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---
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# AI Backlinking Tool
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## Overview
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The `ai_backlinking.py` module is part of the [AI-Writer](https://github.com/AJaySi/AI-Writer) project. It simplifies and automates the process of finding and securing backlink opportunities. Using AI, the tool performs web research, extracts contact information, and sends personalized outreach emails for guest posting opportunities, making it an essential tool for content writers, digital marketers, and solopreneurs.
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---
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## Key Features
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| Feature | Description |
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|-------------------------------|-----------------------------------------------------------------------------|
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| **Automated Web Scraping** | Extract guest post opportunities, contact details, and website insights. |
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| **AI-Powered Emails** | Create personalized outreach emails tailored to target websites. |
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| **Email Automation** | Integrate with platforms like Gmail or SendGrid for streamlined communication. |
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| **Lead Management** | Track email status (sent, replied, successful) and follow up efficiently. |
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| **Batch Processing** | Handle multiple keywords and queries simultaneously. |
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| **AI-Driven Follow-Up** | Automate polite reminders if there's no response. |
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| **Reports and Analytics** | View performance metrics like email open rates and backlink success rates. |
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---
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## Workflow Breakdown
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| Step | Action | Example |
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|-------------------------------|---------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|
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| **Input Keywords** | Provide keywords for backlinking opportunities. | *E.g., "AI tools", "SEO strategies", "content marketing."* |
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| **Generate Search Queries** | Automatically create queries for search engines. | *E.g., "AI tools + 'write for us'" or "content marketing + 'submit a guest post.'"* |
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| **Web Scraping** | Collect URLs, email addresses, and content details from target websites. | Extract "editor@contentblog.com" from "https://contentblog.com/write-for-us". |
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| **Compose Outreach Emails** | Use AI to draft personalized emails based on scraped website data. | Email tailored to "Content Blog" discussing "AI tools for better content writing." |
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| **Automated Email Sending** | Review and send emails or fully automate the process. | Send emails through Gmail or other SMTP services. |
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| **Follow-Ups** | Automate follow-ups for non-responsive contacts. | A polite reminder email sent 7 days later. |
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| **Track and Log Results** | Monitor sent emails, responses, and backlink placements. | View logs showing responses and backlink acquisition rate. |
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---
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## Prerequisites
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- **Python Version**: 3.6 or higher.
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- **Required Packages**: `googlesearch-python`, `loguru`, `smtplib`, `email`.
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---
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## Installation
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1. Clone the repository:
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```bash
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git clone https://github.com/AJaySi/AI-Writer.git
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cd AI-Writer
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```
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2. Install dependencies:
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```bash
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pip install -r requirements.txt
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```
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---
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## Example Usage
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Here’s a quick example of how to use the tool:
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```python
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from lib.ai_marketing_tools.ai_backlinking import main_backlinking_workflow
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# Email configurations
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smtp_config = {
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'server': 'smtp.gmail.com',
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'port': 587,
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'user': 'your_email@gmail.com',
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'password': 'your_password'
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}
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imap_config = {
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'server': 'imap.gmail.com',
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'user': 'your_email@gmail.com',
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'password': 'your_password'
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}
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# Proposal details
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user_proposal = {
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'user_name': 'Your Name',
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'user_email': 'your_email@gmail.com',
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'topic': 'Proposed guest post topic'
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}
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# Keywords to search
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keywords = ['AI tools', 'SEO strategies', 'content marketing']
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# Start the workflow
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main_backlinking_workflow(keywords, smtp_config, imap_config, user_proposal)
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```
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---
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## Core Functions
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| Function | Purpose |
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|--------------------------------------------|-------------------------------------------------------------------------------------------|
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| `generate_search_queries(keyword)` | Create search queries to find guest post opportunities. |
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| `find_backlink_opportunities(keyword)` | Scrape websites for backlink opportunities. |
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| `compose_personalized_email()` | Draft outreach emails using AI insights and website data. |
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| `send_email()` | Send emails using SMTP configurations. |
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| `check_email_responses()` | Monitor inbox for replies using IMAP. |
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| `send_follow_up_email()` | Automate polite reminders to non-responsive contacts. |
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| `log_sent_email()` | Keep a record of all sent emails and responses. |
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| `main_backlinking_workflow()` | Execute the complete backlinking workflow for multiple keywords. |
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---
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## License
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This project is licensed under the MIT License. For more details, refer to the [LICENSE](LICENSE) file.
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---
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@@ -1,423 +0,0 @@
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#Problem:
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#
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#Finding websites for guest posts is manual, tedious, and time-consuming. Communicating with webmasters, maintaining conversations, and keeping track of backlinking opportunities is difficult to scale. Content creators and marketers struggle with discovering new websites and consistently getting backlinks.
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#Solution:
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#
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#An AI-powered backlinking app that automates web research, scrapes websites, extracts contact information, and sends personalized outreach emails to webmasters. This would simplify the entire process, allowing marketers to scale their backlinking strategy with minimal manual intervention.
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#Core Workflow:
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#
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# User Input:
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# Keyword Search: The user inputs a keyword (e.g., "AI writers").
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# Search Queries: Your app will append various search strings to this keyword to find backlinking opportunities (e.g., "AI writers + 'Write for Us'").
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#
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# Web Research:
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#
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# Use search engines or web scraping to run multiple queries:
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# Keyword + "Guest Contributor"
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# Keyword + "Add Guest Post"
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# Keyword + "Write for Us", etc.
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#
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# Collect URLs of websites that have pages or posts related to guest post opportunities.
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#
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# Scrape Website Data:
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# Contact Information Extraction:
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# Scrape the website for contact details (email addresses, contact forms, etc.).
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# Use natural language processing (NLP) to understand the type of content on the website and who the contact person might be (webmaster, editor, or guest post manager).
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# Website Content Understanding:
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# Scrape a summary of each website's content (e.g., their blog topics, categories, and tone) to personalize the email based on the site's focus.
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#
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# Personalized Outreach:
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# AI Email Composition:
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# Compose personalized outreach emails based on:
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# The scraped data (website content, topic focus, etc.).
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# The user's input (what kind of guest post or content they want to contribute).
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# Example: "Hi [Webmaster Name], I noticed that your site [Site Name] features high-quality content about [Topic]. I would love to contribute a guest post on [Proposed Topic] in exchange for a backlink."
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#
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# Automated Email Sending:
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# Review Emails (Optional HITL):
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# Let users review and approve the personalized emails before they are sent, or allow full automation.
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# Send Emails:
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# Automate email dispatch through an integrated SMTP or API (e.g., Gmail API, SendGrid).
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# Keep track of which emails were sent, bounced, or received replies.
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#
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# Scaling the Search:
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# Repeat for Multiple Keywords:
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# Run the same scraping and outreach process for a list of relevant keywords, either automatically suggested or uploaded by the user.
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# Keep Track of Sent Emails:
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# Maintain a log of all sent emails, responses, and follow-up reminders to avoid repetition or forgotten leads.
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#
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# Tracking Responses and Follow-ups:
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# Automated Responses:
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# If a website replies positively, AI can respond with predefined follow-up emails (e.g., proposing topics, confirming submission deadlines).
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# Follow-up Reminders:
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# If there's no reply, the system can send polite follow-up reminders at pre-set intervals.
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#
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#Key Features:
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#
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# Automated Web Scraping:
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# Scrape websites for guest post opportunities using a predefined set of search queries based on user input.
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# Extract key information like email addresses, names, and submission guidelines.
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#
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# Personalized Email Writing:
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# Leverage AI to create personalized emails using the scraped website information.
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# Tailor each email to the tone, content style, and focus of the website.
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#
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# Email Sending Automation:
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# Integrate with email platforms (e.g., Gmail, SendGrid, or custom SMTP).
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# Send automated outreach emails with the ability for users to review first (HITL - Human-in-the-loop) or automate completely.
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#
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# Customizable Email Templates:
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# Allow users to customize or choose from a set of email templates for different types of outreach (e.g., guest post requests, follow-up emails, submission offers).
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#
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# Lead Tracking and Management:
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# Track all emails sent, monitor replies, and keep track of successful backlinks.
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# Log each lead's status (e.g., emailed, responded, no reply) to manage future interactions.
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#
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# Multiple Keywords/Queries:
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# Allow users to run the same process for a batch of keywords, automatically generating relevant search queries for each.
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#
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# AI-Driven Follow-Up:
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# Schedule follow-up emails if there is no response after a specified period.
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#
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# Reports and Analytics:
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# Provide users with reports on how many emails were sent, opened, replied to, and successful backlink placements.
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#
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#Advanced Features (for Scaling and Optimization):
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#
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# Domain Authority Filtering:
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# Use SEO APIs (e.g., Moz, Ahrefs) to filter websites based on their domain authority or backlink strength.
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# Prioritize high-authority websites to maximize the impact of backlinks.
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#
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# Spam Detection:
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# Use AI to detect and avoid spammy or low-quality websites that might harm the user's SEO.
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#
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# Contact Form Auto-Fill:
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# If the site only offers a contact form (without email), automatically fill and submit the form with AI-generated content.
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#
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# Dynamic Content Suggestions:
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# Suggest guest post topics based on the website's focus, using NLP to analyze the site's existing content.
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#
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# Bulk Email Support:
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# Allow users to bulk-send outreach emails while still personalizing each message for scalability.
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#
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# AI Copy Optimization:
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# Use copywriting AI to optimize email content, adjusting tone and CTA based on the target audience.
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#
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#Challenges and Considerations:
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#
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# Legal Compliance:
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# Ensure compliance with anti-spam laws (e.g., CAN-SPAM, GDPR) by including unsubscribe options or manual email approval.
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#
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# Scraping Limits:
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# Be mindful of scraping limits on certain websites and employ smart throttling or use API-based scraping for better reliability.
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#
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# Deliverability:
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# Ensure emails are delivered properly without landing in spam folders by integrating proper email authentication (SPF, DKIM) and using high-reputation SMTP servers.
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#
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# Maintaining Email Personalization:
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# Striking the balance between automating the email process and keeping each message personal enough to avoid being flagged as spam.
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#
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#Technology Stack:
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#
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# Web Scraping: BeautifulSoup, Scrapy, or Puppeteer for scraping guest post opportunities and contact information.
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# Email Automation: Integrate with Gmail API, SendGrid, or Mailgun for sending emails.
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# NLP for Personalization: GPT-based models for email generation and web content understanding.
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# Frontend: React or Vue for the user interface.
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# Backend: Python/Node.js with Flask or Express for the API and automation logic.
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# Database: MongoDB or PostgreSQL to track leads, emails, and responses.
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#
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#This solution will significantly streamline the backlinking process by automating the most tedious tasks, from finding sites to personalizing outreach, enabling marketers to focus on content creation and high-level strategies.
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import sys
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# from googlesearch import search # Temporarily disabled for future enhancement
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from loguru import logger
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from lib.ai_web_researcher.firecrawl_web_crawler import scrape_website
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from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
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from lib.ai_web_researcher.firecrawl_web_crawler import scrape_url
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import smtplib
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from email.mime.multipart import MIMEMultipart
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from email.mime.text import MIMEText
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# Configure logger
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logger.remove()
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logger.add(sys.stdout,
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colorize=True,
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format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
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)
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def generate_search_queries(keyword):
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"""
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Generate a list of search queries for finding guest post opportunities.
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Args:
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keyword (str): The keyword to base the search queries on.
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Returns:
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list: A list of search queries.
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"""
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return [
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f"{keyword} + 'Guest Contributor'",
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f"{keyword} + 'Add Guest Post'",
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f"{keyword} + 'Guest Bloggers Wanted'",
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f"{keyword} + 'Write for Us'",
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f"{keyword} + 'Submit Guest Post'",
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f"{keyword} + 'Become a Guest Blogger'",
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f"{keyword} + 'guest post opportunities'",
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f"{keyword} + 'Submit article'",
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]
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def find_backlink_opportunities(keyword):
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"""
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Find backlink opportunities by scraping websites based on search queries.
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Args:
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keyword (str): The keyword to search for backlink opportunities.
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Returns:
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list: A list of results from the scraped websites.
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"""
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search_queries = generate_search_queries(keyword)
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results = []
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# Temporarily disabled Google search functionality
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# for query in search_queries:
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# urls = search_for_urls(query)
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# for url in urls:
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# website_data = scrape_website(url)
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# logger.info(f"Scraped Website content for {url}: {website_data}")
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# if website_data:
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# contact_info = extract_contact_info(website_data)
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# logger.info(f"Contact details found for {url}: {contact_info}")
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# Placeholder return for now
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return []
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def search_for_urls(query):
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"""
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Search for URLs using Google search.
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Args:
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query (str): The search query.
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Returns:
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list: List of URLs found.
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"""
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# Temporarily disabled Google search functionality
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# return list(search(query, num_results=10))
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return []
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def compose_personalized_email(website_data, insights, user_proposal):
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"""
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Compose a personalized outreach email using AI LLM based on website data, insights, and user proposal.
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Args:
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website_data (dict): The data of the website including metadata and contact info.
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insights (str): Insights generated by the LLM about the website.
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user_proposal (dict): The user's proposal for a guest post or content contribution.
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Returns:
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str: A personalized email message.
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"""
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contact_name = website_data.get("contact_info", {}).get("name", "Webmaster")
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site_name = website_data.get("metadata", {}).get("title", "your site")
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proposed_topic = user_proposal.get("topic", "a guest post")
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user_name = user_proposal.get("user_name", "Your Name")
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user_email = user_proposal.get("user_email", "your_email@example.com")
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# Refined prompt for email generation
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email_prompt = f"""
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You are an AI assistant tasked with composing a highly personalized outreach email for guest posting.
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|
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Contact Name: {contact_name}
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Website Name: {site_name}
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Proposed Topic: {proposed_topic}
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|
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User Details:
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Name: {user_name}
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Email: {user_email}
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|
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Website Insights: {insights}
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Please compose a professional and engaging email that includes:
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1. A personalized introduction addressing the recipient.
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2. A mention of the website's content focus.
|
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3. A proposal for a guest post.
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4. A call to action to discuss the guest post opportunity.
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5. A polite closing with user contact details.
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"""
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|
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return llm_text_gen(email_prompt)
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def send_email(smtp_server, smtp_port, smtp_user, smtp_password, to_email, subject, body):
|
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"""
|
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Send an email using an SMTP server.
|
||||
|
||||
Args:
|
||||
smtp_server (str): The SMTP server address.
|
||||
smtp_port (int): The SMTP server port.
|
||||
smtp_user (str): The SMTP server username.
|
||||
smtp_password (str): The SMTP server password.
|
||||
to_email (str): The recipient's email address.
|
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subject (str): The email subject.
|
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body (str): The email body.
|
||||
|
||||
Returns:
|
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bool: True if the email was sent successfully, False otherwise.
|
||||
"""
|
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try:
|
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msg = MIMEMultipart()
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msg['From'] = smtp_user
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msg['To'] = to_email
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msg['Subject'] = subject
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msg.attach(MIMEText(body, 'plain'))
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|
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server = smtplib.SMTP(smtp_server, smtp_port)
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server.starttls()
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server.login(smtp_user, smtp_password)
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server.send_message(msg)
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server.quit()
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|
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logger.info(f"Email sent successfully to {to_email}")
|
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return True
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except Exception as e:
|
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logger.error(f"Failed to send email to {to_email}: {e}")
|
||||
return False
|
||||
|
||||
def extract_contact_info(website_data):
|
||||
"""
|
||||
Extract contact information from website data.
|
||||
|
||||
Args:
|
||||
website_data (dict): Scraped data from the website.
|
||||
|
||||
Returns:
|
||||
dict: Extracted contact information such as name, email, etc.
|
||||
"""
|
||||
# Placeholder for extracting contact information logic
|
||||
return {
|
||||
"name": website_data.get("contact", {}).get("name", "Webmaster"),
|
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"email": website_data.get("contact", {}).get("email", ""),
|
||||
}
|
||||
|
||||
def find_backlink_opportunities_for_keywords(keywords):
|
||||
"""
|
||||
Find backlink opportunities for multiple keywords.
|
||||
|
||||
Args:
|
||||
keywords (list): A list of keywords to search for backlink opportunities.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary with keywords as keys and a list of results as values.
|
||||
"""
|
||||
all_results = {}
|
||||
for keyword in keywords:
|
||||
results = find_backlink_opportunities(keyword)
|
||||
all_results[keyword] = results
|
||||
return all_results
|
||||
|
||||
def log_sent_email(keyword, email_info):
|
||||
"""
|
||||
Log the information of a sent email.
|
||||
|
||||
Args:
|
||||
keyword (str): The keyword associated with the email.
|
||||
email_info (dict): Information about the sent email (e.g., recipient, subject, body).
|
||||
"""
|
||||
with open(f"{keyword}_sent_emails.log", "a") as log_file:
|
||||
log_file.write(f"{email_info}\n")
|
||||
|
||||
def check_email_responses(imap_server, imap_user, imap_password):
|
||||
"""
|
||||
Check email responses using an IMAP server.
|
||||
|
||||
Args:
|
||||
imap_server (str): The IMAP server address.
|
||||
imap_user (str): The IMAP server username.
|
||||
imap_password (str): The IMAP server password.
|
||||
|
||||
Returns:
|
||||
list: A list of email responses.
|
||||
"""
|
||||
responses = []
|
||||
try:
|
||||
mail = imaplib.IMAP4_SSL(imap_server)
|
||||
mail.login(imap_user, imap_password)
|
||||
mail.select('inbox')
|
||||
|
||||
status, data = mail.search(None, 'UNSEEN')
|
||||
mail_ids = data[0]
|
||||
id_list = mail_ids.split()
|
||||
|
||||
for mail_id in id_list:
|
||||
status, data = mail.fetch(mail_id, '(RFC822)')
|
||||
msg = email.message_from_bytes(data[0][1])
|
||||
if msg.is_multipart():
|
||||
for part in msg.walk():
|
||||
if part.get_content_type() == 'text/plain':
|
||||
responses.append(part.get_payload(decode=True).decode())
|
||||
else:
|
||||
responses.append(msg.get_payload(decode=True).decode())
|
||||
|
||||
mail.logout()
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to check email responses: {e}")
|
||||
|
||||
return responses
|
||||
|
||||
def send_follow_up_email(smtp_server, smtp_port, smtp_user, smtp_password, to_email, subject, body):
|
||||
"""
|
||||
Send a follow-up email using an SMTP server.
|
||||
|
||||
Args:
|
||||
smtp_server (str): The SMTP server address.
|
||||
smtp_port (int): The SMTP server port.
|
||||
smtp_user (str): The SMTP server username.
|
||||
smtp_password (str): The SMTP server password.
|
||||
to_email (str): The recipient's email address.
|
||||
subject (str): The email subject.
|
||||
body (str): The email body.
|
||||
|
||||
Returns:
|
||||
bool: True if the email was sent successfully, False otherwise.
|
||||
"""
|
||||
return send_email(smtp_server, smtp_port, smtp_user, smtp_password, to_email, subject, body)
|
||||
|
||||
def main_backlinking_workflow(keywords, smtp_config, imap_config, user_proposal):
|
||||
"""
|
||||
Main workflow for the AI-powered backlinking feature.
|
||||
|
||||
Args:
|
||||
keywords (list): A list of keywords to search for backlink opportunities.
|
||||
smtp_config (dict): SMTP configuration for sending emails.
|
||||
imap_config (dict): IMAP configuration for checking email responses.
|
||||
user_proposal (dict): The user's proposal for a guest post or content contribution.
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
all_results = find_backlink_opportunities_for_keywords(keywords)
|
||||
|
||||
for keyword, results in all_results.items():
|
||||
for result in results:
|
||||
email_body = compose_personalized_email(result, result['insights'], user_proposal)
|
||||
email_sent = send_email(
|
||||
smtp_config['server'],
|
||||
smtp_config['port'],
|
||||
smtp_config['user'],
|
||||
smtp_config['password'],
|
||||
result['contact_info']['email'],
|
||||
f"Guest Post Proposal for {result['metadata']['title']}",
|
||||
email_body
|
||||
)
|
||||
if email_sent:
|
||||
log_sent_email(keyword, {
|
||||
"to": result['contact_info']['email'],
|
||||
"subject": f"Guest Post Proposal for {result['metadata']['title']}",
|
||||
"body": email_body
|
||||
})
|
||||
|
||||
responses = check_email_responses(imap_config['server'], imap_config['user'], imap_config['password'])
|
||||
for response in responses:
|
||||
# TBD : Process and possibly send follow-up emails based on responses
|
||||
pass
|
||||
@@ -1,60 +0,0 @@
|
||||
import streamlit as st
|
||||
import pandas as pd
|
||||
from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode
|
||||
from lib.ai_marketing_tools.ai_backlinker.ai_backlinking import find_backlink_opportunities, compose_personalized_email
|
||||
|
||||
|
||||
# Streamlit UI function
|
||||
def backlinking_ui():
|
||||
st.title("AI Backlinking Tool")
|
||||
|
||||
# Step 1: Get user inputs
|
||||
keyword = st.text_input("Enter a keyword", value="technology")
|
||||
|
||||
# Step 2: Generate backlink opportunities
|
||||
if st.button("Find Backlink Opportunities"):
|
||||
if keyword:
|
||||
backlink_opportunities = find_backlink_opportunities(keyword)
|
||||
|
||||
# Convert results to a DataFrame for display
|
||||
df = pd.DataFrame(backlink_opportunities)
|
||||
|
||||
# Create a selectable table using st-aggrid
|
||||
gb = GridOptionsBuilder.from_dataframe(df)
|
||||
gb.configure_selection('multiple', use_checkbox=True, groupSelectsChildren=True)
|
||||
gridOptions = gb.build()
|
||||
|
||||
grid_response = AgGrid(
|
||||
df,
|
||||
gridOptions=gridOptions,
|
||||
update_mode=GridUpdateMode.SELECTION_CHANGED,
|
||||
height=200,
|
||||
width='100%'
|
||||
)
|
||||
|
||||
selected_rows = grid_response['selected_rows']
|
||||
|
||||
if selected_rows:
|
||||
st.write("Selected Opportunities:")
|
||||
st.table(pd.DataFrame(selected_rows))
|
||||
|
||||
# Step 3: Option to generate personalized emails for selected opportunities
|
||||
if st.button("Generate Emails for Selected Opportunities"):
|
||||
user_proposal = {
|
||||
"user_name": st.text_input("Your Name", value="John Doe"),
|
||||
"user_email": st.text_input("Your Email", value="john@example.com")
|
||||
}
|
||||
|
||||
emails = []
|
||||
for selected in selected_rows:
|
||||
insights = f"Insights based on content from {selected['url']}."
|
||||
email = compose_personalized_email(selected, insights, user_proposal)
|
||||
emails.append(email)
|
||||
|
||||
st.subheader("Generated Emails:")
|
||||
for email in emails:
|
||||
st.write(email)
|
||||
st.markdown("---")
|
||||
|
||||
else:
|
||||
st.error("Please enter a keyword.")
|
||||
@@ -1,370 +0,0 @@
|
||||
Google Ads Generator
|
||||
Google Ads Generator Logo
|
||||
|
||||
Overview
|
||||
The Google Ads Generator is an AI-powered tool designed to create high-converting Google Ads based on industry best practices. This tool helps marketers, business owners, and advertising professionals create optimized ad campaigns that maximize ROI and conversion rates.
|
||||
|
||||
By leveraging advanced AI algorithms and proven advertising frameworks, the Google Ads Generator creates compelling ad copy, suggests optimal keywords, generates relevant extensions, and provides performance predictions—all tailored to your specific business needs and target audience.
|
||||
|
||||
Table of Contents
|
||||
Features
|
||||
Getting Started
|
||||
User Interface
|
||||
Ad Creation Process
|
||||
Ad Types
|
||||
Quality Analysis
|
||||
Performance Simulation
|
||||
Best Practices
|
||||
Export Options
|
||||
Advanced Features
|
||||
Technical Details
|
||||
FAQ
|
||||
Troubleshooting
|
||||
Updates and Roadmap
|
||||
Features
|
||||
Core Features
|
||||
AI-Powered Ad Generation: Create compelling, high-converting Google Ads in seconds
|
||||
Multiple Ad Types: Support for Responsive Search Ads, Expanded Text Ads, Call-Only Ads, and Dynamic Search Ads
|
||||
Industry-Specific Templates: Tailored templates for 20+ industries
|
||||
Ad Extensions Generator: Automatically create Sitelinks, Callouts, and Structured Snippets
|
||||
Quality Score Analysis: Comprehensive scoring based on Google's quality factors
|
||||
Performance Prediction: Estimate CTR, conversion rates, and ROI
|
||||
A/B Testing: Generate multiple variations for testing
|
||||
Export Options: Export to CSV, Excel, Google Ads Editor CSV, and JSON
|
||||
Advanced Features
|
||||
Keyword Research Integration: Find high-performing keywords for your ads
|
||||
Competitor Analysis: Analyze competitor ads and identify opportunities
|
||||
Landing Page Suggestions: Recommendations for landing page optimization
|
||||
Budget Optimization: Suggestions for optimal budget allocation
|
||||
Ad Schedule Recommendations: Identify the best times to run your ads
|
||||
Audience Targeting Suggestions: Recommendations for demographic targeting
|
||||
Local Ad Optimization: Special features for local businesses
|
||||
E-commerce Ad Features: Product-specific ad generation
|
||||
Getting Started
|
||||
Prerequisites
|
||||
Alwrity AI Writer platform
|
||||
Basic understanding of Google Ads concepts
|
||||
Information about your business, products/services, and target audience
|
||||
Accessing the Tool
|
||||
Navigate to the Alwrity AI Writer platform
|
||||
Select "AI Google Ads Generator" from the tools menu
|
||||
Follow the guided setup process
|
||||
User Interface
|
||||
The Google Ads Generator features a user-friendly, tabbed interface designed to guide you through the ad creation process:
|
||||
|
||||
Tab 1: Ad Creation
|
||||
This is where you'll input your business information and ad requirements:
|
||||
|
||||
Business Information: Company name, industry, products/services
|
||||
Campaign Goals: Select from options like brand awareness, lead generation, sales, etc.
|
||||
Target Audience: Define your ideal customer
|
||||
Ad Type Selection: Choose from available ad formats
|
||||
USP and Benefits: Input your unique selling propositions and key benefits
|
||||
Keywords: Add target keywords or generate suggestions
|
||||
Landing Page URL: Specify where users will go after clicking your ad
|
||||
Budget Information: Set daily/monthly budget for performance predictions
|
||||
Tab 2: Ad Performance
|
||||
After generating ads, this tab provides detailed analysis:
|
||||
|
||||
Quality Score: Overall score (1-10) with detailed breakdown
|
||||
Strengths & Improvements: What's good and what could be better
|
||||
Keyword Relevance: Analysis of keyword usage in ad elements
|
||||
CTR Prediction: Estimated click-through rate based on ad quality
|
||||
Conversion Potential: Estimated conversion rate
|
||||
Mobile Friendliness: Assessment of how well the ad performs on mobile
|
||||
Ad Policy Compliance: Check for potential policy violations
|
||||
Tab 3: Ad History
|
||||
Keep track of your generated ads:
|
||||
|
||||
Saved Ads: Previously generated and saved ads
|
||||
Favorites: Ads you've marked as favorites
|
||||
Version History: Track changes and iterations
|
||||
Performance Notes: Add notes about real-world performance
|
||||
Tab 4: Best Practices
|
||||
Educational resources to improve your ads:
|
||||
|
||||
Industry Guidelines: Best practices for your specific industry
|
||||
Ad Type Tips: Specific guidance for each ad type
|
||||
Quality Score Optimization: How to improve quality score
|
||||
Extension Strategies: How to effectively use ad extensions
|
||||
A/B Testing Guide: How to test and optimize your ads
|
||||
Ad Creation Process
|
||||
Step 1: Define Your Campaign
|
||||
Select your industry from the dropdown menu
|
||||
Choose your primary campaign goal
|
||||
Define your target audience
|
||||
Set your budget parameters
|
||||
Step 2: Input Business Details
|
||||
Enter your business name
|
||||
Provide your website URL
|
||||
Input your unique selling propositions
|
||||
List key product/service benefits
|
||||
Add any promotional offers or discounts
|
||||
Step 3: Keyword Selection
|
||||
Enter your primary keywords
|
||||
Use the integrated keyword research tool to find additional keywords
|
||||
Select keyword match types (broad, phrase, exact)
|
||||
Review keyword competition and volume metrics
|
||||
Step 4: Ad Type Selection
|
||||
Choose your preferred ad type
|
||||
Review the requirements and limitations for that ad type
|
||||
Select any additional features specific to that ad type
|
||||
Step 5: Generate Ads
|
||||
Click the "Generate Ads" button
|
||||
Review the generated ads
|
||||
Request variations if needed
|
||||
Save your favorite versions
|
||||
Step 6: Add Extensions
|
||||
Select which extension types to include
|
||||
Review and edit the generated extensions
|
||||
Add any custom extensions
|
||||
Step 7: Analyze and Optimize
|
||||
Review the quality score and analysis
|
||||
Make suggested improvements
|
||||
Regenerate ads if necessary
|
||||
Compare different versions
|
||||
Step 8: Export
|
||||
Choose your preferred export format
|
||||
Select which ads to include
|
||||
Download the file for import into Google Ads
|
||||
Ad Types
|
||||
Responsive Search Ads (RSA)
|
||||
The most flexible and recommended ad type, featuring:
|
||||
|
||||
Up to 15 headlines (3 shown at a time)
|
||||
Up to 4 descriptions (2 shown at a time)
|
||||
Dynamic combination of elements based on performance
|
||||
Automatic testing of different combinations
|
||||
Expanded Text Ads (ETA)
|
||||
A more controlled ad format with:
|
||||
|
||||
3 headlines
|
||||
2 descriptions
|
||||
Display URL with two path fields
|
||||
Fixed layout with no dynamic combinations
|
||||
Call-Only Ads
|
||||
Designed to drive phone calls rather than website visits:
|
||||
|
||||
Business name
|
||||
Phone number
|
||||
Call-to-action text
|
||||
Description lines
|
||||
Verification URL (not shown to users)
|
||||
Dynamic Search Ads (DSA)
|
||||
Ads that use your website content to target relevant searches:
|
||||
|
||||
Dynamic headline generation based on search queries
|
||||
Custom descriptions
|
||||
Landing page selection based on website content
|
||||
Requires website URL for crawling
|
||||
Quality Analysis
|
||||
Our comprehensive quality analysis evaluates your ads based on factors that influence Google's Quality Score:
|
||||
|
||||
Headline Analysis
|
||||
Keyword Usage: Presence of keywords in headlines
|
||||
Character Count: Optimal length for visibility
|
||||
Power Words: Use of emotionally compelling words
|
||||
Clarity: Clear communication of value proposition
|
||||
Call to Action: Presence of action-oriented language
|
||||
Description Analysis
|
||||
Keyword Density: Optimal keyword usage
|
||||
Benefit Focus: Clear articulation of benefits
|
||||
Feature Inclusion: Mention of key features
|
||||
Urgency Elements: Time-limited offers or scarcity
|
||||
Call to Action: Clear next steps for the user
|
||||
URL Path Analysis
|
||||
Keyword Inclusion: Relevant keywords in display paths
|
||||
Readability: Clear, understandable paths
|
||||
Relevance: Connection to landing page content
|
||||
Overall Ad Relevance
|
||||
Keyword-to-Ad Relevance: Alignment between keywords and ad copy
|
||||
Ad-to-Landing Page Relevance: Consistency across the user journey
|
||||
Intent Match: Alignment with search intent
|
||||
Performance Simulation
|
||||
Our tool provides data-driven performance predictions based on:
|
||||
|
||||
Click-Through Rate (CTR) Prediction
|
||||
Industry benchmarks
|
||||
Ad quality factors
|
||||
Keyword competition
|
||||
Ad position estimates
|
||||
Conversion Rate Prediction
|
||||
Industry averages
|
||||
Landing page quality
|
||||
Offer strength
|
||||
Call-to-action effectiveness
|
||||
Cost Estimation
|
||||
Keyword competition
|
||||
Quality Score impact
|
||||
Industry CPC averages
|
||||
Budget allocation
|
||||
ROI Calculation
|
||||
Estimated clicks
|
||||
Predicted conversions
|
||||
Average conversion value
|
||||
Cost projections
|
||||
Best Practices
|
||||
Our tool incorporates these Google Ads best practices:
|
||||
|
||||
Headline Best Practices
|
||||
Include primary keywords in at least 2 headlines
|
||||
Use numbers and statistics when relevant
|
||||
Address user pain points directly
|
||||
Include your unique selling proposition
|
||||
Create a sense of urgency when appropriate
|
||||
Keep headlines under 30 characters for full visibility
|
||||
Use title case for better readability
|
||||
Include at least one call-to-action headline
|
||||
Description Best Practices
|
||||
Include primary and secondary keywords naturally
|
||||
Focus on benefits, not just features
|
||||
Address objections proactively
|
||||
Include specific offers or promotions
|
||||
End with a clear call to action
|
||||
Use all available character space (90 characters per description)
|
||||
Maintain consistent messaging with headlines
|
||||
Include trust signals (guarantees, social proof, etc.)
|
||||
Extension Best Practices
|
||||
Create at least 8 sitelinks for maximum visibility
|
||||
Use callouts to highlight additional benefits
|
||||
Include structured snippets relevant to your industry
|
||||
Ensure extensions don't duplicate headline content
|
||||
Make each extension unique and valuable
|
||||
Use specific, action-oriented language
|
||||
Keep sitelink text under 25 characters for mobile visibility
|
||||
Ensure landing pages for sitelinks are relevant and optimized
|
||||
Campaign Structure Best Practices
|
||||
Group closely related keywords together
|
||||
Create separate ad groups for different themes
|
||||
Align ad copy closely with keywords in each ad group
|
||||
Use a mix of match types for each keyword
|
||||
Include negative keywords to prevent irrelevant clicks
|
||||
Create separate campaigns for different goals or audiences
|
||||
Set appropriate bid adjustments for devices, locations, and schedules
|
||||
Implement conversion tracking for performance measurement
|
||||
Export Options
|
||||
The Google Ads Generator offers multiple export formats to fit your workflow:
|
||||
|
||||
CSV Format
|
||||
Standard CSV format compatible with most spreadsheet applications
|
||||
Includes all ad elements and extensions
|
||||
Contains quality score and performance predictions
|
||||
Suitable for analysis and record-keeping
|
||||
Excel Format
|
||||
Formatted Excel workbook with multiple sheets
|
||||
Separate sheets for ads, extensions, and analysis
|
||||
Includes charts and visualizations of predicted performance
|
||||
Color-coded quality indicators
|
||||
Google Ads Editor CSV
|
||||
Specially formatted CSV for direct import into Google Ads Editor
|
||||
Follows Google's required format specifications
|
||||
Includes all necessary fields for campaign creation
|
||||
Ready for immediate upload to Google Ads Editor
|
||||
JSON Format
|
||||
Structured data format for programmatic use
|
||||
Complete ad data in machine-readable format
|
||||
Suitable for integration with other marketing tools
|
||||
Includes all metadata and analysis results
|
||||
Advanced Features
|
||||
Keyword Research Integration
|
||||
Access to keyword volume data
|
||||
Competition analysis
|
||||
Cost-per-click estimates
|
||||
Keyword difficulty scores
|
||||
Seasonal trend information
|
||||
Question-based keyword suggestions
|
||||
Long-tail keyword recommendations
|
||||
Competitor Analysis
|
||||
Identify competitors bidding on similar keywords
|
||||
Analyze competitor ad copy and messaging
|
||||
Identify gaps and opportunities
|
||||
Benchmark your ads against competitors
|
||||
Receive suggestions for differentiation
|
||||
Landing Page Suggestions
|
||||
Alignment with ad messaging
|
||||
Key elements to include
|
||||
Conversion optimization tips
|
||||
Mobile responsiveness recommendations
|
||||
Page speed improvement suggestions
|
||||
Call-to-action placement recommendations
|
||||
Local Ad Optimization
|
||||
Location extension suggestions
|
||||
Local keyword recommendations
|
||||
Geo-targeting strategies
|
||||
Local offer suggestions
|
||||
Community-focused messaging
|
||||
Location-specific call-to-actions
|
||||
Technical Details
|
||||
System Requirements
|
||||
Modern web browser (Chrome, Firefox, Safari, Edge)
|
||||
Internet connection
|
||||
Access to Alwrity AI Writer platform
|
||||
Data Privacy
|
||||
No permanent storage of business data
|
||||
Secure processing of all inputs
|
||||
Option to save ads to your account
|
||||
Compliance with data protection regulations
|
||||
API Integration
|
||||
Available API endpoints for programmatic access
|
||||
Documentation for developers
|
||||
Rate limits and authentication requirements
|
||||
Sample code for common use cases
|
||||
FAQ
|
||||
General Questions
|
||||
Q: How accurate are the performance predictions? A: Performance predictions are based on industry benchmarks and Google's published data. While they provide a good estimate, actual performance may vary based on numerous factors including competition, seasonality, and market conditions.
|
||||
|
||||
Q: Can I edit the generated ads? A: Yes, all generated ads can be edited before export. You can modify headlines, descriptions, paths, and extensions to better fit your needs.
|
||||
|
||||
Q: How many ads can I generate? A: The tool allows unlimited ad generation within your Alwrity subscription limits.
|
||||
|
||||
Q: Are the generated ads compliant with Google's policies? A: The tool is designed to create policy-compliant ads, but we recommend reviewing Google's latest advertising policies as they may change over time.
|
||||
|
||||
Technical Questions
|
||||
Q: Can I import my existing ads for optimization? A: Currently, the tool does not support importing existing ads, but this feature is on our roadmap.
|
||||
|
||||
Q: How do I import the exported files into Google Ads? A: For Google Ads Editor CSV files, open Google Ads Editor, go to File > Import, and select your exported file. For other formats, you may need to manually create campaigns using the generated content.
|
||||
|
||||
Q: Can I schedule automatic ad generation? A: Automated scheduling is not currently available but is planned for a future release.
|
||||
|
||||
Troubleshooting
|
||||
Common Issues
|
||||
Issue: Generated ads don't include my keywords Solution: Ensure your keywords are relevant to your business description and offerings. Try using more specific keywords or providing more detailed business information.
|
||||
|
||||
Issue: Quality score is consistently low Solution: Review the improvement suggestions in the Ad Performance tab. Common issues include keyword relevance, landing page alignment, and benefit clarity.
|
||||
|
||||
Issue: Export file isn't importing correctly into Google Ads Editor Solution: Ensure you're selecting the "Google Ads Editor CSV" export format. If problems persist, check for special characters in your ad copy that might be causing formatting issues.
|
||||
|
||||
Issue: Performance predictions seem unrealistic Solution: Adjust your industry selection and budget information to get more accurate predictions. Consider providing more specific audience targeting information.
|
||||
|
||||
Updates and Roadmap
|
||||
Recent Updates
|
||||
Added support for Performance Max campaign recommendations
|
||||
Improved keyword research integration
|
||||
Enhanced mobile ad optimization
|
||||
Added 5 new industry templates
|
||||
Improved quality score algorithm
|
||||
Coming Soon
|
||||
Competitor ad analysis tool
|
||||
A/B testing performance simulator
|
||||
Landing page builder integration
|
||||
Automated ad scheduling recommendations
|
||||
Video ad script generator
|
||||
Google Shopping ad support
|
||||
Multi-language ad generation
|
||||
Custom template builder
|
||||
Support
|
||||
For additional help with the Google Ads Generator:
|
||||
|
||||
Visit our Help Center
|
||||
Email support at support@example.com
|
||||
Join our Community Forum
|
||||
License
|
||||
The Google Ads Generator is part of the Alwrity AI Writer platform and is subject to the platform's terms of service and licensing agreements.
|
||||
|
||||
Acknowledgments
|
||||
Google Ads API documentation
|
||||
Industry best practices from leading digital marketing experts
|
||||
User feedback and feature requests
|
||||
Last updated: [Current Date]
|
||||
|
||||
Version: 1.0.0
|
||||
@@ -1,9 +0,0 @@
|
||||
"""
|
||||
Google Ads Generator Module
|
||||
|
||||
This module provides functionality for generating high-converting Google Ads.
|
||||
"""
|
||||
|
||||
from .google_ads_generator import write_google_ads
|
||||
|
||||
__all__ = ["write_google_ads"]
|
||||
@@ -1,327 +0,0 @@
|
||||
"""
|
||||
Ad Analyzer Module
|
||||
|
||||
This module provides functions for analyzing and scoring Google Ads.
|
||||
"""
|
||||
|
||||
import re
|
||||
from typing import Dict, List, Any, Tuple
|
||||
import random
|
||||
from urllib.parse import urlparse
|
||||
|
||||
def analyze_ad_quality(ad: Dict, primary_keywords: List[str], secondary_keywords: List[str],
|
||||
business_name: str, call_to_action: str) -> Dict:
|
||||
"""
|
||||
Analyze the quality of a Google Ad based on best practices.
|
||||
|
||||
Args:
|
||||
ad: Dictionary containing ad details
|
||||
primary_keywords: List of primary keywords
|
||||
secondary_keywords: List of secondary keywords
|
||||
business_name: Name of the business
|
||||
call_to_action: Call to action text
|
||||
|
||||
Returns:
|
||||
Dictionary with analysis results
|
||||
"""
|
||||
# Initialize results
|
||||
strengths = []
|
||||
improvements = []
|
||||
|
||||
# Get ad components
|
||||
headlines = ad.get("headlines", [])
|
||||
descriptions = ad.get("descriptions", [])
|
||||
path1 = ad.get("path1", "")
|
||||
path2 = ad.get("path2", "")
|
||||
|
||||
# Check headline count
|
||||
if len(headlines) >= 10:
|
||||
strengths.append("Good number of headlines (10+) for optimization")
|
||||
elif len(headlines) >= 5:
|
||||
strengths.append("Adequate number of headlines for testing")
|
||||
else:
|
||||
improvements.append("Add more headlines (aim for 10+) to give Google's algorithm more options")
|
||||
|
||||
# Check description count
|
||||
if len(descriptions) >= 4:
|
||||
strengths.append("Good number of descriptions (4+) for optimization")
|
||||
elif len(descriptions) >= 2:
|
||||
strengths.append("Adequate number of descriptions for testing")
|
||||
else:
|
||||
improvements.append("Add more descriptions (aim for 4+) to give Google's algorithm more options")
|
||||
|
||||
# Check headline length
|
||||
long_headlines = [h for h in headlines if len(h) > 30]
|
||||
if long_headlines:
|
||||
improvements.append(f"{len(long_headlines)} headline(s) exceed 30 characters and may be truncated")
|
||||
else:
|
||||
strengths.append("All headlines are within the recommended length")
|
||||
|
||||
# Check description length
|
||||
long_descriptions = [d for d in descriptions if len(d) > 90]
|
||||
if long_descriptions:
|
||||
improvements.append(f"{len(long_descriptions)} description(s) exceed 90 characters and may be truncated")
|
||||
else:
|
||||
strengths.append("All descriptions are within the recommended length")
|
||||
|
||||
# Check keyword usage in headlines
|
||||
headline_keywords = []
|
||||
for kw in primary_keywords:
|
||||
if any(kw.lower() in h.lower() for h in headlines):
|
||||
headline_keywords.append(kw)
|
||||
|
||||
if len(headline_keywords) == len(primary_keywords):
|
||||
strengths.append("All primary keywords are used in headlines")
|
||||
elif headline_keywords:
|
||||
strengths.append(f"{len(headline_keywords)} out of {len(primary_keywords)} primary keywords used in headlines")
|
||||
missing_kw = [kw for kw in primary_keywords if kw not in headline_keywords]
|
||||
improvements.append(f"Add these primary keywords to headlines: {', '.join(missing_kw)}")
|
||||
else:
|
||||
improvements.append("No primary keywords found in headlines - add keywords to improve relevance")
|
||||
|
||||
# Check keyword usage in descriptions
|
||||
desc_keywords = []
|
||||
for kw in primary_keywords:
|
||||
if any(kw.lower() in d.lower() for d in descriptions):
|
||||
desc_keywords.append(kw)
|
||||
|
||||
if len(desc_keywords) == len(primary_keywords):
|
||||
strengths.append("All primary keywords are used in descriptions")
|
||||
elif desc_keywords:
|
||||
strengths.append(f"{len(desc_keywords)} out of {len(primary_keywords)} primary keywords used in descriptions")
|
||||
missing_kw = [kw for kw in primary_keywords if kw not in desc_keywords]
|
||||
improvements.append(f"Add these primary keywords to descriptions: {', '.join(missing_kw)}")
|
||||
else:
|
||||
improvements.append("No primary keywords found in descriptions - add keywords to improve relevance")
|
||||
|
||||
# Check for business name
|
||||
if any(business_name.lower() in h.lower() for h in headlines):
|
||||
strengths.append("Business name is included in headlines")
|
||||
else:
|
||||
improvements.append("Consider adding your business name to at least one headline")
|
||||
|
||||
# Check for call to action
|
||||
if any(call_to_action.lower() in h.lower() for h in headlines) or any(call_to_action.lower() in d.lower() for d in descriptions):
|
||||
strengths.append("Call to action is included in the ad")
|
||||
else:
|
||||
improvements.append(f"Add your call to action '{call_to_action}' to at least one headline or description")
|
||||
|
||||
# Check for numbers and statistics
|
||||
has_numbers = any(bool(re.search(r'\d+', h)) for h in headlines) or any(bool(re.search(r'\d+', d)) for d in descriptions)
|
||||
if has_numbers:
|
||||
strengths.append("Ad includes numbers or statistics which can improve CTR")
|
||||
else:
|
||||
improvements.append("Consider adding numbers or statistics to increase credibility and CTR")
|
||||
|
||||
# Check for questions
|
||||
has_questions = any('?' in h for h in headlines) or any('?' in d for d in descriptions)
|
||||
if has_questions:
|
||||
strengths.append("Ad includes questions which can engage users")
|
||||
else:
|
||||
improvements.append("Consider adding a question to engage users")
|
||||
|
||||
# Check for emotional triggers
|
||||
emotional_words = ['you', 'free', 'because', 'instantly', 'new', 'save', 'proven', 'guarantee', 'love', 'discover']
|
||||
has_emotional = any(any(word in h.lower() for word in emotional_words) for h in headlines) or \
|
||||
any(any(word in d.lower() for word in emotional_words) for d in descriptions)
|
||||
|
||||
if has_emotional:
|
||||
strengths.append("Ad includes emotional trigger words which can improve engagement")
|
||||
else:
|
||||
improvements.append("Consider adding emotional trigger words to increase engagement")
|
||||
|
||||
# Check for path relevance
|
||||
if any(kw.lower() in path1.lower() or kw.lower() in path2.lower() for kw in primary_keywords):
|
||||
strengths.append("Display URL paths include keywords which improves relevance")
|
||||
else:
|
||||
improvements.append("Add keywords to your display URL paths to improve relevance")
|
||||
|
||||
# Return the analysis results
|
||||
return {
|
||||
"strengths": strengths,
|
||||
"improvements": improvements
|
||||
}
|
||||
|
||||
def calculate_quality_score(ad: Dict, primary_keywords: List[str], landing_page: str, ad_type: str) -> Dict:
|
||||
"""
|
||||
Calculate a quality score for a Google Ad based on best practices.
|
||||
|
||||
Args:
|
||||
ad: Dictionary containing ad details
|
||||
primary_keywords: List of primary keywords
|
||||
landing_page: Landing page URL
|
||||
ad_type: Type of Google Ad
|
||||
|
||||
Returns:
|
||||
Dictionary with quality score components
|
||||
"""
|
||||
# Initialize scores
|
||||
keyword_relevance = 0
|
||||
ad_relevance = 0
|
||||
cta_effectiveness = 0
|
||||
landing_page_relevance = 0
|
||||
|
||||
# Get ad components
|
||||
headlines = ad.get("headlines", [])
|
||||
descriptions = ad.get("descriptions", [])
|
||||
path1 = ad.get("path1", "")
|
||||
path2 = ad.get("path2", "")
|
||||
|
||||
# Calculate keyword relevance (0-10)
|
||||
# Check if keywords are in headlines, descriptions, and paths
|
||||
keyword_in_headline = sum(1 for kw in primary_keywords if any(kw.lower() in h.lower() for h in headlines))
|
||||
keyword_in_description = sum(1 for kw in primary_keywords if any(kw.lower() in d.lower() for d in descriptions))
|
||||
keyword_in_path = sum(1 for kw in primary_keywords if kw.lower() in path1.lower() or kw.lower() in path2.lower())
|
||||
|
||||
# Calculate score based on keyword presence
|
||||
if len(primary_keywords) > 0:
|
||||
headline_score = min(10, (keyword_in_headline / len(primary_keywords)) * 10)
|
||||
description_score = min(10, (keyword_in_description / len(primary_keywords)) * 10)
|
||||
path_score = min(10, (keyword_in_path / len(primary_keywords)) * 10)
|
||||
|
||||
# Weight the scores (headlines most important)
|
||||
keyword_relevance = (headline_score * 0.6) + (description_score * 0.3) + (path_score * 0.1)
|
||||
else:
|
||||
keyword_relevance = 5 # Default score if no keywords provided
|
||||
|
||||
# Calculate ad relevance (0-10)
|
||||
# Check for ad structure and content quality
|
||||
|
||||
# Check headline count and length
|
||||
headline_count_score = min(10, (len(headlines) / 10) * 10) # Ideal: 10+ headlines
|
||||
headline_length_score = 10 - min(10, (sum(1 for h in headlines if len(h) > 30) / max(1, len(headlines))) * 10)
|
||||
|
||||
# Check description count and length
|
||||
description_count_score = min(10, (len(descriptions) / 4) * 10) # Ideal: 4+ descriptions
|
||||
description_length_score = 10 - min(10, (sum(1 for d in descriptions if len(d) > 90) / max(1, len(descriptions))) * 10)
|
||||
|
||||
# Check for emotional triggers, questions, numbers
|
||||
emotional_words = ['you', 'free', 'because', 'instantly', 'new', 'save', 'proven', 'guarantee', 'love', 'discover']
|
||||
emotional_score = min(10, sum(1 for h in headlines if any(word in h.lower() for word in emotional_words)) +
|
||||
sum(1 for d in descriptions if any(word in d.lower() for word in emotional_words)))
|
||||
|
||||
question_score = min(10, (sum(1 for h in headlines if '?' in h) + sum(1 for d in descriptions if '?' in d)) * 2)
|
||||
|
||||
number_score = min(10, (sum(1 for h in headlines if bool(re.search(r'\d+', h))) +
|
||||
sum(1 for d in descriptions if bool(re.search(r'\d+', d)))) * 2)
|
||||
|
||||
# Calculate overall ad relevance score
|
||||
ad_relevance = (headline_count_score * 0.15) + (headline_length_score * 0.15) + \
|
||||
(description_count_score * 0.15) + (description_length_score * 0.15) + \
|
||||
(emotional_score * 0.2) + (question_score * 0.1) + (number_score * 0.1)
|
||||
|
||||
# Calculate CTA effectiveness (0-10)
|
||||
# Check for clear call to action
|
||||
cta_phrases = ['get', 'buy', 'shop', 'order', 'sign up', 'register', 'download', 'learn', 'discover', 'find', 'call',
|
||||
'contact', 'request', 'start', 'try', 'join', 'subscribe', 'book', 'schedule', 'apply']
|
||||
|
||||
cta_in_headline = any(any(phrase in h.lower() for phrase in cta_phrases) for h in headlines)
|
||||
cta_in_description = any(any(phrase in d.lower() for phrase in cta_phrases) for d in descriptions)
|
||||
|
||||
if cta_in_headline and cta_in_description:
|
||||
cta_effectiveness = 10
|
||||
elif cta_in_headline:
|
||||
cta_effectiveness = 8
|
||||
elif cta_in_description:
|
||||
cta_effectiveness = 7
|
||||
else:
|
||||
cta_effectiveness = 4
|
||||
|
||||
# Calculate landing page relevance (0-10)
|
||||
# In a real implementation, this would analyze the landing page content
|
||||
# For this example, we'll use a simplified approach
|
||||
|
||||
if landing_page:
|
||||
# Check if domain seems relevant to keywords
|
||||
domain = urlparse(landing_page).netloc
|
||||
|
||||
# Check if keywords are in the domain or path
|
||||
keyword_in_url = any(kw.lower() in landing_page.lower() for kw in primary_keywords)
|
||||
|
||||
# Check if URL structure seems appropriate
|
||||
has_https = landing_page.startswith('https://')
|
||||
|
||||
# Calculate landing page score
|
||||
landing_page_relevance = 5 # Base score
|
||||
|
||||
if keyword_in_url:
|
||||
landing_page_relevance += 3
|
||||
|
||||
if has_https:
|
||||
landing_page_relevance += 2
|
||||
|
||||
# Cap at 10
|
||||
landing_page_relevance = min(10, landing_page_relevance)
|
||||
else:
|
||||
landing_page_relevance = 5 # Default score if no landing page provided
|
||||
|
||||
# Calculate overall quality score (0-10)
|
||||
overall_score = (keyword_relevance * 0.4) + (ad_relevance * 0.3) + (cta_effectiveness * 0.2) + (landing_page_relevance * 0.1)
|
||||
|
||||
# Calculate estimated CTR based on quality score
|
||||
# This is a simplified model - in reality, CTR depends on many factors
|
||||
base_ctr = {
|
||||
"Responsive Search Ad": 3.17,
|
||||
"Expanded Text Ad": 2.83,
|
||||
"Call-Only Ad": 3.48,
|
||||
"Dynamic Search Ad": 2.69
|
||||
}.get(ad_type, 3.0)
|
||||
|
||||
# Adjust CTR based on quality score (±50%)
|
||||
quality_factor = (overall_score - 5) / 5 # -1 to 1
|
||||
estimated_ctr = base_ctr * (1 + (quality_factor * 0.5))
|
||||
|
||||
# Calculate estimated conversion rate
|
||||
# Again, this is simplified - actual conversion rates depend on many factors
|
||||
base_conversion_rate = 3.75 # Average conversion rate for search ads
|
||||
|
||||
# Adjust conversion rate based on quality score (±40%)
|
||||
estimated_conversion_rate = base_conversion_rate * (1 + (quality_factor * 0.4))
|
||||
|
||||
# Return the quality score components
|
||||
return {
|
||||
"keyword_relevance": round(keyword_relevance, 1),
|
||||
"ad_relevance": round(ad_relevance, 1),
|
||||
"cta_effectiveness": round(cta_effectiveness, 1),
|
||||
"landing_page_relevance": round(landing_page_relevance, 1),
|
||||
"overall_score": round(overall_score, 1),
|
||||
"estimated_ctr": round(estimated_ctr, 2),
|
||||
"estimated_conversion_rate": round(estimated_conversion_rate, 2)
|
||||
}
|
||||
|
||||
def analyze_keyword_relevance(keywords: List[str], ad_text: str) -> Dict:
|
||||
"""
|
||||
Analyze the relevance of keywords to ad text.
|
||||
|
||||
Args:
|
||||
keywords: List of keywords to analyze
|
||||
ad_text: Combined ad text (headlines and descriptions)
|
||||
|
||||
Returns:
|
||||
Dictionary with keyword relevance analysis
|
||||
"""
|
||||
results = {}
|
||||
|
||||
for keyword in keywords:
|
||||
# Check if keyword is in ad text
|
||||
is_present = keyword.lower() in ad_text.lower()
|
||||
|
||||
# Check if keyword is in the first 100 characters
|
||||
is_in_beginning = keyword.lower() in ad_text.lower()[:100]
|
||||
|
||||
# Count occurrences
|
||||
occurrences = ad_text.lower().count(keyword.lower())
|
||||
|
||||
# Calculate density
|
||||
density = (occurrences * len(keyword)) / len(ad_text) * 100 if len(ad_text) > 0 else 0
|
||||
|
||||
# Store results
|
||||
results[keyword] = {
|
||||
"present": is_present,
|
||||
"in_beginning": is_in_beginning,
|
||||
"occurrences": occurrences,
|
||||
"density": round(density, 2),
|
||||
"optimal_density": 0.5 <= density <= 2.5
|
||||
}
|
||||
|
||||
return results
|
||||
@@ -1,320 +0,0 @@
|
||||
"""
|
||||
Ad Extensions Generator Module
|
||||
|
||||
This module provides functions for generating various types of Google Ads extensions.
|
||||
"""
|
||||
|
||||
from typing import Dict, List, Any, Optional
|
||||
import re
|
||||
from ...gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
|
||||
def generate_extensions(business_name: str, business_description: str, industry: str,
|
||||
primary_keywords: List[str], unique_selling_points: List[str],
|
||||
landing_page: str) -> Dict:
|
||||
"""
|
||||
Generate a complete set of ad extensions based on business information.
|
||||
|
||||
Args:
|
||||
business_name: Name of the business
|
||||
business_description: Description of the business
|
||||
industry: Industry of the business
|
||||
primary_keywords: List of primary keywords
|
||||
unique_selling_points: List of unique selling points
|
||||
landing_page: Landing page URL
|
||||
|
||||
Returns:
|
||||
Dictionary with generated extensions
|
||||
"""
|
||||
# Generate sitelinks
|
||||
sitelinks = generate_sitelinks(business_name, business_description, industry, primary_keywords, landing_page)
|
||||
|
||||
# Generate callouts
|
||||
callouts = generate_callouts(business_name, unique_selling_points, industry)
|
||||
|
||||
# Generate structured snippets
|
||||
snippets = generate_structured_snippets(business_name, business_description, industry, primary_keywords)
|
||||
|
||||
# Return all extensions
|
||||
return {
|
||||
"sitelinks": sitelinks,
|
||||
"callouts": callouts,
|
||||
"structured_snippets": snippets
|
||||
}
|
||||
|
||||
def generate_sitelinks(business_name: str, business_description: str, industry: str,
|
||||
primary_keywords: List[str], landing_page: str) -> List[Dict]:
|
||||
"""
|
||||
Generate sitelink extensions based on business information.
|
||||
|
||||
Args:
|
||||
business_name: Name of the business
|
||||
business_description: Description of the business
|
||||
industry: Industry of the business
|
||||
primary_keywords: List of primary keywords
|
||||
landing_page: Landing page URL
|
||||
|
||||
Returns:
|
||||
List of dictionaries with sitelink information
|
||||
"""
|
||||
# Define common sitelink types by industry
|
||||
industry_sitelinks = {
|
||||
"E-commerce": ["Shop Now", "Best Sellers", "New Arrivals", "Sale Items", "Customer Reviews", "About Us"],
|
||||
"SaaS/Technology": ["Features", "Pricing", "Demo", "Case Studies", "Support", "Blog"],
|
||||
"Healthcare": ["Services", "Locations", "Providers", "Insurance", "Patient Portal", "Contact Us"],
|
||||
"Education": ["Programs", "Admissions", "Campus", "Faculty", "Student Life", "Apply Now"],
|
||||
"Finance": ["Services", "Rates", "Calculators", "Locations", "Apply Now", "About Us"],
|
||||
"Real Estate": ["Listings", "Sell Your Home", "Neighborhoods", "Agents", "Mortgage", "Contact Us"],
|
||||
"Legal": ["Practice Areas", "Attorneys", "Results", "Testimonials", "Free Consultation", "Contact"],
|
||||
"Travel": ["Destinations", "Deals", "Book Now", "Reviews", "FAQ", "Contact Us"],
|
||||
"Food & Beverage": ["Menu", "Locations", "Order Online", "Reservations", "Catering", "About Us"]
|
||||
}
|
||||
|
||||
# Get sitelinks for the specified industry, or use default
|
||||
sitelink_types = industry_sitelinks.get(industry, ["About Us", "Services", "Products", "Contact Us", "Testimonials", "FAQ"])
|
||||
|
||||
# Generate sitelinks
|
||||
sitelinks = []
|
||||
base_url = landing_page.rstrip('/') if landing_page else ""
|
||||
|
||||
for sitelink_type in sitelink_types:
|
||||
# Generate URL path based on sitelink type
|
||||
path = sitelink_type.lower().replace(' ', '-')
|
||||
url = f"{base_url}/{path}" if base_url else f"https://example.com/{path}"
|
||||
|
||||
# Generate description based on sitelink type
|
||||
description = ""
|
||||
if sitelink_type == "About Us":
|
||||
description = f"Learn more about {business_name} and our mission."
|
||||
elif sitelink_type == "Services" or sitelink_type == "Products":
|
||||
description = f"Explore our range of {primary_keywords[0] if primary_keywords else 'offerings'}."
|
||||
elif sitelink_type == "Contact Us":
|
||||
description = f"Get in touch with our team for assistance."
|
||||
elif sitelink_type == "Testimonials" or sitelink_type == "Reviews":
|
||||
description = f"See what our customers say about us."
|
||||
elif sitelink_type == "FAQ":
|
||||
description = f"Find answers to common questions."
|
||||
elif sitelink_type == "Pricing" or sitelink_type == "Rates":
|
||||
description = f"View our competitive pricing options."
|
||||
elif sitelink_type == "Shop Now" or sitelink_type == "Order Online":
|
||||
description = f"Browse and purchase our {primary_keywords[0] if primary_keywords else 'products'} online."
|
||||
|
||||
# Add the sitelink
|
||||
sitelinks.append({
|
||||
"text": sitelink_type,
|
||||
"url": url,
|
||||
"description": description
|
||||
})
|
||||
|
||||
return sitelinks
|
||||
|
||||
def generate_callouts(business_name: str, unique_selling_points: List[str], industry: str) -> List[str]:
|
||||
"""
|
||||
Generate callout extensions based on business information.
|
||||
|
||||
Args:
|
||||
business_name: Name of the business
|
||||
unique_selling_points: List of unique selling points
|
||||
industry: Industry of the business
|
||||
|
||||
Returns:
|
||||
List of callout texts
|
||||
"""
|
||||
# Use provided USPs if available
|
||||
if unique_selling_points and len(unique_selling_points) >= 4:
|
||||
# Ensure callouts are not too long (25 characters max)
|
||||
callouts = []
|
||||
for usp in unique_selling_points:
|
||||
if len(usp) <= 25:
|
||||
callouts.append(usp)
|
||||
else:
|
||||
# Try to truncate at a space
|
||||
truncated = usp[:22] + "..."
|
||||
callouts.append(truncated)
|
||||
|
||||
return callouts[:8] # Return up to 8 callouts
|
||||
|
||||
# Define common callouts by industry
|
||||
industry_callouts = {
|
||||
"E-commerce": ["Free Shipping", "24/7 Customer Service", "Secure Checkout", "Easy Returns", "Price Match Guarantee", "Next Day Delivery", "Satisfaction Guaranteed", "Exclusive Deals"],
|
||||
"SaaS/Technology": ["24/7 Support", "Free Trial", "No Credit Card Required", "Easy Integration", "Data Security", "Cloud-Based", "Regular Updates", "Customizable"],
|
||||
"Healthcare": ["Board Certified", "Most Insurance Accepted", "Same-Day Appointments", "Compassionate Care", "State-of-the-Art Facility", "Experienced Staff", "Convenient Location", "Telehealth Available"],
|
||||
"Education": ["Accredited Programs", "Expert Faculty", "Financial Aid", "Career Services", "Small Class Sizes", "Flexible Schedule", "Online Options", "Hands-On Learning"],
|
||||
"Finance": ["FDIC Insured", "No Hidden Fees", "Personalized Service", "Online Banking", "Mobile App", "Low Interest Rates", "Financial Planning", "Retirement Services"],
|
||||
"Real Estate": ["Free Home Valuation", "Virtual Tours", "Experienced Agents", "Local Expertise", "Financing Available", "Property Management", "Commercial & Residential", "Investment Properties"],
|
||||
"Legal": ["Free Consultation", "No Win No Fee", "Experienced Attorneys", "24/7 Availability", "Proven Results", "Personalized Service", "Multiple Practice Areas", "Aggressive Representation"]
|
||||
}
|
||||
|
||||
# Get callouts for the specified industry, or use default
|
||||
callouts = industry_callouts.get(industry, ["Professional Service", "Experienced Team", "Customer Satisfaction", "Quality Guaranteed", "Competitive Pricing", "Fast Service", "Personalized Solutions", "Trusted Provider"])
|
||||
|
||||
return callouts
|
||||
|
||||
def generate_structured_snippets(business_name: str, business_description: str, industry: str, primary_keywords: List[str]) -> Dict:
|
||||
"""
|
||||
Generate structured snippet extensions based on business information.
|
||||
|
||||
Args:
|
||||
business_name: Name of the business
|
||||
business_description: Description of the business
|
||||
industry: Industry of the business
|
||||
primary_keywords: List of primary keywords
|
||||
|
||||
Returns:
|
||||
Dictionary with structured snippet information
|
||||
"""
|
||||
# Define common snippet headers and values by industry
|
||||
industry_snippets = {
|
||||
"E-commerce": {
|
||||
"header": "Brands",
|
||||
"values": ["Nike", "Adidas", "Apple", "Samsung", "Sony", "LG", "Dell", "HP"]
|
||||
},
|
||||
"SaaS/Technology": {
|
||||
"header": "Services",
|
||||
"values": ["Cloud Storage", "Data Analytics", "CRM", "Project Management", "Email Marketing", "Cybersecurity", "API Integration", "Automation"]
|
||||
},
|
||||
"Healthcare": {
|
||||
"header": "Services",
|
||||
"values": ["Preventive Care", "Diagnostics", "Treatment", "Surgery", "Rehabilitation", "Counseling", "Telemedicine", "Wellness Programs"]
|
||||
},
|
||||
"Education": {
|
||||
"header": "Courses",
|
||||
"values": ["Business", "Technology", "Healthcare", "Design", "Engineering", "Education", "Arts", "Sciences"]
|
||||
},
|
||||
"Finance": {
|
||||
"header": "Services",
|
||||
"values": ["Checking Accounts", "Savings Accounts", "Loans", "Mortgages", "Investments", "Retirement Planning", "Insurance", "Wealth Management"]
|
||||
},
|
||||
"Real Estate": {
|
||||
"header": "Types",
|
||||
"values": ["Single-Family Homes", "Condos", "Townhouses", "Apartments", "Commercial", "Land", "New Construction", "Luxury Homes"]
|
||||
},
|
||||
"Legal": {
|
||||
"header": "Services",
|
||||
"values": ["Personal Injury", "Family Law", "Criminal Defense", "Estate Planning", "Business Law", "Immigration", "Real Estate Law", "Intellectual Property"]
|
||||
}
|
||||
}
|
||||
|
||||
# Get snippets for the specified industry, or use default
|
||||
snippet_info = industry_snippets.get(industry, {
|
||||
"header": "Services",
|
||||
"values": ["Consultation", "Assessment", "Implementation", "Support", "Maintenance", "Training", "Customization", "Analysis"]
|
||||
})
|
||||
|
||||
# If we have primary keywords, try to incorporate them
|
||||
if primary_keywords:
|
||||
# Try to determine a better header based on keywords
|
||||
service_keywords = ["service", "support", "consultation", "assistance", "help"]
|
||||
product_keywords = ["product", "item", "good", "merchandise"]
|
||||
brand_keywords = ["brand", "make", "manufacturer"]
|
||||
|
||||
for kw in primary_keywords:
|
||||
kw_lower = kw.lower()
|
||||
if any(service_word in kw_lower for service_word in service_keywords):
|
||||
snippet_info["header"] = "Services"
|
||||
break
|
||||
elif any(product_word in kw_lower for product_word in product_keywords):
|
||||
snippet_info["header"] = "Products"
|
||||
break
|
||||
elif any(brand_word in kw_lower for brand_word in brand_keywords):
|
||||
snippet_info["header"] = "Brands"
|
||||
break
|
||||
|
||||
return snippet_info
|
||||
|
||||
def generate_custom_extensions(business_info: Dict, extension_type: str) -> Any:
|
||||
"""
|
||||
Generate custom extensions using AI based on business information.
|
||||
|
||||
Args:
|
||||
business_info: Dictionary with business information
|
||||
extension_type: Type of extension to generate
|
||||
|
||||
Returns:
|
||||
Generated extension data
|
||||
"""
|
||||
# Extract business information
|
||||
business_name = business_info.get("business_name", "")
|
||||
business_description = business_info.get("business_description", "")
|
||||
industry = business_info.get("industry", "")
|
||||
primary_keywords = business_info.get("primary_keywords", [])
|
||||
unique_selling_points = business_info.get("unique_selling_points", [])
|
||||
|
||||
# Create a prompt based on extension type
|
||||
if extension_type == "sitelinks":
|
||||
prompt = f"""
|
||||
Generate 6 sitelink extensions for a Google Ads campaign for the following business:
|
||||
|
||||
Business Name: {business_name}
|
||||
Business Description: {business_description}
|
||||
Industry: {industry}
|
||||
Keywords: {', '.join(primary_keywords)}
|
||||
|
||||
For each sitelink, provide:
|
||||
1. Link text (max 25 characters)
|
||||
2. Description line 1 (max 35 characters)
|
||||
3. Description line 2 (max 35 characters)
|
||||
|
||||
Format the response as a JSON array of objects with "text", "description1", and "description2" fields.
|
||||
"""
|
||||
elif extension_type == "callouts":
|
||||
prompt = f"""
|
||||
Generate 8 callout extensions for a Google Ads campaign for the following business:
|
||||
|
||||
Business Name: {business_name}
|
||||
Business Description: {business_description}
|
||||
Industry: {industry}
|
||||
Keywords: {', '.join(primary_keywords)}
|
||||
Unique Selling Points: {', '.join(unique_selling_points)}
|
||||
|
||||
Each callout should:
|
||||
1. Be 25 characters or less
|
||||
2. Highlight a feature, benefit, or unique selling point
|
||||
3. Be concise and impactful
|
||||
|
||||
Format the response as a JSON array of strings.
|
||||
"""
|
||||
elif extension_type == "structured_snippets":
|
||||
prompt = f"""
|
||||
Generate structured snippet extensions for a Google Ads campaign for the following business:
|
||||
|
||||
Business Name: {business_name}
|
||||
Business Description: {business_description}
|
||||
Industry: {industry}
|
||||
Keywords: {', '.join(primary_keywords)}
|
||||
|
||||
Provide:
|
||||
1. The most appropriate header type (e.g., Brands, Services, Products, Courses, etc.)
|
||||
2. 8 values that are relevant to the business (each 25 characters or less)
|
||||
|
||||
Format the response as a JSON object with "header" and "values" fields.
|
||||
"""
|
||||
else:
|
||||
return None
|
||||
|
||||
# Generate the extensions using the LLM
|
||||
try:
|
||||
response = llm_text_gen(prompt)
|
||||
|
||||
# Process the response based on extension type
|
||||
# In a real implementation, you would parse the JSON response
|
||||
# For this example, we'll return a placeholder
|
||||
|
||||
if extension_type == "sitelinks":
|
||||
return [
|
||||
{"text": "About Us", "description1": "Learn about our company", "description2": "Our history and mission"},
|
||||
{"text": "Services", "description1": "Explore our service offerings", "description2": "Solutions for your needs"},
|
||||
{"text": "Products", "description1": "Browse our product catalog", "description2": "Quality items at great prices"},
|
||||
{"text": "Contact Us", "description1": "Get in touch with our team", "description2": "We're here to help you"},
|
||||
{"text": "Testimonials", "description1": "See what customers say", "description2": "Real reviews from real people"},
|
||||
{"text": "FAQ", "description1": "Frequently asked questions", "description2": "Find quick answers here"}
|
||||
]
|
||||
elif extension_type == "callouts":
|
||||
return ["Free Shipping", "24/7 Support", "Money-Back Guarantee", "Expert Team", "Premium Quality", "Fast Service", "Affordable Prices", "Satisfaction Guaranteed"]
|
||||
elif extension_type == "structured_snippets":
|
||||
return {"header": "Services", "values": ["Consultation", "Installation", "Maintenance", "Repair", "Training", "Support", "Design", "Analysis"]}
|
||||
else:
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error generating extensions: {str(e)}")
|
||||
return None
|
||||
@@ -1,219 +0,0 @@
|
||||
"""
|
||||
Ad Templates Module
|
||||
|
||||
This module provides templates for different ad types and industries.
|
||||
"""
|
||||
|
||||
from typing import Dict, List, Any
|
||||
|
||||
def get_industry_templates(industry: str) -> Dict:
|
||||
"""
|
||||
Get ad templates specific to an industry.
|
||||
|
||||
Args:
|
||||
industry: The industry to get templates for
|
||||
|
||||
Returns:
|
||||
Dictionary with industry-specific templates
|
||||
"""
|
||||
# Define templates for different industries
|
||||
templates = {
|
||||
"E-commerce": {
|
||||
"headline_templates": [
|
||||
"{product} - {benefit} | {business_name}",
|
||||
"Shop {product} - {discount} Off Today",
|
||||
"Top-Rated {product} - Free Shipping",
|
||||
"{benefit} with Our {product}",
|
||||
"New {product} Collection - {benefit}",
|
||||
"{discount}% Off {product} - Limited Time",
|
||||
"Buy {product} Online - Fast Delivery",
|
||||
"{product} Sale Ends {timeframe}",
|
||||
"Best-Selling {product} from {business_name}",
|
||||
"Premium {product} - {benefit}"
|
||||
],
|
||||
"description_templates": [
|
||||
"Shop our selection of {product} and enjoy {benefit}. Free shipping on orders over ${amount}. Order now!",
|
||||
"Looking for quality {product}? Get {benefit} with our {product}. {discount} off your first order!",
|
||||
"{business_name} offers premium {product} with {benefit}. Shop online or visit our store today!",
|
||||
"Discover our {product} collection. {benefit} guaranteed or your money back. Order now and save {discount}!"
|
||||
],
|
||||
"emotional_triggers": ["exclusive", "limited time", "sale", "discount", "free shipping", "bestseller", "new arrival"],
|
||||
"call_to_actions": ["Shop Now", "Buy Today", "Order Online", "Get Yours", "Add to Cart", "Save Today"]
|
||||
},
|
||||
"SaaS/Technology": {
|
||||
"headline_templates": [
|
||||
"{product} Software - {benefit}",
|
||||
"Try {product} Free for {timeframe}",
|
||||
"{benefit} with Our {product} Platform",
|
||||
"{product} - Rated #1 for {feature}",
|
||||
"New {feature} in Our {product} Software",
|
||||
"{business_name} - {benefit} Software",
|
||||
"Streamline {pain_point} with {product}",
|
||||
"{product} Software - {discount} Off",
|
||||
"Enterprise-Grade {product} for {audience}",
|
||||
"{product} - {benefit} Guaranteed"
|
||||
],
|
||||
"description_templates": [
|
||||
"{business_name}'s {product} helps you {benefit}. Try it free for {timeframe}. No credit card required.",
|
||||
"Struggling with {pain_point}? Our {product} provides {benefit}. Join {number}+ satisfied customers.",
|
||||
"Our {product} platform offers {feature} to help you {benefit}. Rated {rating}/5 by {source}.",
|
||||
"{product} by {business_name}: {benefit} for your business. Plans starting at ${price}/month."
|
||||
],
|
||||
"emotional_triggers": ["efficient", "time-saving", "seamless", "integrated", "secure", "scalable", "innovative"],
|
||||
"call_to_actions": ["Start Free Trial", "Request Demo", "Learn More", "Sign Up Free", "Get Started", "See Plans"]
|
||||
},
|
||||
"Healthcare": {
|
||||
"headline_templates": [
|
||||
"{service} in {location} | {business_name}",
|
||||
"Expert {service} - {benefit}",
|
||||
"Quality {service} for {audience}",
|
||||
"{business_name} - {credential} {professionals}",
|
||||
"Same-Day {service} Appointments",
|
||||
"{service} Specialists in {location}",
|
||||
"Affordable {service} - {benefit}",
|
||||
"{symptom}? Get {service} Today",
|
||||
"Advanced {service} Technology",
|
||||
"Compassionate {service} Care"
|
||||
],
|
||||
"description_templates": [
|
||||
"{business_name} provides expert {service} with {benefit}. Our {credential} team is ready to help. Schedule today!",
|
||||
"Experiencing {symptom}? Our {professionals} offer {service} with {benefit}. Most insurance accepted.",
|
||||
"Quality {service} in {location}. {benefit} from our experienced team. Call now to schedule your appointment.",
|
||||
"Our {service} center provides {benefit} for {audience}. Open {days} with convenient hours."
|
||||
],
|
||||
"emotional_triggers": ["trusted", "experienced", "compassionate", "advanced", "personalized", "comprehensive", "gentle"],
|
||||
"call_to_actions": ["Schedule Now", "Book Appointment", "Call Today", "Free Consultation", "Learn More", "Find Relief"]
|
||||
},
|
||||
"Real Estate": {
|
||||
"headline_templates": [
|
||||
"{property_type} in {location} | {business_name}",
|
||||
"{property_type} for {price_range} - {location}",
|
||||
"Find Your Dream {property_type} in {location}",
|
||||
"{feature} {property_type} - {location}",
|
||||
"New {property_type} Listings in {location}",
|
||||
"Sell Your {property_type} in {timeframe}",
|
||||
"{business_name} - {credential} {professionals}",
|
||||
"{property_type} {benefit} - {location}",
|
||||
"Exclusive {property_type} Listings",
|
||||
"{number}+ {property_type} Available Now"
|
||||
],
|
||||
"description_templates": [
|
||||
"Looking for {property_type} in {location}? {business_name} offers {benefit}. Browse our listings or call us today!",
|
||||
"Sell your {property_type} in {location} with {business_name}. Our {professionals} provide {benefit}. Free valuation!",
|
||||
"{business_name}: {credential} {professionals} helping you find the perfect {property_type} in {location}. Call now!",
|
||||
"Discover {feature} {property_type} in {location}. Prices from {price_range}. Schedule a viewing today!"
|
||||
],
|
||||
"emotional_triggers": ["dream home", "exclusive", "luxury", "investment", "perfect location", "spacious", "modern"],
|
||||
"call_to_actions": ["View Listings", "Schedule Viewing", "Free Valuation", "Call Now", "Learn More", "Get Pre-Approved"]
|
||||
}
|
||||
}
|
||||
|
||||
# Return templates for the specified industry, or a default if not found
|
||||
return templates.get(industry, {
|
||||
"headline_templates": [
|
||||
"{product/service} - {benefit} | {business_name}",
|
||||
"Professional {product/service} - {benefit}",
|
||||
"{benefit} with Our {product/service}",
|
||||
"{business_name} - {credential} {product/service}",
|
||||
"Quality {product/service} for {audience}",
|
||||
"Affordable {product/service} - {benefit}",
|
||||
"{product/service} in {location}",
|
||||
"{feature} {product/service} by {business_name}",
|
||||
"Experienced {product/service} Provider",
|
||||
"{product/service} - Satisfaction Guaranteed"
|
||||
],
|
||||
"description_templates": [
|
||||
"{business_name} offers professional {product/service} with {benefit}. Contact us today to learn more!",
|
||||
"Looking for quality {product/service}? {business_name} provides {benefit}. Call now for more information.",
|
||||
"Our {product/service} helps you {benefit}. Trusted by {number}+ customers. Contact us today!",
|
||||
"{business_name}: {credential} {product/service} provider. We offer {benefit} for {audience}. Learn more!"
|
||||
],
|
||||
"emotional_triggers": ["professional", "quality", "trusted", "experienced", "affordable", "reliable", "satisfaction"],
|
||||
"call_to_actions": ["Contact Us", "Learn More", "Call Now", "Get Quote", "Visit Website", "Schedule Consultation"]
|
||||
})
|
||||
|
||||
def get_ad_type_templates(ad_type: str) -> Dict:
|
||||
"""
|
||||
Get templates specific to an ad type.
|
||||
|
||||
Args:
|
||||
ad_type: The ad type to get templates for
|
||||
|
||||
Returns:
|
||||
Dictionary with ad type-specific templates
|
||||
"""
|
||||
# Define templates for different ad types
|
||||
templates = {
|
||||
"Responsive Search Ad": {
|
||||
"headline_count": 15,
|
||||
"description_count": 4,
|
||||
"headline_max_length": 30,
|
||||
"description_max_length": 90,
|
||||
"best_practices": [
|
||||
"Include at least 3 headlines with keywords",
|
||||
"Create headlines with different lengths",
|
||||
"Include at least 1 headline with a call to action",
|
||||
"Include at least 1 headline with your brand name",
|
||||
"Create descriptions that complement each other",
|
||||
"Include keywords in at least 2 descriptions",
|
||||
"Include a call to action in at least 1 description"
|
||||
]
|
||||
},
|
||||
"Expanded Text Ad": {
|
||||
"headline_count": 3,
|
||||
"description_count": 2,
|
||||
"headline_max_length": 30,
|
||||
"description_max_length": 90,
|
||||
"best_practices": [
|
||||
"Include keywords in Headline 1",
|
||||
"Use a call to action in Headline 2 or 3",
|
||||
"Include your brand name in one headline",
|
||||
"Make descriptions complementary but able to stand alone",
|
||||
"Include keywords in at least one description",
|
||||
"Include a call to action in at least one description"
|
||||
]
|
||||
},
|
||||
"Call-Only Ad": {
|
||||
"headline_count": 2,
|
||||
"description_count": 2,
|
||||
"headline_max_length": 30,
|
||||
"description_max_length": 90,
|
||||
"best_practices": [
|
||||
"Focus on encouraging phone calls",
|
||||
"Include language like 'Call now', 'Speak to an expert', etc.",
|
||||
"Mention phone availability (e.g., '24/7', 'Available now')",
|
||||
"Include benefits of calling rather than clicking",
|
||||
"Be clear about who will answer the call",
|
||||
"Include any special offers for callers"
|
||||
]
|
||||
},
|
||||
"Dynamic Search Ad": {
|
||||
"headline_count": 0, # Headlines are dynamically generated
|
||||
"description_count": 2,
|
||||
"headline_max_length": 0, # N/A
|
||||
"description_max_length": 90,
|
||||
"best_practices": [
|
||||
"Create descriptions that work with any dynamically generated headline",
|
||||
"Focus on your unique selling points",
|
||||
"Include a strong call to action",
|
||||
"Highlight benefits that apply across your product/service range",
|
||||
"Avoid specific product mentions that might not match the dynamic headline"
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
# Return templates for the specified ad type, or a default if not found
|
||||
return templates.get(ad_type, {
|
||||
"headline_count": 3,
|
||||
"description_count": 2,
|
||||
"headline_max_length": 30,
|
||||
"description_max_length": 90,
|
||||
"best_practices": [
|
||||
"Include keywords in headlines",
|
||||
"Use a call to action",
|
||||
"Include your brand name",
|
||||
"Make descriptions informative and compelling",
|
||||
"Include keywords in descriptions",
|
||||
"Highlight unique selling points"
|
||||
]
|
||||
})
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,192 +0,0 @@
|
||||
import os
|
||||
import configparser
|
||||
import streamlit as st
|
||||
from langchain_google_genai import ChatGoogleGenerativeAI
|
||||
|
||||
# Initialize session state variables if not already done
|
||||
if 'progress' not in st.session_state:
|
||||
st.session_state.progress = 0
|
||||
|
||||
|
||||
def create_agents(search_keywords):
|
||||
"""Create agents for content creation."""
|
||||
try:
|
||||
from crewai import Agent
|
||||
from crewai_tools import SerperDevTool
|
||||
except ImportError:
|
||||
raise ImportError("The 'crewai' and/or 'crewai_tools' package is not installed. Please install them to use AI Agents Crew Writer features.")
|
||||
search_tool = SerperDevTool()
|
||||
google_api_key = os.getenv("GEMINI_API_KEY")
|
||||
|
||||
llm = ChatGoogleGenerativeAI(
|
||||
model="gemini-1.5-flash-latest", verbose=True, temperature=0.6, google_api_key=google_api_key
|
||||
)
|
||||
|
||||
try:
|
||||
role, goal, backstory = read_config("content_researcher")
|
||||
content_researcher = Agent(
|
||||
role=role, goal=goal, backstory=backstory, tools=[search_tool], memory=True,
|
||||
verbose=True, max_rpm=None, max_iter=10, allow_delegation=False, llm=llm
|
||||
)
|
||||
|
||||
role, goal, backstory = read_config("content_outliner")
|
||||
content_outliner = Agent(
|
||||
role=role, goal=goal, backstory=backstory, memory=True,
|
||||
verbose=True, tools=[search_tool], max_rpm=10, max_iter=10, allow_delegation=False, llm=llm
|
||||
)
|
||||
|
||||
role, goal, backstory = read_config("content_writer")
|
||||
content_writer = Agent(
|
||||
role=role, goal=goal, backstory=backstory, memory=True,
|
||||
verbose=True, max_rpm=10, max_iter=15, allow_delegation=False, llm=llm
|
||||
)
|
||||
|
||||
role, goal, backstory = read_config("content_reviewer")
|
||||
content_reviewer = Agent(
|
||||
role=role, goal=goal, backstory=backstory, memory=True,
|
||||
verbose=True, max_rpm=10, max_iter=10, allow_delegation=False, llm=llm
|
||||
)
|
||||
|
||||
except Exception as err:
|
||||
st.error(f"Error creating agents: {err}")
|
||||
st.stop()
|
||||
|
||||
return [content_researcher, content_outliner, content_writer, content_reviewer]
|
||||
|
||||
def create_tasks(agents, search_keywords):
|
||||
"""Create tasks for the agents."""
|
||||
try:
|
||||
from crewai import Task
|
||||
except ImportError:
|
||||
raise ImportError("The 'crewai' package is not installed. Please install it to use AI Agents Crew Writer features.")
|
||||
try:
|
||||
task_description, expected_output = read_config("research_task")
|
||||
research_task = Task(
|
||||
description=f"The main focus keywords are: '{search_keywords}'.\n{task_description}.",
|
||||
expected_output=expected_output,
|
||||
agent=agents[0]
|
||||
)
|
||||
|
||||
task_description, expected_output = read_config("outline_task")
|
||||
outline_task = Task(
|
||||
description=f"{task_description}.\nThe main focus keywords are {search_keywords}",
|
||||
expected_output=expected_output,
|
||||
agent=agents[1]
|
||||
)
|
||||
|
||||
task_description, expected_output = read_config("writer_task")
|
||||
writer_task = Task(
|
||||
description=f"{task_description}\nThe main focus keywords are {search_keywords}.",
|
||||
expected_output=expected_output,
|
||||
agent=agents[2]
|
||||
)
|
||||
|
||||
task_description, expected_output = read_config("review_task")
|
||||
proofread_task = Task(
|
||||
description=f"{task_description}.\nThe main focus keywords are: {search_keywords}.",
|
||||
expected_output=expected_output,
|
||||
agent=agents[3]
|
||||
)
|
||||
|
||||
except Exception as err:
|
||||
st.error(f"Error creating tasks: {err}")
|
||||
st.stop()
|
||||
|
||||
return [research_task, outline_task, writer_task, proofread_task]
|
||||
|
||||
def execute_tasks(agents, tasks, lang):
|
||||
"""Execute tasks with the agents."""
|
||||
try:
|
||||
from crewai import Crew
|
||||
except ImportError:
|
||||
raise ImportError("The 'crewai' package is not installed. Please install it to use AI Agents Crew Writer features.")
|
||||
crew = Crew(
|
||||
agents=agents,
|
||||
tasks=tasks,
|
||||
verbose=2,
|
||||
language=lang
|
||||
)
|
||||
try:
|
||||
result = crew.kickoff()
|
||||
except Exception as err:
|
||||
st.error(f"Error executing tasks: {err}")
|
||||
st.stop()
|
||||
return result
|
||||
|
||||
def read_config(which_member):
|
||||
"""Reads configuration for the specified agent or task."""
|
||||
team_dir = os.path.join(os.getcwd(), "lib", "workspace", "my_content_team")
|
||||
config_file = None
|
||||
|
||||
if 'content_researcher' in which_member or 'research_task' in which_member:
|
||||
config_file = os.path.join(team_dir, "content_researcher.txt")
|
||||
elif 'content_writer' in which_member or 'writer_task' in which_member:
|
||||
config_file = os.path.join(team_dir, "content_writer.txt")
|
||||
elif 'content_reviewer' in which_member or 'review_task' in which_member:
|
||||
config_file = os.path.join(team_dir, "content_reviewer.txt")
|
||||
elif 'content_outliner' in which_member or 'outline_task' in which_member:
|
||||
config_file = os.path.join(team_dir, "content_outliner.txt")
|
||||
|
||||
try:
|
||||
config = configparser.ConfigParser()
|
||||
config.read(config_file)
|
||||
role = config.get('main', 'role')
|
||||
goal = config.get('main', 'goal')
|
||||
backstory = config.get('backstory', 'text')
|
||||
except Exception as err:
|
||||
st.error(f"Error reading config: {err}")
|
||||
st.stop()
|
||||
|
||||
if 'task' not in which_member:
|
||||
return role, goal, backstory
|
||||
else:
|
||||
try:
|
||||
task_description = config.get('task', 'task_description')
|
||||
expected_output = config.get('task', 'task_expected_output')
|
||||
except Exception as err:
|
||||
st.error(f"Error reading task config: {err}")
|
||||
st.stop()
|
||||
return task_description, expected_output
|
||||
|
||||
|
||||
def ai_agents_writers(search_keywords, lang="en"):
|
||||
"""Main function to kickoff AI Agents content team."""
|
||||
|
||||
progress_bar = st.progress(0)
|
||||
status_text = st.empty()
|
||||
|
||||
st.session_state.progress = 0
|
||||
status_text.text("Setting up environment...")
|
||||
status_text.text("Creating Agents team...")
|
||||
try:
|
||||
agents = create_agents(search_keywords)
|
||||
st.session_state.progress += 10
|
||||
progress_bar.progress(st.session_state.progress)
|
||||
except Exception as err:
|
||||
st.error(f"Failed in creating Agents team: {err}")
|
||||
st.stop()
|
||||
|
||||
status_text.text("Creating tasks for Agents team...")
|
||||
try:
|
||||
tasks = create_tasks(agents, search_keywords)
|
||||
st.session_state.progress += 25
|
||||
progress_bar.progress(st.session_state.progress)
|
||||
except Exception as err:
|
||||
st.error(f"Failed in creating tasks for Agents team: {err}")
|
||||
st.stop()
|
||||
|
||||
status_text.text("AI Agents busy writing your content...")
|
||||
try:
|
||||
result = execute_tasks(agents, tasks, lang)
|
||||
st.session_state.progress += 60
|
||||
progress_bar.progress(st.session_state.progress)
|
||||
status_text.text("Tasks executed successfully.")
|
||||
st.success("Successfully executed tasks.")
|
||||
|
||||
# Display result with an option to copy the content
|
||||
st.markdown("### Result")
|
||||
st.code(result, language='markdown')
|
||||
st.download_button('Download Content', data=result, file_name='alwrity_result.md')
|
||||
except Exception as err:
|
||||
st.error(f"Failed to execute tasks: {err}")
|
||||
|
||||
@@ -1,192 +0,0 @@
|
||||
# AI-Powered FAQ Generator
|
||||
|
||||
A sophisticated FAQ generation system that creates comprehensive, well-researched FAQs from various content sources. This tool leverages AI to analyze content, conduct web research, and generate detailed FAQs with customizable options.
|
||||
|
||||
## Features
|
||||
|
||||
### Content Processing
|
||||
- **Multiple Input Sources**
|
||||
- Direct text input
|
||||
- File uploads (DOCX, TXT)
|
||||
- URL content extraction
|
||||
- Support for any content type (general, technical, educational, etc.)
|
||||
|
||||
### Research Capabilities
|
||||
- **Multi-level Search Depth**
|
||||
- **Basic**: Google Search for quick, general information
|
||||
- **Comprehensive**: Tavily AI for detailed, in-depth research
|
||||
- **Expert**: Metaphor AI for specialized, expert-level content
|
||||
|
||||
### Customization Options
|
||||
- **Target Audience**
|
||||
- Beginner
|
||||
- Intermediate
|
||||
- Expert
|
||||
|
||||
- **FAQ Style**
|
||||
- Technical
|
||||
- Conversational
|
||||
- Professional
|
||||
|
||||
- **Advanced Features**
|
||||
- Emoji inclusion
|
||||
- Code example generation
|
||||
- Reference integration
|
||||
- Customizable time range for research
|
||||
- Multi-language support
|
||||
|
||||
### Output Formats
|
||||
- Interactive preview
|
||||
- Markdown
|
||||
- HTML
|
||||
- JSON
|
||||
|
||||
## Installation
|
||||
|
||||
1. Clone the repository
|
||||
2. Install dependencies:
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
### Basic Usage
|
||||
```python
|
||||
from lib.ai_writers.ai_blog_faqs_writer.faqs_generator_blog import FAQGenerator, FAQConfig
|
||||
|
||||
# Initialize with default configuration
|
||||
generator = FAQGenerator()
|
||||
|
||||
# Generate FAQs from content
|
||||
faqs = await generator.generate_faqs("Your content here")
|
||||
```
|
||||
|
||||
### Advanced Configuration
|
||||
```python
|
||||
from lib.ai_writers.ai_blog_faqs_writer.faqs_generator_blog import (
|
||||
FAQGenerator, FAQConfig, TargetAudience, FAQStyle, SearchDepth
|
||||
)
|
||||
|
||||
# Custom configuration
|
||||
config = FAQConfig(
|
||||
num_faqs=10,
|
||||
target_audience=TargetAudience.INTERMEDIATE,
|
||||
faq_style=FAQStyle.TECHNICAL,
|
||||
include_emojis=True,
|
||||
include_code_examples=True,
|
||||
include_references=True,
|
||||
search_depth=SearchDepth.COMPREHENSIVE,
|
||||
time_range="last_6_months",
|
||||
language="English"
|
||||
)
|
||||
|
||||
generator = FAQGenerator(config)
|
||||
```
|
||||
|
||||
### Web Interface
|
||||
Run the Streamlit interface:
|
||||
```bash
|
||||
streamlit run lib/ai_writers/ai_blog_faqs_writer/faqs_ui.py
|
||||
```
|
||||
|
||||
## Research Process
|
||||
|
||||
1. **Content Analysis**
|
||||
- Identifies key topics and concepts
|
||||
- Extracts potential questions
|
||||
- Determines research requirements
|
||||
|
||||
2. **Web Research**
|
||||
- Selects appropriate search function based on depth
|
||||
- Gathers relevant information
|
||||
- Validates and cross-references data
|
||||
|
||||
3. **FAQ Generation**
|
||||
- Creates comprehensive questions
|
||||
- Provides detailed answers
|
||||
- Includes code examples (if applicable)
|
||||
- Adds references and citations
|
||||
|
||||
## Output Structure
|
||||
|
||||
Each FAQ item includes:
|
||||
- Question
|
||||
- Detailed answer
|
||||
- Category
|
||||
- Code example (if applicable)
|
||||
- References
|
||||
- Confidence score
|
||||
- Last updated timestamp
|
||||
|
||||
## Configuration Options
|
||||
|
||||
### FAQConfig Parameters
|
||||
- `num_faqs`: Number of FAQs to generate (default: 5)
|
||||
- `target_audience`: Target audience level (default: INTERMEDIATE)
|
||||
- `faq_style`: Writing style (default: PROFESSIONAL)
|
||||
- `include_emojis`: Whether to include emojis (default: True)
|
||||
- `include_code_examples`: Whether to include code examples (default: True)
|
||||
- `include_references`: Whether to include references (default: True)
|
||||
- `search_depth`: Research depth level (default: COMPREHENSIVE)
|
||||
- `time_range`: Time range for research (default: "last_6_months")
|
||||
- `language`: Output language (default: "English")
|
||||
|
||||
## Research Depth Options
|
||||
|
||||
### Basic (Google Search)
|
||||
- Quick, general information
|
||||
- Broad coverage
|
||||
- Suitable for basic topics
|
||||
|
||||
### Comprehensive (Tavily AI)
|
||||
- Detailed, in-depth research
|
||||
- Multiple source integration
|
||||
- Best for most use cases
|
||||
|
||||
### Expert (Metaphor AI)
|
||||
- Specialized, expert-level content
|
||||
- Advanced topic coverage
|
||||
- Technical and academic focus
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Content Preparation**
|
||||
- Provide clear, well-structured content
|
||||
- Include key terms and concepts
|
||||
- Specify target audience and style
|
||||
|
||||
2. **Research Selection**
|
||||
- Use Basic for general topics
|
||||
- Choose Comprehensive for detailed analysis
|
||||
- Select Expert for technical subjects
|
||||
|
||||
3. **Output Review**
|
||||
- Verify accuracy of information
|
||||
- Check code examples
|
||||
- Validate references
|
||||
|
||||
## Contributing
|
||||
|
||||
1. Fork the repository
|
||||
2. Create a feature branch
|
||||
3. Commit your changes
|
||||
4. Push to the branch
|
||||
5. Create a Pull Request
|
||||
|
||||
## License
|
||||
|
||||
This project is licensed under the MIT License - see the LICENSE file for details.
|
||||
|
||||
## Support
|
||||
|
||||
For support, please open an issue in the repository or contact the maintainers.
|
||||
|
||||
## Acknowledgments
|
||||
|
||||
- OpenAI for GPT integration
|
||||
- Google Search API
|
||||
- Tavily AI
|
||||
- Metaphor AI
|
||||
- BeautifulSoup for web scraping
|
||||
- Streamlit for UI
|
||||
@@ -1,444 +0,0 @@
|
||||
"""
|
||||
Enhanced FAQ Generator
|
||||
|
||||
This module provides a comprehensive FAQ generation system that can create detailed,
|
||||
well-researched FAQs from various content sources with customizable options.
|
||||
"""
|
||||
|
||||
import sys
|
||||
import json
|
||||
import re
|
||||
from typing import Dict, List, Optional, Union
|
||||
from pathlib import Path
|
||||
from enum import Enum
|
||||
from dataclasses import dataclass
|
||||
from loguru import logger
|
||||
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
from lib.ai_web_researcher.google_serp_search import google_search
|
||||
from lib.ai_web_researcher.tavily_ai_search import do_tavily_ai_search
|
||||
from lib.ai_web_researcher.metaphor_basic_neural_web_search import metaphor_search_articles
|
||||
|
||||
logger.remove()
|
||||
logger.add(sys.stdout,
|
||||
colorize=True,
|
||||
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}")
|
||||
|
||||
class TargetAudience(Enum):
|
||||
BEGINNER = "beginner"
|
||||
INTERMEDIATE = "intermediate"
|
||||
EXPERT = "expert"
|
||||
|
||||
class FAQStyle(Enum):
|
||||
TECHNICAL = "technical"
|
||||
CONVERSATIONAL = "conversational"
|
||||
PROFESSIONAL = "professional"
|
||||
|
||||
class SearchDepth(Enum):
|
||||
BASIC = "basic"
|
||||
COMPREHENSIVE = "comprehensive"
|
||||
EXPERT = "expert"
|
||||
|
||||
@dataclass
|
||||
class FAQConfig:
|
||||
"""Configuration for FAQ generation."""
|
||||
num_faqs: int = 5
|
||||
target_audience: TargetAudience = TargetAudience.INTERMEDIATE
|
||||
faq_style: FAQStyle = FAQStyle.PROFESSIONAL
|
||||
include_emojis: bool = True
|
||||
include_code_examples: bool = True
|
||||
include_references: bool = True
|
||||
search_depth: SearchDepth = SearchDepth.COMPREHENSIVE
|
||||
time_range: str = "last_6_months"
|
||||
exclude_domains: List[str] = None
|
||||
language: str = "English"
|
||||
selected_search_queries: List[str] = None
|
||||
|
||||
@dataclass
|
||||
class FAQItem:
|
||||
"""Individual FAQ item with metadata."""
|
||||
question: str
|
||||
answer: str
|
||||
category: str
|
||||
code_example: Optional[str] = None
|
||||
references: List[Dict[str, str]] = None
|
||||
confidence_score: float = 0.0
|
||||
last_updated: str = None
|
||||
|
||||
class FAQGenerator:
|
||||
"""Enhanced FAQ Generator with research capabilities."""
|
||||
|
||||
def __init__(self, config: Optional[FAQConfig] = None):
|
||||
"""Initialize the FAQ generator with optional configuration."""
|
||||
self.config = config or FAQConfig()
|
||||
self.faqs: List[FAQItem] = []
|
||||
self.research_results = {}
|
||||
self.search_queries = []
|
||||
|
||||
def generate_search_queries(self, content: str) -> List[str]:
|
||||
"""Generate search queries based on the content."""
|
||||
try:
|
||||
prompt = f"""Based on the following content, generate 5 specific search queries that would help create comprehensive FAQs.
|
||||
Content: {content}
|
||||
|
||||
Guidelines for search queries:
|
||||
1. Focus on key concepts and terms
|
||||
2. Include common questions users might have
|
||||
3. Cover technical aspects that need clarification
|
||||
4. Include best practices and recommendations
|
||||
5. Make queries specific and focused
|
||||
|
||||
Please provide exactly 5 search queries, one per line.
|
||||
Do not include numbers or bullet points in the queries.
|
||||
"""
|
||||
|
||||
response = llm_text_gen(prompt)
|
||||
# Clean up the queries by removing numbers and extra spaces
|
||||
queries = []
|
||||
for line in response.split('\n'):
|
||||
# Remove any leading numbers, dots, or spaces
|
||||
cleaned = re.sub(r'^\d+\.\s*', '', line.strip())
|
||||
if cleaned:
|
||||
queries.append(cleaned)
|
||||
|
||||
self.search_queries = queries[:5] # Ensure we only get 5 queries
|
||||
return self.search_queries
|
||||
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to generate search queries: {err}")
|
||||
return []
|
||||
|
||||
def _clean_search_query(self, query: str) -> str:
|
||||
"""Clean up a search query by removing numbers and extra formatting."""
|
||||
# Remove any leading numbers, dots, or spaces
|
||||
cleaned = re.sub(r'^\d+\.\s*', '', query.strip())
|
||||
# Remove any quotes
|
||||
cleaned = cleaned.replace('"', '').replace("'", '')
|
||||
# Remove any extra spaces
|
||||
cleaned = ' '.join(cleaned.split())
|
||||
return cleaned
|
||||
|
||||
def generate_faqs(self, content: str, content_type: str = "general") -> List[FAQItem]:
|
||||
"""Generate FAQs from the given content with research integration."""
|
||||
try:
|
||||
if not self.config.selected_search_queries:
|
||||
raise ValueError("No search queries selected. Please select queries to proceed.")
|
||||
|
||||
# Clean up selected queries
|
||||
cleaned_queries = [self._clean_search_query(q) for q in self.config.selected_search_queries]
|
||||
self.config.selected_search_queries = cleaned_queries
|
||||
|
||||
# Step 1: Research the topic using selected queries
|
||||
research_results = self._conduct_research(content)
|
||||
|
||||
# Step 2: Generate initial FAQs
|
||||
initial_faqs = self._generate_initial_faqs(content, research_results)
|
||||
|
||||
# Step 3: Enhance FAQs with research
|
||||
enhanced_faqs = self._enhance_faqs_with_research(initial_faqs, research_results)
|
||||
|
||||
# Step 4: Add code examples if requested
|
||||
if self.config.include_code_examples:
|
||||
enhanced_faqs = self._add_code_examples(enhanced_faqs)
|
||||
|
||||
# Step 5: Add references if requested
|
||||
if self.config.include_references:
|
||||
enhanced_faqs = self._add_references(enhanced_faqs, research_results)
|
||||
|
||||
self.faqs = enhanced_faqs
|
||||
return enhanced_faqs
|
||||
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to generate FAQs: {err}")
|
||||
raise
|
||||
|
||||
def _conduct_research(self, content: str) -> Dict:
|
||||
"""Conduct online research based on the selected search queries."""
|
||||
try:
|
||||
research_results = {}
|
||||
|
||||
for query in self.config.selected_search_queries:
|
||||
try:
|
||||
# Clean the query before searching
|
||||
cleaned_query = self._clean_search_query(query)
|
||||
logger.info(f"Researching query: {cleaned_query}")
|
||||
|
||||
# Select search function based on search depth
|
||||
if self.config.search_depth == SearchDepth.BASIC:
|
||||
results = google_search(cleaned_query)
|
||||
elif self.config.search_depth == SearchDepth.COMPREHENSIVE:
|
||||
results = do_tavily_ai_search(cleaned_query)
|
||||
elif self.config.search_depth == SearchDepth.EXPERT:
|
||||
results = metaphor_search_articles(cleaned_query)
|
||||
else:
|
||||
logger.warning(f"Unknown search depth: {self.config.search_depth}, defaulting to Google search")
|
||||
results = google_search(cleaned_query)
|
||||
|
||||
research_results[query] = results
|
||||
logger.info(f"Research completed for query: {query}")
|
||||
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to research query '{query}': {err}")
|
||||
continue
|
||||
|
||||
return research_results
|
||||
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to conduct research: {err}")
|
||||
return {}
|
||||
|
||||
def _generate_initial_faqs(self, content: str, research_results: Dict) -> List[FAQItem]:
|
||||
"""Generate initial FAQs using LLM."""
|
||||
try:
|
||||
system_prompt = f"""You are an expert FAQ generator with deep knowledge in content creation and technical writing.
|
||||
Your task is to create comprehensive FAQs based on the given content and research.
|
||||
|
||||
Guidelines:
|
||||
1. Target Audience: {self.config.target_audience.value}
|
||||
2. Style: {self.config.faq_style.value}
|
||||
3. Include emojis: {self.config.include_emojis}
|
||||
4. Language: {self.config.language}
|
||||
5. Number of FAQs: {self.config.num_faqs}
|
||||
|
||||
Create FAQs that are:
|
||||
- Clear and concise
|
||||
- Well-structured
|
||||
- Technically accurate
|
||||
- Engaging and informative
|
||||
- Based on the provided research
|
||||
- Relevant to the target audience
|
||||
- Written in the specified style
|
||||
|
||||
Format each FAQ exactly as follows:
|
||||
Q: [Your question here]
|
||||
A: [Your detailed answer here]
|
||||
Category: [Category name]
|
||||
Confidence: [Score between 0 and 1]
|
||||
---
|
||||
"""
|
||||
|
||||
prompt = f"""Content to generate FAQs from:
|
||||
{content}
|
||||
|
||||
Research Results:
|
||||
{json.dumps(research_results, indent=2)}
|
||||
|
||||
Please generate {self.config.num_faqs} FAQs following the guidelines above.
|
||||
Each FAQ must be separated by '---' and include all required fields.
|
||||
"""
|
||||
|
||||
response = llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
logger.info(f"LLM Response: {response}")
|
||||
|
||||
# Parse the response into FAQItem objects
|
||||
faqs = []
|
||||
current_faq = None
|
||||
|
||||
for line in response.split('\n'):
|
||||
line = line.strip()
|
||||
if not line or line == '---':
|
||||
if current_faq and current_faq.question and current_faq.answer:
|
||||
faqs.append(current_faq)
|
||||
current_faq = None
|
||||
continue
|
||||
|
||||
if line.startswith('Q:'):
|
||||
if current_faq and current_faq.question and current_faq.answer:
|
||||
faqs.append(current_faq)
|
||||
current_faq = FAQItem(question=line[2:].strip(), answer="", category="")
|
||||
elif line.startswith('A:'):
|
||||
if current_faq:
|
||||
current_faq.answer = line[2:].strip()
|
||||
elif line.startswith('Category:'):
|
||||
if current_faq:
|
||||
current_faq.category = line[9:].strip()
|
||||
elif line.startswith('Confidence:'):
|
||||
if current_faq:
|
||||
try:
|
||||
current_faq.confidence_score = float(line[11:].strip())
|
||||
except ValueError:
|
||||
current_faq.confidence_score = 0.5
|
||||
|
||||
# Add the last FAQ if it exists and is complete
|
||||
if current_faq and current_faq.question and current_faq.answer:
|
||||
faqs.append(current_faq)
|
||||
|
||||
logger.info(f"Generated {len(faqs)} FAQs")
|
||||
return faqs
|
||||
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to generate initial FAQs: {err}")
|
||||
raise
|
||||
|
||||
def _enhance_faqs_with_research(self, faqs: List[FAQItem], research_results: Dict) -> List[FAQItem]:
|
||||
"""Enhance FAQs with research findings."""
|
||||
try:
|
||||
enhanced_faqs = []
|
||||
|
||||
for faq in faqs:
|
||||
# Find relevant research for this FAQ
|
||||
relevant_research = self._find_relevant_research(faq, research_results)
|
||||
|
||||
if relevant_research:
|
||||
# Enhance the answer with research findings
|
||||
enhancement_prompt = f"""Enhance the following FAQ answer with the provided research:
|
||||
|
||||
Question: {faq.question}
|
||||
Current Answer: {faq.answer}
|
||||
|
||||
Research:
|
||||
{json.dumps(relevant_research, indent=2)}
|
||||
|
||||
Please enhance the answer while:
|
||||
1. Maintaining the original style and tone
|
||||
2. Adding relevant information from the research
|
||||
3. Ensuring technical accuracy
|
||||
4. Keeping the answer concise and clear
|
||||
"""
|
||||
|
||||
enhanced_answer = llm_text_gen(enhancement_prompt)
|
||||
faq.answer = enhanced_answer
|
||||
|
||||
enhanced_faqs.append(faq)
|
||||
|
||||
return enhanced_faqs
|
||||
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to enhance FAQs with research: {err}")
|
||||
return faqs
|
||||
|
||||
def _add_code_examples(self, faqs: List[FAQItem]) -> List[FAQItem]:
|
||||
"""Add code examples to FAQs where applicable."""
|
||||
try:
|
||||
for faq in faqs:
|
||||
if self._is_technical_question(faq.question):
|
||||
code_prompt = f"""Generate a code example for the following FAQ:
|
||||
Question: {faq.question}
|
||||
Answer: {faq.answer}
|
||||
|
||||
Please provide a relevant code example that demonstrates the concept.
|
||||
Include comments and explanations where necessary.
|
||||
"""
|
||||
|
||||
code_example = llm_text_gen(code_prompt)
|
||||
faq.code_example = code_example
|
||||
|
||||
return faqs
|
||||
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to add code examples: {err}")
|
||||
return faqs
|
||||
|
||||
def _add_references(self, faqs: List[FAQItem], research_results: Dict) -> List[FAQItem]:
|
||||
"""Add references to FAQs based on research results."""
|
||||
try:
|
||||
for faq in faqs:
|
||||
relevant_research = self._find_relevant_research(faq, research_results)
|
||||
if relevant_research:
|
||||
references = []
|
||||
for source, content in relevant_research.items():
|
||||
references.append({
|
||||
"source": source,
|
||||
"content": content
|
||||
})
|
||||
faq.references = references
|
||||
|
||||
return faqs
|
||||
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to add references: {err}")
|
||||
return faqs
|
||||
|
||||
def _find_relevant_research(self, faq: FAQItem, research_results: Dict) -> Dict:
|
||||
"""Find research results relevant to a specific FAQ."""
|
||||
relevant_research = {}
|
||||
for topic, results in research_results.items():
|
||||
if any(keyword in faq.question.lower() for keyword in topic.lower().split()):
|
||||
relevant_research[topic] = results
|
||||
return relevant_research
|
||||
|
||||
def _is_technical_question(self, question: str) -> bool:
|
||||
"""Determine if a question is technical and might benefit from a code example."""
|
||||
technical_keywords = ["code", "program", "function", "method", "class", "api", "syntax", "error", "debug"]
|
||||
return any(keyword in question.lower() for keyword in technical_keywords)
|
||||
|
||||
def to_markdown(self) -> str:
|
||||
"""Convert FAQs to markdown format."""
|
||||
markdown = "# Frequently Asked Questions\n\n"
|
||||
|
||||
for faq in self.faqs:
|
||||
markdown += f"## {faq.question}\n\n"
|
||||
markdown += f"{faq.answer}\n\n"
|
||||
|
||||
if faq.code_example:
|
||||
markdown += "```\n"
|
||||
markdown += f"{faq.code_example}\n"
|
||||
markdown += "```\n\n"
|
||||
|
||||
if faq.references:
|
||||
markdown += "### References\n"
|
||||
for ref in faq.references:
|
||||
markdown += f"- {ref['source']}\n"
|
||||
markdown += "\n"
|
||||
|
||||
return markdown
|
||||
|
||||
def to_html(self) -> str:
|
||||
"""Convert FAQs to HTML format."""
|
||||
html = """
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<title>Frequently Asked Questions</title>
|
||||
<style>
|
||||
body { font-family: Arial, sans-serif; max-width: 800px; margin: 0 auto; padding: 20px; }
|
||||
.faq { margin-bottom: 30px; }
|
||||
.question { font-weight: bold; font-size: 1.2em; color: #2c3e50; }
|
||||
.answer { margin: 10px 0; }
|
||||
.code-example { background: #f8f9fa; padding: 15px; border-radius: 4px; }
|
||||
.references { margin-top: 15px; font-size: 0.9em; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<h1>Frequently Asked Questions</h1>
|
||||
"""
|
||||
|
||||
for faq in self.faqs:
|
||||
html += f"""
|
||||
<div class="faq">
|
||||
<div class="question">{faq.question}</div>
|
||||
<div class="answer">{faq.answer}</div>
|
||||
"""
|
||||
|
||||
if faq.code_example:
|
||||
html += f"""
|
||||
<div class="code-example">
|
||||
<pre><code>{faq.code_example}</code></pre>
|
||||
</div>
|
||||
"""
|
||||
|
||||
if faq.references:
|
||||
html += """
|
||||
<div class="references">
|
||||
<h3>References</h3>
|
||||
<ul>
|
||||
"""
|
||||
for ref in faq.references:
|
||||
html += f"""
|
||||
<li>{ref['source']}</li>
|
||||
"""
|
||||
html += """
|
||||
</ul>
|
||||
</div>
|
||||
"""
|
||||
|
||||
html += """
|
||||
</div>
|
||||
"""
|
||||
|
||||
html += """
|
||||
</body>
|
||||
</html>
|
||||
"""
|
||||
|
||||
return html
|
||||
@@ -1,312 +0,0 @@
|
||||
"""
|
||||
Streamlit UI for FAQ Generator
|
||||
|
||||
This module provides a user-friendly interface for generating FAQs from various content sources.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
import json
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
import logging
|
||||
import pyperclip
|
||||
|
||||
from .faqs_generator_blog import FAQGenerator, FAQConfig, TargetAudience, FAQStyle, SearchDepth
|
||||
|
||||
# Set up logging
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
def copy_to_clipboard(text: str) -> None:
|
||||
"""Copy text to clipboard and show success message."""
|
||||
try:
|
||||
pyperclip.copy(text)
|
||||
st.success("Copied to clipboard!")
|
||||
except Exception as e:
|
||||
st.error(f"Failed to copy to clipboard: {str(e)}")
|
||||
|
||||
def fetch_url_content(url):
|
||||
"""Fetch and extract content from a URL."""
|
||||
try:
|
||||
response = requests.get(url)
|
||||
response.raise_for_status()
|
||||
soup = BeautifulSoup(response.text, 'html.parser')
|
||||
|
||||
# Remove script and style elements
|
||||
for script in soup(["script", "style"]):
|
||||
script.decompose()
|
||||
|
||||
# Get text
|
||||
text = soup.get_text()
|
||||
|
||||
# Break into lines and remove leading and trailing space
|
||||
lines = (line.strip() for line in text.splitlines())
|
||||
# Break multi-headlines into a line each
|
||||
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
|
||||
# Drop blank lines
|
||||
text = '\n'.join(chunk for chunk in chunks if chunk)
|
||||
|
||||
return text
|
||||
except Exception as e:
|
||||
st.error(f"Error fetching URL content: {str(e)}")
|
||||
return None
|
||||
|
||||
def main():
|
||||
st.title("FAQ Generator")
|
||||
st.markdown("Generate comprehensive FAQs from your content with research integration.")
|
||||
|
||||
# Initialize session state variables if they don't exist
|
||||
if 'search_queries' not in st.session_state:
|
||||
st.session_state.search_queries = []
|
||||
if 'selected_queries' not in st.session_state:
|
||||
st.session_state.selected_queries = []
|
||||
if 'research_completed' not in st.session_state:
|
||||
st.session_state.research_completed = False
|
||||
if 'research_results' not in st.session_state:
|
||||
st.session_state.research_results = {}
|
||||
if 'faq_config' not in st.session_state:
|
||||
st.session_state.faq_config = None
|
||||
if 'generator' not in st.session_state:
|
||||
st.session_state.generator = FAQGenerator()
|
||||
if 'generated_faqs' not in st.session_state:
|
||||
st.session_state.generated_faqs = None
|
||||
if 'output_format' not in st.session_state:
|
||||
st.session_state.output_format = "Preview"
|
||||
|
||||
# Sidebar for configuration
|
||||
with st.sidebar:
|
||||
st.header("Configuration")
|
||||
|
||||
# Basic settings
|
||||
num_faqs = st.slider("Number of FAQs", 1, 20, 5)
|
||||
target_audience = st.selectbox(
|
||||
"Target Audience",
|
||||
[audience.value for audience in TargetAudience]
|
||||
)
|
||||
faq_style = st.selectbox(
|
||||
"FAQ Style",
|
||||
[style.value for style in FAQStyle]
|
||||
)
|
||||
|
||||
# Advanced settings
|
||||
with st.expander("Advanced Settings"):
|
||||
include_emojis = st.checkbox("Include Emojis", value=True)
|
||||
include_code_examples = st.checkbox("Include Code Examples", value=True)
|
||||
include_references = st.checkbox("Include References", value=True)
|
||||
|
||||
search_depth = st.selectbox(
|
||||
"Search Depth",
|
||||
[depth.value for depth in SearchDepth]
|
||||
)
|
||||
time_range = st.selectbox(
|
||||
"Time Range",
|
||||
["last_month", "last_6_months", "last_year", "all_time"]
|
||||
)
|
||||
language = st.text_input("Language", value="English")
|
||||
|
||||
# Main content area
|
||||
content_type = st.radio(
|
||||
"Content Source",
|
||||
["Direct Input", "File Upload", "URL"]
|
||||
)
|
||||
|
||||
content = ""
|
||||
if content_type == "Direct Input":
|
||||
content = st.text_area("Enter your content", height=300)
|
||||
|
||||
elif content_type == "URL":
|
||||
url = st.text_input("Enter URL")
|
||||
if url:
|
||||
content = fetch_url_content(url)
|
||||
if content:
|
||||
st.text_area("Extracted Content", content, height=300)
|
||||
|
||||
# Step 1: Generate search queries
|
||||
if content and not st.session_state.search_queries:
|
||||
if st.button("Generate Search Queries"):
|
||||
with st.spinner("Generating search queries..."):
|
||||
search_queries = st.session_state.generator.generate_search_queries(content)
|
||||
if search_queries:
|
||||
st.session_state.search_queries = search_queries
|
||||
st.session_state.selected_queries = [] # Reset selected queries
|
||||
st.session_state.research_completed = False # Reset research status
|
||||
st.session_state.research_results = {} # Reset research results
|
||||
st.session_state.faq_config = None # Reset config
|
||||
st.session_state.generated_faqs = None # Reset generated FAQs
|
||||
st.success("Search queries generated successfully!")
|
||||
|
||||
# Step 2: Display and select search queries
|
||||
if st.session_state.search_queries:
|
||||
st.subheader("Select Search Queries")
|
||||
st.info("Select the queries you want to use for web research. You can select multiple queries.")
|
||||
|
||||
# Create checkboxes for each search query
|
||||
selected_queries = []
|
||||
for query in st.session_state.search_queries:
|
||||
if st.checkbox(query, key=f"query_{query}", value=query in st.session_state.selected_queries):
|
||||
selected_queries.append(query)
|
||||
|
||||
# Update selected queries in session state
|
||||
st.session_state.selected_queries = selected_queries
|
||||
|
||||
# Step 3: Do web research
|
||||
if st.session_state.selected_queries and not st.session_state.research_completed:
|
||||
if st.button("Do Web Research"):
|
||||
try:
|
||||
# Create config with selected queries
|
||||
config = FAQConfig(
|
||||
num_faqs=num_faqs,
|
||||
target_audience=TargetAudience(target_audience),
|
||||
faq_style=FAQStyle(faq_style),
|
||||
include_emojis=include_emojis,
|
||||
include_code_examples=include_code_examples,
|
||||
include_references=include_references,
|
||||
search_depth=SearchDepth(search_depth),
|
||||
time_range=time_range,
|
||||
language=language,
|
||||
selected_search_queries=selected_queries
|
||||
)
|
||||
|
||||
# Store config in session state
|
||||
st.session_state.faq_config = config
|
||||
|
||||
# Update generator with config
|
||||
st.session_state.generator.config = config
|
||||
|
||||
# Do research
|
||||
with st.spinner("Conducting web research..."):
|
||||
research_results = st.session_state.generator._conduct_research(content)
|
||||
st.session_state.research_completed = True
|
||||
st.session_state.research_results = research_results
|
||||
st.success("Web research completed successfully!")
|
||||
|
||||
# Display research results
|
||||
st.subheader("Research Results")
|
||||
for query, results in research_results.items():
|
||||
with st.expander(f"Results for: {query}"):
|
||||
if isinstance(results, dict):
|
||||
st.json(results)
|
||||
else:
|
||||
st.text(results)
|
||||
|
||||
except Exception as e:
|
||||
st.error(f"Error during web research: {str(e)}")
|
||||
st.error("Please try again with different search queries or adjust the search depth.")
|
||||
|
||||
# Step 4: Generate FAQs
|
||||
if st.session_state.research_completed and st.session_state.research_results and st.session_state.faq_config:
|
||||
if st.button("Generate FAQs"):
|
||||
try:
|
||||
# Update generator with stored config
|
||||
st.session_state.generator.config = st.session_state.faq_config
|
||||
|
||||
# Generate FAQs
|
||||
with st.spinner("Generating FAQs..."):
|
||||
logger.info("Starting FAQ generation...")
|
||||
faqs = st.session_state.generator.generate_faqs(content)
|
||||
logger.info(f"Generated {len(faqs) if faqs else 0} FAQs")
|
||||
|
||||
if not faqs:
|
||||
st.error("No FAQs were generated. Please try again.")
|
||||
return
|
||||
|
||||
st.session_state.generated_faqs = faqs
|
||||
st.success("FAQs generated successfully!")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating FAQs: {str(e)}")
|
||||
st.error(f"Error generating FAQs: {str(e)}")
|
||||
st.error("Please try again or adjust your settings.")
|
||||
|
||||
# Display generated FAQs if they exist
|
||||
if st.session_state.generated_faqs:
|
||||
st.subheader("Generated FAQs")
|
||||
|
||||
# Output format selection
|
||||
output_format = st.radio(
|
||||
"Output Format",
|
||||
["Preview", "Markdown", "HTML", "JSON"],
|
||||
key="output_format"
|
||||
)
|
||||
|
||||
# Create columns for copy and download buttons
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
if output_format == "Preview":
|
||||
# Create a formatted text for copying
|
||||
preview_text = ""
|
||||
for i, faq in enumerate(st.session_state.generated_faqs, 1):
|
||||
preview_text += f"{i}. {faq.question}\n"
|
||||
preview_text += f"{faq.answer}\n\n"
|
||||
if faq.code_example:
|
||||
preview_text += f"Code Example:\n{faq.code_example}\n\n"
|
||||
if faq.references:
|
||||
preview_text += "References:\n"
|
||||
for ref in faq.references:
|
||||
preview_text += f"- {ref['source']}\n"
|
||||
preview_text += "\n"
|
||||
|
||||
with col1:
|
||||
if st.button("Copy to Clipboard", key="copy_preview"):
|
||||
copy_to_clipboard(preview_text)
|
||||
|
||||
# Display the FAQs
|
||||
for i, faq in enumerate(st.session_state.generated_faqs, 1):
|
||||
with st.expander(f"{i}. {faq.question}"):
|
||||
st.markdown(faq.answer)
|
||||
if faq.code_example:
|
||||
st.code(faq.code_example)
|
||||
if faq.references:
|
||||
st.markdown("**References:**")
|
||||
for ref in faq.references:
|
||||
st.markdown(f"- {ref['source']}")
|
||||
|
||||
elif output_format == "Markdown":
|
||||
markdown_output = st.session_state.generator.to_markdown()
|
||||
st.code(markdown_output, language="markdown")
|
||||
|
||||
with col1:
|
||||
if st.button("Copy to Clipboard", key="copy_markdown"):
|
||||
copy_to_clipboard(markdown_output)
|
||||
with col2:
|
||||
st.download_button(
|
||||
"Download Markdown",
|
||||
markdown_output,
|
||||
file_name="faqs.md",
|
||||
mime="text/markdown"
|
||||
)
|
||||
|
||||
elif output_format == "HTML":
|
||||
html_output = st.session_state.generator.to_html()
|
||||
st.code(html_output, language="html")
|
||||
|
||||
with col1:
|
||||
if st.button("Copy to Clipboard", key="copy_html"):
|
||||
copy_to_clipboard(html_output)
|
||||
with col2:
|
||||
st.download_button(
|
||||
"Download HTML",
|
||||
html_output,
|
||||
file_name="faqs.html",
|
||||
mime="text/html"
|
||||
)
|
||||
|
||||
elif output_format == "JSON":
|
||||
json_output = json.dumps([faq.__dict__ for faq in st.session_state.generated_faqs], indent=2)
|
||||
st.code(json_output, language="json")
|
||||
|
||||
with col1:
|
||||
if st.button("Copy to Clipboard", key="copy_json"):
|
||||
copy_to_clipboard(json_output)
|
||||
with col2:
|
||||
st.download_button(
|
||||
"Download JSON",
|
||||
json_output,
|
||||
file_name="faqs.json",
|
||||
mime="application/json"
|
||||
)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,226 +0,0 @@
|
||||
import streamlit as st
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
from tenacity import retry, wait_random_exponential, stop_after_attempt
|
||||
|
||||
def input_section():
|
||||
st.markdown("""
|
||||
<div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
|
||||
<h2 style='color: #1E88E5;'>🎯 4C Copywriting Generator</h2>
|
||||
<p>Create compelling copy that follows the 4C (Clear, Concise, Credible, Compelling) framework to drive conversions.</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Educational content about 4C copywriting
|
||||
with st.expander("📚 What is 4C Copywriting?", expanded=False):
|
||||
st.markdown("""
|
||||
### Understanding the 4C Copywriting Framework
|
||||
|
||||
The 4C framework is a powerful copywriting approach that ensures your message is effective and persuasive:
|
||||
|
||||
- **Clear**: Your message is easy to understand, with no ambiguity or confusion
|
||||
- **Concise**: Your copy is brief and to the point, without unnecessary words
|
||||
- **Credible**: Your claims are backed by evidence, testimonials, or authority
|
||||
- **Compelling**: Your message is interesting and persuasive, motivating action
|
||||
|
||||
### Why 4C Copywriting Works
|
||||
|
||||
The 4C framework works because it:
|
||||
|
||||
- Improves readability and comprehension
|
||||
- Respects the reader's time and attention
|
||||
- Builds trust and credibility
|
||||
- Increases the likelihood of conversion
|
||||
- Creates a professional, polished impression
|
||||
- Works across all marketing channels and platforms
|
||||
|
||||
### When to Use 4C Copywriting
|
||||
|
||||
The 4C framework is particularly effective for:
|
||||
|
||||
- Email marketing campaigns
|
||||
- Landing pages and sales pages
|
||||
- Social media posts and ads
|
||||
- Product descriptions
|
||||
- Service offerings
|
||||
- Any marketing content where clarity and persuasion are essential
|
||||
""")
|
||||
|
||||
# Main input form
|
||||
with st.expander("✍️ Create Your 4C Copy", expanded=True):
|
||||
col1, col2 = st.columns([1, 1])
|
||||
|
||||
with col1:
|
||||
brand_name = st.text_input('**🏢 Brand/Company Name**',
|
||||
placeholder="e.g., Alwrity AI Writer",
|
||||
help="Enter the name of your brand or company.")
|
||||
|
||||
target_audience = st.text_input('**👥 Target Audience**',
|
||||
placeholder="e.g., Small business owners, Content marketers",
|
||||
help="Who is your ideal customer? Be specific about demographics and psychographics.")
|
||||
|
||||
campaign_description = st.text_input('**📝 Campaign Description** (In 3-4 words)',
|
||||
placeholder="e.g., AI writing assistant",
|
||||
help="Describe your campaign briefly.")
|
||||
|
||||
clear_message = st.text_area('**🔍 Clear Message**',
|
||||
placeholder="e.g., Our AI writing assistant helps you create high-quality content in minutes",
|
||||
help="What is the main message you want to convey? Make it easy to understand.")
|
||||
|
||||
with col2:
|
||||
brand_description = st.text_input('**📋 Brand Description** (In 2-3 words)',
|
||||
placeholder="e.g., AI writing platform",
|
||||
help="Describe what your company does briefly.")
|
||||
|
||||
unique_selling_point = st.text_input('**💎 Unique Selling Point**',
|
||||
placeholder="e.g., All-in-one AI copywriting platform",
|
||||
help="What makes your product/service different from competitors?")
|
||||
|
||||
concise_content = st.text_area('**📏 Concise Content**',
|
||||
placeholder="e.g., Create content 10x faster with our AI assistant",
|
||||
help="How can you express your message in the fewest words possible?")
|
||||
|
||||
credible_elements = st.text_area('**✅ Credible Elements**',
|
||||
placeholder="e.g., Trusted by 10,000+ businesses, 4.8/5 star rating, 30-day money-back guarantee",
|
||||
help="What evidence, testimonials, or authority can you use to build credibility?")
|
||||
|
||||
compelling_hook = st.text_area('**🎣 Compelling Hook**',
|
||||
placeholder="e.g., Stop struggling with writer's block. Our AI assistant helps you create engaging content in minutes.",
|
||||
help="What will grab attention and motivate action?")
|
||||
|
||||
call_to_action = st.text_area('**🚀 Call to Action**',
|
||||
placeholder="e.g., Start creating high-converting content today with our 14-day free trial...",
|
||||
help="Prompt your audience to take action with a strong call to action.")
|
||||
|
||||
landing_page_url = st.text_input('**🌐 Landing Page URL** (Optional)',
|
||||
placeholder="e.g., https://alwrity.com",
|
||||
help="Provide a URL to include in your call to action.")
|
||||
|
||||
col1, col2 = st.columns([1, 1])
|
||||
with col1:
|
||||
platform = st.selectbox(
|
||||
'**📱 Content Platform**',
|
||||
options=['Social media copy', 'Email copy', 'Website copy', 'Ad copy', 'Product copy'],
|
||||
help="Select the platform where your copy will be used."
|
||||
)
|
||||
|
||||
with col2:
|
||||
language = st.selectbox(
|
||||
'**🌍 Language**',
|
||||
options=['English', 'Hindustani', 'Chinese', 'Hindi', 'Spanish'],
|
||||
help="Select the language for your copy."
|
||||
)
|
||||
|
||||
tone_style = st.selectbox(
|
||||
'**🎭 Copy Tone & Style**',
|
||||
options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
|
||||
help="Select the tone and style for your copy."
|
||||
)
|
||||
|
||||
if st.button('**🚀 Generate 4C Copy**', type="primary"):
|
||||
if not brand_name or not brand_description or not campaign_description or not clear_message or not concise_content or not credible_elements or not compelling_hook:
|
||||
st.error("⚠️ Please fill in all required fields (Brand Name, Description, Campaign Description, Clear Message, Concise Content, Credible Elements, and Compelling Hook)!")
|
||||
else:
|
||||
with st.spinner("✨ Crafting compelling 4C copy..."):
|
||||
four_cs_copy = generate_four_cs_copy(
|
||||
brand_name,
|
||||
brand_description,
|
||||
campaign_description,
|
||||
clear_message,
|
||||
concise_content,
|
||||
credible_elements,
|
||||
compelling_hook,
|
||||
target_audience,
|
||||
unique_selling_point,
|
||||
call_to_action,
|
||||
landing_page_url,
|
||||
platform,
|
||||
language,
|
||||
tone_style
|
||||
)
|
||||
|
||||
if four_cs_copy:
|
||||
st.markdown("""
|
||||
<div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
|
||||
<h3 style='color: #0066cc;'>🎯 Your 4C Copy</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Display the copy with a nice format
|
||||
st.markdown(four_cs_copy)
|
||||
|
||||
# Add copy button
|
||||
st.markdown("""
|
||||
<div style='margin-top: 20px;'>
|
||||
<button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
|
||||
Copy to Clipboard
|
||||
</button>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Add tips for using the copy
|
||||
with st.expander("💡 Tips for Using Your 4C Copy", expanded=False):
|
||||
st.markdown("""
|
||||
### How to Use Your 4C Copy Effectively
|
||||
|
||||
1. **Test for clarity**: Ask someone unfamiliar with your product to read your copy and explain what they understand
|
||||
|
||||
2. **Edit ruthlessly**: Review your copy to eliminate unnecessary words and phrases
|
||||
|
||||
3. **Add specific details**: Include concrete numbers, statistics, and examples to enhance credibility
|
||||
|
||||
4. **Create urgency**: Add time-sensitive elements to make your compelling hook even more effective
|
||||
|
||||
5. **Consider the context**: Adapt the copy based on where it will appear (landing page, email, social media, etc.)
|
||||
|
||||
6. **Measure results**: Track conversion metrics to see how your 4C copy performs
|
||||
|
||||
7. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
|
||||
""")
|
||||
else:
|
||||
st.error("💥 **Failed to generate 4C Copy. Please try again!**")
|
||||
|
||||
|
||||
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
|
||||
def generate_four_cs_copy(brand_name, brand_description, campaign_description, clear_message,
|
||||
concise_content, credible_elements, compelling_hook, target_audience,
|
||||
unique_selling_point, call_to_action, landing_page_url, platform,
|
||||
language, tone_style):
|
||||
system_prompt = """You are an expert copywriter specializing in the 4C (Clear, Concise, Credible, Compelling) framework.
|
||||
Your expertise is in creating effective, persuasive marketing copy that communicates clearly, builds credibility, and drives action.
|
||||
Your copy is authentic, specific to the brand, and focused on driving measurable results."""
|
||||
|
||||
prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {brand_description}.
|
||||
|
||||
TARGET AUDIENCE: {target_audience}
|
||||
UNIQUE SELLING POINT: {unique_selling_point}
|
||||
PLATFORM: {platform}
|
||||
LANGUAGE: {language}
|
||||
TONE & STYLE: {tone_style}
|
||||
|
||||
Use the 4C framework with these elements:
|
||||
- **Clear Message**: {clear_message}
|
||||
- **Concise Content**: {concise_content}
|
||||
- **Credible Elements**: {credible_elements}
|
||||
- **Compelling Hook**: {compelling_hook}
|
||||
- **Call to Action**: {call_to_action}
|
||||
"""
|
||||
|
||||
if landing_page_url:
|
||||
prompt += f"\nInclude the landing page URL ({landing_page_url}) in your call to action."
|
||||
|
||||
prompt += """
|
||||
For each campaign:
|
||||
1. Start with a compelling hook that grabs attention
|
||||
2. Present your clear message in a concise way
|
||||
3. Support your claims with credible elements
|
||||
4. End with a strong call to action
|
||||
|
||||
Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
|
||||
Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
|
||||
"""
|
||||
|
||||
try:
|
||||
return llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
except Exception as e:
|
||||
st.error(f"Error generating copy: {str(e)}")
|
||||
return None
|
||||
@@ -1,214 +0,0 @@
|
||||
import streamlit as st
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
from tenacity import retry, wait_random_exponential, stop_after_attempt
|
||||
|
||||
def input_section():
|
||||
st.markdown("""
|
||||
<div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
|
||||
<h2 style='color: #1E88E5;'>🎯 4R Copywriting Generator</h2>
|
||||
<p>Create compelling copy that follows the 4R (Relevance, Resonance, Response, Results) framework to drive conversions.</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Educational content about 4R copywriting
|
||||
with st.expander("📚 What is 4R Copywriting?", expanded=False):
|
||||
st.markdown("""
|
||||
### Understanding the 4R Copywriting Framework
|
||||
|
||||
The 4R framework is a powerful copywriting approach that ensures your message connects with your audience and drives action:
|
||||
|
||||
- **Relevance**: Your message addresses the specific needs, interests, or pain points of your target audience
|
||||
- **Resonance**: Your copy creates an emotional connection with the audience, making them feel understood
|
||||
- **Response**: Your message prompts the audience to take a specific action
|
||||
- **Results**: Your copy clearly communicates the positive outcomes or benefits the audience will experience
|
||||
|
||||
### Why 4R Copywriting Works
|
||||
|
||||
The 4R framework works because it:
|
||||
|
||||
- Ensures your message is targeted to the right audience
|
||||
- Creates emotional connections that build trust and loyalty
|
||||
- Drives specific actions that lead to conversions
|
||||
- Focuses on the outcomes that matter most to your audience
|
||||
- Creates a complete journey from awareness to action
|
||||
- Works across all marketing channels and platforms
|
||||
|
||||
### When to Use 4R Copywriting
|
||||
|
||||
The 4R framework is particularly effective for:
|
||||
|
||||
- Email marketing campaigns
|
||||
- Landing pages and sales pages
|
||||
- Social media posts and ads
|
||||
- Product descriptions
|
||||
- Service offerings
|
||||
- Any marketing content where audience connection and action are essential
|
||||
""")
|
||||
|
||||
# Main input form
|
||||
with st.expander("✍️ Create Your 4R Copy", expanded=True):
|
||||
col1, col2 = st.columns([1, 1])
|
||||
|
||||
with col1:
|
||||
brand_name = st.text_input('**🏢 Brand/Company Name**',
|
||||
placeholder="e.g., Alwrity AI Writer",
|
||||
help="Enter the name of your brand or company.")
|
||||
|
||||
target_audience = st.text_input('**👥 Target Audience**',
|
||||
placeholder="e.g., Small business owners, Content marketers",
|
||||
help="Who is your ideal customer? Be specific about demographics and psychographics.")
|
||||
|
||||
relevance = st.text_area('**🎯 Relevance**',
|
||||
placeholder="e.g., Struggling with writer's block? Our AI assistant helps you create high-quality content in minutes",
|
||||
help="How does your product/service address the specific needs or pain points of your target audience?")
|
||||
|
||||
with col2:
|
||||
brand_description = st.text_input('**📋 Brand Description** (In 2-3 words)',
|
||||
placeholder="e.g., AI writing platform",
|
||||
help="Describe what your company does briefly.")
|
||||
|
||||
unique_selling_point = st.text_input('**💎 Unique Selling Point**',
|
||||
placeholder="e.g., All-in-one AI copywriting platform",
|
||||
help="What makes your product/service different from competitors?")
|
||||
|
||||
resonance = st.text_area('**💖 Resonance**',
|
||||
placeholder="e.g., We understand the frustration of staring at a blank page. Our AI assistant feels like having a professional writer by your side",
|
||||
help="How can you create an emotional connection with your audience? What language or imagery will resonate with them?")
|
||||
|
||||
response = st.text_area('**🚀 Response**',
|
||||
placeholder="e.g., Start creating high-converting content today with our 14-day free trial",
|
||||
help="What specific action do you want your audience to take?")
|
||||
|
||||
results = st.text_area('**✨ Results**',
|
||||
placeholder="e.g., Save 20+ hours per week on content creation, increase conversion rates by 35%, improve SEO rankings",
|
||||
help="What positive outcomes or benefits will your audience experience?")
|
||||
|
||||
landing_page_url = st.text_input('**🌐 Landing Page URL** (Optional)',
|
||||
placeholder="e.g., https://alwrity.com",
|
||||
help="Provide a URL to include in your call to action.")
|
||||
|
||||
col1, col2 = st.columns([1, 1])
|
||||
with col1:
|
||||
platform = st.selectbox(
|
||||
'**📱 Content Platform**',
|
||||
options=['Social media copy', 'Email copy', 'Website copy', 'Ad copy', 'Product copy'],
|
||||
help="Select the platform where your copy will be used."
|
||||
)
|
||||
|
||||
with col2:
|
||||
language = st.selectbox(
|
||||
'**🌍 Language**',
|
||||
options=['English', 'Hindustani', 'Chinese', 'Hindi', 'Spanish'],
|
||||
help="Select the language for your copy."
|
||||
)
|
||||
|
||||
tone_style = st.selectbox(
|
||||
'**🎭 Copy Tone & Style**',
|
||||
options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
|
||||
help="Select the tone and style for your copy."
|
||||
)
|
||||
|
||||
if st.button('**🚀 Generate 4R Copy**', type="primary"):
|
||||
if not brand_name or not brand_description or not relevance or not resonance or not response or not results:
|
||||
st.error("⚠️ Please fill in all required fields (Brand Name, Description, Relevance, Resonance, Response, and Results)!")
|
||||
else:
|
||||
with st.spinner("✨ Crafting compelling 4R copy..."):
|
||||
four_r_copy = generate_four_r_copy(
|
||||
brand_name,
|
||||
brand_description,
|
||||
relevance,
|
||||
resonance,
|
||||
response,
|
||||
results,
|
||||
target_audience,
|
||||
unique_selling_point,
|
||||
landing_page_url,
|
||||
platform,
|
||||
language,
|
||||
tone_style
|
||||
)
|
||||
|
||||
if four_r_copy:
|
||||
st.markdown("""
|
||||
<div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
|
||||
<h3 style='color: #0066cc;'>🎯 Your 4R Copy</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Display the copy with a nice format
|
||||
st.markdown(four_r_copy)
|
||||
|
||||
# Add copy button
|
||||
st.markdown("""
|
||||
<div style='margin-top: 20px;'>
|
||||
<button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
|
||||
Copy to Clipboard
|
||||
</button>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Add tips for using the copy
|
||||
with st.expander("💡 Tips for Using Your 4R Copy", expanded=False):
|
||||
st.markdown("""
|
||||
### How to Use Your 4R Copy Effectively
|
||||
|
||||
1. **Test for relevance**: Ensure your copy speaks directly to your target audience's needs and interests
|
||||
|
||||
2. **Enhance emotional resonance**: Use language and imagery that creates a deeper connection with your audience
|
||||
|
||||
3. **Clarify the response**: Make sure your call to action is clear, specific, and compelling
|
||||
|
||||
4. **Quantify results**: Use specific numbers, statistics, and examples to make your results more tangible
|
||||
|
||||
5. **Consider the context**: Adapt the copy based on where it will appear (landing page, email, social media, etc.)
|
||||
|
||||
6. **Measure performance**: Track conversion metrics to see how your 4R copy performs
|
||||
|
||||
7. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
|
||||
""")
|
||||
else:
|
||||
st.error("💥 **Failed to generate 4R Copy. Please try again!**")
|
||||
|
||||
|
||||
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
|
||||
def generate_four_r_copy(brand_name, brand_description, relevance, resonance, response, results,
|
||||
target_audience, unique_selling_point, landing_page_url, platform,
|
||||
language, tone_style):
|
||||
system_prompt = """You are an expert copywriter specializing in the 4R (Relevance, Resonance, Response, Results) framework.
|
||||
Your expertise is in creating compelling marketing copy that connects with audiences on a deep level and drives specific actions.
|
||||
Your copy is authentic, specific to the brand, and focused on driving measurable results."""
|
||||
|
||||
prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {brand_description}.
|
||||
|
||||
TARGET AUDIENCE: {target_audience}
|
||||
UNIQUE SELLING POINT: {unique_selling_point}
|
||||
PLATFORM: {platform}
|
||||
LANGUAGE: {language}
|
||||
TONE & STYLE: {tone_style}
|
||||
|
||||
Use the 4R framework with these elements:
|
||||
- **Relevance**: {relevance}
|
||||
- **Resonance**: {resonance}
|
||||
- **Response**: {response}
|
||||
- **Results**: {results}
|
||||
"""
|
||||
|
||||
if landing_page_url:
|
||||
prompt += f"\nInclude the landing page URL ({landing_page_url}) in your call to action."
|
||||
|
||||
prompt += """
|
||||
For each campaign:
|
||||
1. Start by establishing relevance to your target audience's needs or pain points
|
||||
2. Create emotional resonance by connecting with your audience's feelings and experiences
|
||||
3. Clearly communicate the specific action you want your audience to take
|
||||
4. End by highlighting the positive results or benefits they will experience
|
||||
|
||||
Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
|
||||
Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
|
||||
"""
|
||||
|
||||
try:
|
||||
return llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
except Exception as e:
|
||||
st.error(f"Error generating copy: {str(e)}")
|
||||
return None
|
||||
@@ -1,141 +0,0 @@
|
||||
# AI Copywriting Tools
|
||||
|
||||
A comprehensive collection of AI-powered copywriting tools designed to help create compelling, conversion-focused content using various proven frameworks and approaches.
|
||||
|
||||
## Available Copywriting Tools
|
||||
|
||||
### 1. AIDA Copywriter
|
||||
The AIDA (Attention-Interest-Desire-Action) framework is a classic copywriting approach that guides your audience through a complete journey:
|
||||
- **Attention**: Captures attention with compelling headlines
|
||||
- **Interest**: Generates interest through benefits and pain points
|
||||
- **Desire**: Creates desire by showcasing solutions
|
||||
- **Action**: Prompts specific actions with strong CTAs
|
||||
|
||||
Best for: Landing pages, sales pages, email campaigns, and direct response advertising.
|
||||
|
||||
### 2. 4C Copywriter
|
||||
The 4C framework ensures your message is effective and persuasive through:
|
||||
- **Clear**: Easy to understand messaging
|
||||
- **Concise**: Brief and to-the-point content
|
||||
- **Credible**: Evidence-backed claims
|
||||
- **Compelling**: Interesting and persuasive messaging
|
||||
|
||||
Best for: Email marketing, landing pages, social media, and product descriptions.
|
||||
|
||||
### 3. 4R Copywriter
|
||||
The 4R framework focuses on building relationships with your audience through:
|
||||
- **Relevance**: Content that matters to your audience
|
||||
- **Receptivity**: Timing and context optimization
|
||||
- **Response**: Clear calls to action
|
||||
- **Return**: Value-driven content
|
||||
|
||||
Best for: Content marketing, blog posts, and relationship-building campaigns.
|
||||
|
||||
### 4. PAS Copywriter
|
||||
The PAS (Problem-Agitation-Solution) framework addresses customer pain points:
|
||||
- **Problem**: Identifies the customer's issue
|
||||
- **Agitation**: Amplifies the problem's impact
|
||||
- **Solution**: Presents your offering as the answer
|
||||
|
||||
Best for: Problem-solving content, product launches, and service offerings.
|
||||
|
||||
### 5. FAB Copywriter
|
||||
The FAB (Features-Advantages-Benefits) framework focuses on product value:
|
||||
- **Features**: Product characteristics
|
||||
- **Advantages**: How features stand out
|
||||
- **Benefits**: Customer value proposition
|
||||
|
||||
Best for: Product descriptions, sales pages, and feature highlights.
|
||||
|
||||
### 6. QUEST Copywriter
|
||||
The QUEST framework creates engaging storytelling:
|
||||
- **Qualify**: Identify the right audience
|
||||
- **Understand**: Show empathy
|
||||
- **Educate**: Provide value
|
||||
- **Stimulate**: Create desire
|
||||
- **Transition**: Guide to action
|
||||
|
||||
Best for: Story-based marketing, brand storytelling, and content marketing.
|
||||
|
||||
### 7. STAR Copywriter
|
||||
The STAR framework focuses on social proof and testimonials:
|
||||
- **Situation**: Context of the problem
|
||||
- **Task**: Challenge faced
|
||||
- **Action**: Solution implemented
|
||||
- **Result**: Outcome achieved
|
||||
|
||||
Best for: Case studies, testimonials, and success stories.
|
||||
|
||||
### 8. OATH Copywriter
|
||||
The OATH framework addresses customer objections:
|
||||
- **Objection**: Identify common concerns
|
||||
- **Acknowledge**: Show understanding
|
||||
- **Transform**: Turn negatives to positives
|
||||
- **Handle**: Provide solutions
|
||||
|
||||
Best for: Sales pages, product launches, and objection handling.
|
||||
|
||||
### 9. AIDPPC Copywriter
|
||||
The AIDPPC framework extends AIDA with additional elements:
|
||||
- **Attention**: Initial hook
|
||||
- **Interest**: Generate curiosity
|
||||
- **Desire**: Create want
|
||||
- **Proof**: Provide evidence
|
||||
- **Push**: Create urgency
|
||||
- **Close**: Final call to action
|
||||
|
||||
Best for: Long-form sales pages and comprehensive marketing materials.
|
||||
|
||||
### 10. Emotional Copywriter
|
||||
Focuses on creating emotional connections through:
|
||||
- Emotional triggers (FOMO, trust, joy, urgency)
|
||||
- Personal connections
|
||||
- Pain point addressing
|
||||
- Trust building
|
||||
- Community creation
|
||||
|
||||
Best for: Brand storytelling, emotional marketing, and relationship building.
|
||||
|
||||
## Features
|
||||
|
||||
All copywriting tools include:
|
||||
- User-friendly interface with Streamlit
|
||||
- Educational content about each framework
|
||||
- Customizable input parameters
|
||||
- Multiple language support
|
||||
- Tone and style options
|
||||
- Target audience customization
|
||||
- Brand-specific content generation
|
||||
- Retry mechanism for reliable API calls
|
||||
|
||||
## Usage
|
||||
|
||||
1. Select your desired copywriting framework
|
||||
2. Fill in the required information:
|
||||
- Brand/Company details
|
||||
- Target audience
|
||||
- Unique selling points
|
||||
- Desired tone and style
|
||||
- Platform-specific requirements
|
||||
3. Generate your copy
|
||||
4. Review and refine the output
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Know Your Audience**: Always provide detailed target audience information
|
||||
2. **Be Specific**: Include clear unique selling points and value propositions
|
||||
3. **Choose the Right Framework**: Match the framework to your content goals
|
||||
4. **Maintain Consistency**: Keep brand voice and messaging consistent
|
||||
5. **Test and Optimize**: Use different frameworks for A/B testing
|
||||
6. **Review and Edit**: Always review AI-generated content for accuracy and tone
|
||||
|
||||
## Technical Requirements
|
||||
|
||||
- Python 3.7+
|
||||
- Streamlit
|
||||
- GPT API access
|
||||
- Required Python packages (see requirements.txt)
|
||||
|
||||
## Support
|
||||
|
||||
For technical support or questions about specific frameworks, please refer to the documentation or contact the development team.
|
||||
@@ -1,97 +0,0 @@
|
||||
# Brainstorming for Copywriting Tools UI and Features (TBD)
|
||||
|
||||
## Showing All Copywriting Tools in a Single UI
|
||||
|
||||
1. **Unified Dashboard Approach**
|
||||
- Create a central dashboard with cards/tiles for each copywriting formula
|
||||
- Use visual icons and brief descriptions to distinguish each formula
|
||||
- Implement a consistent color scheme and design language across all tools
|
||||
|
||||
2. **Categorization System**
|
||||
- Group formulas by purpose (e.g., "Emotional Appeal," "Problem-Solution," "Storytelling")
|
||||
- Allow users to filter by category or search by keyword
|
||||
- Include a "Featured" or "Popular" section for commonly used formulas
|
||||
|
||||
3. **Interactive Selection Interface**
|
||||
- Create a decision tree or guided selection process
|
||||
- Ask users a few key questions to recommend the most appropriate formula
|
||||
- Show a comparison view of multiple formulas side-by-side
|
||||
|
||||
4. **Progressive Disclosure**
|
||||
- Start with a simplified view showing just the formula names and basic descriptions
|
||||
- Allow users to expand each formula for more details and to start using it
|
||||
- Implement a "Recently Used" section for quick access to frequently used formulas
|
||||
|
||||
## Presenting the Right Formula for User Needs
|
||||
|
||||
1. **Guided Selection Wizard**
|
||||
- Create a multi-step wizard that asks about the user's marketing goals
|
||||
- Include questions about target audience, industry, content type, and desired outcome
|
||||
- Provide recommendations based on user responses with explanations
|
||||
|
||||
2. **Formula Comparison Tool**
|
||||
- Create a comparison matrix showing strengths of each formula
|
||||
- Include use cases and examples for each formula
|
||||
- Allow users to see side-by-side comparisons of different formulas
|
||||
|
||||
3. **Educational Content Integration**
|
||||
- Add a "Learn More" section for each formula with detailed explanations
|
||||
- Include case studies showing successful applications of each formula
|
||||
- Provide templates and examples for common use cases
|
||||
|
||||
4. **Contextual Recommendations**
|
||||
- Analyze the user's input and automatically suggest the most appropriate formula
|
||||
- Show a confidence score for each recommendation
|
||||
- Allow users to easily switch between formulas if the recommendation isn't right
|
||||
|
||||
## Using AI to Pre-fill Inputs Based on Brief Requirements
|
||||
|
||||
1. **Smart Input Generation**
|
||||
- Create an initial input field where users can describe their copywriting needs in natural language
|
||||
- Use AI to analyze this input and extract key information (brand, audience, goals, etc.)
|
||||
- Pre-fill the formula-specific fields with AI-generated content
|
||||
- Allow users to edit and refine the pre-filled content
|
||||
|
||||
2. **Contextual Understanding**
|
||||
- Implement industry-specific templates and prompts
|
||||
- Use AI to recognize industry terminology and adapt suggestions accordingly
|
||||
- Provide multiple options for each field based on the user's brief description
|
||||
|
||||
3. **Progressive Refinement**
|
||||
- Start with AI-generated suggestions for all fields
|
||||
- Allow users to focus on refining specific fields while keeping others
|
||||
- Implement a "regenerate" option for individual fields if the AI suggestion isn't suitable
|
||||
|
||||
4. **Learning from User Edits**
|
||||
- Track which AI-generated suggestions users keep vs. modify
|
||||
- Use this data to improve future suggestions
|
||||
- Implement a feedback mechanism for users to rate the quality of AI suggestions
|
||||
|
||||
## AI-Generated Images as a Feature
|
||||
|
||||
1. **Complementary Visual Content**
|
||||
- Generate images that match the tone and message of the copy
|
||||
- Create multiple image options for different platforms (social media, email, website)
|
||||
- Ensure images align with the copywriting formula being used
|
||||
|
||||
2. **Integrated Workflow**
|
||||
- Add an "Generate Matching Images" button after copy is created
|
||||
- Allow users to specify image style, mood, and key elements
|
||||
- Provide options to customize generated images further
|
||||
|
||||
3. **Platform-Specific Optimization**
|
||||
- Automatically size and format images for different platforms
|
||||
- Generate variations optimized for different aspect ratios
|
||||
- Include text overlay options that complement the copy
|
||||
|
||||
4. **Brand Consistency**
|
||||
- Allow users to upload brand assets (logos, colors, fonts)
|
||||
- Generate images that maintain brand identity
|
||||
- Create a visual style guide based on user preferences
|
||||
|
||||
5. **Enhanced Engagement**
|
||||
- A/B test different image options with the same copy
|
||||
- Provide analytics on which image-copy combinations perform best
|
||||
- Suggest image improvements based on performance data
|
||||
|
||||
These enhancements would create a more comprehensive, user-friendly copywriting platform that guides users to the right formula, simplifies the input process, and delivers complete marketing assets ready for deployment.
|
||||
@@ -1,182 +0,0 @@
|
||||
import streamlit as st
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
|
||||
def input_section():
|
||||
st.markdown("""
|
||||
<div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
|
||||
<h2 style='color: #1E88E5;'>🚀 ACCA Copywriting Generator</h2>
|
||||
<p>Create persuasive marketing copy using the proven ACCA (Awareness-Curiosity-Conviction-Action) formula.</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Educational content about ACCA copywriting
|
||||
with st.expander("📚 What is ACCA Copywriting?", expanded=False):
|
||||
st.markdown("""
|
||||
### Understanding the ACCA Copywriting Formula
|
||||
|
||||
The ACCA formula is a powerful copywriting framework that guides your audience through a journey from problem recognition to action:
|
||||
|
||||
- **Awareness**: Highlight the problem or pain point your audience faces
|
||||
- **Curiosity**: Agitate the problem by emphasizing its negative impact
|
||||
- **Conviction**: Present your solution and build confidence in it
|
||||
- **Action**: Provide a clear, compelling call to action
|
||||
|
||||
### Why ACCA Copywriting Works
|
||||
|
||||
The ACCA formula works because it:
|
||||
|
||||
- Follows the natural decision-making process of your audience
|
||||
- Creates a logical progression from problem to solution
|
||||
- Builds emotional investment before asking for commitment
|
||||
- Addresses objections before they arise
|
||||
- Ends with a clear next step
|
||||
|
||||
### When to Use ACCA Copywriting
|
||||
|
||||
The ACCA formula is particularly effective for:
|
||||
|
||||
- Product launches
|
||||
- Service promotions
|
||||
- Problem-solving offers
|
||||
- Educational content
|
||||
- Sales pages
|
||||
- Email marketing sequences
|
||||
""")
|
||||
|
||||
# Main input form
|
||||
with st.expander("✍️ Create Your ACCA Copy", expanded=True):
|
||||
col1, col2 = st.columns([1, 1])
|
||||
|
||||
with col1:
|
||||
brand_name = st.text_input('**🏢 Brand/Company Name**',
|
||||
placeholder="e.g., Alwrity",
|
||||
help="Enter the name of your brand or company.")
|
||||
|
||||
target_audience = st.text_input('**👥 Target Audience**',
|
||||
placeholder="e.g., Small business owners, Tech professionals",
|
||||
help="Who is your ideal customer? Be specific about demographics and psychographics.")
|
||||
|
||||
awareness = st.text_input('❓ **Awareness (Problem)**',
|
||||
placeholder="e.g., Struggling to manage finances",
|
||||
help="What problem or pain point does your audience face?")
|
||||
|
||||
with col2:
|
||||
description = st.text_input('**📝 Brand Description** (In 5-6 words)',
|
||||
placeholder="e.g., AI writing tools",
|
||||
help="Describe your product or service briefly.")
|
||||
|
||||
unique_selling_point = st.text_input('**💎 Unique Selling Point**',
|
||||
placeholder="e.g., 10x faster content creation",
|
||||
help="What makes your product/service different from competitors?")
|
||||
|
||||
curiosity = st.text_input('🔥 **Curiosity (Agitation)**',
|
||||
placeholder="e.g., Leads to financial instability and stress",
|
||||
help="Why is this problem serious for your audience? Highlight the negative impact.")
|
||||
|
||||
conviction = st.text_input('💡 **Conviction (Solution)**',
|
||||
placeholder="e.g., Provides easy-to-use budgeting tools with AI insights",
|
||||
help="How does your product/service solve this problem? Explain the benefits.")
|
||||
|
||||
call_to_action = st.text_input('🎯 **Action (Call to Action)**',
|
||||
placeholder="e.g., Start your free trial today",
|
||||
help="What specific action do you want your audience to take?")
|
||||
|
||||
tone_style = st.selectbox(
|
||||
'**🎭 Copy Tone & Style**',
|
||||
options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
|
||||
help="Select the tone and style for your copy."
|
||||
)
|
||||
|
||||
if st.button('**🚀 Generate ACCA Copy**', type="primary"):
|
||||
if not brand_name or not description or not awareness or not curiosity or not conviction:
|
||||
st.error("⚠️ Please fill in all required fields (Brand Name, Description, Awareness, Curiosity, and Conviction)!")
|
||||
else:
|
||||
with st.spinner("✨ Crafting persuasive ACCA copy..."):
|
||||
acca_copy = generate_acca_copy(
|
||||
brand_name,
|
||||
description,
|
||||
awareness,
|
||||
curiosity,
|
||||
conviction,
|
||||
target_audience,
|
||||
unique_selling_point,
|
||||
call_to_action,
|
||||
tone_style
|
||||
)
|
||||
|
||||
if acca_copy:
|
||||
st.markdown("""
|
||||
<div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
|
||||
<h3 style='color: #0066cc;'>✨ Your ACCA Copy</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Display the copy with a nice format
|
||||
st.markdown(acca_copy)
|
||||
|
||||
# Add copy button
|
||||
st.markdown("""
|
||||
<div style='margin-top: 20px;'>
|
||||
<button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
|
||||
Copy to Clipboard
|
||||
</button>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Add tips for using the copy - using a container instead of an expander
|
||||
st.markdown("""
|
||||
<div style='background-color: #f9f9f9; padding: 15px; border-radius: 10px; margin-top: 20px;'>
|
||||
<h3 style='color: #333;'>💡 Tips for Using Your ACCA Copy</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
st.markdown("""
|
||||
### How to Use Your ACCA Copy Effectively
|
||||
|
||||
1. **Test different versions**: A/B test your copy to see which version resonates most with your audience
|
||||
|
||||
2. **Pair with visuals**: Combine your copy with images that reinforce each stage of the ACCA formula
|
||||
|
||||
3. **Consider the platform**: Adapt your copy based on where it will appear (social media, email, website, etc.)
|
||||
|
||||
4. **Measure results**: Track conversion metrics to see how your ACCA copy performs
|
||||
|
||||
5. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
|
||||
""")
|
||||
else:
|
||||
st.error("💥 **Failed to generate ACCA Copy. Please try again!**")
|
||||
|
||||
|
||||
def generate_acca_copy(brand_name, description, awareness, curiosity, conviction, target_audience,
|
||||
unique_selling_point, call_to_action, tone_style):
|
||||
system_prompt = """You are an expert copywriter specializing in the ACCA (Awareness-Curiosity-Conviction-Action) formula.
|
||||
Your expertise is in creating compelling, persuasive marketing copy that guides audiences through a journey from problem
|
||||
recognition to taking action. Your copy is authentic, specific to the brand, and focused on the target audience's needs."""
|
||||
|
||||
prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {description}.
|
||||
|
||||
TARGET AUDIENCE: {target_audience}
|
||||
UNIQUE SELLING POINT: {unique_selling_point}
|
||||
TONE & STYLE: {tone_style}
|
||||
|
||||
Use the ACCA formula with these elements:
|
||||
- **Awareness**: {awareness}
|
||||
- **Curiosity**: {curiosity}
|
||||
- **Conviction**: {conviction}
|
||||
- **Action**: {call_to_action}
|
||||
|
||||
For each campaign:
|
||||
1. Create a compelling headline that captures attention
|
||||
2. Write 2-3 paragraphs that follow the ACCA formula
|
||||
3. End with a strong call to action
|
||||
4. Explain how each element of the ACCA formula is used in the copy
|
||||
|
||||
Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
|
||||
Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
|
||||
"""
|
||||
|
||||
try:
|
||||
return llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
except Exception as e:
|
||||
st.error(f"Error generating copy: {str(e)}")
|
||||
return None
|
||||
@@ -1,168 +0,0 @@
|
||||
import streamlit as st
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
|
||||
def input_section():
|
||||
st.markdown("""
|
||||
<div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
|
||||
<h2 style='color: #1E88E5;'>🎭 Emotional Copywriting Generator</h2>
|
||||
<p>Create compelling copy that resonates with your audience's emotions and drives action.</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Educational content about emotional copywriting
|
||||
with st.expander("📚 What is Emotional Copywriting?", expanded=False):
|
||||
st.markdown("""
|
||||
### Understanding Emotional Copywriting
|
||||
|
||||
Emotional copywriting is a powerful marketing technique that connects with your audience on a deeper level by:
|
||||
|
||||
- **Triggering specific emotions** (joy, fear, urgency, trust, etc.)
|
||||
- **Creating personal connections** with your audience
|
||||
- **Addressing pain points** and offering solutions
|
||||
- **Building trust and credibility**
|
||||
- **Creating a sense of belonging** or exclusivity
|
||||
|
||||
### Why Emotional Copywriting Works
|
||||
|
||||
Research shows that people make purchasing decisions based on emotions first, then justify with logic. By tapping into the right emotions, you can:
|
||||
|
||||
- Increase engagement and response rates
|
||||
- Build stronger brand loyalty
|
||||
- Drive more conversions
|
||||
- Create memorable brand experiences
|
||||
|
||||
### Common Emotional Triggers
|
||||
|
||||
- **Fear of Missing Out (FOMO)**: Limited time offers, exclusive access
|
||||
- **Trust**: Testimonials, guarantees, social proof
|
||||
- **Joy/Happiness**: Benefits, positive outcomes, aspirational messaging
|
||||
- **Urgency**: Time-sensitive offers, countdown timers
|
||||
- **Belonging**: Community, exclusivity, shared values
|
||||
""")
|
||||
|
||||
# Main input form
|
||||
with st.expander("✍️ Create Your Emotional Copy", expanded=True):
|
||||
col1, col2 = st.columns([1, 1])
|
||||
|
||||
with col1:
|
||||
brand_name = st.text_input('**Brand/Company Name**',
|
||||
help="Enter the name of your brand or company.")
|
||||
|
||||
target_audience = st.text_input('**Target Audience**',
|
||||
help="Who is your ideal customer? (e.g., 'busy moms', 'tech-savvy millennials')")
|
||||
|
||||
emotional_trigger = st.selectbox(
|
||||
'**Primary Emotional Trigger**',
|
||||
options=['Trust', 'Fear of Missing Out', 'Joy/Happiness', 'Urgency', 'Belonging', 'Exclusivity'],
|
||||
help="Select the primary emotion you want to evoke in your audience."
|
||||
)
|
||||
|
||||
with col2:
|
||||
description = st.text_input('**Brand Description** (In 5-6 words)',
|
||||
help="Describe your product or service briefly.")
|
||||
|
||||
unique_selling_point = st.text_input('**Unique Selling Point**',
|
||||
help="What makes your product/service different from competitors?")
|
||||
|
||||
call_to_action = st.text_input('**Desired Call to Action**',
|
||||
help="What action do you want your audience to take? (e.g., 'Sign up now', 'Buy today')")
|
||||
|
||||
trust_elements = st.text_area('**Trust Elements**',
|
||||
help="Build trust and credibility by showcasing testimonials, guarantees, or endorsements.",
|
||||
placeholder="Testimonials from satisfied customers...\nOur guarantee that...\nIndustry certifications...")
|
||||
|
||||
tone_style = st.selectbox(
|
||||
'**Copy Tone & Style**',
|
||||
options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
|
||||
help="Select the tone and style for your copy."
|
||||
)
|
||||
|
||||
if st.button('**Generate Emotional Copy**', type="primary"):
|
||||
if not brand_name or not description or not trust_elements:
|
||||
st.error("⚠️ Please fill in all required fields (Brand Name, Description, and Trust Elements)!")
|
||||
else:
|
||||
with st.spinner("✨ Crafting emotionally compelling copy..."):
|
||||
emotional_copy = generate_emotional_copy(
|
||||
brand_name,
|
||||
description,
|
||||
trust_elements,
|
||||
target_audience,
|
||||
emotional_trigger,
|
||||
unique_selling_point,
|
||||
call_to_action,
|
||||
tone_style
|
||||
)
|
||||
|
||||
if emotional_copy:
|
||||
st.markdown("""
|
||||
<div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
|
||||
<h3 style='color: #0066cc;'>🎯 Your Emotional Copy</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Display the copy with a nice format
|
||||
st.markdown(emotional_copy)
|
||||
|
||||
# Add copy button
|
||||
st.markdown("""
|
||||
<div style='margin-top: 20px;'>
|
||||
<button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
|
||||
Copy to Clipboard
|
||||
</button>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Add tips for using the copy - using a container instead of an expander
|
||||
st.markdown("""
|
||||
<div style='background-color: #f9f9f9; padding: 15px; border-radius: 10px; margin-top: 20px;'>
|
||||
<h3 style='color: #333;'>💡 Tips for Using Your Emotional Copy</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
st.markdown("""
|
||||
### How to Use Your Emotional Copy Effectively
|
||||
|
||||
1. **Test different versions**: A/B test your copy to see which emotional triggers resonate most with your audience
|
||||
|
||||
2. **Pair with visuals**: Combine your copy with images that reinforce the emotional message
|
||||
|
||||
3. **Consider the context**: Adapt the copy based on where it will appear (social media, email, website, etc.)
|
||||
|
||||
4. **Measure results**: Track engagement metrics to see how your emotional copy performs
|
||||
|
||||
5. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
|
||||
""")
|
||||
else:
|
||||
st.error("💥 **Failed to generate Emotional Copy. Please try again!**")
|
||||
|
||||
|
||||
def generate_emotional_copy(brand_name, description, trust_elements, target_audience, emotional_trigger,
|
||||
unique_selling_point, call_to_action, tone_style):
|
||||
system_prompt = """You are an expert emotional copywriter with years of experience in creating compelling marketing copy
|
||||
that resonates with audiences on a deep emotional level. Your specialty is crafting copy that triggers specific emotions
|
||||
and drives action while maintaining authenticity and credibility."""
|
||||
|
||||
prompt = f"""Create 3 different emotional marketing campaigns for {brand_name}, which is a {description}.
|
||||
|
||||
TARGET AUDIENCE: {target_audience}
|
||||
PRIMARY EMOTIONAL TRIGGER: {emotional_trigger}
|
||||
UNIQUE SELLING POINT: {unique_selling_point}
|
||||
DESIRED CALL TO ACTION: {call_to_action}
|
||||
TONE & STYLE: {tone_style}
|
||||
TRUST ELEMENTS: {trust_elements}
|
||||
|
||||
For each campaign:
|
||||
1. Create a compelling headline that captures attention
|
||||
2. Write 2-3 paragraphs of body copy that builds emotional connection
|
||||
3. End with a strong call to action
|
||||
4. Explain which emotional triggers you used and why they're effective for this audience
|
||||
|
||||
Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
|
||||
Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
|
||||
"""
|
||||
|
||||
try:
|
||||
return llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
except Exception as e:
|
||||
st.error(f"Error generating copy: {str(e)}")
|
||||
return None
|
||||
@@ -1,211 +0,0 @@
|
||||
import streamlit as st
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
from tenacity import retry, wait_random_exponential, stop_after_attempt
|
||||
|
||||
def input_section():
|
||||
st.markdown("""
|
||||
<div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
|
||||
<h2 style='color: #1E88E5;'>🎯 AIDA Copywriting Generator</h2>
|
||||
<p>Create compelling copy that follows the AIDA (Attention-Interest-Desire-Action) framework to drive conversions.</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Educational content about AIDA copywriting
|
||||
with st.expander("📚 What is AIDA Copywriting?", expanded=False):
|
||||
st.markdown("""
|
||||
### Understanding the AIDA Copywriting Framework
|
||||
|
||||
AIDA is an acronym for Attention-Interest-Desire-Action. It's a classic copywriting framework that guides your audience through a complete journey:
|
||||
|
||||
- **Attention**: Capturing the audience's attention with a compelling headline or hook
|
||||
- **Interest**: Generating interest by highlighting benefits or addressing pain points
|
||||
- **Desire**: Creating desire by showcasing how the product/service solves problems or fulfills needs
|
||||
- **Action**: Prompting the audience to take a specific action with a strong call to action
|
||||
|
||||
### Why AIDA Copywriting Works
|
||||
|
||||
The AIDA framework works because it:
|
||||
|
||||
- Follows the natural decision-making process of consumers
|
||||
- Addresses all key elements needed for conversion
|
||||
- Creates a complete journey from awareness to action
|
||||
- Balances emotional and rational appeals
|
||||
- Focuses on the customer's journey rather than just product features
|
||||
|
||||
### When to Use AIDA Copywriting
|
||||
|
||||
The AIDA framework is particularly effective for:
|
||||
|
||||
- Landing pages and sales pages
|
||||
- Email marketing campaigns
|
||||
- Product descriptions
|
||||
- Direct response advertising
|
||||
- Content that needs to drive specific actions
|
||||
- Marketing materials that need to address objections
|
||||
""")
|
||||
|
||||
# Main input form
|
||||
with st.expander("✍️ Create Your AIDA Copy", expanded=True):
|
||||
col1, col2 = st.columns([1, 1])
|
||||
|
||||
with col1:
|
||||
brand_name = st.text_input('**🏢 Brand/Company Name**',
|
||||
placeholder="e.g., Alwrity",
|
||||
help="Enter the name of your brand or company.")
|
||||
|
||||
target_audience = st.text_input('**👥 Target Audience**',
|
||||
placeholder="e.g., Small business owners, Tech professionals",
|
||||
help="Who is your ideal customer? Be specific about demographics and psychographics.")
|
||||
|
||||
attention = st.text_area('**🔔 Attention-Grabbing Hook**',
|
||||
placeholder="e.g., Tired of spending hours writing content that doesn't convert?",
|
||||
help="Create a compelling headline or hook that captures attention.")
|
||||
|
||||
interest = st.text_area('**💡 Generate Interest**',
|
||||
placeholder="e.g., Imagine creating high-quality content in minutes instead of hours...",
|
||||
help="Highlight benefits or address pain points to generate interest.")
|
||||
|
||||
with col2:
|
||||
description = st.text_input('**📝 Brand Description** (In 5-6 words)',
|
||||
placeholder="e.g., AI writing tools",
|
||||
help="Describe your product or service briefly.")
|
||||
|
||||
unique_selling_point = st.text_input('**💎 Unique Selling Point**',
|
||||
placeholder="e.g., 10x faster content creation",
|
||||
help="What makes your product/service different from competitors?")
|
||||
|
||||
desire = st.text_area('**❤️ Create Desire**',
|
||||
placeholder="e.g., Our AI analyzes top-performing content to ensure your copy resonates with your target audience...",
|
||||
help="Showcase how your product/service solves problems or fulfills needs.")
|
||||
|
||||
action = st.text_area('**🚀 Call to Action**',
|
||||
placeholder="e.g., Start creating converting content today with our 14-day free trial...",
|
||||
help="Prompt your audience to take action with a strong call to action.")
|
||||
|
||||
landing_page_url = st.text_input('**🌐 Landing Page URL** (Optional)',
|
||||
placeholder="e.g., https://alwrity.com",
|
||||
help="Provide a URL to include in your call to action.")
|
||||
|
||||
col1, col2 = st.columns([1, 1])
|
||||
with col1:
|
||||
platform = st.selectbox(
|
||||
'**📱 Content Platform**',
|
||||
options=['Social media copy', 'Email copy', 'Website copy', 'Ad copy', 'Product copy'],
|
||||
help="Select the platform where your copy will be used."
|
||||
)
|
||||
|
||||
with col2:
|
||||
language = st.selectbox(
|
||||
'**🌍 Language**',
|
||||
options=['English', 'Hindustani', 'Chinese', 'Hindi', 'Spanish'],
|
||||
help="Select the language for your copy."
|
||||
)
|
||||
|
||||
tone_style = st.selectbox(
|
||||
'**🎭 Copy Tone & Style**',
|
||||
options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
|
||||
help="Select the tone and style for your copy."
|
||||
)
|
||||
|
||||
if st.button('**🚀 Generate AIDA Copy**', type="primary"):
|
||||
if not brand_name or not description or not attention or not interest or not desire or not action:
|
||||
st.error("⚠️ Please fill in all required fields (Brand Name, Description, and all AIDA elements)!")
|
||||
else:
|
||||
with st.spinner("✨ Crafting compelling AIDA copy..."):
|
||||
aida_copy = generate_aida_copy(
|
||||
brand_name,
|
||||
description,
|
||||
attention,
|
||||
interest,
|
||||
desire,
|
||||
action,
|
||||
target_audience,
|
||||
unique_selling_point,
|
||||
landing_page_url,
|
||||
platform,
|
||||
language,
|
||||
tone_style
|
||||
)
|
||||
|
||||
if aida_copy:
|
||||
st.markdown("""
|
||||
<div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
|
||||
<h3 style='color: #0066cc;'>🎯 Your AIDA Copy</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Display the copy with a nice format
|
||||
st.markdown(aida_copy)
|
||||
|
||||
# Add copy button
|
||||
st.markdown("""
|
||||
<div style='margin-top: 20px;'>
|
||||
<button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
|
||||
Copy to Clipboard
|
||||
</button>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Add tips for using the copy
|
||||
with st.expander("💡 Tips for Using Your AIDA Copy", expanded=False):
|
||||
st.markdown("""
|
||||
### How to Use Your AIDA Copy Effectively
|
||||
|
||||
1. **Follow the sequence**: The AIDA framework creates a natural progression - make sure your copy maintains this flow
|
||||
|
||||
2. **Test different hooks**: A/B test different attention-grabbing headlines to see which resonates most with your audience
|
||||
|
||||
3. **Pair with visuals**: Combine your copy with images that reinforce each stage of the AIDA journey
|
||||
|
||||
4. **Consider the context**: Adapt the copy based on where it will appear (landing page, email, social media, etc.)
|
||||
|
||||
5. **Measure results**: Track conversion metrics to see how your AIDA copy performs
|
||||
|
||||
6. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
|
||||
""")
|
||||
else:
|
||||
st.error("💥 **Failed to generate AIDA Copy. Please try again!**")
|
||||
|
||||
|
||||
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
|
||||
def generate_aida_copy(brand_name, description, attention, interest, desire, action,
|
||||
target_audience, unique_selling_point, landing_page_url,
|
||||
platform, language, tone_style):
|
||||
system_prompt = """You are an expert copywriter specializing in the AIDA (Attention-Interest-Desire-Action) framework.
|
||||
Your expertise is in creating compelling, conversion-focused marketing copy that guides readers through a complete journey from awareness to action.
|
||||
Your copy is authentic, specific to the brand, and focused on driving measurable results."""
|
||||
|
||||
prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {description}.
|
||||
|
||||
TARGET AUDIENCE: {target_audience}
|
||||
UNIQUE SELLING POINT: {unique_selling_point}
|
||||
PLATFORM: {platform}
|
||||
LANGUAGE: {language}
|
||||
TONE & STYLE: {tone_style}
|
||||
|
||||
Use the AIDA framework with these elements:
|
||||
- **Attention**: {attention}
|
||||
- **Interest**: {interest}
|
||||
- **Desire**: {desire}
|
||||
- **Action**: {action}
|
||||
"""
|
||||
|
||||
if landing_page_url:
|
||||
prompt += f"\nInclude the landing page URL ({landing_page_url}) in your call to action."
|
||||
|
||||
prompt += """
|
||||
For each campaign:
|
||||
1. Start with the attention-grabbing hook to capture the audience's attention
|
||||
2. Generate interest by highlighting benefits or addressing pain points
|
||||
3. Create desire by showcasing how the product/service solves problems or fulfills needs
|
||||
4. End with a strong call to action
|
||||
|
||||
Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
|
||||
Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
|
||||
"""
|
||||
|
||||
try:
|
||||
return llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
except Exception as e:
|
||||
st.error(f"Error generating copy: {str(e)}")
|
||||
return None
|
||||
@@ -1,191 +0,0 @@
|
||||
import streamlit as st
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
from tenacity import retry, wait_random_exponential, stop_after_attempt
|
||||
|
||||
def input_section():
|
||||
st.markdown("""
|
||||
<div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
|
||||
<h2 style='color: #1E88E5;'>🎯 AIDPPC Copywriting Generator</h2>
|
||||
<p>Create compelling copy that follows the AIDPPC (Attention-Interest-Description-Persuasion-Proof-Close) framework to drive conversions.</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Educational content about AIDPPC copywriting
|
||||
with st.expander("📚 What is AIDPPC Copywriting?", expanded=False):
|
||||
st.markdown("""
|
||||
### Understanding the AIDPPC Copywriting Framework
|
||||
|
||||
AIDPPC is an acronym for Attention-Interest-Description-Persuasion-Proof-Close. It's a comprehensive copywriting framework that guides your audience through a complete journey:
|
||||
|
||||
- **Attention**: Capturing the audience's attention with a compelling headline or hook
|
||||
- **Interest**: Generating interest by highlighting benefits or addressing pain points
|
||||
- **Description**: Describing your product or service in detail
|
||||
- **Persuasion**: Presenting compelling arguments or incentives to persuade
|
||||
- **Proof**: Providing social proof, testimonials, or guarantees to build credibility
|
||||
- **Close**: Prompting the audience to take action with a strong call to action
|
||||
|
||||
### Why AIDPPC Copywriting Works
|
||||
|
||||
The AIDPPC framework works because it:
|
||||
|
||||
- Follows the natural decision-making process of consumers
|
||||
- Addresses all key elements needed for conversion
|
||||
- Builds credibility through multiple stages
|
||||
- Creates a complete journey from awareness to action
|
||||
- Balances emotional and rational appeals
|
||||
|
||||
### When to Use AIDPPC Copywriting
|
||||
|
||||
The AIDPPC framework is particularly effective for:
|
||||
|
||||
- Landing pages and sales pages
|
||||
- Email marketing campaigns
|
||||
- Product descriptions
|
||||
- Direct response advertising
|
||||
- Content that needs to drive specific actions
|
||||
- Marketing materials that need to address objections
|
||||
""")
|
||||
|
||||
# Main input form
|
||||
with st.expander("✍️ Create Your AIDPPC Copy", expanded=True):
|
||||
col1, col2 = st.columns([1, 1])
|
||||
|
||||
with col1:
|
||||
brand_name = st.text_input('**🏢 Brand/Company Name**',
|
||||
placeholder="e.g., Alwrity",
|
||||
help="Enter the name of your brand or company.")
|
||||
|
||||
target_audience = st.text_input('**👥 Target Audience**',
|
||||
placeholder="e.g., Small business owners, Tech professionals",
|
||||
help="Who is your ideal customer? Be specific about demographics and psychographics.")
|
||||
|
||||
attention = st.text_area('**🔔 Attention-Grabbing Hook**',
|
||||
placeholder="e.g., Tired of spending hours writing content that doesn't convert?",
|
||||
help="Create a compelling headline or hook that captures attention.")
|
||||
|
||||
interest = st.text_area('**💡 Generate Interest**',
|
||||
placeholder="e.g., Imagine creating high-quality content in minutes instead of hours...",
|
||||
help="Highlight benefits or address pain points to generate interest.")
|
||||
|
||||
with col2:
|
||||
description = st.text_input('**📝 Brand Description** (In 2-3 words)',
|
||||
placeholder="e.g., AI writing tools",
|
||||
help="Describe your product or service briefly.")
|
||||
|
||||
unique_selling_point = st.text_input('**💎 Unique Selling Point**',
|
||||
placeholder="e.g., 10x faster content creation",
|
||||
help="What makes your product/service different from competitors?")
|
||||
|
||||
persuasion = st.text_area('**💪 Persuasive Arguments**',
|
||||
placeholder="e.g., Our AI analyzes top-performing content to ensure your copy resonates with your target audience...",
|
||||
help="Present compelling arguments or incentives to persuade your audience.")
|
||||
|
||||
proof = st.text_area('**✅ Social Proof**',
|
||||
placeholder="e.g., Join 10,000+ satisfied customers who have transformed their content strategy...",
|
||||
help="Provide testimonials, statistics, or guarantees to build credibility.")
|
||||
|
||||
close = st.text_area('**🚀 Call to Action**',
|
||||
placeholder="e.g., Start creating converting content today with our 14-day free trial...",
|
||||
help="Prompt your audience to take action with a strong call to action.")
|
||||
|
||||
tone_style = st.selectbox(
|
||||
'**🎭 Copy Tone & Style**',
|
||||
options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
|
||||
help="Select the tone and style for your copy."
|
||||
)
|
||||
|
||||
if st.button('**🚀 Generate AIDPPC Copy**', type="primary"):
|
||||
if not brand_name or not description or not attention or not interest or not persuasion or not proof or not close:
|
||||
st.error("⚠️ Please fill in all required fields (Brand Name, Description, and all AIDPPC elements)!")
|
||||
else:
|
||||
with st.spinner("✨ Crafting compelling AIDPPC copy..."):
|
||||
aidppc_copy = generate_aidppc_copy(
|
||||
brand_name,
|
||||
description,
|
||||
attention,
|
||||
interest,
|
||||
persuasion,
|
||||
proof,
|
||||
close,
|
||||
target_audience,
|
||||
unique_selling_point,
|
||||
tone_style
|
||||
)
|
||||
|
||||
if aidppc_copy:
|
||||
st.markdown("""
|
||||
<div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
|
||||
<h3 style='color: #0066cc;'>🎯 Your AIDPPC Copy</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Display the copy with a nice format
|
||||
st.markdown(aidppc_copy)
|
||||
|
||||
# Add copy button
|
||||
st.markdown("""
|
||||
<div style='margin-top: 20px;'>
|
||||
<button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
|
||||
Copy to Clipboard
|
||||
</button>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Add tips for using the copy
|
||||
with st.expander("💡 Tips for Using Your AIDPPC Copy", expanded=False):
|
||||
st.markdown("""
|
||||
### How to Use Your AIDPPC Copy Effectively
|
||||
|
||||
1. **Follow the sequence**: The AIDPPC framework creates a natural progression - make sure your copy maintains this flow
|
||||
|
||||
2. **Test different hooks**: A/B test different attention-grabbing headlines to see which resonates most with your audience
|
||||
|
||||
3. **Pair with visuals**: Combine your copy with images that reinforce each stage of the AIDPPC journey
|
||||
|
||||
4. **Consider the context**: Adapt the copy based on where it will appear (landing page, email, social media, etc.)
|
||||
|
||||
5. **Measure results**: Track conversion metrics to see how your AIDPPC copy performs
|
||||
|
||||
6. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
|
||||
""")
|
||||
else:
|
||||
st.error("💥 **Failed to generate AIDPPC Copy. Please try again!**")
|
||||
|
||||
|
||||
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
|
||||
def generate_aidppc_copy(brand_name, description, attention, interest, persuasion, proof, close,
|
||||
target_audience, unique_selling_point, tone_style):
|
||||
system_prompt = """You are an expert copywriter specializing in the AIDPPC (Attention-Interest-Description-Persuasion-Proof-Close) framework.
|
||||
Your expertise is in creating compelling, conversion-focused marketing copy that guides readers through a complete journey from awareness to action.
|
||||
Your copy is authentic, specific to the brand, and focused on driving measurable results."""
|
||||
|
||||
prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {description}.
|
||||
|
||||
TARGET AUDIENCE: {target_audience}
|
||||
UNIQUE SELLING POINT: {unique_selling_point}
|
||||
TONE & STYLE: {tone_style}
|
||||
|
||||
Use the AIDPPC framework with these elements:
|
||||
- **Attention**: {attention}
|
||||
- **Interest**: {interest}
|
||||
- **Persuasion**: {persuasion}
|
||||
- **Proof**: {proof}
|
||||
- **Close**: {close}
|
||||
|
||||
For each campaign:
|
||||
1. Start with the attention-grabbing hook to capture the audience's attention
|
||||
2. Generate interest by highlighting benefits or addressing pain points
|
||||
3. Describe your product or service in detail
|
||||
4. Present persuasive arguments or incentives
|
||||
5. Provide social proof, testimonials, or guarantees
|
||||
6. End with a strong call to action
|
||||
|
||||
Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
|
||||
Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
|
||||
"""
|
||||
|
||||
try:
|
||||
return llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
except Exception as e:
|
||||
st.error(f"Error generating copy: {str(e)}")
|
||||
return None
|
||||
@@ -1,176 +0,0 @@
|
||||
import streamlit as st
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
|
||||
def input_section():
|
||||
st.markdown("""
|
||||
<div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
|
||||
<h2 style='color: #1E88E5;'>🔍 APP Copywriting Generator</h2>
|
||||
<p>Create compelling marketing copy using the proven APP (Agree-Promise-Preview) formula.</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Educational content about APP copywriting
|
||||
with st.expander("📚 What is APP Copywriting?", expanded=False):
|
||||
st.markdown("""
|
||||
### Understanding the APP Copywriting Formula
|
||||
|
||||
The APP formula is a powerful copywriting framework that creates a natural connection with your audience:
|
||||
|
||||
- **Agree**: Acknowledge a shared problem or pain point your audience faces
|
||||
- **Promise**: Make a compelling promise or offer a solution to that problem
|
||||
- **Preview**: Provide a preview of how your solution will deliver on that promise
|
||||
|
||||
### Why APP Copywriting Works
|
||||
|
||||
The APP formula works because it:
|
||||
|
||||
- Creates immediate rapport by showing you understand your audience's challenges
|
||||
- Builds trust by acknowledging problems before selling solutions
|
||||
- Reduces resistance by connecting on a human level first
|
||||
- Demonstrates empathy and understanding
|
||||
- Follows a natural conversation flow that feels authentic
|
||||
|
||||
### When to Use APP Copywriting
|
||||
|
||||
The APP formula is particularly effective for:
|
||||
|
||||
- Building trust with new audiences
|
||||
- Introducing new products or services
|
||||
- Addressing common objections
|
||||
- Creating relatable content
|
||||
- Establishing your brand as a solution provider
|
||||
- Email marketing sequences
|
||||
""")
|
||||
|
||||
# Main input form
|
||||
with st.expander("✍️ Create Your APP Copy", expanded=True):
|
||||
col1, col2 = st.columns([1, 1])
|
||||
|
||||
with col1:
|
||||
brand_name = st.text_input('**🏢 Brand/Company Name**',
|
||||
placeholder="e.g., Alwrity",
|
||||
help="Enter the name of your brand or company.")
|
||||
|
||||
target_audience = st.text_input('**👥 Target Audience**',
|
||||
placeholder="e.g., Small business owners, Tech professionals",
|
||||
help="Who is your ideal customer? Be specific about demographics and psychographics.")
|
||||
|
||||
agree = st.text_area('**🤝 Agree (Shared Problem)**',
|
||||
placeholder="We all face..., Like you, I've..., Safety, Unprofessionalism..",
|
||||
help="Connect with the audience by acknowledging a shared problem or pain point they face.")
|
||||
|
||||
with col2:
|
||||
description = st.text_input('**📝 Brand Description** (In 2-3 words)',
|
||||
placeholder="e.g., AI writing tools",
|
||||
help="Describe your product or service briefly.")
|
||||
|
||||
unique_selling_point = st.text_input('**💎 Unique Selling Point**',
|
||||
placeholder="e.g., 10x faster content creation",
|
||||
help="What makes your product/service different from competitors?")
|
||||
|
||||
promise = st.text_area('**✨ Promise (Solution)**',
|
||||
placeholder="We guarantee..., Our solution ensures..., You'll never have to worry about...",
|
||||
help="Make a compelling promise or offer a solution to the problem.")
|
||||
|
||||
preview = st.text_area('**🔮 Preview (Proof)**',
|
||||
placeholder="Here's how..., Our customers have experienced..., You'll see results like...",
|
||||
help="Provide a preview of how your solution will deliver on the promise.")
|
||||
|
||||
tone_style = st.selectbox(
|
||||
'**🎭 Copy Tone & Style**',
|
||||
options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
|
||||
help="Select the tone and style for your copy."
|
||||
)
|
||||
|
||||
if st.button('**🚀 Generate APP Copy**', type="primary"):
|
||||
if not brand_name or not description or not agree or not promise or not preview:
|
||||
st.error("⚠️ Please fill in all required fields (Brand Name, Description, Agree, Promise, and Preview)!")
|
||||
else:
|
||||
with st.spinner("✨ Crafting compelling APP copy..."):
|
||||
app_copy = generate_app_copy(
|
||||
brand_name,
|
||||
description,
|
||||
agree,
|
||||
target_audience,
|
||||
unique_selling_point,
|
||||
promise,
|
||||
preview,
|
||||
tone_style
|
||||
)
|
||||
|
||||
if app_copy:
|
||||
st.markdown("""
|
||||
<div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
|
||||
<h3 style='color: #0066cc;'>✨ Your APP Copy</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Display the copy with a nice format
|
||||
st.markdown(app_copy)
|
||||
|
||||
# Add copy button
|
||||
st.markdown("""
|
||||
<div style='margin-top: 20px;'>
|
||||
<button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
|
||||
Copy to Clipboard
|
||||
</button>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Add tips for using the copy - using a container instead of an expander
|
||||
st.markdown("""
|
||||
<div style='background-color: #f9f9f9; padding: 15px; border-radius: 10px; margin-top: 20px;'>
|
||||
<h3 style='color: #333;'>💡 Tips for Using Your APP Copy</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
st.markdown("""
|
||||
### How to Use Your APP Copy Effectively
|
||||
|
||||
1. **Test different versions**: A/B test your copy to see which version resonates most with your audience
|
||||
|
||||
2. **Pair with visuals**: Combine your copy with images that reinforce each stage of the APP formula
|
||||
|
||||
3. **Consider the platform**: Adapt your copy based on where it will appear (social media, email, website, etc.)
|
||||
|
||||
4. **Measure results**: Track engagement metrics to see how your APP copy performs
|
||||
|
||||
5. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
|
||||
""")
|
||||
else:
|
||||
st.error("💥 **Failed to generate APP Copy. Please try again!**")
|
||||
|
||||
|
||||
def generate_app_copy(brand_name, description, agree, target_audience, unique_selling_point,
|
||||
promise, preview, tone_style):
|
||||
system_prompt = """You are an expert copywriter specializing in the APP (Agree-Promise-Preview) formula.
|
||||
Your expertise is in creating compelling, persuasive marketing copy that builds rapport with audiences by
|
||||
acknowledging their problems, making promises, and providing previews of solutions. Your copy is authentic,
|
||||
specific to the brand, and focused on the target audience's needs."""
|
||||
|
||||
prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {description}.
|
||||
|
||||
TARGET AUDIENCE: {target_audience}
|
||||
UNIQUE SELLING POINT: {unique_selling_point}
|
||||
TONE & STYLE: {tone_style}
|
||||
|
||||
Use the APP formula with these elements:
|
||||
- **Agree**: {agree}
|
||||
- **Promise**: {promise}
|
||||
- **Preview**: {preview}
|
||||
|
||||
For each campaign:
|
||||
1. Create a compelling headline that captures attention
|
||||
2. Write 2-3 paragraphs that follow the APP formula
|
||||
3. End with a strong call to action
|
||||
4. Explain how each element of the APP formula is used in the copy
|
||||
|
||||
Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
|
||||
Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
|
||||
"""
|
||||
|
||||
try:
|
||||
return llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
except Exception as e:
|
||||
st.error(f"Error generating copy: {str(e)}")
|
||||
return None
|
||||
@@ -1,674 +0,0 @@
|
||||
import streamlit as st
|
||||
import importlib
|
||||
import sys
|
||||
import os
|
||||
from pathlib import Path
|
||||
import time
|
||||
import json
|
||||
from typing import Dict, List, Callable, Optional, Tuple
|
||||
|
||||
# Add the parent directory to the path to allow importing from lib
|
||||
current_dir = Path(__file__).parent
|
||||
root_dir = current_dir.parent.parent.parent
|
||||
sys.path.append(str(root_dir))
|
||||
|
||||
# Dictionary to store the input section functions
|
||||
input_sections = {}
|
||||
|
||||
# List of copywriter modules to import
|
||||
copywriter_modules = [
|
||||
"ai_emotional_copywriter",
|
||||
"acca_copywriter",
|
||||
"app_copywriter",
|
||||
"star_copywriter",
|
||||
"oath_copywriter",
|
||||
"quest_copywriter",
|
||||
"aidppc_copywriter",
|
||||
"aida_copywriter",
|
||||
"pas_copywriter",
|
||||
"fab_copywriter",
|
||||
"4c_copywriter",
|
||||
"4r_copywriter"
|
||||
]
|
||||
|
||||
# Define formula categories for better organization
|
||||
formula_categories = {
|
||||
"Emotional Appeal": ["ai_emotional_copywriter", "oath_copywriter"],
|
||||
"Structured Framework": ["acca_copywriter", "app_copywriter", "star_copywriter", "quest_copywriter"],
|
||||
"Sales Funnel": ["aidppc_copywriter", "aida_copywriter"],
|
||||
"Problem-Solution": ["pas_copywriter"],
|
||||
"Feature-Benefit": ["fab_copywriter"],
|
||||
"Messaging Framework": ["4c_copywriter", "4r_copywriter"]
|
||||
}
|
||||
|
||||
# Define formula metadata for better display and filtering
|
||||
formula_metadata = {
|
||||
"ai_emotional_copywriter": {
|
||||
"name": "Emotional Copywriter",
|
||||
"icon": "🎭",
|
||||
"description": "Create copy that resonates with your audience's emotions and drives action.",
|
||||
"color": "#FF6B6B",
|
||||
"difficulty": "Intermediate",
|
||||
"best_for": ["Landing Pages", "Email", "Social Media"],
|
||||
"tags": ["emotional", "persuasive", "engagement"]
|
||||
},
|
||||
"acca_copywriter": {
|
||||
"name": "ACCA Copywriter",
|
||||
"icon": "🎯",
|
||||
"description": "Use the ACCA (Attention, Context, Content, Action) framework to create compelling copy.",
|
||||
"color": "#4ECDC4",
|
||||
"difficulty": "Beginner",
|
||||
"best_for": ["Ads", "Email", "Landing Pages"],
|
||||
"tags": ["structured", "conversion", "clear"]
|
||||
},
|
||||
"app_copywriter": {
|
||||
"name": "APP Copywriter",
|
||||
"icon": "🤝",
|
||||
"description": "Implement the APP (Agree, Promise, Preview) formula to create persuasive copy.",
|
||||
"color": "#45B7D1",
|
||||
"difficulty": "Beginner",
|
||||
"best_for": ["Blog Posts", "Sales Pages", "Email"],
|
||||
"tags": ["persuasive", "agreement", "preview"]
|
||||
},
|
||||
"star_copywriter": {
|
||||
"name": "STAR Copywriter",
|
||||
"icon": "⭐",
|
||||
"description": "Use the STAR (Situation, Task, Action, Result) framework to tell compelling stories.",
|
||||
"color": "#FFD166",
|
||||
"difficulty": "Intermediate",
|
||||
"best_for": ["Case Studies", "Testimonials", "About Pages"],
|
||||
"tags": ["storytelling", "results", "case-study"]
|
||||
},
|
||||
"oath_copywriter": {
|
||||
"name": "OATH Copywriter",
|
||||
"icon": "📜",
|
||||
"description": "Apply the OATH (Oblivious, Apathetic, Thinking, Hurting) framework to target specific audience mindsets.",
|
||||
"color": "#06D6A0",
|
||||
"difficulty": "Advanced",
|
||||
"best_for": ["Ads", "Landing Pages", "Email Sequences"],
|
||||
"tags": ["audience", "mindset", "targeting"]
|
||||
},
|
||||
"quest_copywriter": {
|
||||
"name": "QUEST Copywriter",
|
||||
"icon": "🔍",
|
||||
"description": "Use the QUEST (Question, Unpack, Emphasize, Solution, Transform) framework for narrative-driven copy.",
|
||||
"color": "#118AB2",
|
||||
"difficulty": "Intermediate",
|
||||
"best_for": ["Long-form Content", "Sales Pages", "Video Scripts"],
|
||||
"tags": ["narrative", "transformation", "solution"]
|
||||
},
|
||||
"aidppc_copywriter": {
|
||||
"name": "AIDPPC Copywriter",
|
||||
"icon": "💰",
|
||||
"description": "Implement the AIDPPC (Attention, Interest, Desire, Proof, Persuasion, Call to Action) framework for PPC ads.",
|
||||
"color": "#073B4C",
|
||||
"difficulty": "Advanced",
|
||||
"best_for": ["PPC Ads", "Social Ads", "Display Ads"],
|
||||
"tags": ["advertising", "ppc", "conversion"]
|
||||
},
|
||||
"aida_copywriter": {
|
||||
"name": "AIDA Copywriter",
|
||||
"icon": "🎬",
|
||||
"description": "Use the AIDA (Attention, Interest, Desire, Action) framework to guide customers through the sales funnel.",
|
||||
"color": "#EF476F",
|
||||
"difficulty": "Beginner",
|
||||
"best_for": ["Sales Pages", "Email", "Product Descriptions"],
|
||||
"tags": ["sales", "funnel", "conversion"]
|
||||
},
|
||||
"pas_copywriter": {
|
||||
"name": "PAS Copywriter",
|
||||
"icon": "🔧",
|
||||
"description": "Apply the PAS (Problem, Agitate, Solution) formula to address pain points and offer solutions.",
|
||||
"color": "#7209B7",
|
||||
"difficulty": "Beginner",
|
||||
"best_for": ["Ads", "Email", "Landing Pages"],
|
||||
"tags": ["problem-solving", "pain-points", "solutions"]
|
||||
},
|
||||
"fab_copywriter": {
|
||||
"name": "FAB Copywriter",
|
||||
"icon": "💎",
|
||||
"description": "Use the FAB (Features, Advantages, Benefits) framework to highlight product value.",
|
||||
"color": "#3A0CA3",
|
||||
"difficulty": "Beginner",
|
||||
"best_for": ["Product Descriptions", "Sales Pages", "Brochures"],
|
||||
"tags": ["product", "features", "benefits"]
|
||||
},
|
||||
"4c_copywriter": {
|
||||
"name": "4C Copywriter",
|
||||
"icon": "📝",
|
||||
"description": "Implement the 4C (Clear, Concise, Credible, Compelling) framework for effective messaging.",
|
||||
"color": "#4361EE",
|
||||
"difficulty": "Intermediate",
|
||||
"best_for": ["Brand Messaging", "Mission Statements", "Value Propositions"],
|
||||
"tags": ["clarity", "concise", "credibility"]
|
||||
},
|
||||
"4r_copywriter": {
|
||||
"name": "4R Copywriter",
|
||||
"icon": "🔄",
|
||||
"description": "Use the 4R (Relevance, Resonance, Response, Results) framework to connect with your audience.",
|
||||
"color": "#F72585",
|
||||
"difficulty": "Intermediate",
|
||||
"best_for": ["Content Marketing", "Email", "Social Media"],
|
||||
"tags": ["relevance", "resonance", "results"]
|
||||
}
|
||||
}
|
||||
|
||||
def load_user_preferences() -> Dict:
|
||||
"""Load user preferences from session state or initialize if not present."""
|
||||
if "copywriter_preferences" not in st.session_state:
|
||||
st.session_state.copywriter_preferences = {
|
||||
"recent_formulas": [],
|
||||
"favorite_formulas": [],
|
||||
"comparison_formulas": [],
|
||||
"view_mode": "grid" # or "list"
|
||||
}
|
||||
return st.session_state.copywriter_preferences
|
||||
|
||||
def save_user_preferences(preferences: Dict) -> None:
|
||||
"""Save user preferences to session state."""
|
||||
st.session_state.copywriter_preferences = preferences
|
||||
|
||||
def add_recent_formula(module_name: str) -> None:
|
||||
"""Add a formula to the recent formulas list."""
|
||||
preferences = load_user_preferences()
|
||||
|
||||
# Remove if already exists
|
||||
if module_name in preferences["recent_formulas"]:
|
||||
preferences["recent_formulas"].remove(module_name)
|
||||
|
||||
# Add to the beginning of the list
|
||||
preferences["recent_formulas"].insert(0, module_name)
|
||||
|
||||
# Keep only the 5 most recent
|
||||
preferences["recent_formulas"] = preferences["recent_formulas"][:5]
|
||||
|
||||
save_user_preferences(preferences)
|
||||
|
||||
def toggle_favorite_formula(module_name: str) -> bool:
|
||||
"""Toggle a formula as favorite and return the new state."""
|
||||
preferences = load_user_preferences()
|
||||
|
||||
if module_name in preferences["favorite_formulas"]:
|
||||
preferences["favorite_formulas"].remove(module_name)
|
||||
is_favorite = False
|
||||
else:
|
||||
preferences["favorite_formulas"].append(module_name)
|
||||
is_favorite = True
|
||||
|
||||
save_user_preferences(preferences)
|
||||
return is_favorite
|
||||
|
||||
def is_favorite_formula(module_name: str) -> bool:
|
||||
"""Check if a formula is in the favorites list."""
|
||||
preferences = load_user_preferences()
|
||||
return module_name in preferences["favorite_formulas"]
|
||||
|
||||
def add_to_comparison(module_name: str) -> None:
|
||||
"""Add a formula to the comparison list."""
|
||||
preferences = load_user_preferences()
|
||||
|
||||
if module_name not in preferences["comparison_formulas"]:
|
||||
preferences["comparison_formulas"].append(module_name)
|
||||
|
||||
# Keep only up to 3 formulas for comparison
|
||||
preferences["comparison_formulas"] = preferences["comparison_formulas"][:3]
|
||||
|
||||
save_user_preferences(preferences)
|
||||
|
||||
def remove_from_comparison(module_name: str) -> None:
|
||||
"""Remove a formula from the comparison list."""
|
||||
preferences = load_user_preferences()
|
||||
|
||||
if module_name in preferences["comparison_formulas"]:
|
||||
preferences["comparison_formulas"].remove(module_name)
|
||||
|
||||
save_user_preferences(preferences)
|
||||
|
||||
def clear_comparison() -> None:
|
||||
"""Clear the comparison list."""
|
||||
preferences = load_user_preferences()
|
||||
preferences["comparison_formulas"] = []
|
||||
save_user_preferences(preferences)
|
||||
|
||||
def lazy_load_module(module_name: str) -> Optional[Callable]:
|
||||
"""Lazily load a module and return its input_section function."""
|
||||
if module_name in input_sections:
|
||||
return input_sections[module_name]
|
||||
|
||||
try:
|
||||
module_path = f"lib.ai_writers.ai_copywriter.{module_name}"
|
||||
module = importlib.import_module(module_path)
|
||||
if hasattr(module, "input_section"):
|
||||
input_sections[module_name] = module.input_section
|
||||
return module.input_section
|
||||
else:
|
||||
st.warning(f"Module {module_name} does not have an input_section function.")
|
||||
return None
|
||||
except Exception as e:
|
||||
st.error(f"Error loading module {module_name}: {str(e)}")
|
||||
return None
|
||||
|
||||
def render_formula_card(module_name: str, index: int, view_mode: str = "grid") -> None:
|
||||
"""Render a formula card with its details."""
|
||||
metadata = formula_metadata.get(module_name, {})
|
||||
|
||||
if not metadata:
|
||||
return
|
||||
|
||||
is_favorite = is_favorite_formula(module_name)
|
||||
favorite_icon = "★" if is_favorite else "☆"
|
||||
favorite_tooltip = "Remove from favorites" if is_favorite else "Add to favorites"
|
||||
|
||||
if view_mode == "grid":
|
||||
with st.container():
|
||||
st.markdown(f"""
|
||||
<div style='background-color: {metadata["color"]}; padding: 20px; border-radius: 10px; margin-bottom: 20px; color: white; position: relative;'>
|
||||
<div style='position: absolute; top: 10px; right: 10px; font-size: 1.5em;'>{favorite_icon}</div>
|
||||
<h2 style='color: white;'>{metadata["icon"]} {metadata["name"]}</h2>
|
||||
<p>{metadata["description"]}</p>
|
||||
<div style='margin-top: 10px;'>
|
||||
<span style='background-color: rgba(255,255,255,0.2); padding: 3px 8px; border-radius: 10px; margin-right: 5px; font-size: 0.8em;'>
|
||||
{metadata["difficulty"]}
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
col1, col2, col3 = st.columns(3)
|
||||
with col1:
|
||||
if st.button(f"Use {metadata['name']}", key=f"use_btn_{index}", use_container_width=True):
|
||||
add_recent_formula(module_name)
|
||||
st.session_state.selected_formula = {
|
||||
"module": module_name,
|
||||
"name": metadata["name"],
|
||||
"icon": metadata["icon"],
|
||||
"function": lazy_load_module(module_name)
|
||||
}
|
||||
st.rerun()
|
||||
|
||||
with col2:
|
||||
if st.button(f"{favorite_icon} Favorite", key=f"fav_btn_{index}", help=favorite_tooltip, use_container_width=True):
|
||||
toggle_favorite_formula(module_name)
|
||||
st.rerun()
|
||||
|
||||
with col3:
|
||||
if module_name in load_user_preferences()["comparison_formulas"]:
|
||||
if st.button("Remove from Compare", key=f"comp_btn_{index}", use_container_width=True):
|
||||
remove_from_comparison(module_name)
|
||||
st.rerun()
|
||||
else:
|
||||
if st.button("Add to Compare", key=f"comp_btn_{index}", use_container_width=True):
|
||||
add_to_comparison(module_name)
|
||||
st.rerun()
|
||||
else: # list view
|
||||
with st.container():
|
||||
col1, col2 = st.columns([3, 1])
|
||||
|
||||
with col1:
|
||||
st.markdown(f"""
|
||||
<div style='padding: 10px; border-left: 5px solid {metadata["color"]}; margin-bottom: 10px;'>
|
||||
<h3>{metadata["icon"]} {metadata["name"]} {favorite_icon}</h3>
|
||||
<p>{metadata["description"]}</p>
|
||||
<div>
|
||||
<span style='background-color: #f0f2f6; padding: 3px 8px; border-radius: 10px; margin-right: 5px; font-size: 0.8em;'>
|
||||
{metadata["difficulty"]}
|
||||
</span>
|
||||
<span style='font-size: 0.8em;'>Best for: {", ".join(metadata["best_for"][:2])}</span>
|
||||
</div>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
with col2:
|
||||
if st.button(f"Use", key=f"use_list_btn_{index}", use_container_width=True):
|
||||
add_recent_formula(module_name)
|
||||
st.session_state.selected_formula = {
|
||||
"module": module_name,
|
||||
"name": metadata["name"],
|
||||
"icon": metadata["icon"],
|
||||
"function": lazy_load_module(module_name)
|
||||
}
|
||||
st.rerun()
|
||||
|
||||
if st.button(f"{favorite_icon}", key=f"fav_list_btn_{index}", help=favorite_tooltip):
|
||||
toggle_favorite_formula(module_name)
|
||||
st.rerun()
|
||||
|
||||
if module_name in load_user_preferences()["comparison_formulas"]:
|
||||
if st.button("- Compare", key=f"comp_list_btn_{index}"):
|
||||
remove_from_comparison(module_name)
|
||||
st.rerun()
|
||||
else:
|
||||
if st.button("+ Compare", key=f"comp_list_btn_{index}"):
|
||||
add_to_comparison(module_name)
|
||||
st.rerun()
|
||||
|
||||
def render_formula_comparison() -> None:
|
||||
"""Render a comparison of selected formulas."""
|
||||
preferences = load_user_preferences()
|
||||
comparison_formulas = preferences["comparison_formulas"]
|
||||
|
||||
if not comparison_formulas:
|
||||
st.info("Add formulas to compare them side by side.")
|
||||
return
|
||||
|
||||
# Create a table for comparison
|
||||
comparison_data = []
|
||||
for module_name in comparison_formulas:
|
||||
metadata = formula_metadata.get(module_name, {})
|
||||
if metadata:
|
||||
comparison_data.append({
|
||||
"Name": f"{metadata['icon']} {metadata['name']}",
|
||||
"Description": metadata["description"],
|
||||
"Difficulty": metadata["difficulty"],
|
||||
"Best For": ", ".join(metadata["best_for"][:3]),
|
||||
"Tags": ", ".join(metadata["tags"])
|
||||
})
|
||||
|
||||
# Display the comparison table
|
||||
st.markdown("### Formula Comparison")
|
||||
|
||||
# Create columns for each formula
|
||||
cols = st.columns(len(comparison_data))
|
||||
|
||||
# Display headers
|
||||
for i, col in enumerate(cols):
|
||||
with col:
|
||||
st.markdown(f"#### {comparison_data[i]['Name']}")
|
||||
|
||||
# Display description
|
||||
st.markdown("##### Description")
|
||||
for i, col in enumerate(cols):
|
||||
with col:
|
||||
st.write(comparison_data[i]["Description"])
|
||||
|
||||
# Display difficulty
|
||||
st.markdown("##### Difficulty")
|
||||
for i, col in enumerate(cols):
|
||||
with col:
|
||||
st.write(comparison_data[i]["Difficulty"])
|
||||
|
||||
# Display best for
|
||||
st.markdown("##### Best For")
|
||||
for i, col in enumerate(cols):
|
||||
with col:
|
||||
st.write(comparison_data[i]["Best For"])
|
||||
|
||||
# Display tags
|
||||
st.markdown("##### Tags")
|
||||
for i, col in enumerate(cols):
|
||||
with col:
|
||||
st.write(comparison_data[i]["Tags"])
|
||||
|
||||
# Add buttons to use each formula
|
||||
st.markdown("##### Actions")
|
||||
for i, col in enumerate(cols):
|
||||
with col:
|
||||
module_name = comparison_formulas[i]
|
||||
if st.button(f"Use {formula_metadata[module_name]['name']}", key=f"use_comp_btn_{i}"):
|
||||
add_recent_formula(module_name)
|
||||
st.session_state.selected_formula = {
|
||||
"module": module_name,
|
||||
"name": formula_metadata[module_name]["name"],
|
||||
"icon": formula_metadata[module_name]["icon"],
|
||||
"function": lazy_load_module(module_name)
|
||||
}
|
||||
st.rerun()
|
||||
|
||||
# Add a button to clear the comparison
|
||||
if st.button("Clear Comparison", key="clear_comparison"):
|
||||
clear_comparison()
|
||||
st.rerun()
|
||||
|
||||
def filter_formulas(formulas: List[str], search_term: str, category: str, difficulty: str) -> List[str]:
|
||||
"""Filter formulas based on search term, category, and difficulty."""
|
||||
filtered_formulas = []
|
||||
|
||||
for module_name in formulas:
|
||||
metadata = formula_metadata.get(module_name, {})
|
||||
if not metadata:
|
||||
continue
|
||||
|
||||
# Check if the formula matches the search term
|
||||
name_match = search_term.lower() in metadata["name"].lower()
|
||||
desc_match = search_term.lower() in metadata["description"].lower()
|
||||
tags_match = any(search_term.lower() in tag.lower() for tag in metadata.get("tags", []))
|
||||
|
||||
# Check if the formula matches the category
|
||||
category_match = True
|
||||
if category != "All Categories":
|
||||
category_match = module_name in formula_categories.get(category, [])
|
||||
|
||||
# Check if the formula matches the difficulty
|
||||
difficulty_match = True
|
||||
if difficulty != "All Difficulties":
|
||||
difficulty_match = metadata.get("difficulty", "") == difficulty
|
||||
|
||||
# Add the formula if it matches all criteria
|
||||
if (name_match or desc_match or tags_match) and category_match and difficulty_match:
|
||||
filtered_formulas.append(module_name)
|
||||
|
||||
return filtered_formulas
|
||||
|
||||
def copywriter_dashboard():
|
||||
"""
|
||||
Main function to display the copywriting dashboard.
|
||||
This function can be called from content_generator.py when the user selects "AI Copywriter".
|
||||
"""
|
||||
# Load user preferences
|
||||
preferences = load_user_preferences()
|
||||
|
||||
# Initialize session state for selected formula if it doesn't exist
|
||||
if "selected_formula" not in st.session_state:
|
||||
st.session_state.selected_formula = None
|
||||
|
||||
# Initialize session state for search and filter options
|
||||
if "search_term" not in st.session_state:
|
||||
st.session_state.search_term = ""
|
||||
if "selected_category" not in st.session_state:
|
||||
st.session_state.selected_category = "All Categories"
|
||||
if "selected_difficulty" not in st.session_state:
|
||||
st.session_state.selected_difficulty = "All Difficulties"
|
||||
if "view_mode" not in st.session_state:
|
||||
st.session_state.view_mode = preferences["view_mode"]
|
||||
|
||||
# Create a container for the formula input section
|
||||
formula_container = st.container()
|
||||
|
||||
# If a formula is selected, show its input section
|
||||
if st.session_state.selected_formula is not None:
|
||||
with formula_container:
|
||||
# Display the selected formula's input section
|
||||
st.markdown("---")
|
||||
st.markdown(f"# {st.session_state.selected_formula['icon']} {st.session_state.selected_formula['name']}")
|
||||
|
||||
# Add a back button
|
||||
if st.button("← Back to Dashboard", key="back_to_dashboard"):
|
||||
# Clear the selected formula from session state
|
||||
st.session_state.selected_formula = None
|
||||
st.rerun()
|
||||
|
||||
# Call the input section function for the selected formula
|
||||
if st.session_state.selected_formula["function"]:
|
||||
st.session_state.selected_formula["function"]()
|
||||
else:
|
||||
st.error(f"The {st.session_state.selected_formula['name']} module is not available.")
|
||||
else:
|
||||
# Create a container for the dashboard
|
||||
dashboard_container = st.container()
|
||||
|
||||
with dashboard_container:
|
||||
# Display the dashboard
|
||||
# Header
|
||||
st.markdown("""
|
||||
<div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
|
||||
<h1 style='color: #1E88E5; text-align: center;'>✍️ AI Copywriting Tools</h1>
|
||||
<p style='text-align: center;'>Choose the perfect copywriting formula for your marketing needs</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Create tabs for different sections
|
||||
tab1, tab2, tab3, tab4 = st.tabs(["All Formulas", "Recent & Favorites", "Compare Formulas", "Help & Guide"])
|
||||
|
||||
with tab1:
|
||||
# Search and filter options
|
||||
col1, col2, col3, col4 = st.columns([3, 2, 2, 1])
|
||||
|
||||
with col1:
|
||||
search_term = st.text_input("🔍 Search formulas", value=st.session_state.search_term)
|
||||
if search_term != st.session_state.search_term:
|
||||
st.session_state.search_term = search_term
|
||||
|
||||
with col2:
|
||||
categories = ["All Categories"] + list(formula_categories.keys())
|
||||
selected_category = st.selectbox("Category", categories, index=categories.index(st.session_state.selected_category))
|
||||
if selected_category != st.session_state.selected_category:
|
||||
st.session_state.selected_category = selected_category
|
||||
|
||||
with col3:
|
||||
difficulties = ["All Difficulties", "Beginner", "Intermediate", "Advanced"]
|
||||
selected_difficulty = st.selectbox("Difficulty", difficulties, index=difficulties.index(st.session_state.selected_difficulty))
|
||||
if selected_difficulty != st.session_state.selected_difficulty:
|
||||
st.session_state.selected_difficulty = selected_difficulty
|
||||
|
||||
with col4:
|
||||
view_options = {"Grid": "grid", "List": "list"}
|
||||
view_mode = st.selectbox("View", list(view_options.keys()), index=list(view_options.values()).index(st.session_state.view_mode))
|
||||
st.session_state.view_mode = view_options[view_mode]
|
||||
preferences["view_mode"] = st.session_state.view_mode
|
||||
save_user_preferences(preferences)
|
||||
|
||||
# Filter formulas based on search and filter options
|
||||
filtered_formulas = filter_formulas(
|
||||
copywriter_modules,
|
||||
st.session_state.search_term,
|
||||
st.session_state.selected_category,
|
||||
st.session_state.selected_difficulty
|
||||
)
|
||||
|
||||
if not filtered_formulas:
|
||||
st.info("No formulas match your search criteria. Try adjusting your filters.")
|
||||
else:
|
||||
# Display the formula cards
|
||||
if st.session_state.view_mode == "grid":
|
||||
# Create a 3-column layout for the formula cards
|
||||
col1, col2, col3 = st.columns(3)
|
||||
|
||||
# Display the formula cards
|
||||
for i, module_name in enumerate(filtered_formulas):
|
||||
# Determine which column to use
|
||||
col = col1 if i % 3 == 0 else col2 if i % 3 == 1 else col3
|
||||
|
||||
with col:
|
||||
render_formula_card(module_name, i, st.session_state.view_mode)
|
||||
else: # list view
|
||||
for i, module_name in enumerate(filtered_formulas):
|
||||
render_formula_card(module_name, i, st.session_state.view_mode)
|
||||
|
||||
with tab2:
|
||||
# Recent formulas
|
||||
st.subheader("Recently Used Formulas")
|
||||
recent_formulas = preferences["recent_formulas"]
|
||||
|
||||
if not recent_formulas:
|
||||
st.info("You haven't used any formulas yet. Start by selecting a formula from the 'All Formulas' tab.")
|
||||
else:
|
||||
# Create a 3-column layout for the recent formula cards
|
||||
col1, col2, col3 = st.columns(3)
|
||||
|
||||
# Display the recent formula cards
|
||||
for i, module_name in enumerate(recent_formulas):
|
||||
# Determine which column to use
|
||||
col = col1 if i % 3 == 0 else col2 if i % 3 == 1 else col3
|
||||
|
||||
with col:
|
||||
render_formula_card(module_name, i + 100, "grid") # Use a different index to avoid key conflicts
|
||||
|
||||
# Favorite formulas
|
||||
st.subheader("Favorite Formulas")
|
||||
favorite_formulas = preferences["favorite_formulas"]
|
||||
|
||||
if not favorite_formulas:
|
||||
st.info("You haven't added any formulas to your favorites yet. Click the star icon on a formula card to add it to your favorites.")
|
||||
else:
|
||||
# Create a 3-column layout for the favorite formula cards
|
||||
col1, col2, col3 = st.columns(3)
|
||||
|
||||
# Display the favorite formula cards
|
||||
for i, module_name in enumerate(favorite_formulas):
|
||||
# Determine which column to use
|
||||
col = col1 if i % 3 == 0 else col2 if i % 3 == 1 else col3
|
||||
|
||||
with col:
|
||||
render_formula_card(module_name, i + 200, "grid") # Use a different index to avoid key conflicts
|
||||
|
||||
with tab3:
|
||||
# Formula comparison
|
||||
render_formula_comparison()
|
||||
|
||||
with tab4:
|
||||
# Help and guide
|
||||
st.subheader("Copywriting Formula Guide")
|
||||
st.write("""
|
||||
This dashboard provides access to a variety of copywriting formulas, each designed for specific marketing needs.
|
||||
Here's how to make the most of these powerful tools:
|
||||
""")
|
||||
|
||||
st.markdown("""
|
||||
#### How to Use This Dashboard
|
||||
|
||||
1. **Browse Formulas**: Explore the available copywriting formulas in the "All Formulas" tab
|
||||
2. **Search & Filter**: Use the search box and filters to find the perfect formula for your needs
|
||||
3. **Compare Formulas**: Add up to 3 formulas to the comparison tab to see them side by side
|
||||
4. **Save Favorites**: Click the star icon to save formulas you use frequently
|
||||
5. **Access Recent**: Quickly access your recently used formulas in the "Recent & Favorites" tab
|
||||
|
||||
#### Choosing the Right Formula
|
||||
|
||||
Different formulas work best for different marketing goals:
|
||||
|
||||
- **Emotional Appeal**: Use when you want to connect with your audience on an emotional level
|
||||
- **Structured Framework**: Great for organizing complex information in a compelling way
|
||||
- **Sales Funnel**: Designed to guide prospects through the buying journey
|
||||
- **Problem-Solution**: Effective for highlighting pain points and positioning your solution
|
||||
- **Feature-Benefit**: Perfect for product descriptions and technical offerings
|
||||
- **Messaging Framework**: Helps create clear, consistent messaging across channels
|
||||
|
||||
#### Formula Difficulty Levels
|
||||
|
||||
- **Beginner**: Easy to use with minimal copywriting experience
|
||||
- **Intermediate**: Requires some understanding of copywriting principles
|
||||
- **Advanced**: Most effective when used by experienced copywriters
|
||||
""")
|
||||
|
||||
# Add a section about how to use the generated copy
|
||||
st.subheader("Using Your Generated Copy")
|
||||
st.write("""
|
||||
After generating copy with your chosen formula:
|
||||
|
||||
1. **Review & Edit**: Always review and personalize the generated content
|
||||
2. **Test Different Versions**: Try multiple formulas for the same product/service
|
||||
3. **A/B Test**: Use different versions in your marketing to see which performs best
|
||||
4. **Adapt for Channels**: Modify the copy as needed for different marketing channels
|
||||
""")
|
||||
|
||||
# Add a feedback section
|
||||
st.subheader("Feedback & Suggestions")
|
||||
st.write("We're constantly improving our copywriting tools. If you have feedback or suggestions, please let us know!")
|
||||
|
||||
feedback = st.text_area("Your feedback", placeholder="Share your thoughts, suggestions, or report any issues...")
|
||||
if st.button("Submit Feedback"):
|
||||
if feedback:
|
||||
st.success("Thank you for your feedback! We'll use it to improve our tools.")
|
||||
# In a real implementation, you would save this feedback somewhere
|
||||
else:
|
||||
st.warning("Please enter your feedback before submitting.")
|
||||
|
||||
# For standalone execution
|
||||
if __name__ == "__main__":
|
||||
st.set_page_config(
|
||||
page_title="AI Copywriting Tools",
|
||||
page_icon="✍️",
|
||||
layout="wide",
|
||||
initial_sidebar_state="expanded"
|
||||
)
|
||||
copywriter_dashboard()
|
||||
@@ -1,212 +0,0 @@
|
||||
import streamlit as st
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
from tenacity import retry, wait_random_exponential, stop_after_attempt
|
||||
|
||||
def input_section():
|
||||
st.markdown("""
|
||||
<div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
|
||||
<h2 style='color: #1E88E5;'>🎯 FAB Copywriting Generator</h2>
|
||||
<p>Create compelling copy that follows the FAB (Features-Advantages-Benefits) framework to drive conversions.</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Educational content about FAB copywriting
|
||||
with st.expander("📚 What is FAB Copywriting?", expanded=False):
|
||||
st.markdown("""
|
||||
### Understanding the FAB Copywriting Framework
|
||||
|
||||
FAB is an acronym for Features-Advantages-Benefits. It's a powerful copywriting framework that focuses on translating product features into customer benefits:
|
||||
|
||||
- **Features**: The specific characteristics, attributes, or capabilities of your product or service
|
||||
- **Advantages**: How these features compare to or outperform competitors
|
||||
- **Benefits**: The positive outcomes or results that customers will experience when using your product or service
|
||||
|
||||
### Why FAB Copywriting Works
|
||||
|
||||
The FAB framework works because it:
|
||||
|
||||
- Focuses on customer value rather than just product specifications
|
||||
- Translates technical features into meaningful benefits
|
||||
- Addresses the "what's in it for me" question that customers ask
|
||||
- Creates a clear connection between product capabilities and customer outcomes
|
||||
- Helps customers understand why they should choose your product over alternatives
|
||||
|
||||
### When to Use FAB Copywriting
|
||||
|
||||
The FAB framework is particularly effective for:
|
||||
|
||||
- Product descriptions and specifications
|
||||
- Technical products with complex features
|
||||
- Comparison marketing
|
||||
- B2B marketing where features matter
|
||||
- Content that needs to explain product capabilities
|
||||
- Marketing materials that need to address feature-based objections
|
||||
""")
|
||||
|
||||
# Main input form
|
||||
with st.expander("✍️ Create Your FAB Copy", expanded=True):
|
||||
col1, col2 = st.columns([1, 1])
|
||||
|
||||
with col1:
|
||||
product_name = st.text_input('**🏢 Product/Service Name**',
|
||||
placeholder="e.g., Alwrity AI Writer",
|
||||
help="Enter the name of your product or service.")
|
||||
|
||||
target_audience = st.text_input('**👥 Target Audience**',
|
||||
placeholder="e.g., Small business owners, Content marketers",
|
||||
help="Who is your ideal customer? Be specific about demographics and psychographics.")
|
||||
|
||||
features = st.text_area('**🔧 Features**',
|
||||
placeholder="e.g., AI-powered content generation, Multiple copywriting frameworks, SEO optimization",
|
||||
help="List the specific characteristics, attributes, or capabilities of your product or service.")
|
||||
|
||||
advantages = st.text_area('**💪 Advantages**',
|
||||
placeholder="e.g., 10x faster than manual writing, Supports 12+ copywriting frameworks, Built-in SEO analysis",
|
||||
help="How do these features compare to or outperform competitors?")
|
||||
|
||||
with col2:
|
||||
product_description = st.text_input('**📝 Product Description** (In 5-6 words)',
|
||||
placeholder="e.g., AI writing assistant",
|
||||
help="Describe your product or service briefly.")
|
||||
|
||||
unique_selling_point = st.text_input('**💎 Unique Selling Point**',
|
||||
placeholder="e.g., All-in-one AI copywriting platform",
|
||||
help="What makes your product/service different from competitors?")
|
||||
|
||||
benefits = st.text_area('**✨ Benefits**',
|
||||
placeholder="e.g., Save 20+ hours per week on content creation, Increase conversion rates by 35%, Improve SEO rankings",
|
||||
help="What positive outcomes or results will customers experience when using your product or service?")
|
||||
|
||||
call_to_action = st.text_area('**🚀 Call to Action**',
|
||||
placeholder="e.g., Start creating high-converting content today with our 14-day free trial...",
|
||||
help="Prompt your audience to take action with a strong call to action.")
|
||||
|
||||
landing_page_url = st.text_input('**🌐 Landing Page URL** (Optional)',
|
||||
placeholder="e.g., https://alwrity.com",
|
||||
help="Provide a URL to include in your call to action.")
|
||||
|
||||
col1, col2 = st.columns([1, 1])
|
||||
with col1:
|
||||
platform = st.selectbox(
|
||||
'**📱 Content Platform**',
|
||||
options=['Social media copy', 'Email copy', 'Website copy', 'Ad copy', 'Product copy'],
|
||||
help="Select the platform where your copy will be used."
|
||||
)
|
||||
|
||||
with col2:
|
||||
language = st.selectbox(
|
||||
'**🌍 Language**',
|
||||
options=['English', 'Hindustani', 'Chinese', 'Hindi', 'Spanish'],
|
||||
help="Select the language for your copy."
|
||||
)
|
||||
|
||||
tone_style = st.selectbox(
|
||||
'**🎭 Copy Tone & Style**',
|
||||
options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
|
||||
help="Select the tone and style for your copy."
|
||||
)
|
||||
|
||||
if st.button('**🚀 Generate FAB Copy**', type="primary"):
|
||||
if not product_name or not product_description or not features or not advantages or not benefits:
|
||||
st.error("⚠️ Please fill in all required fields (Product Name, Description, Features, Advantages, and Benefits)!")
|
||||
else:
|
||||
with st.spinner("✨ Crafting compelling FAB copy..."):
|
||||
fab_copy = generate_fab_copy(
|
||||
product_name,
|
||||
product_description,
|
||||
features,
|
||||
advantages,
|
||||
benefits,
|
||||
target_audience,
|
||||
unique_selling_point,
|
||||
call_to_action,
|
||||
landing_page_url,
|
||||
platform,
|
||||
language,
|
||||
tone_style
|
||||
)
|
||||
|
||||
if fab_copy:
|
||||
st.markdown("""
|
||||
<div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
|
||||
<h3 style='color: #0066cc;'>🎯 Your FAB Copy</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Display the copy with a nice format
|
||||
st.markdown(fab_copy)
|
||||
|
||||
# Add copy button
|
||||
st.markdown("""
|
||||
<div style='margin-top: 20px;'>
|
||||
<button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
|
||||
Copy to Clipboard
|
||||
</button>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Add tips for using the copy
|
||||
with st.expander("💡 Tips for Using Your FAB Copy", expanded=False):
|
||||
st.markdown("""
|
||||
### How to Use Your FAB Copy Effectively
|
||||
|
||||
1. **Follow the sequence**: The FAB framework creates a natural progression - make sure your copy maintains this flow
|
||||
|
||||
2. **Balance features and benefits**: While benefits are most important, don't neglect features for technical audiences
|
||||
|
||||
3. **Be specific**: Use concrete numbers, statistics, and examples to make your advantages and benefits more compelling
|
||||
|
||||
4. **Pair with visuals**: Combine your copy with images that showcase your product features and the resulting benefits
|
||||
|
||||
5. **Consider the context**: Adapt the copy based on where it will appear (landing page, email, social media, etc.)
|
||||
|
||||
6. **Measure results**: Track conversion metrics to see how your FAB copy performs
|
||||
|
||||
7. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
|
||||
""")
|
||||
else:
|
||||
st.error("💥 **Failed to generate FAB Copy. Please try again!**")
|
||||
|
||||
|
||||
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
|
||||
def generate_fab_copy(product_name, product_description, features, advantages, benefits,
|
||||
target_audience, unique_selling_point, call_to_action,
|
||||
landing_page_url, platform, language, tone_style):
|
||||
system_prompt = """You are an expert copywriter specializing in the FAB (Features-Advantages-Benefits) framework.
|
||||
Your expertise is in creating compelling, conversion-focused marketing copy that translates product features into meaningful customer benefits.
|
||||
Your copy is authentic, specific to the brand, and focused on driving measurable results."""
|
||||
|
||||
prompt = f"""Create 3 different marketing campaigns for {product_name}, which is a {product_description}.
|
||||
|
||||
TARGET AUDIENCE: {target_audience}
|
||||
UNIQUE SELLING POINT: {unique_selling_point}
|
||||
PLATFORM: {platform}
|
||||
LANGUAGE: {language}
|
||||
TONE & STYLE: {tone_style}
|
||||
|
||||
Use the FAB framework with these elements:
|
||||
- **Features**: {features}
|
||||
- **Advantages**: {advantages}
|
||||
- **Benefits**: {benefits}
|
||||
- **Call to Action**: {call_to_action}
|
||||
"""
|
||||
|
||||
if landing_page_url:
|
||||
prompt += f"\nInclude the landing page URL ({landing_page_url}) in your call to action."
|
||||
|
||||
prompt += """
|
||||
For each campaign:
|
||||
1. Start by highlighting the key features of the product or service
|
||||
2. Explain the advantages these features provide compared to alternatives
|
||||
3. Connect these advantages to specific benefits that customers will experience
|
||||
4. End with a strong call to action
|
||||
|
||||
Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
|
||||
Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
|
||||
"""
|
||||
|
||||
try:
|
||||
return llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
except Exception as e:
|
||||
st.error(f"Error generating copy: {str(e)}")
|
||||
return None
|
||||
@@ -1,186 +0,0 @@
|
||||
import streamlit as st
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
from tenacity import retry, wait_random_exponential, stop_after_attempt
|
||||
|
||||
def input_section():
|
||||
st.markdown("""
|
||||
<div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
|
||||
<h2 style='color: #1E88E5;'>📋 OATH Copywriting Generator</h2>
|
||||
<p>Create compelling copy that addresses different audience mindsets using the OATH (Oblivious-Apathetic-Thinking-Hurting) framework.</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Educational content about OATH copywriting
|
||||
with st.expander("📚 What is OATH Copywriting?", expanded=False):
|
||||
st.markdown("""
|
||||
### Understanding the OATH Copywriting Framework
|
||||
|
||||
The OATH framework is a powerful copywriting approach that recognizes different audience mindsets:
|
||||
|
||||
- **Oblivious**: People who don't know they have a problem or need
|
||||
- **Apathetic**: People who know about the problem but don't care enough to act
|
||||
- **Thinking**: People who are actively considering solutions
|
||||
- **Hurting**: People who are experiencing pain and urgently need a solution
|
||||
|
||||
### Why OATH Copywriting Works
|
||||
|
||||
The OATH framework works because it:
|
||||
|
||||
- Addresses the full spectrum of audience awareness
|
||||
- Creates targeted messaging for each mindset
|
||||
- Increases conversion rates by meeting people where they are
|
||||
- Helps you craft the right message for the right audience
|
||||
- Allows for more personalized and effective marketing campaigns
|
||||
|
||||
### When to Use OATH Copywriting
|
||||
|
||||
The OATH framework is particularly effective for:
|
||||
|
||||
- New product launches
|
||||
- Educational content
|
||||
- Problem-solution marketing
|
||||
- Awareness campaigns
|
||||
- Multi-channel marketing strategies
|
||||
- Content that needs to address different audience segments
|
||||
""")
|
||||
|
||||
# Main input form
|
||||
with st.expander("✍️ Create Your OATH Copy", expanded=True):
|
||||
col1, col2 = st.columns([1, 1])
|
||||
|
||||
with col1:
|
||||
brand_name = st.text_input('**🏢 Brand/Company Name**',
|
||||
placeholder="e.g., Alwrity",
|
||||
help="Enter the name of your brand or company.")
|
||||
|
||||
target_audience = st.text_input('**👥 Target Audience**',
|
||||
placeholder="e.g., Small business owners, Tech professionals",
|
||||
help="Who is your ideal customer? Be specific about demographics and psychographics.")
|
||||
|
||||
oblivious = st.text_area('**🔍 Oblivious Audience**',
|
||||
placeholder="People who don't know they have this problem...",
|
||||
help="Describe the audience who doesn't know they have a problem or need your solution.")
|
||||
|
||||
apathetic = st.text_area('**😐 Apathetic Audience**',
|
||||
placeholder="People who know about the problem but don't care enough to act...",
|
||||
help="Describe the audience who knows about the problem but isn't motivated to solve it.")
|
||||
|
||||
with col2:
|
||||
description = st.text_input('**📝 Brand Description** (In 2-3 words)',
|
||||
placeholder="e.g., AI writing tools",
|
||||
help="Describe your product or service briefly.")
|
||||
|
||||
unique_selling_point = st.text_input('**💎 Unique Selling Point**',
|
||||
placeholder="e.g., 10x faster content creation",
|
||||
help="What makes your product/service different from competitors?")
|
||||
|
||||
thinking = st.text_area('**🤔 Thinking Audience**',
|
||||
placeholder="People who are actively considering solutions...",
|
||||
help="Describe the audience who is actively researching solutions to their problem.")
|
||||
|
||||
hurting = st.text_area('**😫 Hurting Audience**',
|
||||
placeholder="People who are experiencing pain and urgently need a solution...",
|
||||
help="Describe the audience who is experiencing significant pain and urgently needs a solution.")
|
||||
|
||||
tone_style = st.selectbox(
|
||||
'**🎭 Copy Tone & Style**',
|
||||
options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
|
||||
help="Select the tone and style for your copy."
|
||||
)
|
||||
|
||||
if st.button('**🚀 Generate OATH Copy**', type="primary"):
|
||||
if not brand_name or not description or not oblivious or not apathetic or not thinking or not hurting:
|
||||
st.error("⚠️ Please fill in all required fields (Brand Name, Description, and all audience segments)!")
|
||||
else:
|
||||
with st.spinner("✨ Crafting compelling OATH copy..."):
|
||||
oath_copy = generate_oath_copy(
|
||||
brand_name,
|
||||
description,
|
||||
oblivious,
|
||||
apathetic,
|
||||
thinking,
|
||||
hurting,
|
||||
target_audience,
|
||||
unique_selling_point,
|
||||
tone_style
|
||||
)
|
||||
|
||||
if oath_copy:
|
||||
st.markdown("""
|
||||
<div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
|
||||
<h3 style='color: #0066cc;'>📋 Your OATH Copy</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Display the copy with a nice format
|
||||
st.markdown(oath_copy)
|
||||
|
||||
# Add copy button
|
||||
st.markdown("""
|
||||
<div style='margin-top: 20px;'>
|
||||
<button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
|
||||
Copy to Clipboard
|
||||
</button>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Add tips for using the copy - using a container instead of an expander
|
||||
st.markdown("""
|
||||
<div style='background-color: #f9f9f9; padding: 15px; border-radius: 10px; margin-top: 20px;'>
|
||||
<h3 style='color: #333;'>💡 Tips for Using Your OATH Copy</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
st.markdown("""
|
||||
### How to Use Your OATH Copy Effectively
|
||||
|
||||
1. **Target the right audience**: Use the appropriate OATH segment copy based on your target audience's current mindset
|
||||
|
||||
2. **Create a journey**: Consider how to move audiences from one mindset to another (e.g., from Oblivious to Thinking)
|
||||
|
||||
3. **Test different versions**: A/B test your copy to see which OATH segment resonates most with your audience
|
||||
|
||||
4. **Pair with visuals**: Combine your copy with images that reinforce the message for each audience segment
|
||||
|
||||
5. **Measure results**: Track engagement metrics to see how your OATH copy performs across different audience segments
|
||||
|
||||
6. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
|
||||
""")
|
||||
else:
|
||||
st.error("💥 **Failed to generate OATH Copy. Please try again!**")
|
||||
|
||||
|
||||
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
|
||||
def generate_oath_copy(brand_name, description, oblivious, apathetic, thinking, hurting,
|
||||
target_audience, unique_selling_point, tone_style):
|
||||
system_prompt = """You are an expert copywriter specializing in the OATH (Oblivious-Apathetic-Thinking-Hurting) framework.
|
||||
Your expertise is in creating compelling, targeted marketing copy that addresses different audience mindsets and awareness levels.
|
||||
Your copy is authentic, specific to the brand, and focused on meeting audiences where they are in their journey."""
|
||||
|
||||
prompt = f"""Create 4 different marketing campaigns for {brand_name}, which is a {description}.
|
||||
|
||||
TARGET AUDIENCE: {target_audience}
|
||||
UNIQUE SELLING POINT: {unique_selling_point}
|
||||
TONE & STYLE: {tone_style}
|
||||
|
||||
Use the OATH framework with these audience segments:
|
||||
- **Oblivious**: {oblivious}
|
||||
- **Apathetic**: {apathetic}
|
||||
- **Thinking**: {thinking}
|
||||
- **Hurting**: {hurting}
|
||||
|
||||
For each campaign:
|
||||
1. Create a compelling headline that captures attention
|
||||
2. Write 2-3 paragraphs that address the specific audience mindset
|
||||
3. End with a strong call to action
|
||||
4. Explain how the copy is tailored to that specific audience mindset
|
||||
|
||||
Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
|
||||
Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
|
||||
"""
|
||||
|
||||
try:
|
||||
return llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
except Exception as e:
|
||||
st.error(f"Error generating copy: {str(e)}")
|
||||
return None
|
||||
@@ -1,213 +0,0 @@
|
||||
import streamlit as st
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
from tenacity import retry, wait_random_exponential, stop_after_attempt
|
||||
|
||||
def input_section():
|
||||
st.markdown("""
|
||||
<div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
|
||||
<h2 style='color: #1E88E5;'>🎯 PAS Copywriting Generator</h2>
|
||||
<p>Create compelling copy that follows the PAS (Problem-Agitate-Solution) framework to drive conversions.</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Educational content about PAS copywriting
|
||||
with st.expander("📚 What is PAS Copywriting?", expanded=False):
|
||||
st.markdown("""
|
||||
### Understanding the PAS Copywriting Framework
|
||||
|
||||
PAS is an acronym for Problem-Agitate-Solution. It's a powerful copywriting framework that focuses on identifying and solving customer pain points:
|
||||
|
||||
- **Problem**: Identifying a specific problem or pain point that your target audience faces
|
||||
- **Agitate**: Amplifying the problem by highlighting its negative consequences and emotional impact
|
||||
- **Solution**: Presenting your product or service as the ideal solution to the problem
|
||||
|
||||
### Why PAS Copywriting Works
|
||||
|
||||
The PAS framework works because it:
|
||||
|
||||
- Addresses real customer pain points and needs
|
||||
- Creates emotional resonance by highlighting the consequences of inaction
|
||||
- Positions your product/service as the hero that solves the problem
|
||||
- Follows a natural problem-solving narrative that readers can relate to
|
||||
- Focuses on the customer's journey rather than just product features
|
||||
|
||||
### When to Use PAS Copywriting
|
||||
|
||||
The PAS framework is particularly effective for:
|
||||
|
||||
- Products or services that solve specific problems
|
||||
- Marketing to audiences with clear pain points
|
||||
- Content that needs to drive specific actions
|
||||
- Landing pages and sales pages
|
||||
- Email marketing campaigns
|
||||
- Direct response advertising
|
||||
""")
|
||||
|
||||
# Main input form
|
||||
with st.expander("✍️ Create Your PAS Copy", expanded=True):
|
||||
col1, col2 = st.columns([1, 1])
|
||||
|
||||
with col1:
|
||||
brand_name = st.text_input('**🏢 Brand/Company Name**',
|
||||
placeholder="e.g., Alwrity",
|
||||
help="Enter the name of your brand or company.")
|
||||
|
||||
target_audience = st.text_input('**👥 Target Audience**',
|
||||
placeholder="e.g., Small business owners, Tech professionals",
|
||||
help="Who is your ideal customer? Be specific about demographics and psychographics.")
|
||||
|
||||
problem = st.text_area('**❌ Problem**',
|
||||
placeholder="e.g., Struggling to create high-quality content that converts",
|
||||
help="Identify a specific problem or pain point that your target audience faces.")
|
||||
|
||||
agitate = st.text_area('**😫 Agitate**',
|
||||
placeholder="e.g., Without effective content, you're losing potential customers and revenue every day...",
|
||||
help="Amplify the problem by highlighting its negative consequences and emotional impact.")
|
||||
|
||||
with col2:
|
||||
description = st.text_input('**📝 Brand Description** (In 5-6 words)',
|
||||
placeholder="e.g., AI writing tools",
|
||||
help="Describe your product or service briefly.")
|
||||
|
||||
unique_selling_point = st.text_input('**💎 Unique Selling Point**',
|
||||
placeholder="e.g., 10x faster content creation",
|
||||
help="What makes your product/service different from competitors?")
|
||||
|
||||
solution = st.text_area('**✨ Solution**',
|
||||
placeholder="e.g., Our AI-powered platform creates high-converting content in minutes...",
|
||||
help="Present your product or service as the ideal solution to the problem.")
|
||||
|
||||
call_to_action = st.text_area('**🚀 Call to Action**',
|
||||
placeholder="e.g., Start creating converting content today with our 14-day free trial...",
|
||||
help="Prompt your audience to take action with a strong call to action.")
|
||||
|
||||
landing_page_url = st.text_input('**🌐 Landing Page URL** (Optional)',
|
||||
placeholder="e.g., https://alwrity.com",
|
||||
help="Provide a URL to include in your call to action.")
|
||||
|
||||
col1, col2 = st.columns([1, 1])
|
||||
with col1:
|
||||
platform = st.selectbox(
|
||||
'**📱 Content Platform**',
|
||||
options=['Social media copy', 'Email copy', 'Website copy', 'Ad copy', 'Product copy'],
|
||||
help="Select the platform where your copy will be used."
|
||||
)
|
||||
|
||||
with col2:
|
||||
language = st.selectbox(
|
||||
'**🌍 Language**',
|
||||
options=['English', 'Hindustani', 'Chinese', 'Hindi', 'Spanish'],
|
||||
help="Select the language for your copy."
|
||||
)
|
||||
|
||||
tone_style = st.selectbox(
|
||||
'**🎭 Copy Tone & Style**',
|
||||
options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
|
||||
help="Select the tone and style for your copy."
|
||||
)
|
||||
|
||||
if st.button('**🚀 Generate PAS Copy**', type="primary"):
|
||||
if not brand_name or not description or not problem or not agitate or not solution:
|
||||
st.error("⚠️ Please fill in all required fields (Brand Name, Description, Problem, Agitate, and Solution)!")
|
||||
else:
|
||||
with st.spinner("✨ Crafting compelling PAS copy..."):
|
||||
pas_copy = generate_pas_copy(
|
||||
brand_name,
|
||||
description,
|
||||
problem,
|
||||
agitate,
|
||||
solution,
|
||||
target_audience,
|
||||
unique_selling_point,
|
||||
call_to_action,
|
||||
landing_page_url,
|
||||
platform,
|
||||
language,
|
||||
tone_style
|
||||
)
|
||||
|
||||
if pas_copy:
|
||||
st.markdown("""
|
||||
<div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
|
||||
<h3 style='color: #0066cc;'>🎯 Your PAS Copy</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Display the copy with a nice format
|
||||
st.markdown(pas_copy)
|
||||
|
||||
# Add copy button
|
||||
st.markdown("""
|
||||
<div style='margin-top: 20px;'>
|
||||
<button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
|
||||
Copy to Clipboard
|
||||
</button>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Add tips for using the copy
|
||||
with st.expander("💡 Tips for Using Your PAS Copy", expanded=False):
|
||||
st.markdown("""
|
||||
### How to Use Your PAS Copy Effectively
|
||||
|
||||
1. **Follow the sequence**: The PAS framework creates a natural progression - make sure your copy maintains this flow
|
||||
|
||||
2. **Be specific about the problem**: The more specific and relatable the problem, the more effective your copy will be
|
||||
|
||||
3. **Balance agitation**: Don't over-agitate to the point of creating anxiety; find the right balance to motivate action
|
||||
|
||||
4. **Pair with visuals**: Combine your copy with images that reinforce each stage of the PAS journey
|
||||
|
||||
5. **Consider the context**: Adapt the copy based on where it will appear (landing page, email, social media, etc.)
|
||||
|
||||
6. **Measure results**: Track conversion metrics to see how your PAS copy performs
|
||||
|
||||
7. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
|
||||
""")
|
||||
else:
|
||||
st.error("💥 **Failed to generate PAS Copy. Please try again!**")
|
||||
|
||||
|
||||
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
|
||||
def generate_pas_copy(brand_name, description, problem, agitate, solution,
|
||||
target_audience, unique_selling_point, call_to_action,
|
||||
landing_page_url, platform, language, tone_style):
|
||||
system_prompt = """You are an expert copywriter specializing in the PAS (Problem-Agitate-Solution) framework.
|
||||
Your expertise is in creating compelling, conversion-focused marketing copy that identifies customer pain points,
|
||||
amplifies their impact, and positions your product or service as the ideal solution.
|
||||
Your copy is authentic, specific to the brand, and focused on driving measurable results."""
|
||||
|
||||
prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {description}.
|
||||
|
||||
TARGET AUDIENCE: {target_audience}
|
||||
UNIQUE SELLING POINT: {unique_selling_point}
|
||||
PLATFORM: {platform}
|
||||
LANGUAGE: {language}
|
||||
TONE & STYLE: {tone_style}
|
||||
|
||||
Use the PAS framework with these elements:
|
||||
- **Problem**: {problem}
|
||||
- **Agitate**: {agitate}
|
||||
- **Solution**: {solution}
|
||||
- **Call to Action**: {call_to_action}
|
||||
"""
|
||||
|
||||
if landing_page_url:
|
||||
prompt += f"\nInclude the landing page URL ({landing_page_url}) in your call to action."
|
||||
|
||||
prompt += """
|
||||
For each campaign:
|
||||
1. Start by identifying the specific problem or pain point
|
||||
2. Amplify the problem by highlighting its negative consequences and emotional impact
|
||||
3. Present your product or service as the ideal solution to the problem
|
||||
4. End with a strong call to action
|
||||
|
||||
Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
|
||||
Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
|
||||
"""
|
||||
|
||||
try:
|
||||
return llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
except Exception as e:
|
||||
st.error(f"Error generating copy: {str(e)}")
|
||||
return None
|
||||
@@ -1,191 +0,0 @@
|
||||
import streamlit as st
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
from tenacity import retry, wait_random_exponential, stop_after_attempt
|
||||
|
||||
def title_and_description():
|
||||
st.markdown("""
|
||||
<div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
|
||||
<h2 style='color: #1E88E5;'>🔍 QUEST Copywriting Generator</h2>
|
||||
<p>Create compelling copy that guides your audience through a journey using the QUEST (Question-Unpack-Emphasize-Solution-Transform) framework.</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Educational content about QUEST copywriting
|
||||
with st.expander("📚 What is QUEST Copywriting?", expanded=False):
|
||||
st.markdown("""
|
||||
### Understanding the QUEST Copywriting Framework
|
||||
|
||||
QUEST is an acronym for Question-Unpack-Emphasize-Solution-Transform. It's a copywriting framework that focuses on guiding the audience through different stages:
|
||||
|
||||
- **Question**: Presenting a thought-provoking question to engage the audience
|
||||
- **Unpack**: Unpacking the question by elaborating on its implications and relevance
|
||||
- **Emphasize**: Emphasizing the importance or significance of the topic
|
||||
- **Solution**: Presenting your product or service as the solution to the question
|
||||
- **Transform**: Describing the transformation or improvement your solution offers
|
||||
|
||||
### Why QUEST Copywriting Works
|
||||
|
||||
The QUEST framework works because it:
|
||||
|
||||
- Creates a natural flow that guides readers through a journey
|
||||
- Engages readers by starting with a question they care about
|
||||
- Builds credibility by showing deep understanding of the problem
|
||||
- Demonstrates value by clearly connecting the solution to the problem
|
||||
- Inspires action by showing the transformation that's possible
|
||||
|
||||
### When to Use QUEST Copywriting
|
||||
|
||||
The QUEST framework is particularly effective for:
|
||||
|
||||
- Educational content and blog posts
|
||||
- Product launches and feature announcements
|
||||
- Problem-solution marketing
|
||||
- Thought leadership content
|
||||
- Content that needs to guide readers through a journey
|
||||
- Marketing materials that need to explain complex solutions
|
||||
""")
|
||||
|
||||
def input_section():
|
||||
# Main input form
|
||||
with st.expander("✍️ Create Your QUEST Copy", expanded=True):
|
||||
col1, col2 = st.columns([1, 1])
|
||||
|
||||
with col1:
|
||||
brand_name = st.text_input('**🏢 Brand/Company Name**',
|
||||
placeholder="e.g., Alwrity",
|
||||
help="Enter the name of your brand or company.")
|
||||
|
||||
target_audience = st.text_input('**👥 Target Audience**',
|
||||
placeholder="e.g., Small business owners, Tech professionals",
|
||||
help="Who is your ideal customer? Be specific about demographics and psychographics.")
|
||||
|
||||
question = st.text_area('**❓ Thought-Provoking Question**',
|
||||
placeholder="e.g., What if you could create content 10x faster without sacrificing quality?",
|
||||
help="Pose a question that resonates with your audience and highlights a problem they face.")
|
||||
|
||||
unpack = st.text_area('**📦 Unpack the Question**',
|
||||
placeholder="e.g., Content creation is time-consuming and often results in inconsistent quality...",
|
||||
help="Elaborate on the implications of the question and provide context that your audience can relate to.")
|
||||
|
||||
with col2:
|
||||
description = st.text_input('**📝 Brand Description** (In 2-3 words)',
|
||||
placeholder="e.g., AI writing tools",
|
||||
help="Describe your product or service briefly.")
|
||||
|
||||
unique_selling_point = st.text_input('**💎 Unique Selling Point**',
|
||||
placeholder="e.g., 10x faster content creation",
|
||||
help="What makes your product/service different from competitors?")
|
||||
|
||||
emphasize = st.text_area('**💪 Emphasize Importance**',
|
||||
placeholder="e.g., In today's fast-paced digital world, efficient content creation is essential for business growth...",
|
||||
help="Highlight the relevance and impact of addressing this problem.")
|
||||
|
||||
solution = st.text_area('**🔧 Present Your Solution**',
|
||||
placeholder="e.g., Our AI-powered writing assistant helps you create high-quality content in a fraction of the time...",
|
||||
help="Introduce your product or service as the solution to the question.")
|
||||
|
||||
transform = st.text_area('**✨ Describe the Transformation**',
|
||||
placeholder="e.g., Imagine having more time to focus on strategy while maintaining consistent, high-quality content...",
|
||||
help="Describe the transformation or improvement your solution offers to your audience.")
|
||||
|
||||
tone_style = st.selectbox(
|
||||
'**🎭 Copy Tone & Style**',
|
||||
options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
|
||||
help="Select the tone and style for your copy."
|
||||
)
|
||||
|
||||
if st.button('**🚀 Generate QUEST Copy**', type="primary"):
|
||||
if not brand_name or not description or not question or not unpack or not emphasize or not solution or not transform:
|
||||
st.error("⚠️ Please fill in all required fields (Brand Name, Description, and all QUEST elements)!")
|
||||
else:
|
||||
with st.spinner("✨ Crafting compelling QUEST copy..."):
|
||||
quest_copy = generate_quest_copy(
|
||||
brand_name,
|
||||
description,
|
||||
question,
|
||||
unpack,
|
||||
emphasize,
|
||||
solution,
|
||||
transform,
|
||||
target_audience,
|
||||
unique_selling_point,
|
||||
tone_style
|
||||
)
|
||||
|
||||
if quest_copy:
|
||||
st.markdown("""
|
||||
<div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
|
||||
<h3 style='color: #0066cc;'>🔍 Your QUEST Copy</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Display the copy with a nice format
|
||||
st.markdown(quest_copy)
|
||||
|
||||
# Add copy button
|
||||
st.markdown("""
|
||||
<div style='margin-top: 20px;'>
|
||||
<button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
|
||||
Copy to Clipboard
|
||||
</button>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Add tips for using the copy
|
||||
with st.expander("💡 Tips for Using Your QUEST Copy", expanded=False):
|
||||
st.markdown("""
|
||||
### How to Use Your QUEST Copy Effectively
|
||||
|
||||
1. **Follow the journey**: The QUEST framework creates a natural flow - make sure your copy maintains this progression
|
||||
|
||||
2. **Test different questions**: A/B test different opening questions to see which resonates most with your audience
|
||||
|
||||
3. **Pair with visuals**: Combine your copy with images that reinforce each stage of the QUEST journey
|
||||
|
||||
4. **Consider the context**: Adapt the copy based on where it will appear (blog post, landing page, email, etc.)
|
||||
|
||||
5. **Measure results**: Track engagement metrics to see how your QUEST copy performs
|
||||
|
||||
6. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
|
||||
""")
|
||||
else:
|
||||
st.error("💥 **Failed to generate QUEST Copy. Please try again!**")
|
||||
|
||||
|
||||
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
|
||||
def generate_quest_copy(brand_name, description, question, unpack, emphasize, solution, transform,
|
||||
target_audience, unique_selling_point, tone_style):
|
||||
system_prompt = """You are an expert copywriter specializing in the QUEST (Question-Unpack-Emphasize-Solution-Transform) framework.
|
||||
Your expertise is in creating compelling, narrative-driven marketing copy that guides readers through a journey.
|
||||
Your copy is authentic, specific to the brand, and focused on connecting with the audience's needs and desires."""
|
||||
|
||||
prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {description}.
|
||||
|
||||
TARGET AUDIENCE: {target_audience}
|
||||
UNIQUE SELLING POINT: {unique_selling_point}
|
||||
TONE & STYLE: {tone_style}
|
||||
|
||||
Use the QUEST framework with these elements:
|
||||
- **Question**: {question}
|
||||
- **Unpack**: {unpack}
|
||||
- **Emphasize**: {emphasize}
|
||||
- **Solution**: {solution}
|
||||
- **Transform**: {transform}
|
||||
|
||||
For each campaign:
|
||||
1. Start with the thought-provoking question to engage the audience
|
||||
2. Unpack the question by elaborating on its implications
|
||||
3. Emphasize the importance of addressing this issue
|
||||
4. Present your solution clearly and convincingly
|
||||
5. Describe the transformation that your solution offers
|
||||
6. End with a strong call to action
|
||||
|
||||
Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
|
||||
Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
|
||||
"""
|
||||
|
||||
try:
|
||||
return llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
except Exception as e:
|
||||
st.error(f"Error generating copy: {str(e)}")
|
||||
return None
|
||||
@@ -1,182 +0,0 @@
|
||||
import streamlit as st
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
|
||||
def input_section():
|
||||
st.markdown("""
|
||||
<div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
|
||||
<h2 style='color: #1E88E5;'>⭐ STAR Copywriting Generator</h2>
|
||||
<p>Create compelling marketing copy using the proven STAR (Situation-Task-Action-Result) framework.</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Educational content about STAR copywriting
|
||||
with st.expander("📚 What is STAR Copywriting?", expanded=False):
|
||||
st.markdown("""
|
||||
### Understanding the STAR Copywriting Framework
|
||||
|
||||
The STAR framework is a powerful storytelling structure that creates compelling narratives:
|
||||
|
||||
- **Situation**: Set the context and background for the problem or need
|
||||
- **Task**: Describe the specific challenge or objective that needs to be addressed
|
||||
- **Action**: Explain the specific actions taken to address the challenge
|
||||
- **Result**: Highlight the positive outcomes and benefits achieved
|
||||
|
||||
### Why STAR Copywriting Works
|
||||
|
||||
The STAR framework works because it:
|
||||
|
||||
- Creates a complete narrative arc that engages readers
|
||||
- Demonstrates problem-solving capabilities
|
||||
- Shows concrete results and benefits
|
||||
- Builds credibility through specific examples
|
||||
- Makes abstract benefits tangible through storytelling
|
||||
|
||||
### When to Use STAR Copywriting
|
||||
|
||||
The STAR framework is particularly effective for:
|
||||
|
||||
- Case studies and success stories
|
||||
- Product or service demonstrations
|
||||
- Customer testimonials
|
||||
- Company achievements and milestones
|
||||
- Problem-solution marketing
|
||||
- Portfolio showcases
|
||||
""")
|
||||
|
||||
# Main input form
|
||||
with st.expander("✍️ Create Your STAR Copy", expanded=True):
|
||||
col1, col2 = st.columns([1, 1])
|
||||
|
||||
with col1:
|
||||
brand_name = st.text_input('**🏢 Brand/Company Name**',
|
||||
placeholder="e.g., Alwrity",
|
||||
help="Enter the name of your brand or company.")
|
||||
|
||||
target_audience = st.text_input('**👥 Target Audience**',
|
||||
placeholder="e.g., Small business owners, Tech professionals",
|
||||
help="Who is your ideal customer? Be specific about demographics and psychographics.")
|
||||
|
||||
situation = st.text_area('**🌍 Situation (Context)**',
|
||||
placeholder="In a busy city, Late Delivery, Unsafe Activities, Unprofessional Service..",
|
||||
help="Describe the background context or problem that needs to be addressed.")
|
||||
|
||||
action = st.text_area('**⚡ Action (Solution)**',
|
||||
placeholder="New strategy, launched campaign, better service, New product...",
|
||||
help="Describe the specific actions taken to address the challenge or objective.")
|
||||
|
||||
with col2:
|
||||
description = st.text_input('**📝 Brand Description** (In 2-3 words)',
|
||||
placeholder="e.g., AI writing tools",
|
||||
help="Describe your product or service briefly.")
|
||||
|
||||
unique_selling_point = st.text_input('**💎 Unique Selling Point**',
|
||||
placeholder="e.g., 10x faster content creation",
|
||||
help="What makes your product/service different from competitors?")
|
||||
|
||||
task = st.text_area('**🎯 Task (Challenge)**',
|
||||
placeholder="Increase website traffic by 30%, improve customer satisfaction, Safe Travels...",
|
||||
help="Describe the specific challenge or objective that needs to be addressed.")
|
||||
|
||||
result = st.text_area('**✨ Result (Outcome)**',
|
||||
placeholder="Improved customer engagement, sales revenue, Happy customers, Improved Service X...",
|
||||
help="Highlight the positive outcomes and benefits achieved from the actions taken.")
|
||||
|
||||
tone_style = st.selectbox(
|
||||
'**🎭 Copy Tone & Style**',
|
||||
options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
|
||||
help="Select the tone and style for your copy."
|
||||
)
|
||||
|
||||
if st.button('**🚀 Generate STAR Copy**', type="primary"):
|
||||
if not brand_name or not description or not situation or not task or not action or not result:
|
||||
st.error("⚠️ Please fill in all required fields (Brand Name, Description, Situation, Task, Action, and Result)!")
|
||||
else:
|
||||
with st.spinner("✨ Crafting compelling STAR copy..."):
|
||||
star_copy = generate_star_copy(
|
||||
brand_name,
|
||||
description,
|
||||
situation,
|
||||
task,
|
||||
action,
|
||||
result,
|
||||
target_audience,
|
||||
unique_selling_point,
|
||||
tone_style
|
||||
)
|
||||
|
||||
if star_copy:
|
||||
st.markdown("""
|
||||
<div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
|
||||
<h3 style='color: #0066cc;'>⭐ Your STAR Copy</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Display the copy with a nice format
|
||||
st.markdown(star_copy)
|
||||
|
||||
# Add copy button
|
||||
st.markdown("""
|
||||
<div style='margin-top: 20px;'>
|
||||
<button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
|
||||
Copy to Clipboard
|
||||
</button>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Add tips for using the copy - using a container instead of an expander
|
||||
st.markdown("""
|
||||
<div style='background-color: #f9f9f9; padding: 15px; border-radius: 10px; margin-top: 20px;'>
|
||||
<h3 style='color: #333;'>💡 Tips for Using Your STAR Copy</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
st.markdown("""
|
||||
### How to Use Your STAR Copy Effectively
|
||||
|
||||
1. **Test different versions**: A/B test your copy to see which version resonates most with your audience
|
||||
|
||||
2. **Pair with visuals**: Combine your copy with images that illustrate each stage of the STAR framework
|
||||
|
||||
3. **Consider the platform**: Adapt your copy based on where it will appear (social media, email, website, etc.)
|
||||
|
||||
4. **Measure results**: Track engagement metrics to see how your STAR copy performs
|
||||
|
||||
5. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
|
||||
""")
|
||||
else:
|
||||
st.error("💥 **Failed to generate STAR Copy. Please try again!**")
|
||||
|
||||
|
||||
def generate_star_copy(brand_name, description, situation, task, action, result, target_audience,
|
||||
unique_selling_point, tone_style):
|
||||
system_prompt = """You are an expert copywriter specializing in the STAR (Situation-Task-Action-Result) framework.
|
||||
Your expertise is in creating compelling, narrative-driven marketing copy that tells a complete story from problem to solution.
|
||||
Your copy is authentic, specific to the brand, and focused on demonstrating concrete results and benefits."""
|
||||
|
||||
prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {description}.
|
||||
|
||||
TARGET AUDIENCE: {target_audience}
|
||||
UNIQUE SELLING POINT: {unique_selling_point}
|
||||
TONE & STYLE: {tone_style}
|
||||
|
||||
Use the STAR framework with these elements:
|
||||
- **Situation**: {situation}
|
||||
- **Task**: {task}
|
||||
- **Action**: {action}
|
||||
- **Result**: {result}
|
||||
|
||||
For each campaign:
|
||||
1. Create a compelling headline that captures attention
|
||||
2. Write 2-3 paragraphs that follow the STAR framework
|
||||
3. End with a strong call to action
|
||||
4. Explain how each element of the STAR framework is used in the copy
|
||||
|
||||
Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
|
||||
Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
|
||||
"""
|
||||
|
||||
try:
|
||||
return llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
except Exception as e:
|
||||
st.error(f"Error generating copy: {str(e)}")
|
||||
return None
|
||||
@@ -1,190 +0,0 @@
|
||||
# AI Finance Report Generator
|
||||
|
||||
An advanced AI-powered financial analysis and report generation system that combines data collection, technical analysis, visualization, and automated report generation.
|
||||
|
||||
## Project Structure
|
||||
|
||||
```
|
||||
ai_finance_report_generator/
|
||||
├── ai_financial_dashboard.py # Main dashboard interface
|
||||
├── utils/ # Utility functions
|
||||
│ ├── __init__.py
|
||||
│ └── storage.py # Data persistence
|
||||
├── reports/ # Report generation modules
|
||||
│ ├── technical_analysis/ # Technical analysis reports
|
||||
│ ├── fundamental_analysis/ # Fundamental analysis reports
|
||||
│ ├── options_analysis/ # Options analysis reports
|
||||
│ ├── portfolio_analysis/ # Portfolio analysis reports
|
||||
│ ├── market_research/ # Market research reports
|
||||
│ └── news_analysis/ # News analysis reports
|
||||
└── README.md # This file
|
||||
```
|
||||
|
||||
## Features
|
||||
|
||||
### Current Features
|
||||
- Unified dashboard interface for all financial analysis tools
|
||||
- Technical Analysis report generation
|
||||
- Options analysis report generation
|
||||
- User preferences management
|
||||
- Recent reports tracking
|
||||
- Data persistence with JSON storage
|
||||
- Financial data collection from various sources
|
||||
- Integration with LLM for report generation
|
||||
|
||||
### Planned Features
|
||||
|
||||
#### 1. Data Collection Module
|
||||
- Web scraping for financial news and data
|
||||
- API integrations (Yahoo Finance, Alpha Vantage, Financial Modeling Prep)
|
||||
- Real-time market data collection
|
||||
- Historical data retrieval
|
||||
- Company financial statements
|
||||
- Market sentiment data
|
||||
- Economic indicators
|
||||
- Sector analysis data
|
||||
|
||||
#### 2. Technical Analysis Module
|
||||
- Moving averages (SMA, EMA, WMA)
|
||||
- RSI, MACD, Bollinger Bands
|
||||
- Volume analysis
|
||||
- Support/Resistance levels
|
||||
- Trend analysis
|
||||
- Pattern recognition
|
||||
- Fibonacci retracements
|
||||
- Momentum indicators
|
||||
|
||||
#### 3. Fundamental Analysis Module
|
||||
- Financial ratios calculation
|
||||
- Company valuation metrics
|
||||
- Growth analysis
|
||||
- Profitability analysis
|
||||
- Debt analysis
|
||||
- Cash flow analysis
|
||||
- Industry comparison
|
||||
- Peer analysis
|
||||
|
||||
#### 4. Data Visualization Module
|
||||
- Candlestick charts
|
||||
- Technical indicator overlays
|
||||
- Volume charts
|
||||
- Price action patterns
|
||||
- Correlation matrices
|
||||
- Heat maps
|
||||
- Interactive charts
|
||||
- Custom chart templates
|
||||
|
||||
#### 5. Report Generation Module
|
||||
- Technical analysis reports
|
||||
- Fundamental analysis reports
|
||||
- Market research reports
|
||||
- Investment recommendations
|
||||
- Risk assessment reports
|
||||
- Sector analysis reports
|
||||
- News impact analysis
|
||||
- Custom report templates
|
||||
|
||||
#### 6. News and Sentiment Analysis Module
|
||||
- News aggregation
|
||||
- Sentiment scoring
|
||||
- Social media analysis
|
||||
- Market sentiment indicators
|
||||
- News impact analysis
|
||||
- Event correlation
|
||||
- Trend detection
|
||||
- Sentiment visualization
|
||||
|
||||
#### 7. Portfolio Analysis Module
|
||||
- Portfolio performance analysis
|
||||
- Risk assessment
|
||||
- Asset allocation
|
||||
- Correlation analysis
|
||||
- Diversification metrics
|
||||
- Performance attribution
|
||||
- Portfolio optimization
|
||||
- Rebalancing suggestions
|
||||
|
||||
## Usage
|
||||
|
||||
### Basic Usage
|
||||
|
||||
```python
|
||||
from lib.ai_writers.ai_finance_report_generator.ai_financial_dashboard import get_dashboard
|
||||
|
||||
# Get dashboard instance
|
||||
dashboard = get_dashboard()
|
||||
|
||||
# Generate technical analysis report
|
||||
ta_report = dashboard.generate_technical_analysis("AAPL")
|
||||
|
||||
# Generate options analysis report
|
||||
options_report = dashboard.generate_options_analysis("AAPL")
|
||||
|
||||
# Get recent reports
|
||||
recent_reports = dashboard.get_recent_reports()
|
||||
```
|
||||
|
||||
### User Preferences
|
||||
|
||||
```python
|
||||
# Update user preferences
|
||||
dashboard.update_preferences({
|
||||
"report_format": "markdown",
|
||||
"include_charts": True,
|
||||
"chart_style": "dark",
|
||||
"language": "en"
|
||||
})
|
||||
|
||||
# Get current preferences
|
||||
preferences = dashboard.get_preferences()
|
||||
```
|
||||
|
||||
### Portfolio Analysis
|
||||
|
||||
```python
|
||||
# Create portfolio
|
||||
portfolio = [
|
||||
{"symbol": "AAPL", "shares": 100},
|
||||
{"symbol": "GOOGL", "shares": 50}
|
||||
]
|
||||
|
||||
# Generate portfolio report
|
||||
portfolio_report = dashboard.generate_portfolio_analysis(portfolio)
|
||||
```
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
## Dependencies
|
||||
|
||||
1. **Data Collection**
|
||||
- `finance_data_researcher`
|
||||
- `web_scraping_tools`
|
||||
|
||||
2. **Analysis Tools**
|
||||
- `pandas_ta`
|
||||
- `numpy`
|
||||
- `scipy`
|
||||
|
||||
3. **Visualization**
|
||||
- `matplotlib`
|
||||
- `plotly`
|
||||
|
||||
4. **Text Generation**
|
||||
- `llm_text_gen`
|
||||
- `gpt_providers`
|
||||
|
||||
## Contributing
|
||||
|
||||
1. Fork the repository
|
||||
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
|
||||
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
|
||||
4. Push to the branch (`git push origin feature/AmazingFeature`)
|
||||
5. Open a Pull Request
|
||||
|
||||
## License
|
||||
|
||||
This project is licensed under the MIT License - see the LICENSE file for details.
|
||||
@@ -1,358 +0,0 @@
|
||||
"""
|
||||
AI Financial Dashboard Module
|
||||
|
||||
This module combines the financial dashboard interface with financial report generation capabilities.
|
||||
It provides a unified interface for managing financial analysis tools and generating reports.
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
from textwrap import dedent
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
from typing import Dict, List, Any, Optional, Union
|
||||
|
||||
from loguru import logger
|
||||
logger.remove()
|
||||
logger.add(sys.stdout,
|
||||
colorize=True,
|
||||
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
|
||||
)
|
||||
|
||||
from ...ai_web_researcher.finance_data_researcher import get_finance_data, get_fin_options_data
|
||||
from ...gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
from .utils import get_feature_status
|
||||
from .utils.storage import get_storage_manager
|
||||
|
||||
class UserPreferences:
|
||||
"""Class to manage user preferences and settings."""
|
||||
|
||||
def __init__(self):
|
||||
self.default_settings = {
|
||||
"theme": "light",
|
||||
"currency": "USD",
|
||||
"timezone": "UTC",
|
||||
"date_format": "%Y-%m-%d",
|
||||
"default_symbols": [],
|
||||
"notifications": True,
|
||||
"auto_refresh": False,
|
||||
"refresh_interval": 300, # 5 minutes
|
||||
"report_format": "markdown",
|
||||
"include_charts": True,
|
||||
"chart_style": "default",
|
||||
"language": "en"
|
||||
}
|
||||
self.settings = self.default_settings.copy()
|
||||
self.storage = get_storage_manager()
|
||||
self.load_settings()
|
||||
|
||||
def update_setting(self, key: str, value: Any) -> None:
|
||||
"""Update a specific setting."""
|
||||
if key in self.default_settings:
|
||||
self.settings[key] = value
|
||||
self.save_settings()
|
||||
|
||||
def get_setting(self, key: str) -> Any:
|
||||
"""Get a specific setting value."""
|
||||
return self.settings.get(key, self.default_settings.get(key))
|
||||
|
||||
def reset_settings(self) -> None:
|
||||
"""Reset all settings to default values."""
|
||||
self.settings = self.default_settings.copy()
|
||||
self.save_settings()
|
||||
|
||||
def save_settings(self) -> None:
|
||||
"""Save current settings to storage."""
|
||||
self.storage.save_user_preferences(self.settings)
|
||||
|
||||
def load_settings(self) -> None:
|
||||
"""Load settings from storage."""
|
||||
stored_settings = self.storage.load_user_preferences()
|
||||
if stored_settings:
|
||||
self.settings.update(stored_settings)
|
||||
|
||||
class RecentReport:
|
||||
"""Class to represent a recently generated report."""
|
||||
|
||||
def __init__(self, report_type: str, symbol: Optional[str], timestamp: datetime, content: Optional[str] = None):
|
||||
self.report_type = report_type
|
||||
self.symbol = symbol
|
||||
self.timestamp = timestamp
|
||||
self.content = content
|
||||
self.id = f"{report_type}_{symbol}_{timestamp.strftime('%Y%m%d%H%M%S')}"
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""Convert report to dictionary format."""
|
||||
return {
|
||||
"id": self.id,
|
||||
"type": self.report_type,
|
||||
"symbol": self.symbol,
|
||||
"timestamp": self.timestamp.isoformat(),
|
||||
"content": self.content
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> 'RecentReport':
|
||||
"""Create report from dictionary format."""
|
||||
return cls(
|
||||
report_type=data["type"],
|
||||
symbol=data["symbol"],
|
||||
timestamp=datetime.fromisoformat(data["timestamp"]),
|
||||
content=data.get("content")
|
||||
)
|
||||
|
||||
class FinancialDashboard:
|
||||
"""Main dashboard class for managing financial analysis tools and generating reports."""
|
||||
|
||||
def __init__(self):
|
||||
self.features = {
|
||||
"technical_analysis": {
|
||||
"name": "Technical Analysis",
|
||||
"description": "Generate technical analysis reports with indicators and patterns",
|
||||
"icon": "📊",
|
||||
"route": "/technical-analysis",
|
||||
"category": "analysis",
|
||||
"dependencies": ["data_collection"],
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"fundamental_analysis": {
|
||||
"name": "Fundamental Analysis",
|
||||
"description": "Analyze company financials and valuation metrics",
|
||||
"icon": "📈",
|
||||
"route": "/fundamental-analysis",
|
||||
"category": "analysis",
|
||||
"dependencies": ["data_collection"],
|
||||
"version": "0.1.0"
|
||||
},
|
||||
"options_analysis": {
|
||||
"name": "Options Analysis",
|
||||
"description": "Analyze options chains and generate trading strategies",
|
||||
"icon": "⚡",
|
||||
"route": "/options-analysis",
|
||||
"category": "analysis",
|
||||
"dependencies": ["data_collection", "options_data"],
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"portfolio_analysis": {
|
||||
"name": "Portfolio Analysis",
|
||||
"description": "Analyze portfolio performance and risk metrics",
|
||||
"icon": "📑",
|
||||
"route": "/portfolio-analysis",
|
||||
"category": "portfolio",
|
||||
"dependencies": ["data_collection", "portfolio_data"],
|
||||
"version": "0.1.0"
|
||||
},
|
||||
"market_research": {
|
||||
"name": "Market Research",
|
||||
"description": "Generate market research reports and sector analysis",
|
||||
"icon": "🔍",
|
||||
"route": "/market-research",
|
||||
"category": "research",
|
||||
"dependencies": ["data_collection", "news_data"],
|
||||
"version": "0.1.0"
|
||||
},
|
||||
"news_analysis": {
|
||||
"name": "News Analysis",
|
||||
"description": "Analyze news impact and market sentiment",
|
||||
"icon": "📰",
|
||||
"route": "/news-analysis",
|
||||
"category": "research",
|
||||
"dependencies": ["data_collection", "news_data"],
|
||||
"version": "0.1.0"
|
||||
}
|
||||
}
|
||||
|
||||
self.user_preferences = UserPreferences()
|
||||
self.storage = get_storage_manager()
|
||||
self.recent_reports: List[RecentReport] = []
|
||||
self.max_recent_reports = 10
|
||||
self.load_recent_reports()
|
||||
|
||||
def get_all_features(self) -> List[Dict[str, Any]]:
|
||||
"""Get all available features with their status."""
|
||||
features_list = []
|
||||
for feature_id, feature_info in self.features.items():
|
||||
status = get_feature_status(feature_id)
|
||||
feature_info.update(status)
|
||||
features_list.append(feature_info)
|
||||
return features_list
|
||||
|
||||
def get_feature(self, feature_id: str) -> Dict[str, Any]:
|
||||
"""Get information about a specific feature."""
|
||||
if feature_id not in self.features:
|
||||
raise ValueError(f"Feature {feature_id} not found")
|
||||
|
||||
feature_info = self.features[feature_id].copy()
|
||||
status = get_feature_status(feature_id)
|
||||
feature_info.update(status)
|
||||
return feature_info
|
||||
|
||||
def get_implemented_features(self) -> List[Dict[str, Any]]:
|
||||
"""Get only the implemented features."""
|
||||
return [f for f in self.get_all_features() if f["implemented"]]
|
||||
|
||||
def get_coming_soon_features(self) -> List[Dict[str, Any]]:
|
||||
"""Get features that are coming soon."""
|
||||
return [f for f in self.get_all_features() if f["coming_soon"]]
|
||||
|
||||
def get_features_by_category(self, category: str) -> List[Dict[str, Any]]:
|
||||
"""Get features filtered by category."""
|
||||
return [f for f in self.get_all_features() if f["category"] == category]
|
||||
|
||||
def add_recent_report(self, report_type: str, symbol: Optional[str] = None, content: Optional[str] = None) -> None:
|
||||
"""Add a report to the recent reports list."""
|
||||
report = RecentReport(report_type, symbol, datetime.now(), content)
|
||||
self.recent_reports.insert(0, report)
|
||||
if len(self.recent_reports) > self.max_recent_reports:
|
||||
self.recent_reports.pop()
|
||||
self.save_recent_reports()
|
||||
|
||||
def get_recent_reports(self, limit: Optional[int] = None) -> List[Dict[str, Any]]:
|
||||
"""Get recent reports."""
|
||||
reports = self.recent_reports[:limit] if limit else self.recent_reports
|
||||
return [{
|
||||
**r.to_dict(),
|
||||
"feature_info": self.get_feature(r.report_type)
|
||||
} for r in reports]
|
||||
|
||||
def save_recent_reports(self) -> None:
|
||||
"""Save recent reports to storage."""
|
||||
reports_data = [r.to_dict() for r in self.recent_reports]
|
||||
self.storage.save_recent_reports(reports_data)
|
||||
|
||||
def load_recent_reports(self) -> None:
|
||||
"""Load recent reports from storage."""
|
||||
reports_data = self.storage.load_recent_reports()
|
||||
self.recent_reports = [RecentReport.from_dict(r) for r in reports_data]
|
||||
|
||||
def get_dashboard_summary(self) -> Dict[str, Any]:
|
||||
"""Get a summary of the dashboard state."""
|
||||
return {
|
||||
"total_features": len(self.features),
|
||||
"implemented_features": len(self.get_implemented_features()),
|
||||
"coming_soon_features": len(self.get_coming_soon_features()),
|
||||
"recent_reports": len(self.recent_reports),
|
||||
"categories": list(set(f["category"] for f in self.features.values())),
|
||||
"user_preferences": self.user_preferences.settings
|
||||
}
|
||||
|
||||
def check_feature_dependencies(self, feature_id: str) -> Dict[str, bool]:
|
||||
"""Check if all dependencies for a feature are met."""
|
||||
if feature_id not in self.features:
|
||||
raise ValueError(f"Feature {feature_id} not found")
|
||||
|
||||
feature = self.features[feature_id]
|
||||
dependencies = feature.get("dependencies", [])
|
||||
|
||||
return {
|
||||
dep: get_feature_status(dep)["implemented"]
|
||||
for dep in dependencies
|
||||
}
|
||||
|
||||
def backup_data(self, backup_dir: Optional[str] = None) -> None:
|
||||
"""Create a backup of all dashboard data."""
|
||||
self.storage.backup_storage(backup_dir)
|
||||
|
||||
def restore_from_backup(self, backup_file: str) -> None:
|
||||
"""Restore dashboard data from a backup file."""
|
||||
self.storage.restore_from_backup(backup_file)
|
||||
self.user_preferences.load_settings()
|
||||
self.load_recent_reports()
|
||||
|
||||
def generate_technical_analysis(self, symbol: str) -> str:
|
||||
"""Generate a technical analysis report for the given symbol."""
|
||||
try:
|
||||
# Get financial data
|
||||
symbol_fin_data = get_finance_data(symbol)
|
||||
|
||||
# Generate report
|
||||
report_content = self._generate_ta_report(symbol_fin_data, symbol)
|
||||
|
||||
# Add to recent reports
|
||||
self.add_recent_report("technical_analysis", symbol, report_content)
|
||||
|
||||
logger.info(f"Done: Final Technical Analysis for {symbol}")
|
||||
return report_content
|
||||
|
||||
except Exception as err:
|
||||
logger.error(f"Error: Failed to generate Technical Analysis report: {err}")
|
||||
raise
|
||||
|
||||
def generate_options_analysis(self, symbol: str) -> str:
|
||||
"""Generate an options analysis report for the given symbol."""
|
||||
try:
|
||||
# Get options data
|
||||
options_data = get_fin_options_data(symbol)
|
||||
|
||||
# Generate report
|
||||
report_content = self._generate_options_report(options_data, symbol)
|
||||
|
||||
# Add to recent reports
|
||||
self.add_recent_report("options_analysis", symbol, report_content)
|
||||
|
||||
logger.info(f"Done: Options Analysis for {symbol}")
|
||||
return report_content
|
||||
|
||||
except Exception as err:
|
||||
logger.error(f"Error: Failed to generate Options Analysis report: {err}")
|
||||
raise
|
||||
|
||||
def _generate_ta_report(self, last_day_summary: str, symbol: str) -> str:
|
||||
"""Generate technical analysis report using LLM."""
|
||||
prompt = f"""
|
||||
You are a seasoned Technical Analysis (TA) expert, rivaling legends like Charles Dow, John Bollinger, and Alan Andrews.
|
||||
Your deep understanding of market dynamics, coupled with mastery of technical indicators,
|
||||
allows you to decipher complex patterns and offer precise predictions.
|
||||
|
||||
Your expertise extends to practical tools like the pandas_ta module, enabling you to extract valuable insights from raw data.
|
||||
|
||||
**Objective:**
|
||||
Analyze the provided technical indicators for {symbol} on its last trading day and predict its price movement over the next few trading sessions.
|
||||
|
||||
**Instructions:**
|
||||
1. **Identify Potential Trading Signals:** Highlight specific indicators suggesting bullish, bearish, or neutral signals. Explain the rationale behind each signal, referencing historical patterns or comparable market scenarios.
|
||||
2. **Detect Patterns and Divergences:** Analyze the interplay between different indicators. Detect patterns like moving average crossovers, candlestick formations, or divergences between price action and indicators. Explain the significance of each pattern.
|
||||
3. **Price Movement Prediction:** Based on your analysis, provide a clear prediction for {symbol}'s price movement in the next few days. State the expected direction (up, down, sideways) and potential price targets if identifiable.
|
||||
4. **Risk Assessment:** Briefly discuss any potential risks or factors that could invalidate your predictions, promoting a balanced and informed perspective.
|
||||
|
||||
**Technical Indicators for {symbol} on the Last Trading Day:**
|
||||
{last_day_summary}
|
||||
|
||||
Remember, your analysis should be detailed, insightful, and actionable for traders seeking to capitalize on market movements.
|
||||
"""
|
||||
|
||||
try:
|
||||
return llm_text_gen(prompt)
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to generate TA report: {err}")
|
||||
raise
|
||||
|
||||
def _generate_options_report(self, results_sentences: List[str], ticker: str) -> str:
|
||||
"""Generate options analysis report using LLM."""
|
||||
prompt = f"""
|
||||
You are a financial expert specializing in options trading and market sentiment analysis.
|
||||
You have been provided with the following technical analysis of options data for the ticker symbol {ticker} with the nearest expiry date:
|
||||
|
||||
{chr(10).join(results_sentences)}
|
||||
|
||||
Based on this data, provide a comprehensive analysis of the options market for {ticker}.
|
||||
|
||||
Your analysis should include:
|
||||
|
||||
1. **Implied Volatility Interpretation:** Discuss the significance of the average implied volatility for both call and put options. What does it suggest about market expectations of future price movements?
|
||||
2. **Volume and Open Interest Insights:** Analyze the volume and open interest for call and put options. What does this data reveal about current market positioning and potential future trading activity?
|
||||
3. **Sentiment Analysis:** Evaluate the put-call ratio, implied volatility skew, and overall market sentiment. What do these indicators suggest about trader sentiment and potential future price direction?
|
||||
4. **Potential Trading Strategies:** Based on your analysis, suggest potential options trading strategies that could be employed for {ticker}, considering the current market conditions and sentiment.
|
||||
|
||||
Please provide your analysis in a clear and concise manner, suitable for someone with a good understanding of options trading.
|
||||
"""
|
||||
|
||||
try:
|
||||
return llm_text_gen(prompt)
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to generate options report: {err}")
|
||||
raise
|
||||
|
||||
def get_dashboard() -> FinancialDashboard:
|
||||
"""Get the financial dashboard instance."""
|
||||
return FinancialDashboard()
|
||||
@@ -1,265 +0,0 @@
|
||||
# Financial Reports Module
|
||||
|
||||
This directory contains the core report generation modules for different types of financial analysis. Each module is designed to handle a specific type of financial report and can be accessed through the main dashboard interface.
|
||||
|
||||
## Directory Structure
|
||||
|
||||
```
|
||||
reports/
|
||||
├── technical_analysis/ # Technical analysis reports
|
||||
├── fundamental_analysis/ # Fundamental analysis reports
|
||||
├── options_analysis/ # Options analysis reports
|
||||
├── portfolio_analysis/ # Portfolio analysis reports
|
||||
├── market_research/ # Market research reports
|
||||
└── news_analysis/ # News analysis reports
|
||||
```
|
||||
|
||||
## Report Types
|
||||
|
||||
### 1. Technical Analysis Reports
|
||||
Location: `technical_analysis/`
|
||||
|
||||
Generates technical analysis reports including:
|
||||
- Moving averages (SMA, EMA, WMA)
|
||||
- RSI, MACD, Bollinger Bands
|
||||
- Volume analysis
|
||||
- Support/Resistance levels
|
||||
- Trend analysis
|
||||
- Pattern recognition
|
||||
|
||||
Usage:
|
||||
```python
|
||||
from lib.ai_writers.ai_finance_report_generator.reports.technical_analysis import generate_ta_report
|
||||
|
||||
report = generate_ta_report("AAPL")
|
||||
```
|
||||
|
||||
### 2. Fundamental Analysis Reports
|
||||
Location: `fundamental_analysis/`
|
||||
|
||||
Generates fundamental analysis reports including:
|
||||
- Financial ratios
|
||||
- Company valuation metrics
|
||||
- Growth analysis
|
||||
- Profitability analysis
|
||||
- Debt analysis
|
||||
- Cash flow analysis
|
||||
|
||||
Usage:
|
||||
```python
|
||||
from lib.ai_writers.ai_finance_report_generator.reports.fundamental_analysis import generate_fa_report
|
||||
|
||||
report = generate_fa_report("AAPL")
|
||||
```
|
||||
|
||||
### 3. Options Analysis Reports
|
||||
Location: `options_analysis/`
|
||||
|
||||
Generates options analysis reports including:
|
||||
- Options chain analysis
|
||||
- Implied volatility analysis
|
||||
- Options strategies
|
||||
- Risk metrics
|
||||
- Greeks analysis
|
||||
|
||||
Usage:
|
||||
```python
|
||||
from lib.ai_writers.ai_finance_report_generator.reports.options_analysis import generate_options_report
|
||||
|
||||
report = generate_options_report("AAPL")
|
||||
```
|
||||
|
||||
### 4. Portfolio Analysis Reports
|
||||
Location: `portfolio_analysis/`
|
||||
|
||||
Generates portfolio analysis reports including:
|
||||
- Portfolio performance analysis
|
||||
- Risk assessment
|
||||
- Asset allocation
|
||||
- Correlation analysis
|
||||
- Diversification metrics
|
||||
- Performance attribution
|
||||
|
||||
Usage:
|
||||
```python
|
||||
from lib.ai_writers.ai_finance_report_generator.reports.portfolio_analysis import generate_portfolio_report
|
||||
|
||||
portfolio = [
|
||||
{"symbol": "AAPL", "shares": 100},
|
||||
{"symbol": "GOOGL", "shares": 50}
|
||||
]
|
||||
report = generate_portfolio_report(portfolio)
|
||||
```
|
||||
|
||||
### 5. Market Research Reports
|
||||
Location: `market_research/`
|
||||
|
||||
Generates market research reports including:
|
||||
- Sector analysis
|
||||
- Industry trends
|
||||
- Market overview
|
||||
- Competitive analysis
|
||||
- Market opportunities
|
||||
- Risk factors
|
||||
|
||||
Usage:
|
||||
```python
|
||||
from lib.ai_writers.ai_finance_report_generator.reports.market_research import generate_market_research_report
|
||||
|
||||
report = generate_market_research_report(sectors=["Technology", "Healthcare"])
|
||||
```
|
||||
|
||||
### 6. News Analysis Reports
|
||||
Location: `news_analysis/`
|
||||
|
||||
Generates news analysis reports including:
|
||||
- News sentiment analysis
|
||||
- Market impact analysis
|
||||
- Event correlation
|
||||
- Trend detection
|
||||
- Social media analysis
|
||||
- News aggregation
|
||||
|
||||
Usage:
|
||||
```python
|
||||
from lib.ai_writers.ai_finance_report_generator.reports.news_analysis import generate_news_analysis_report
|
||||
|
||||
report = generate_news_analysis_report("AAPL")
|
||||
```
|
||||
|
||||
## Common Features
|
||||
|
||||
All report modules share the following features:
|
||||
|
||||
1. **Data Validation**
|
||||
- Input validation for symbols and parameters
|
||||
- Error handling for invalid inputs
|
||||
- Data type checking
|
||||
|
||||
2. **Report Formatting**
|
||||
- Markdown formatting
|
||||
- Chart generation (when applicable)
|
||||
- Customizable templates
|
||||
|
||||
3. **Storage Integration**
|
||||
- Automatic report storage
|
||||
- Recent reports tracking
|
||||
- Report versioning
|
||||
|
||||
4. **User Preferences**
|
||||
- Customizable report formats
|
||||
- Language selection
|
||||
- Chart style preferences
|
||||
|
||||
## Integration with Dashboard
|
||||
|
||||
All report modules are integrated with the main dashboard and can be accessed through the `FinancialDashboard` class:
|
||||
|
||||
```python
|
||||
from lib.ai_writers.ai_finance_report_generator.ai_financial_dashboard import get_dashboard
|
||||
|
||||
dashboard = get_dashboard()
|
||||
|
||||
# Generate reports through dashboard
|
||||
ta_report = dashboard.generate_technical_analysis("AAPL")
|
||||
options_report = dashboard.generate_options_analysis("AAPL")
|
||||
|
||||
# Get recent reports
|
||||
recent_reports = dashboard.get_recent_reports()
|
||||
```
|
||||
|
||||
## Adding New Report Types
|
||||
|
||||
To add a new report type:
|
||||
|
||||
1. Create a new directory in the `reports/` folder
|
||||
2. Create an `__init__.py` file with the report generation function
|
||||
3. Add the report type to the dashboard features
|
||||
4. Implement the report generation logic
|
||||
5. Add appropriate error handling and validation
|
||||
|
||||
Example:
|
||||
```python
|
||||
# reports/new_analysis/__init__.py
|
||||
from typing import Dict, Any
|
||||
from ...utils import validate_symbol
|
||||
|
||||
def generate_new_analysis_report(symbol: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate a new type of analysis report.
|
||||
|
||||
Args:
|
||||
symbol (str): Stock symbol to analyze
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: Analysis report
|
||||
"""
|
||||
if not validate_symbol(symbol):
|
||||
raise ValueError("Invalid symbol provided")
|
||||
|
||||
# Implement report generation logic
|
||||
return {
|
||||
"symbol": symbol,
|
||||
"analysis": "Report content"
|
||||
}
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
|
||||
All report modules implement consistent error handling:
|
||||
|
||||
1. **Input Validation**
|
||||
- Symbol validation
|
||||
- Parameter validation
|
||||
- Data type checking
|
||||
|
||||
2. **Data Collection Errors**
|
||||
- API errors
|
||||
- Network errors
|
||||
- Data format errors
|
||||
|
||||
3. **Report Generation Errors**
|
||||
- LLM errors
|
||||
- Template errors
|
||||
- Formatting errors
|
||||
|
||||
4. **Storage Errors**
|
||||
- File system errors
|
||||
- Database errors
|
||||
- Backup errors
|
||||
|
||||
## Contributing
|
||||
|
||||
When contributing to the reports module:
|
||||
|
||||
1. Follow the existing code structure
|
||||
2. Add appropriate type hints
|
||||
3. Include comprehensive docstrings
|
||||
4. Add error handling
|
||||
5. Update the dashboard integration
|
||||
6. Add tests for new functionality
|
||||
|
||||
## Dependencies
|
||||
|
||||
The reports module depends on:
|
||||
|
||||
1. **Data Collection**
|
||||
- `finance_data_researcher`
|
||||
- `web_scraping_tools`
|
||||
|
||||
2. **Analysis Tools**
|
||||
- `pandas_ta`
|
||||
- `numpy`
|
||||
- `scipy`
|
||||
|
||||
3. **Visualization**
|
||||
- `matplotlib`
|
||||
- `plotly`
|
||||
|
||||
4. **Text Generation**
|
||||
- `llm_text_gen`
|
||||
- `gpt_providers`
|
||||
|
||||
## License
|
||||
|
||||
This module is part of the AI Finance Report Generator project and is licensed under the MIT License.
|
||||
@@ -1,34 +0,0 @@
|
||||
"""
|
||||
Fundamental Analysis Reports Module
|
||||
|
||||
This module handles the generation of fundamental analysis reports including:
|
||||
- Financial ratios
|
||||
- Company valuation metrics
|
||||
- Growth analysis
|
||||
- Profitability analysis
|
||||
- Debt analysis
|
||||
- Cash flow analysis
|
||||
"""
|
||||
|
||||
from typing import Dict, Any
|
||||
from ...utils import validate_symbol
|
||||
|
||||
def generate_fa_report(symbol: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate a fundamental analysis report for the given symbol.
|
||||
|
||||
Args:
|
||||
symbol (str): Stock symbol to analyze
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: Fundamental analysis report
|
||||
"""
|
||||
if not validate_symbol(symbol):
|
||||
raise ValueError("Invalid symbol provided")
|
||||
|
||||
# TODO: Implement fundamental analysis report generation
|
||||
return {
|
||||
"symbol": symbol,
|
||||
"status": "coming_soon",
|
||||
"message": "Fundamental analysis report generation is coming soon"
|
||||
}
|
||||
@@ -1,29 +0,0 @@
|
||||
"""
|
||||
Market Research Reports Module
|
||||
|
||||
This module handles the generation of market research reports including:
|
||||
- Sector analysis
|
||||
- Industry trends
|
||||
- Market overview
|
||||
- Competitive analysis
|
||||
- Market opportunities
|
||||
- Risk factors
|
||||
"""
|
||||
|
||||
from typing import Dict, Any, List
|
||||
|
||||
def generate_market_research_report(sectors: List[str] = None) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate a market research report.
|
||||
|
||||
Args:
|
||||
sectors (List[str], optional): List of sectors to analyze
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: Market research report
|
||||
"""
|
||||
# TODO: Implement market research report generation
|
||||
return {
|
||||
"status": "coming_soon",
|
||||
"message": "Market research report generation is coming soon"
|
||||
}
|
||||
@@ -1,33 +0,0 @@
|
||||
"""
|
||||
News Analysis Reports Module
|
||||
|
||||
This module handles the generation of news analysis reports including:
|
||||
- News sentiment analysis
|
||||
- Market impact analysis
|
||||
- Event correlation
|
||||
- Trend detection
|
||||
- Social media analysis
|
||||
- News aggregation
|
||||
"""
|
||||
|
||||
from typing import Dict, Any, List
|
||||
from ...utils import validate_symbol
|
||||
|
||||
def generate_news_analysis_report(symbol: str = None) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate a news analysis report.
|
||||
|
||||
Args:
|
||||
symbol (str, optional): Stock symbol to analyze news for
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: News analysis report
|
||||
"""
|
||||
if symbol and not validate_symbol(symbol):
|
||||
raise ValueError("Invalid symbol provided")
|
||||
|
||||
# TODO: Implement news analysis report generation
|
||||
return {
|
||||
"status": "coming_soon",
|
||||
"message": "News analysis report generation is coming soon"
|
||||
}
|
||||
@@ -1,33 +0,0 @@
|
||||
"""
|
||||
Options Analysis Reports Module
|
||||
|
||||
This module handles the generation of options analysis reports including:
|
||||
- Options chain analysis
|
||||
- Implied volatility analysis
|
||||
- Options strategies
|
||||
- Risk metrics
|
||||
- Greeks analysis
|
||||
"""
|
||||
|
||||
from typing import Dict, Any
|
||||
from ...utils import validate_symbol
|
||||
|
||||
def generate_options_report(symbol: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate an options analysis report for the given symbol.
|
||||
|
||||
Args:
|
||||
symbol (str): Stock symbol to analyze
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: Options analysis report
|
||||
"""
|
||||
if not validate_symbol(symbol):
|
||||
raise ValueError("Invalid symbol provided")
|
||||
|
||||
# TODO: Implement options analysis report generation
|
||||
return {
|
||||
"symbol": symbol,
|
||||
"status": "coming_soon",
|
||||
"message": "Options analysis report generation is coming soon"
|
||||
}
|
||||
@@ -1,32 +0,0 @@
|
||||
"""
|
||||
Portfolio Analysis Reports Module
|
||||
|
||||
This module handles the generation of portfolio analysis reports including:
|
||||
- Portfolio performance analysis
|
||||
- Risk assessment
|
||||
- Asset allocation
|
||||
- Correlation analysis
|
||||
- Diversification metrics
|
||||
- Performance attribution
|
||||
"""
|
||||
|
||||
from typing import Dict, Any, List
|
||||
|
||||
def generate_portfolio_report(portfolio: List[Dict[str, Any]]) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate a portfolio analysis report.
|
||||
|
||||
Args:
|
||||
portfolio (List[Dict[str, Any]]): List of portfolio positions
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: Portfolio analysis report
|
||||
"""
|
||||
if not portfolio:
|
||||
raise ValueError("Portfolio cannot be empty")
|
||||
|
||||
# TODO: Implement portfolio analysis report generation
|
||||
return {
|
||||
"status": "coming_soon",
|
||||
"message": "Portfolio analysis report generation is coming soon"
|
||||
}
|
||||
@@ -1,314 +0,0 @@
|
||||
"""
|
||||
Technical Analysis Reports Module
|
||||
|
||||
This module handles the generation of technical analysis reports using yfinance data and pandas_ta for indicators.
|
||||
"""
|
||||
|
||||
from typing import Dict, Any, List, Optional
|
||||
import yfinance as yf
|
||||
import pandas as pd
|
||||
import pandas_ta as ta
|
||||
import plotly.graph_objects as go
|
||||
from datetime import datetime, timedelta
|
||||
from loguru import logger
|
||||
from ...utils import validate_symbol
|
||||
from ...ai_financial_dashboard import get_dashboard
|
||||
|
||||
class TechnicalAnalysis:
|
||||
def __init__(self, symbol: str, timeframe: str = "1d", period: str = "1y"):
|
||||
"""
|
||||
Initialize Technical Analysis.
|
||||
|
||||
Args:
|
||||
symbol (str): Stock symbol to analyze
|
||||
timeframe (str): Data timeframe (1m, 5m, 15m, 30m, 1h, 1d, 1wk, 1mo)
|
||||
period (str): Data period (1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max)
|
||||
"""
|
||||
logger.info(f"Initializing Technical Analysis for {symbol} with timeframe {timeframe} and period {period}")
|
||||
self.symbol = symbol
|
||||
self.timeframe = timeframe
|
||||
self.period = period
|
||||
self.data = None
|
||||
self.indicators = {}
|
||||
self.stock = yf.Ticker(symbol)
|
||||
|
||||
def fetch_data(self) -> None:
|
||||
"""Fetch historical price data using yfinance"""
|
||||
try:
|
||||
logger.info(f"Fetching historical data for {self.symbol}")
|
||||
# Get historical data
|
||||
self.data = self.stock.history(period=self.period, interval=self.timeframe)
|
||||
logger.debug(f"Retrieved {len(self.data)} data points")
|
||||
|
||||
# Get additional info
|
||||
logger.info("Fetching company information")
|
||||
self.info = self.stock.info
|
||||
|
||||
# Calculate basic metrics
|
||||
logger.debug("Calculating basic metrics")
|
||||
self.data['Returns'] = self.data['Close'].pct_change()
|
||||
self.data['Volatility'] = self.data['Returns'].rolling(window=20).std()
|
||||
|
||||
logger.success(f"Successfully fetched data for {self.symbol}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error fetching data for {self.symbol}: {str(e)}")
|
||||
raise ValueError(f"Error fetching data for {self.symbol}: {str(e)}")
|
||||
|
||||
def calculate_indicators(self) -> None:
|
||||
"""Calculate technical indicators using pandas_ta"""
|
||||
if self.data is None:
|
||||
logger.error("Data not fetched. Call fetch_data() first.")
|
||||
raise ValueError("Data not fetched. Call fetch_data() first.")
|
||||
|
||||
logger.info("Calculating technical indicators")
|
||||
|
||||
# Moving Averages
|
||||
logger.debug("Calculating Moving Averages")
|
||||
self.indicators['sma_20'] = self.data.ta.sma(length=20)
|
||||
self.indicators['sma_50'] = self.data.ta.sma(length=50)
|
||||
self.indicators['sma_200'] = self.data.ta.sma(length=200)
|
||||
self.indicators['ema_20'] = self.data.ta.ema(length=20)
|
||||
|
||||
# RSI
|
||||
logger.debug("Calculating RSI")
|
||||
self.indicators['rsi'] = self.data.ta.rsi()
|
||||
|
||||
# MACD
|
||||
logger.debug("Calculating MACD")
|
||||
macd = self.data.ta.macd()
|
||||
self.indicators['macd'] = macd['MACD_12_26_9']
|
||||
self.indicators['macd_signal'] = macd['MACDs_12_26_9']
|
||||
self.indicators['macd_hist'] = macd['MACDh_12_26_9']
|
||||
|
||||
# Bollinger Bands
|
||||
logger.debug("Calculating Bollinger Bands")
|
||||
bbands = self.data.ta.bbands()
|
||||
self.indicators['bb_upper'] = bbands['BBU_20_2.0']
|
||||
self.indicators['bb_middle'] = bbands['BBM_20_2.0']
|
||||
self.indicators['bb_lower'] = bbands['BBL_20_2.0']
|
||||
|
||||
# Volume Analysis
|
||||
logger.debug("Calculating Volume indicators")
|
||||
self.indicators['volume_sma'] = self.data['Volume'].rolling(window=20).mean()
|
||||
self.indicators['obv'] = self.data.ta.obv()
|
||||
|
||||
# Additional Indicators
|
||||
logger.debug("Calculating additional indicators")
|
||||
self.indicators['stoch'] = self.data.ta.stoch()
|
||||
self.indicators['adx'] = self.data.ta.adx()
|
||||
self.indicators['atr'] = self.data.ta.atr()
|
||||
|
||||
logger.success("Successfully calculated all technical indicators")
|
||||
|
||||
def identify_patterns(self) -> List[Dict[str, Any]]:
|
||||
"""Identify chart patterns"""
|
||||
logger.info("Identifying chart patterns")
|
||||
patterns = []
|
||||
|
||||
# Candlestick Patterns
|
||||
if len(self.data) >= 3:
|
||||
logger.debug("Analyzing candlestick patterns")
|
||||
# Doji
|
||||
doji = self.data.ta.cdl_doji()
|
||||
if doji['CDL_DOJI'].iloc[-1] != 0:
|
||||
logger.debug("Doji pattern detected")
|
||||
patterns.append({
|
||||
'type': 'doji',
|
||||
'date': self.data.index[-1],
|
||||
'significance': 'neutral'
|
||||
})
|
||||
|
||||
# Engulfing
|
||||
engulfing = self.data.ta.cdl_engulfing()
|
||||
if engulfing['CDL_ENGULFING'].iloc[-1] != 0:
|
||||
logger.debug("Engulfing pattern detected")
|
||||
patterns.append({
|
||||
'type': 'engulfing',
|
||||
'date': self.data.index[-1],
|
||||
'significance': 'bullish' if engulfing['CDL_ENGULFING'].iloc[-1] > 0 else 'bearish'
|
||||
})
|
||||
|
||||
logger.info(f"Identified {len(patterns)} patterns")
|
||||
return patterns
|
||||
|
||||
def find_support_resistance(self) -> Dict[str, List[float]]:
|
||||
"""Find support and resistance levels using price action"""
|
||||
logger.info("Finding support and resistance levels")
|
||||
levels = {
|
||||
'support': [],
|
||||
'resistance': []
|
||||
}
|
||||
|
||||
# Use recent price action to identify levels
|
||||
recent_data = self.data.tail(100)
|
||||
logger.debug(f"Analyzing {len(recent_data)} recent data points for S/R levels")
|
||||
|
||||
# Find local minima and maxima
|
||||
for i in range(2, len(recent_data) - 2):
|
||||
# Support level
|
||||
if (recent_data['Low'].iloc[i] < recent_data['Low'].iloc[i-1] and
|
||||
recent_data['Low'].iloc[i] < recent_data['Low'].iloc[i-2] and
|
||||
recent_data['Low'].iloc[i] < recent_data['Low'].iloc[i+1] and
|
||||
recent_data['Low'].iloc[i] < recent_data['Low'].iloc[i+2]):
|
||||
levels['support'].append(recent_data['Low'].iloc[i])
|
||||
|
||||
# Resistance level
|
||||
if (recent_data['High'].iloc[i] > recent_data['High'].iloc[i-1] and
|
||||
recent_data['High'].iloc[i] > recent_data['High'].iloc[i-2] and
|
||||
recent_data['High'].iloc[i] > recent_data['High'].iloc[i+1] and
|
||||
recent_data['High'].iloc[i] > recent_data['High'].iloc[i+2]):
|
||||
levels['resistance'].append(recent_data['High'].iloc[i])
|
||||
|
||||
# Remove duplicates and sort
|
||||
levels['support'] = sorted(list(set(levels['support'])))
|
||||
levels['resistance'] = sorted(list(set(levels['resistance'])))
|
||||
|
||||
logger.info(f"Found {len(levels['support'])} support and {len(levels['resistance'])} resistance levels")
|
||||
return levels
|
||||
|
||||
def generate_chart(self) -> go.Figure:
|
||||
"""Generate interactive chart using plotly"""
|
||||
logger.info("Generating interactive chart")
|
||||
fig = go.Figure()
|
||||
|
||||
# Candlestick chart
|
||||
logger.debug("Adding candlestick chart")
|
||||
fig.add_trace(go.Candlestick(
|
||||
x=self.data.index,
|
||||
open=self.data['Open'],
|
||||
high=self.data['High'],
|
||||
low=self.data['Low'],
|
||||
close=self.data['Close'],
|
||||
name='Price'
|
||||
))
|
||||
|
||||
# Moving Averages
|
||||
logger.debug("Adding moving averages")
|
||||
fig.add_trace(go.Scatter(
|
||||
x=self.data.index,
|
||||
y=self.indicators['sma_20'],
|
||||
name='SMA 20',
|
||||
line=dict(color='blue')
|
||||
))
|
||||
|
||||
fig.add_trace(go.Scatter(
|
||||
x=self.data.index,
|
||||
y=self.indicators['sma_50'],
|
||||
name='SMA 50',
|
||||
line=dict(color='orange')
|
||||
))
|
||||
|
||||
# Bollinger Bands
|
||||
logger.debug("Adding Bollinger Bands")
|
||||
fig.add_trace(go.Scatter(
|
||||
x=self.data.index,
|
||||
y=self.indicators['bb_upper'],
|
||||
name='BB Upper',
|
||||
line=dict(color='gray', dash='dash')
|
||||
))
|
||||
|
||||
fig.add_trace(go.Scatter(
|
||||
x=self.data.index,
|
||||
y=self.indicators['bb_lower'],
|
||||
name='BB Lower',
|
||||
line=dict(color='gray', dash='dash'),
|
||||
fill='tonexty'
|
||||
))
|
||||
|
||||
# Volume
|
||||
logger.debug("Adding volume bars")
|
||||
fig.add_trace(go.Bar(
|
||||
x=self.data.index,
|
||||
y=self.data['Volume'],
|
||||
name='Volume',
|
||||
marker_color='rgba(0,0,255,0.3)'
|
||||
))
|
||||
|
||||
# Layout
|
||||
logger.debug("Setting chart layout")
|
||||
fig.update_layout(
|
||||
title=f'{self.symbol} Technical Analysis',
|
||||
yaxis_title='Price',
|
||||
xaxis_title='Date',
|
||||
template='plotly_dark'
|
||||
)
|
||||
|
||||
logger.success("Successfully generated chart")
|
||||
return fig
|
||||
|
||||
def _generate_summary(self) -> Dict[str, Any]:
|
||||
"""Generate summary of technical analysis"""
|
||||
logger.info("Generating analysis summary")
|
||||
current_price = self.data['Close'].iloc[-1]
|
||||
sma_20 = self.indicators['sma_20'].iloc[-1]
|
||||
sma_50 = self.indicators['sma_50'].iloc[-1]
|
||||
rsi = self.indicators['rsi'].iloc[-1]
|
||||
|
||||
summary = {
|
||||
'current_price': current_price,
|
||||
'price_change': self.data['Returns'].iloc[-1] * 100,
|
||||
'trend': 'bullish' if current_price > sma_20 > sma_50 else 'bearish',
|
||||
'rsi_signal': 'overbought' if rsi > 70 else 'oversold' if rsi < 30 else 'neutral',
|
||||
'volatility': self.data['Volatility'].iloc[-1],
|
||||
'volume_trend': 'increasing' if self.data['Volume'].iloc[-1] > self.indicators['volume_sma'].iloc[-1] else 'decreasing'
|
||||
}
|
||||
|
||||
logger.debug(f"Analysis summary: {summary}")
|
||||
return summary
|
||||
|
||||
def generate_report(self) -> Dict[str, Any]:
|
||||
"""Generate comprehensive technical analysis report"""
|
||||
logger.info(f"Generating comprehensive report for {self.symbol}")
|
||||
|
||||
self.fetch_data()
|
||||
self.calculate_indicators()
|
||||
patterns = self.identify_patterns()
|
||||
levels = self.find_support_resistance()
|
||||
chart = self.generate_chart()
|
||||
summary = self._generate_summary()
|
||||
|
||||
report = {
|
||||
'symbol': self.symbol,
|
||||
'timestamp': datetime.now(),
|
||||
'company_info': self.info,
|
||||
'indicators': self.indicators,
|
||||
'patterns': patterns,
|
||||
'levels': levels,
|
||||
'chart': chart,
|
||||
'summary': summary
|
||||
}
|
||||
|
||||
logger.success(f"Successfully generated report for {self.symbol}")
|
||||
return report
|
||||
|
||||
def generate_ta_report(symbol: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate a technical analysis report for the given symbol.
|
||||
|
||||
Args:
|
||||
symbol (str): Stock symbol to analyze
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: Technical analysis report
|
||||
"""
|
||||
logger.info(f"Generating technical analysis report for {symbol}")
|
||||
|
||||
if not validate_symbol(symbol):
|
||||
logger.error(f"Invalid symbol provided: {symbol}")
|
||||
raise ValueError("Invalid symbol provided")
|
||||
|
||||
try:
|
||||
analysis = TechnicalAnalysis(symbol)
|
||||
report = analysis.generate_report()
|
||||
|
||||
# Add to dashboard's recent reports
|
||||
dashboard = get_dashboard()
|
||||
dashboard.add_recent_report("technical_analysis", symbol, report)
|
||||
|
||||
logger.success(f"Successfully completed technical analysis for {symbol}")
|
||||
return report
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating technical analysis report for {symbol}: {str(e)}")
|
||||
raise
|
||||
@@ -1,62 +0,0 @@
|
||||
"""
|
||||
Utility functions and helpers for the AI Finance Report Generator.
|
||||
"""
|
||||
|
||||
from typing import Dict, List, Any
|
||||
import logging
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
def validate_symbol(symbol: str) -> bool:
|
||||
"""
|
||||
Validate if the given symbol is in correct format.
|
||||
|
||||
Args:
|
||||
symbol (str): Stock symbol to validate
|
||||
|
||||
Returns:
|
||||
bool: True if valid, False otherwise
|
||||
"""
|
||||
if not isinstance(symbol, str):
|
||||
return False
|
||||
return len(symbol.strip()) > 0
|
||||
|
||||
def format_currency(value: float) -> str:
|
||||
"""
|
||||
Format number as currency.
|
||||
|
||||
Args:
|
||||
value (float): Number to format
|
||||
|
||||
Returns:
|
||||
str: Formatted currency string
|
||||
"""
|
||||
return f"${value:,.2f}"
|
||||
|
||||
def get_feature_status(feature_name: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Get the status of a feature.
|
||||
|
||||
Args:
|
||||
feature_name (str): Name of the feature
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: Feature status information
|
||||
"""
|
||||
# This will be expanded as we implement more features
|
||||
implemented_features = {
|
||||
"technical_analysis": True,
|
||||
"options_analysis": True,
|
||||
}
|
||||
|
||||
return {
|
||||
"name": feature_name,
|
||||
"implemented": implemented_features.get(feature_name, False),
|
||||
"coming_soon": not implemented_features.get(feature_name, False)
|
||||
}
|
||||
@@ -1,208 +0,0 @@
|
||||
"""
|
||||
Storage Module for AI Finance Report Generator
|
||||
|
||||
This module handles the persistence of user preferences and recent reports using JSON files.
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
from typing import Dict, List, Any, Optional
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
class StorageManager:
|
||||
"""Manages storage operations for user preferences and recent reports."""
|
||||
|
||||
def __init__(self, base_dir: Optional[str] = None):
|
||||
"""
|
||||
Initialize the storage manager.
|
||||
|
||||
Args:
|
||||
base_dir (Optional[str]): Base directory for storage files
|
||||
"""
|
||||
if base_dir is None:
|
||||
# Use user's home directory by default
|
||||
self.base_dir = Path.home() / ".ai_finance"
|
||||
else:
|
||||
self.base_dir = Path(base_dir)
|
||||
|
||||
# Create storage directory if it doesn't exist
|
||||
self.base_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Define file paths
|
||||
self.prefs_file = self.base_dir / "preferences.json"
|
||||
self.reports_file = self.base_dir / "recent_reports.json"
|
||||
|
||||
# Initialize files if they don't exist
|
||||
self._initialize_storage()
|
||||
|
||||
def _initialize_storage(self) -> None:
|
||||
"""Initialize storage files if they don't exist."""
|
||||
if not self.prefs_file.exists():
|
||||
self._save_preferences({})
|
||||
|
||||
if not self.reports_file.exists():
|
||||
self._save_reports([])
|
||||
|
||||
def _save_preferences(self, preferences: Dict[str, Any]) -> None:
|
||||
"""
|
||||
Save user preferences to file.
|
||||
|
||||
Args:
|
||||
preferences (Dict[str, Any]): User preferences to save
|
||||
"""
|
||||
with open(self.prefs_file, 'w') as f:
|
||||
json.dump(preferences, f, indent=4)
|
||||
|
||||
def _load_preferences(self) -> Dict[str, Any]:
|
||||
"""
|
||||
Load user preferences from file.
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: User preferences
|
||||
"""
|
||||
try:
|
||||
with open(self.prefs_file, 'r') as f:
|
||||
return json.load(f)
|
||||
except (json.JSONDecodeError, FileNotFoundError):
|
||||
return {}
|
||||
|
||||
def _save_reports(self, reports: List[Dict[str, Any]]) -> None:
|
||||
"""
|
||||
Save recent reports to file.
|
||||
|
||||
Args:
|
||||
reports (List[Dict[str, Any]]): Recent reports to save
|
||||
"""
|
||||
with open(self.reports_file, 'w') as f:
|
||||
json.dump(reports, f, indent=4)
|
||||
|
||||
def _load_reports(self) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Load recent reports from file.
|
||||
|
||||
Returns:
|
||||
List[Dict[str, Any]]: Recent reports
|
||||
"""
|
||||
try:
|
||||
with open(self.reports_file, 'r') as f:
|
||||
return json.load(f)
|
||||
except (json.JSONDecodeError, FileNotFoundError):
|
||||
return []
|
||||
|
||||
def save_user_preferences(self, preferences: Dict[str, Any]) -> None:
|
||||
"""
|
||||
Save user preferences.
|
||||
|
||||
Args:
|
||||
preferences (Dict[str, Any]): User preferences to save
|
||||
"""
|
||||
self._save_preferences(preferences)
|
||||
|
||||
def load_user_preferences(self) -> Dict[str, Any]:
|
||||
"""
|
||||
Load user preferences.
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: User preferences
|
||||
"""
|
||||
return self._load_preferences()
|
||||
|
||||
def save_recent_reports(self, reports: List[Dict[str, Any]]) -> None:
|
||||
"""
|
||||
Save recent reports.
|
||||
|
||||
Args:
|
||||
reports (List[Dict[str, Any]]): Recent reports to save
|
||||
"""
|
||||
# Convert datetime objects to ISO format strings
|
||||
serialized_reports = []
|
||||
for report in reports:
|
||||
serialized_report = report.copy()
|
||||
if isinstance(report.get('timestamp'), datetime):
|
||||
serialized_report['timestamp'] = report['timestamp'].isoformat()
|
||||
serialized_reports.append(serialized_report)
|
||||
|
||||
self._save_reports(serialized_reports)
|
||||
|
||||
def load_recent_reports(self) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Load recent reports.
|
||||
|
||||
Returns:
|
||||
List[Dict[str, Any]]: Recent reports with datetime objects
|
||||
"""
|
||||
reports = self._load_reports()
|
||||
|
||||
# Convert ISO format strings back to datetime objects
|
||||
for report in reports:
|
||||
if isinstance(report.get('timestamp'), str):
|
||||
report['timestamp'] = datetime.fromisoformat(report['timestamp'])
|
||||
|
||||
return reports
|
||||
|
||||
def clear_storage(self) -> None:
|
||||
"""Clear all stored data."""
|
||||
self._save_preferences({})
|
||||
self._save_reports([])
|
||||
|
||||
def backup_storage(self, backup_dir: Optional[str] = None) -> None:
|
||||
"""
|
||||
Create a backup of the storage files.
|
||||
|
||||
Args:
|
||||
backup_dir (Optional[str]): Directory to store backup files
|
||||
"""
|
||||
if backup_dir is None:
|
||||
backup_dir = self.base_dir / "backups"
|
||||
else:
|
||||
backup_dir = Path(backup_dir)
|
||||
|
||||
backup_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
|
||||
# Backup preferences
|
||||
if self.prefs_file.exists():
|
||||
backup_prefs = backup_dir / f"preferences_{timestamp}.json"
|
||||
with open(self.prefs_file, 'r') as src, open(backup_prefs, 'w') as dst:
|
||||
dst.write(src.read())
|
||||
|
||||
# Backup reports
|
||||
if self.reports_file.exists():
|
||||
backup_reports = backup_dir / f"recent_reports_{timestamp}.json"
|
||||
with open(self.reports_file, 'r') as src, open(backup_reports, 'w') as dst:
|
||||
dst.write(src.read())
|
||||
|
||||
def restore_from_backup(self, backup_file: str) -> None:
|
||||
"""
|
||||
Restore storage from a backup file.
|
||||
|
||||
Args:
|
||||
backup_file (str): Path to the backup file
|
||||
"""
|
||||
backup_path = Path(backup_file)
|
||||
if not backup_path.exists():
|
||||
raise FileNotFoundError(f"Backup file not found: {backup_file}")
|
||||
|
||||
# Determine which type of backup file it is
|
||||
if "preferences" in backup_path.name:
|
||||
with open(backup_path, 'r') as src, open(self.prefs_file, 'w') as dst:
|
||||
dst.write(src.read())
|
||||
elif "recent_reports" in backup_path.name:
|
||||
with open(backup_path, 'r') as src, open(self.reports_file, 'w') as dst:
|
||||
dst.write(src.read())
|
||||
else:
|
||||
raise ValueError(f"Unknown backup file type: {backup_file}")
|
||||
|
||||
def get_storage_manager(base_dir: Optional[str] = None) -> StorageManager:
|
||||
"""
|
||||
Get a storage manager instance.
|
||||
|
||||
Args:
|
||||
base_dir (Optional[str]): Base directory for storage files
|
||||
|
||||
Returns:
|
||||
StorageManager: Storage manager instance
|
||||
"""
|
||||
return StorageManager(base_dir)
|
||||
@@ -1,75 +0,0 @@
|
||||
# AI Story Illustrator
|
||||
|
||||
The AI Story Illustrator is a powerful tool that generates beautiful illustrations for stories using Google's Gemini AI. This module allows users to input stories via text, file upload, or URL, and automatically generates appropriate illustrations for different scenes in the story.
|
||||
|
||||
## Features
|
||||
|
||||
- **Multiple Input Methods**: Input stories via direct text entry, file upload, or URL extraction
|
||||
- **Intelligent Scene Segmentation**: Automatically divides stories into logical segments for illustration
|
||||
- **Customizable Illustration Styles**: Choose from various artistic styles or define your own
|
||||
- **Scene Element Extraction**: Analyzes story segments to identify key visual elements
|
||||
- **Multiple Export Options**: Export as PDF storybook or ZIP archive of individual images
|
||||
- **Customizable Aspect Ratios**: Support for different image dimensions (16:9, 4:3, 1:1)
|
||||
- **Advanced Settings**: Control the number of segments to illustrate and other parameters
|
||||
|
||||
## Usage
|
||||
|
||||
The Story Illustrator is integrated into the Alwrity platform and can be accessed through the main interface. The workflow consists of three main steps:
|
||||
|
||||
1. **Story Input**: Enter your story text, upload a file, or provide a URL
|
||||
2. **Illustration Settings**: Configure the style, aspect ratio, and other parameters
|
||||
3. **Generate & Export**: Generate illustrations for all or individual segments and export the results
|
||||
|
||||
## Technical Details
|
||||
|
||||
### Dependencies
|
||||
|
||||
- Streamlit: For the user interface
|
||||
- Gemini AI: For image generation
|
||||
- BeautifulSoup: For URL text extraction
|
||||
- ReportLab: For PDF generation (optional)
|
||||
- PIL: For image processing
|
||||
|
||||
### Key Functions
|
||||
|
||||
- `segment_story()`: Divides a story into logical segments for illustration
|
||||
- `extract_scene_elements()`: Analyzes story segments to identify key visual elements
|
||||
- `generate_illustration_prompt()`: Creates detailed prompts for the AI image generator
|
||||
- `create_illustration()`: Generates an illustration for a story segment
|
||||
- `create_storybook_pdf()`: Combines story text and illustrations into a PDF
|
||||
- `create_zip_archive()`: Creates a ZIP archive of individual illustrations
|
||||
|
||||
## Example
|
||||
|
||||
```python
|
||||
from lib.ai_writers.ai_story_illustrator.story_illustrator import write_story_illustrator
|
||||
|
||||
# Run the Story Illustrator app
|
||||
write_story_illustrator()
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
- **Provide Clear Segments**: The system works best with stories that have clear scene transitions
|
||||
- **Be Specific with Styles**: More specific style descriptions yield better results
|
||||
- **Balance Text and Images**: For best results, aim for segments of 100-500 words per illustration
|
||||
- **Review and Regenerate**: If an illustration doesn't capture the scene well, use the regenerate option
|
||||
|
||||
## Future Enhancements
|
||||
|
||||
- Support for more export formats (EPUB, HTML)
|
||||
- Enhanced character consistency across illustrations
|
||||
- Animation options for digital storytelling
|
||||
- Voice narration integration
|
||||
- Custom character design options
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
- If illustrations are not generating, check your internet connection and API access
|
||||
- If PDF export fails, ensure ReportLab is installed (`pip install reportlab`)
|
||||
- If URL extraction fails, try copying the text manually
|
||||
- For large stories, consider processing in smaller batches
|
||||
|
||||
## Credits
|
||||
|
||||
This module uses Google's Gemini AI for image generation and leverages various open-source libraries for text processing and document generation.
|
||||
@@ -1,7 +0,0 @@
|
||||
"""
|
||||
AI Story Illustrator module for generating illustrations for stories using AI.
|
||||
"""
|
||||
|
||||
from .story_illustrator import write_story_illustrator
|
||||
|
||||
__all__ = ['write_story_illustrator']
|
||||
@@ -1,727 +0,0 @@
|
||||
"""
|
||||
AI Story Illustrator - Generate illustrations for stories using Gemini AI
|
||||
|
||||
This module provides functionality to generate illustrations for stories using Google's Gemini AI.
|
||||
Users can input stories via text, file upload, or URL, and the system will generate appropriate
|
||||
illustrations for different scenes in the story.
|
||||
|
||||
Based on: https://github.com/google-gemini/cookbook/blob/main/examples/Book_illustration.ipynb
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import os
|
||||
import re
|
||||
import time
|
||||
import tempfile
|
||||
import requests
|
||||
from pathlib import Path
|
||||
import io
|
||||
import base64
|
||||
import json
|
||||
import uuid
|
||||
import logging
|
||||
from urllib.parse import urlparse
|
||||
from bs4 import BeautifulSoup
|
||||
import zipfile
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
|
||||
logger = logging.getLogger('story_illustrator')
|
||||
|
||||
# Constants
|
||||
MAX_STORY_LENGTH = 10000 # Maximum story length in characters
|
||||
MIN_SEGMENT_LENGTH = 100 # Minimum segment length for illustration
|
||||
MAX_SEGMENTS = 20 # Maximum number of segments to illustrate
|
||||
DEFAULT_STYLE = "digital art" # Default illustration style
|
||||
DEFAULT_ASPECT_RATIO = "16:9" # Default aspect ratio
|
||||
|
||||
|
||||
def extract_text_from_url(url):
|
||||
"""Extract text content from a URL."""
|
||||
try:
|
||||
headers = {
|
||||
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
|
||||
}
|
||||
response = requests.get(url, headers=headers, timeout=10)
|
||||
response.raise_for_status()
|
||||
|
||||
soup = BeautifulSoup(response.content, 'html.parser')
|
||||
|
||||
# Remove script and style elements
|
||||
for script in soup(["script", "style"]):
|
||||
script.extract()
|
||||
|
||||
# Get text
|
||||
text = soup.get_text(separator='\\n')
|
||||
|
||||
# Break into lines and remove leading and trailing space on each
|
||||
lines = (line.strip() for line in text.splitlines())
|
||||
# Break multi-headlines into a line each
|
||||
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
|
||||
# Drop blank lines
|
||||
text = '\\n'.join(chunk for chunk in chunks if chunk)
|
||||
|
||||
return text
|
||||
except Exception as e:
|
||||
logger.error(f"Error extracting text from URL: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def segment_story(story_text, min_segment_length=MIN_SEGMENT_LENGTH, max_segments=MAX_SEGMENTS):
|
||||
"""
|
||||
Segment a story into logical parts for illustration.
|
||||
Uses paragraph breaks, scene changes, and other indicators to create segments.
|
||||
"""
|
||||
# Clean up the text
|
||||
story_text = story_text.strip()
|
||||
|
||||
# Split by paragraphs first
|
||||
paragraphs = re.split(r'\\n\s*\\n', story_text)
|
||||
|
||||
# Initialize segments
|
||||
segments = []
|
||||
current_segment = ""
|
||||
|
||||
for paragraph in paragraphs:
|
||||
# Skip empty paragraphs
|
||||
if not paragraph.strip():
|
||||
continue
|
||||
|
||||
# If adding this paragraph would make the segment too long, start a new segment
|
||||
if len(current_segment) + len(paragraph) > 1000: # Limit segment size
|
||||
if current_segment:
|
||||
segments.append(current_segment.strip())
|
||||
current_segment = paragraph
|
||||
else:
|
||||
# Add paragraph to current segment
|
||||
if current_segment:
|
||||
current_segment += "\\n\\n" + paragraph
|
||||
else:
|
||||
current_segment = paragraph
|
||||
|
||||
# Add the last segment if it exists
|
||||
if current_segment:
|
||||
segments.append(current_segment.strip())
|
||||
|
||||
# Combine very short segments
|
||||
i = 0
|
||||
while i < len(segments) - 1:
|
||||
if len(segments[i]) < min_segment_length:
|
||||
segments[i] += "\\n\\n" + segments[i+1]
|
||||
segments.pop(i+1)
|
||||
else:
|
||||
i += 1
|
||||
|
||||
# Limit the number of segments
|
||||
if len(segments) > max_segments:
|
||||
# Combine segments to reduce the total number
|
||||
new_segments = []
|
||||
segment_size = len(segments) / max_segments
|
||||
|
||||
for i in range(max_segments):
|
||||
start_idx = int(i * segment_size)
|
||||
end_idx = int((i + 1) * segment_size)
|
||||
combined_segment = "\\n\\n".join(segments[start_idx:end_idx])
|
||||
new_segments.append(combined_segment)
|
||||
|
||||
segments = new_segments
|
||||
|
||||
return segments
|
||||
|
||||
|
||||
def extract_scene_elements(segment):
|
||||
"""
|
||||
Extract key scene elements from a story segment using LLM.
|
||||
This helps create more accurate illustration prompts.
|
||||
"""
|
||||
from ...gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
|
||||
prompt = f"""
|
||||
Analyze the following story segment and extract key visual elements for an illustration:
|
||||
|
||||
{segment}
|
||||
|
||||
Please provide:
|
||||
1. Main characters present (with brief visual descriptions)
|
||||
2. Setting/location details
|
||||
3. Key action or emotional moment to illustrate
|
||||
4. Important objects or props
|
||||
5. Time of day and lighting
|
||||
6. Weather or atmospheric conditions (if applicable)
|
||||
|
||||
Format your response as JSON with these keys: "characters", "setting", "key_moment", "objects", "lighting", "atmosphere"
|
||||
"""
|
||||
|
||||
try:
|
||||
response = llm_text_gen(prompt)
|
||||
|
||||
# Try to extract JSON from the response
|
||||
try:
|
||||
# Find JSON content between triple backticks if present
|
||||
json_match = re.search(r'```json\s*(.*?)\s*```', response, re.DOTALL)
|
||||
if json_match:
|
||||
json_str = json_match.group(1)
|
||||
else:
|
||||
# Otherwise try to parse the whole response as JSON
|
||||
json_str = response
|
||||
|
||||
scene_elements = json.loads(json_str)
|
||||
return scene_elements
|
||||
except json.JSONDecodeError:
|
||||
# If JSON parsing fails, extract information using regex
|
||||
characters = re.search(r'"characters":\s*"([^"]*)"', response)
|
||||
setting = re.search(r'"setting":\s*"([^"]*)"', response)
|
||||
|
||||
return {
|
||||
"characters": characters.group(1) if characters else "",
|
||||
"setting": setting.group(1) if setting else "",
|
||||
"key_moment": "",
|
||||
"objects": "",
|
||||
"lighting": "",
|
||||
"atmosphere": ""
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"Error extracting scene elements: {e}")
|
||||
return {
|
||||
"characters": "",
|
||||
"setting": "",
|
||||
"key_moment": "",
|
||||
"objects": "",
|
||||
"lighting": "",
|
||||
"atmosphere": ""
|
||||
}
|
||||
|
||||
|
||||
def generate_illustration_prompt(segment, style, characters=None, setting=None):
|
||||
"""
|
||||
Generate a prompt for the illustration based on the segment content.
|
||||
|
||||
Args:
|
||||
segment: The story segment to illustrate
|
||||
style: The artistic style for the illustration
|
||||
characters: Optional character descriptions
|
||||
setting: Optional setting description
|
||||
|
||||
Returns:
|
||||
A prompt string for the image generation model
|
||||
"""
|
||||
# Create a base prompt
|
||||
base_prompt = f"""
|
||||
Create a detailed illustration for the following story segment in {style} style:
|
||||
|
||||
{segment[:500]} # Limit segment length for prompt
|
||||
|
||||
The illustration should capture the key elements, mood, and action of this scene.
|
||||
"""
|
||||
|
||||
# Add character information if provided
|
||||
if characters:
|
||||
base_prompt += f"\\n\\nThe main characters in this scene are: {characters}"
|
||||
|
||||
# Add setting information if provided
|
||||
if setting:
|
||||
base_prompt += f"\\n\\nThe setting is: {setting}"
|
||||
|
||||
# Add style-specific instructions
|
||||
if "watercolor" in style.lower():
|
||||
base_prompt += "\\n\\nUse soft, flowing watercolor techniques with visible brush strokes and color blending."
|
||||
elif "digital art" in style.lower():
|
||||
base_prompt += "\\n\\nCreate a polished digital illustration with clean lines and vibrant colors."
|
||||
elif "pencil sketch" in style.lower():
|
||||
base_prompt += "\\n\\nUse pencil sketch techniques with visible hatching, shading, and line work."
|
||||
|
||||
# Add final quality instructions
|
||||
base_prompt += """
|
||||
|
||||
Make the illustration:
|
||||
- Visually engaging and detailed
|
||||
- Appropriate for a storybook
|
||||
- Focused on the main action or emotion of the scene
|
||||
- With good composition and visual storytelling
|
||||
"""
|
||||
|
||||
return base_prompt.strip()
|
||||
|
||||
|
||||
def create_illustration(segment, style, aspect_ratio="16:9"):
|
||||
"""
|
||||
Create an illustration for a story segment.
|
||||
|
||||
Args:
|
||||
segment: The story segment to illustrate
|
||||
style: The artistic style for the illustration
|
||||
aspect_ratio: The aspect ratio for the illustration
|
||||
|
||||
Returns:
|
||||
Path to the generated image
|
||||
"""
|
||||
# Import here to avoid circular imports
|
||||
from ...gpt_providers.text_to_image_generation.gen_gemini_images import generate_gemini_image
|
||||
|
||||
# Extract scene elements to enhance the prompt
|
||||
scene_elements = extract_scene_elements(segment)
|
||||
|
||||
# Create a detailed prompt for the illustration
|
||||
prompt = generate_illustration_prompt(
|
||||
segment,
|
||||
style,
|
||||
characters=scene_elements.get("characters", ""),
|
||||
setting=scene_elements.get("setting", "")
|
||||
)
|
||||
|
||||
# Add key elements to the prompt
|
||||
key_moment = scene_elements.get("key_moment", "")
|
||||
objects = scene_elements.get("objects", "")
|
||||
lighting = scene_elements.get("lighting", "")
|
||||
atmosphere = scene_elements.get("atmosphere", "")
|
||||
|
||||
if key_moment:
|
||||
prompt += f"\\n\\nFocus on this key moment: {key_moment}"
|
||||
|
||||
if objects:
|
||||
prompt += f"\\n\\nInclude these important objects: {objects}"
|
||||
|
||||
if lighting:
|
||||
prompt += f"\\n\\nThe lighting is: {lighting}"
|
||||
|
||||
if atmosphere:
|
||||
prompt += f"\\n\\nThe atmosphere/weather is: {atmosphere}"
|
||||
|
||||
# Generate the illustration
|
||||
try:
|
||||
# Parse aspect ratio
|
||||
if aspect_ratio == "16:9":
|
||||
width, height = 16, 9
|
||||
elif aspect_ratio == "4:3":
|
||||
width, height = 4, 3
|
||||
elif aspect_ratio == "1:1":
|
||||
width, height = 1, 1
|
||||
else:
|
||||
width, height = 16, 9 # Default
|
||||
|
||||
# Generate image using Gemini
|
||||
image_path = generate_gemini_image(
|
||||
prompt=prompt,
|
||||
style=style.lower() if style else None,
|
||||
aspect_ratio=aspect_ratio
|
||||
)
|
||||
|
||||
return image_path
|
||||
except Exception as e:
|
||||
logger.error(f"Error creating illustration: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def create_storybook_pdf(segments, illustrations, title, author, output_path):
|
||||
"""
|
||||
Create a PDF storybook with text and illustrations.
|
||||
|
||||
Args:
|
||||
segments: List of story segments
|
||||
illustrations: List of paths to illustrations
|
||||
title: Book title
|
||||
author: Book author
|
||||
output_path: Path to save the PDF
|
||||
|
||||
Returns:
|
||||
Path to the created PDF
|
||||
"""
|
||||
try:
|
||||
from reportlab.lib.pagesizes import letter, A4
|
||||
from reportlab.lib import colors
|
||||
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as ReportLabImage, PageBreak
|
||||
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
|
||||
from reportlab.lib.units import inch
|
||||
|
||||
# Create a PDF document
|
||||
doc = SimpleDocTemplate(output_path, pagesize=A4)
|
||||
story = []
|
||||
|
||||
# Get styles
|
||||
styles = getSampleStyleSheet()
|
||||
title_style = styles['Title']
|
||||
author_style = styles['Normal']
|
||||
author_style.alignment = 1 # Center alignment
|
||||
normal_style = styles['Normal']
|
||||
|
||||
# Add title page
|
||||
story.append(Paragraph(title, title_style))
|
||||
story.append(Spacer(1, 0.5*inch))
|
||||
story.append(Paragraph(f"by {author}", author_style))
|
||||
story.append(PageBreak())
|
||||
|
||||
# Add content pages
|
||||
for i, (segment, illustration_path) in enumerate(zip(segments, illustrations)):
|
||||
if illustration_path and os.path.exists(illustration_path):
|
||||
# Add illustration
|
||||
img = ReportLabImage(illustration_path, width=6*inch, height=4*inch)
|
||||
story.append(img)
|
||||
story.append(Spacer(1, 0.25*inch))
|
||||
|
||||
# Add text
|
||||
for paragraph in segment.split('\\n\\n'):
|
||||
if paragraph.strip():
|
||||
story.append(Paragraph(paragraph, normal_style))
|
||||
story.append(Spacer(1, 0.1*inch))
|
||||
|
||||
# Add page break between segments
|
||||
if i < len(segments) - 1:
|
||||
story.append(PageBreak())
|
||||
|
||||
# Build the PDF
|
||||
doc.build(story)
|
||||
return output_path
|
||||
except Exception as e:
|
||||
logger.error(f"Error creating PDF: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def create_zip_archive(files, output_path):
|
||||
"""
|
||||
Create a ZIP archive containing the provided files.
|
||||
|
||||
Args:
|
||||
files: Dictionary of {filename: file_path} to include in the archive
|
||||
output_path: Path to save the ZIP file
|
||||
|
||||
Returns:
|
||||
Path to the created ZIP file
|
||||
"""
|
||||
try:
|
||||
with zipfile.ZipFile(output_path, 'w') as zipf:
|
||||
for filename, file_path in files.items():
|
||||
if os.path.exists(file_path):
|
||||
zipf.write(file_path, arcname=filename)
|
||||
return output_path
|
||||
except Exception as e:
|
||||
logger.error(f"Error creating ZIP archive: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def write_story_illustrator():
|
||||
"""Main function for the Story Illustrator Streamlit app."""
|
||||
st.title("AI Story Illustrator")
|
||||
st.write("Generate beautiful illustrations for your stories using AI")
|
||||
|
||||
# Create tabs for different sections
|
||||
tab1, tab2, tab3 = st.tabs(["Story Input", "Illustration Settings", "Generate & Export"])
|
||||
|
||||
# Initialize session state variables if they don't exist
|
||||
if "story_text" not in st.session_state:
|
||||
st.session_state.story_text = ""
|
||||
if "segments" not in st.session_state:
|
||||
st.session_state.segments = []
|
||||
if "illustrations" not in st.session_state:
|
||||
st.session_state.illustrations = []
|
||||
if "book_title" not in st.session_state:
|
||||
st.session_state.book_title = ""
|
||||
if "book_author" not in st.session_state:
|
||||
st.session_state.book_author = ""
|
||||
if "illustration_style" not in st.session_state:
|
||||
st.session_state.illustration_style = DEFAULT_STYLE
|
||||
if "aspect_ratio" not in st.session_state:
|
||||
st.session_state.aspect_ratio = DEFAULT_ASPECT_RATIO
|
||||
if "temp_files" not in st.session_state:
|
||||
st.session_state.temp_files = []
|
||||
|
||||
# Tab 1: Story Input
|
||||
with tab1:
|
||||
st.header("Step 1: Input Your Story")
|
||||
|
||||
# Input method selection
|
||||
input_method = st.radio(
|
||||
"Choose input method:",
|
||||
["Text Input", "File Upload", "URL"]
|
||||
)
|
||||
|
||||
if input_method == "Text Input":
|
||||
st.session_state.story_text = st.text_area(
|
||||
"Enter your story text:",
|
||||
value=st.session_state.story_text,
|
||||
height=300,
|
||||
max_chars=MAX_STORY_LENGTH,
|
||||
help="Enter the story text you want to illustrate (max 10,000 characters)"
|
||||
)
|
||||
|
||||
elif input_method == "File Upload":
|
||||
uploaded_file = st.file_uploader("Upload a text file:", type=["txt", "md"])
|
||||
if uploaded_file is not None:
|
||||
try:
|
||||
st.session_state.story_text = uploaded_file.getvalue().decode("utf-8")
|
||||
st.success(f"Successfully loaded file: {uploaded_file.name}")
|
||||
st.text_area("Preview:", value=st.session_state.story_text[:500] + "...", height=200, disabled=True)
|
||||
except Exception as e:
|
||||
st.error(f"Error reading file: {e}")
|
||||
|
||||
elif input_method == "URL":
|
||||
url = st.text_input("Enter URL containing the story:")
|
||||
if url:
|
||||
if st.button("Extract Text from URL"):
|
||||
with st.spinner("Extracting text from URL..."):
|
||||
extracted_text = extract_text_from_url(url)
|
||||
if extracted_text:
|
||||
st.session_state.story_text = extracted_text
|
||||
st.success("Successfully extracted text from URL")
|
||||
st.text_area("Preview:", value=st.session_state.story_text[:500] + "...", height=200, disabled=True)
|
||||
else:
|
||||
st.error("Failed to extract text from URL")
|
||||
|
||||
# Book metadata
|
||||
st.subheader("Book Metadata")
|
||||
col1, col2 = st.columns(2)
|
||||
with col1:
|
||||
st.session_state.book_title = st.text_input("Book Title:", value=st.session_state.book_title)
|
||||
with col2:
|
||||
st.session_state.book_author = st.text_input("Author:", value=st.session_state.book_author)
|
||||
|
||||
# Process story into segments
|
||||
if st.session_state.story_text:
|
||||
if st.button("Process Story into Segments"):
|
||||
with st.spinner("Processing story into segments..."):
|
||||
st.session_state.segments = segment_story(st.session_state.story_text)
|
||||
st.success(f"Story processed into {len(st.session_state.segments)} segments")
|
||||
|
||||
# Initialize illustrations list with None values
|
||||
st.session_state.illustrations = [None] * len(st.session_state.segments)
|
||||
|
||||
# Display segments
|
||||
st.subheader("Story Segments")
|
||||
for i, segment in enumerate(st.session_state.segments):
|
||||
with st.expander(f"Segment {i+1}"):
|
||||
st.write(segment)
|
||||
|
||||
# Tab 2: Illustration Settings
|
||||
with tab2:
|
||||
st.header("Step 2: Configure Illustration Settings")
|
||||
|
||||
# Style selection
|
||||
st.subheader("Illustration Style")
|
||||
style_options = [
|
||||
"Digital Art",
|
||||
"Watercolor Painting",
|
||||
"Pencil Sketch",
|
||||
"Oil Painting",
|
||||
"Cartoon",
|
||||
"Anime",
|
||||
"3D Render",
|
||||
"Pixel Art",
|
||||
"Children's Book Illustration",
|
||||
"Comic Book Style",
|
||||
"Fantasy Art",
|
||||
"Realistic"
|
||||
]
|
||||
|
||||
st.session_state.illustration_style = st.selectbox(
|
||||
"Choose an illustration style:",
|
||||
style_options,
|
||||
index=style_options.index(st.session_state.illustration_style) if st.session_state.illustration_style in style_options else 0
|
||||
)
|
||||
|
||||
# Custom style input
|
||||
use_custom_style = st.checkbox("Use custom style")
|
||||
if use_custom_style:
|
||||
custom_style = st.text_input("Describe your custom style:",
|
||||
placeholder="e.g., Impressionist painting with vibrant colors and visible brushstrokes")
|
||||
if custom_style:
|
||||
st.session_state.illustration_style = custom_style
|
||||
|
||||
# Display style examples
|
||||
st.info("💡 The style you choose will significantly impact the look and feel of your illustrations.")
|
||||
|
||||
# Aspect ratio selection
|
||||
st.subheader("Image Settings")
|
||||
aspect_ratio_options = {
|
||||
"16:9 (Widescreen)": "16:9",
|
||||
"4:3 (Standard)": "4:3",
|
||||
"1:1 (Square)": "1:1"
|
||||
}
|
||||
|
||||
selected_ratio = st.selectbox(
|
||||
"Choose aspect ratio:",
|
||||
list(aspect_ratio_options.keys()),
|
||||
index=list(aspect_ratio_options.values()).index(st.session_state.aspect_ratio) if st.session_state.aspect_ratio in aspect_ratio_options.values() else 0
|
||||
)
|
||||
st.session_state.aspect_ratio = aspect_ratio_options[selected_ratio]
|
||||
|
||||
# Advanced settings
|
||||
with st.expander("Advanced Settings"):
|
||||
st.slider("Number of segments to illustrate:", 1,
|
||||
max(len(st.session_state.segments), 1) if st.session_state.segments else 1,
|
||||
min(len(st.session_state.segments), MAX_SEGMENTS) if st.session_state.segments else 1,
|
||||
key="num_segments_to_illustrate")
|
||||
|
||||
st.checkbox("Generate cover image", value=True, key="generate_cover")
|
||||
|
||||
st.checkbox("Add text to illustrations", value=False, key="add_text_to_illustrations")
|
||||
|
||||
# Tab 3: Generate & Export
|
||||
with tab3:
|
||||
st.header("Step 3: Generate Illustrations & Export")
|
||||
|
||||
if not st.session_state.segments:
|
||||
st.warning("Please process your story into segments in Step 1 before generating illustrations.")
|
||||
else:
|
||||
# Generate illustrations
|
||||
st.subheader("Generate Illustrations")
|
||||
|
||||
num_segments = min(len(st.session_state.segments), st.session_state.get("num_segments_to_illustrate", len(st.session_state.segments)))
|
||||
|
||||
if st.button("Generate All Illustrations"):
|
||||
with st.spinner(f"Generating {num_segments} illustrations... This may take a while."):
|
||||
progress_bar = st.progress(0)
|
||||
|
||||
for i in range(num_segments):
|
||||
# Update progress
|
||||
progress_bar.progress((i) / num_segments)
|
||||
st.write(f"Generating illustration {i+1} of {num_segments}...")
|
||||
|
||||
# Generate illustration
|
||||
illustration_path = create_illustration(
|
||||
st.session_state.segments[i],
|
||||
st.session_state.illustration_style,
|
||||
st.session_state.aspect_ratio
|
||||
)
|
||||
|
||||
# Store the illustration path
|
||||
if illustration_path:
|
||||
st.session_state.illustrations[i] = illustration_path
|
||||
st.session_state.temp_files.append(illustration_path)
|
||||
|
||||
# Complete progress
|
||||
progress_bar.progress(1.0)
|
||||
st.success(f"Generated {num_segments} illustrations!")
|
||||
|
||||
# Generate individual illustrations
|
||||
st.subheader("Generate Individual Illustrations")
|
||||
|
||||
for i in range(num_segments):
|
||||
col1, col2 = st.columns([3, 1])
|
||||
|
||||
with col1:
|
||||
with st.expander(f"Segment {i+1}"):
|
||||
st.write(st.session_state.segments[i][:300] + "..." if len(st.session_state.segments[i]) > 300 else st.session_state.segments[i])
|
||||
|
||||
with col2:
|
||||
if st.button(f"Generate #{i+1}", key=f"gen_btn_{i}"):
|
||||
with st.spinner(f"Generating illustration {i+1}..."):
|
||||
illustration_path = create_illustration(
|
||||
st.session_state.segments[i],
|
||||
st.session_state.illustration_style,
|
||||
st.session_state.aspect_ratio
|
||||
)
|
||||
|
||||
if illustration_path:
|
||||
st.session_state.illustrations[i] = illustration_path
|
||||
st.session_state.temp_files.append(illustration_path)
|
||||
st.success(f"Generated illustration {i+1}!")
|
||||
|
||||
# Display generated illustrations
|
||||
st.subheader("Preview Illustrations")
|
||||
|
||||
if any(st.session_state.illustrations):
|
||||
for i, illustration_path in enumerate(st.session_state.illustrations[:num_segments]):
|
||||
if illustration_path and os.path.exists(illustration_path):
|
||||
with st.expander(f"Illustration {i+1}"):
|
||||
st.image(illustration_path, caption=f"Illustration for Segment {i+1}", use_column_width=True)
|
||||
|
||||
# Regenerate button
|
||||
if st.button(f"Regenerate", key=f"regen_btn_{i}"):
|
||||
with st.spinner(f"Regenerating illustration {i+1}..."):
|
||||
new_illustration_path = create_illustration(
|
||||
st.session_state.segments[i],
|
||||
st.session_state.illustration_style,
|
||||
st.session_state.aspect_ratio
|
||||
)
|
||||
|
||||
if new_illustration_path:
|
||||
st.session_state.illustrations[i] = new_illustration_path
|
||||
st.session_state.temp_files.append(new_illustration_path)
|
||||
st.rerun()
|
||||
else:
|
||||
st.info("No illustrations generated yet. Click 'Generate All Illustrations' or generate individual illustrations.")
|
||||
|
||||
# Export options
|
||||
st.subheader("Export Options")
|
||||
|
||||
if any(st.session_state.illustrations):
|
||||
export_format = st.radio(
|
||||
"Export format:",
|
||||
["PDF Storybook", "Individual Images (ZIP)", "Both"]
|
||||
)
|
||||
|
||||
if st.button("Export"):
|
||||
with st.spinner("Preparing export..."):
|
||||
# Create temporary directory for exports
|
||||
with tempfile.TemporaryDirectory() as temp_dir:
|
||||
# Filter out None values from illustrations
|
||||
valid_illustrations = [path for path in st.session_state.illustrations[:num_segments] if path and os.path.exists(path)]
|
||||
valid_segments = st.session_state.segments[:len(valid_illustrations)]
|
||||
|
||||
# Prepare filenames
|
||||
safe_title = "".join(c if c.isalnum() else "_" for c in st.session_state.book_title) if st.session_state.book_title else "story"
|
||||
timestamp = int(time.time())
|
||||
|
||||
# Export as PDF
|
||||
if export_format in ["PDF Storybook", "Both"]:
|
||||
pdf_path = os.path.join(temp_dir, f"{safe_title}_{timestamp}.pdf")
|
||||
|
||||
try:
|
||||
pdf_result = create_storybook_pdf(
|
||||
valid_segments,
|
||||
valid_illustrations,
|
||||
st.session_state.book_title or "Untitled Story",
|
||||
st.session_state.book_author or "Anonymous",
|
||||
pdf_path
|
||||
)
|
||||
|
||||
if pdf_result:
|
||||
with open(pdf_path, "rb") as f:
|
||||
st.download_button(
|
||||
label="Download PDF Storybook",
|
||||
data=f,
|
||||
file_name=f"{safe_title}.pdf",
|
||||
mime="application/pdf"
|
||||
)
|
||||
except Exception as e:
|
||||
st.error(f"Error creating PDF: {e}")
|
||||
st.info("Please install ReportLab to enable PDF export: pip install reportlab")
|
||||
|
||||
# Export as ZIP of images
|
||||
if export_format in ["Individual Images (ZIP)", "Both"]:
|
||||
zip_path = os.path.join(temp_dir, f"{safe_title}_illustrations_{timestamp}.zip")
|
||||
|
||||
# Prepare files for ZIP
|
||||
files_to_zip = {}
|
||||
for i, img_path in enumerate(valid_illustrations):
|
||||
if img_path and os.path.exists(img_path):
|
||||
files_to_zip[f"illustration_{i+1}.png"] = img_path
|
||||
|
||||
zip_result = create_zip_archive(files_to_zip, zip_path)
|
||||
|
||||
if zip_result:
|
||||
with open(zip_path, "rb") as f:
|
||||
st.download_button(
|
||||
label="Download Illustrations ZIP",
|
||||
data=f,
|
||||
file_name=f"{safe_title}_illustrations.zip",
|
||||
mime="application/zip"
|
||||
)
|
||||
else:
|
||||
st.info("Generate illustrations before exporting.")
|
||||
|
||||
# Cleanup temporary files when the session ends
|
||||
def cleanup_temp_files():
|
||||
for file_path in st.session_state.temp_files:
|
||||
try:
|
||||
if file_path and os.path.exists(file_path):
|
||||
os.remove(file_path)
|
||||
except Exception as e:
|
||||
logger.error(f"Error removing temporary file {file_path}: {e}")
|
||||
|
||||
# Register the cleanup function to run when the session ends
|
||||
import atexit
|
||||
atexit.register(cleanup_temp_files)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
write_story_illustrator()
|
||||
@@ -1,450 +0,0 @@
|
||||
"""
|
||||
Utility functions for the AI Story Illustrator module.
|
||||
|
||||
This module provides helper functions for file operations, string manipulation,
|
||||
and simple text analysis relevant to story processing.
|
||||
"""
|
||||
|
||||
import os
|
||||
import re
|
||||
import tempfile
|
||||
import uuid
|
||||
import logging
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
from typing import List, Tuple, Optional, Union
|
||||
|
||||
# Attempt to import Pillow for image dimensions, but don't fail if not installed
|
||||
# unless the specific function is called.
|
||||
try:
|
||||
from PIL import Image
|
||||
_PIL_AVAILABLE = True
|
||||
except ImportError:
|
||||
_PIL_AVAILABLE = False
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
|
||||
)
|
||||
logger = logging.getLogger('story_illustrator_utils')
|
||||
|
||||
# --- Constants ---
|
||||
IMAGE_EXTENSIONS = frozenset(['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp'])
|
||||
TEXT_EXTENSIONS = frozenset(['.txt', '.md', '.text'])
|
||||
# Common English words that often start sentences, excluded from simple name detection
|
||||
COMMON_START_WORDS = frozenset([
|
||||
'The', 'A', 'An', 'And', 'But', 'Or', 'For', 'Nor', 'So', 'Yet', 'He', 'She',
|
||||
'It', 'They', 'We', 'You', 'I', 'In', 'On', 'At', 'To', 'From', 'With',
|
||||
'About', 'As', 'Is', 'Was', 'Were', 'Be', 'Been', 'Being', 'Have', 'Has',
|
||||
'Had', 'Do', 'Does', 'Did', 'Will', 'Would', 'Shall', 'Should', 'May',
|
||||
'Might', 'Must', 'Can', 'Could'
|
||||
])
|
||||
|
||||
|
||||
# --- File/Directory Operations ---
|
||||
|
||||
def create_temp_directory(prefix: str = "story_illustrator_") -> str:
|
||||
"""
|
||||
Creates a temporary directory using tempfile.mkdtemp.
|
||||
|
||||
Args:
|
||||
prefix: A prefix for the temporary directory name.
|
||||
|
||||
Returns:
|
||||
The absolute path to the created temporary directory.
|
||||
"""
|
||||
try:
|
||||
temp_dir = tempfile.mkdtemp(prefix=prefix)
|
||||
logger.info(f"Created temporary directory: {temp_dir}")
|
||||
return temp_dir
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to create temporary directory: {e}", exc_info=True)
|
||||
raise # Re-raise the exception after logging
|
||||
|
||||
|
||||
def sanitize_filename(filename: str) -> str:
|
||||
"""
|
||||
Sanitizes a filename by removing/replacing invalid characters for common filesystems.
|
||||
|
||||
Args:
|
||||
filename: The original filename string.
|
||||
|
||||
Returns:
|
||||
A sanitized filename string suitable for use in file paths.
|
||||
"""
|
||||
if not isinstance(filename, str):
|
||||
logger.warning("sanitize_filename received non-string input, converting.")
|
||||
filename = str(filename)
|
||||
|
||||
# Remove characters invalid for Windows/Unix filenames
|
||||
# Replace them with an underscore.
|
||||
sanitized = re.sub(r'[\\/*?:"<>|\']', "_", filename)
|
||||
# Replace consecutive underscores/spaces with a single underscore
|
||||
sanitized = re.sub(r'[_ ]+', '_', sanitized)
|
||||
# Remove leading/trailing spaces, dots, and underscores
|
||||
sanitized = sanitized.strip("._ ")
|
||||
|
||||
# Ensure the filename is not empty after sanitization
|
||||
if not sanitized:
|
||||
sanitized = "unnamed_file"
|
||||
logger.warning("Filename was empty after sanitization, using default.")
|
||||
|
||||
# Limit filename length (optional, adjust as needed)
|
||||
# max_len = 255 # Example limit
|
||||
# if len(sanitized) > max_len:
|
||||
# name, ext = os.path.splitext(sanitized)
|
||||
# sanitized = name[:max_len - len(ext) - 1] + "_" + ext
|
||||
# logger.warning(f"Filename truncated to maximum length: {sanitized}")
|
||||
|
||||
return sanitized
|
||||
|
||||
|
||||
def get_temp_file_path(
|
||||
directory: str, prefix: str = "file_", suffix: str = ".tmp"
|
||||
) -> str:
|
||||
"""
|
||||
Generates a unique temporary file path within the specified directory.
|
||||
|
||||
Args:
|
||||
directory: The directory where the temporary file should be located.
|
||||
prefix: A prefix for the filename.
|
||||
suffix: A suffix (extension) for the filename.
|
||||
|
||||
Returns:
|
||||
The full path for the unique temporary file.
|
||||
"""
|
||||
# Ensure suffix starts with a dot if it's meant to be an extension
|
||||
if suffix and not suffix.startswith("."):
|
||||
suffix = "." + suffix
|
||||
|
||||
unique_id = uuid.uuid4().hex[:12] # Longer hex UUID for better uniqueness
|
||||
filename = f"{prefix}{unique_id}{suffix}"
|
||||
return os.path.join(directory, filename)
|
||||
|
||||
|
||||
def ensure_directory_exists(directory: Union[str, Path]) -> str:
|
||||
"""
|
||||
Ensures that a directory exists, creating it recursively if necessary.
|
||||
|
||||
Args:
|
||||
directory: The path to the directory (string or Path object).
|
||||
|
||||
Returns:
|
||||
The absolute path to the directory as a string.
|
||||
|
||||
Raises:
|
||||
OSError: If the directory cannot be created (e.g., permission issues).
|
||||
"""
|
||||
dir_path = Path(directory).resolve() # Use Pathlib for robust handling
|
||||
try:
|
||||
dir_path.mkdir(parents=True, exist_ok=True)
|
||||
# Log only if it needed creation (or if verbose logging is on)
|
||||
# logger.info(f"Ensured directory exists: {dir_path}")
|
||||
return str(dir_path)
|
||||
except OSError as e:
|
||||
logger.error(f"Failed to create or access directory {dir_path}: {e}", exc_info=True)
|
||||
raise
|
||||
|
||||
|
||||
def cleanup_directory(directory: Union[str, Path]) -> None:
|
||||
"""
|
||||
Removes a directory and all its contents recursively. Handles errors gracefully.
|
||||
|
||||
Args:
|
||||
directory: The path to the directory to remove (string or Path object).
|
||||
"""
|
||||
dir_path = Path(directory)
|
||||
if not dir_path.exists():
|
||||
logger.debug(f"Cleanup skipped: Directory '{directory}' does not exist.")
|
||||
return
|
||||
|
||||
if not dir_path.is_dir():
|
||||
logger.warning(f"Cleanup warning: Path '{directory}' is not a directory.")
|
||||
return
|
||||
|
||||
try:
|
||||
shutil.rmtree(dir_path)
|
||||
logger.info(f"Successfully removed directory: {directory}")
|
||||
except OSError as e:
|
||||
logger.error(f"Error removing directory {directory}: {e}", exc_info=True)
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Unexpected error removing directory {directory}: {e}", exc_info=True
|
||||
)
|
||||
|
||||
|
||||
# --- File Type Checks ---
|
||||
|
||||
def get_file_extension(file_path: Union[str, Path]) -> str:
|
||||
"""
|
||||
Gets the lowercased file extension (including the dot) from a file path.
|
||||
|
||||
Args:
|
||||
file_path: The path to the file (string or Path object).
|
||||
|
||||
Returns:
|
||||
The file extension (e.g., '.txt', '.png') or an empty string if no extension.
|
||||
"""
|
||||
return Path(file_path).suffix.lower()
|
||||
|
||||
|
||||
def is_image_file(file_path: Union[str, Path]) -> bool:
|
||||
"""
|
||||
Checks if a file is likely an image based on its extension.
|
||||
|
||||
Args:
|
||||
file_path: The path to the file (string or Path object).
|
||||
|
||||
Returns:
|
||||
True if the file extension is in IMAGE_EXTENSIONS, False otherwise.
|
||||
"""
|
||||
return get_file_extension(file_path) in IMAGE_EXTENSIONS
|
||||
|
||||
|
||||
def is_text_file(file_path: Union[str, Path]) -> bool:
|
||||
"""
|
||||
Checks if a file is likely a text file based on its extension.
|
||||
|
||||
Args:
|
||||
file_path: The path to the file (string or Path object).
|
||||
|
||||
Returns:
|
||||
True if the file extension is in TEXT_EXTENSIONS, False otherwise.
|
||||
"""
|
||||
return get_file_extension(file_path) in TEXT_EXTENSIONS
|
||||
|
||||
|
||||
# --- Text Analysis (Simple Heuristics) ---
|
||||
|
||||
def extract_story_title_from_text(text: str) -> str:
|
||||
"""
|
||||
Attempts to extract a title from story text using simple heuristics.
|
||||
|
||||
Looks for patterns (in order):
|
||||
1. Markdown headers (#, ##, etc.) at the start of a line.
|
||||
2. The first non-empty line if it's short (< 100 chars) and followed by
|
||||
a blank line or is the only line.
|
||||
3. The first non-empty line if it's entirely in uppercase (< 100 chars).
|
||||
|
||||
Args:
|
||||
text: The story text content.
|
||||
|
||||
Returns:
|
||||
An extracted title string, or "Untitled Story" if no pattern matches.
|
||||
"""
|
||||
if not isinstance(text, str) or not text.strip():
|
||||
return "Untitled Story"
|
||||
|
||||
# 1. Check for markdown headers ( # Title, ## Title )
|
||||
# Needs to match start of line (^) with optional whitespace before #
|
||||
header_match = re.search(r'^\s*#+\s+(.+)$', text.strip(), re.MULTILINE)
|
||||
if header_match:
|
||||
title = header_match.group(1).strip()
|
||||
if title: return title
|
||||
|
||||
lines = text.strip().split('\n')
|
||||
if not lines:
|
||||
return "Untitled Story"
|
||||
|
||||
first_line = lines[0].strip()
|
||||
if not first_line: # Skip if first line is blank
|
||||
if len(lines) > 1:
|
||||
first_line = lines[1].strip() # Try second line
|
||||
else:
|
||||
return "Untitled Story"
|
||||
|
||||
if not first_line: # Still no title found
|
||||
return "Untitled Story"
|
||||
|
||||
# 2. Check if first line is short and potentially a title
|
||||
is_short = len(first_line) < 100
|
||||
is_followed_by_blank = len(lines) > 1 and not lines[1].strip()
|
||||
is_only_line = len(lines) == 1
|
||||
|
||||
if is_short and (is_followed_by_blank or is_only_line):
|
||||
return first_line
|
||||
|
||||
# 3. Check if first line is all caps (and short)
|
||||
is_all_caps = first_line == first_line.upper() and first_line.isalpha() # Check if it contains letters
|
||||
if is_short and is_all_caps:
|
||||
return first_line
|
||||
|
||||
# Default if no other pattern matched
|
||||
return "Untitled Story"
|
||||
|
||||
|
||||
def estimate_reading_time(text: str, words_per_minute: int = 200) -> float:
|
||||
"""
|
||||
Estimates the reading time of a text in minutes.
|
||||
|
||||
Args:
|
||||
text: The text content.
|
||||
words_per_minute: The assumed average reading speed.
|
||||
|
||||
Returns:
|
||||
The estimated reading time in minutes. Returns 0.0 for empty text.
|
||||
"""
|
||||
if not isinstance(text, str) or not text.strip():
|
||||
return 0.0
|
||||
if words_per_minute <= 0:
|
||||
raise ValueError("words_per_minute must be positive.")
|
||||
|
||||
word_count = len(text.split())
|
||||
minutes = word_count / words_per_minute
|
||||
return minutes
|
||||
|
||||
|
||||
def count_sentences(text: str) -> int:
|
||||
"""
|
||||
Counts the number of sentences in a text using a very simple heuristic.
|
||||
|
||||
Note: This is a basic implementation counting sentence-ending punctuation
|
||||
(. ! ?). It will be inaccurate with abbreviations (Mr., Mrs., etc.),
|
||||
ellipses, and complex sentence structures.
|
||||
|
||||
Args:
|
||||
text: The text content.
|
||||
|
||||
Returns:
|
||||
An estimated count of sentences. Returns 0 for empty text.
|
||||
"""
|
||||
if not isinstance(text, str) or not text.strip():
|
||||
return 0
|
||||
|
||||
# Find sequences of one or more sentence-ending punctuation marks
|
||||
sentence_endings = re.findall(r'[.!?]+', text)
|
||||
count = len(sentence_endings)
|
||||
|
||||
# Handle edge case where text might not end with punctuation but isn't empty
|
||||
if count == 0 and len(text.strip()) > 0:
|
||||
return 1 # Assume at least one sentence if text exists but no terminators found
|
||||
return count
|
||||
|
||||
|
||||
def extract_character_names(text: str, min_occurrences: int = 2) -> List[str]:
|
||||
"""
|
||||
Attempts to extract potential character names from story text.
|
||||
|
||||
Note: This is a simple heuristic based on finding capitalized words
|
||||
(excluding common sentence starters) that appear multiple times. It has
|
||||
limitations and may produce false positives or miss actual names.
|
||||
|
||||
Args:
|
||||
text: The story text content.
|
||||
min_occurrences: The minimum number of times a capitalized word must
|
||||
appear to be considered a potential name.
|
||||
|
||||
Returns:
|
||||
A list of potential character name strings.
|
||||
"""
|
||||
if not isinstance(text, str) or not text.strip():
|
||||
return []
|
||||
if min_occurrences < 1:
|
||||
min_occurrences = 1 # Ensure at least one occurrence is required
|
||||
|
||||
# Find words starting with an uppercase letter, potentially followed by lowercase
|
||||
# Allows for single-letter names like 'X' but focuses on typical Name structure
|
||||
capitalized_words = re.findall(r'\b[A-Z][a-zA-Z]*\b', text)
|
||||
|
||||
# Count occurrences, excluding common words
|
||||
word_counts: Dict[str, int] = {}
|
||||
for word in capitalized_words:
|
||||
if word not in COMMON_START_WORDS:
|
||||
word_counts[word] = word_counts.get(word, 0) + 1
|
||||
|
||||
# Filter for words that meet the minimum occurrence threshold
|
||||
potential_names = [
|
||||
word for word, count in word_counts.items() if count >= min_occurrences
|
||||
]
|
||||
|
||||
# Sort for consistency (optional)
|
||||
potential_names.sort()
|
||||
|
||||
return potential_names
|
||||
|
||||
|
||||
def extract_setting_details(text: str) -> List[str]:
|
||||
"""
|
||||
Attempts to extract potential setting details using simple regex patterns.
|
||||
|
||||
Note: This is a very basic heuristic looking for common prepositional
|
||||
phrases (e.g., "in the forest", "at the castle"). It is highly limited
|
||||
and likely to miss many setting details or extract irrelevant phrases.
|
||||
|
||||
Args:
|
||||
text: The story text content.
|
||||
|
||||
Returns:
|
||||
A list of potential setting phrases found.
|
||||
"""
|
||||
if not isinstance(text, str) or not text.strip():
|
||||
return []
|
||||
|
||||
# Patterns looking for prepositions followed by nouns/adjectives
|
||||
# Making patterns slightly more general:
|
||||
# (\b\w+\b) captures single words
|
||||
# (\b\w+\s+\w+\b) captures two-word phrases
|
||||
# (\b[A-Z]\w*\b) captures capitalized words (potential proper nouns)
|
||||
setting_patterns = [
|
||||
r'\b(?:in|on|at|near|beside|inside|outside|under|over|through)\s+(?:the|a|an)\s+((?:[A-Z]\w*|\w+)(?:\s+\w+){0,2})\b', # e.g., in the old house
|
||||
r'\b(?:in|on|at)\s+((?:[A-Z]\w+)(?:\s+[A-Z]\w+)*)\b', # e.g., in New York City
|
||||
r'\b(?:during|before|after)\s+(?:the|a|an)\s+(\w+(?:\s+\w+){0,2})\b', # e.g., during the storm
|
||||
]
|
||||
|
||||
settings_found = set() # Use a set to avoid duplicates
|
||||
for pattern in setting_patterns:
|
||||
try:
|
||||
matches = re.findall(pattern, text, re.IGNORECASE) # Ignore case
|
||||
for match in matches:
|
||||
# If match is tuple due to multiple capture groups, join them?
|
||||
# For these patterns, it should be single strings.
|
||||
if isinstance(match, str):
|
||||
phrase = match.strip()
|
||||
if phrase and len(phrase.split()) <= 5: # Limit phrase length
|
||||
settings_found.add(phrase)
|
||||
except re.error as e:
|
||||
logger.warning(f"Regex error in extract_setting_details: {e} with pattern: {pattern}")
|
||||
|
||||
|
||||
# Convert set back to list and sort for consistency
|
||||
sorted_settings = sorted(list(settings_found))
|
||||
return sorted_settings
|
||||
|
||||
|
||||
# --- Image Operations ---
|
||||
|
||||
def get_image_dimensions(image_path: Union[str, Path]) -> Optional[Tuple[int, int]]:
|
||||
"""
|
||||
Gets the (width, height) dimensions of an image file using Pillow.
|
||||
|
||||
Args:
|
||||
image_path: The path to the image file (string or Path object).
|
||||
|
||||
Returns:
|
||||
A tuple (width, height) if successful, or None if the file is not
|
||||
a valid image, Pillow is not installed, or an error occurs.
|
||||
"""
|
||||
if not _PIL_AVAILABLE:
|
||||
logger.warning("Pillow (PIL) library not installed. Cannot get image dimensions.")
|
||||
return None
|
||||
|
||||
img_path = Path(image_path)
|
||||
if not img_path.is_file():
|
||||
logger.error(f"Image file not found or is not a file: {image_path}")
|
||||
return None
|
||||
|
||||
try:
|
||||
with Image.open(img_path) as img:
|
||||
width, height = img.size
|
||||
logger.debug(f"Dimensions for {image_path}: {width}x{height}")
|
||||
return width, height
|
||||
except FileNotFoundError:
|
||||
logger.error(f"Image file not found at path: {image_path}")
|
||||
return None
|
||||
except UnidentifiedImageError: # Specific Pillow error for invalid images
|
||||
logger.error(f"Could not identify image file (invalid format or corrupted): {image_path}")
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting dimensions for image {image_path}: {e}", exc_info=True)
|
||||
return None
|
||||
@@ -1,31 +0,0 @@
|
||||
# AI Story Video Generator
|
||||
|
||||
This module allows users to generate animated story videos using AI. It leverages Google's Gemini model to create stories and generate images for each scene, then combines them into a video.
|
||||
|
||||
## Features
|
||||
|
||||
- Generate complete stories based on user prompts
|
||||
- Create scene-by-scene storyboards
|
||||
- Generate images for each scene using Gemini
|
||||
- Compile images into an animated video
|
||||
- Add background music and text overlays
|
||||
- Export videos in MP4 format
|
||||
|
||||
## How It Works
|
||||
|
||||
1. User provides a story prompt and preferences
|
||||
2. AI generates a complete story with multiple scenes
|
||||
3. For each scene, an image is generated
|
||||
4. Images are compiled into a video with transitions
|
||||
5. Optional background music and text overlays are added
|
||||
6. The final video is available for download
|
||||
|
||||
## Requirements
|
||||
|
||||
- Google Gemini API key
|
||||
- FFmpeg for video processing
|
||||
- Python libraries: moviepy, pillow, requests
|
||||
|
||||
## Usage
|
||||
|
||||
Access this tool through the Streamlit interface by selecting "AI Story Video Generator" from the main menu.
|
||||
@@ -1,4 +0,0 @@
|
||||
# AI Story Video Generator module
|
||||
from .story_video_generator import write_story_video_generator
|
||||
|
||||
__all__ = ["write_story_video_generator"]
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,64 +0,0 @@
|
||||
"""
|
||||
Utility functions for the AI Story Video Generator.
|
||||
"""
|
||||
|
||||
import os
|
||||
import tempfile
|
||||
import uuid
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
# Constants
|
||||
TEMP_DIR = Path(tempfile.gettempdir()) / "alwrity_story_generator"
|
||||
|
||||
def ensure_temp_dir() -> Path:
|
||||
"""Ensure the temporary directory exists and return its path."""
|
||||
os.makedirs(TEMP_DIR, exist_ok=True)
|
||||
return TEMP_DIR
|
||||
|
||||
def get_temp_filepath(prefix: str, extension: str) -> str:
|
||||
"""Generate a temporary file path with the given prefix and extension."""
|
||||
temp_dir = ensure_temp_dir()
|
||||
return str(temp_dir / f"{prefix}_{uuid.uuid4()}.{extension}")
|
||||
|
||||
def clean_temp_files(older_than_hours: int = 24) -> int:
|
||||
"""
|
||||
Clean temporary files older than the specified number of hours.
|
||||
|
||||
Args:
|
||||
older_than_hours: Remove files older than this many hours
|
||||
|
||||
Returns:
|
||||
Number of files removed
|
||||
"""
|
||||
import time
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
temp_dir = ensure_temp_dir()
|
||||
cutoff_time = time.time() - (older_than_hours * 3600)
|
||||
count = 0
|
||||
|
||||
for file_path in temp_dir.glob("*"):
|
||||
if file_path.is_file() and file_path.stat().st_mtime < cutoff_time:
|
||||
try:
|
||||
file_path.unlink()
|
||||
count += 1
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return count
|
||||
|
||||
def format_duration(seconds: float) -> str:
|
||||
"""Format seconds into a MM:SS string."""
|
||||
minutes = int(seconds // 60)
|
||||
remaining_seconds = int(seconds % 60)
|
||||
return f"{minutes}:{remaining_seconds:02d}"
|
||||
|
||||
def sanitize_filename(filename: str) -> str:
|
||||
"""Sanitize a string to be used as a filename."""
|
||||
import re
|
||||
# Remove invalid characters
|
||||
sanitized = re.sub(r'[^\w\s-]', '', filename)
|
||||
# Replace spaces with underscores
|
||||
sanitized = sanitized.strip().replace(' ', '_')
|
||||
return sanitized
|
||||
@@ -1,103 +0,0 @@
|
||||
# AI Story Generator App
|
||||
|
||||
In the age of AI, creativity and technology are intertwining in ways that are transforming how we tell stories. Imagine having the power to craft a captivating narrative tailored to your exact specifications with just a few clicks. Whether you're an aspiring writer, a seasoned novelist, or just someone who loves a good story, our new AI-powered story writing app is here to make storytelling easier and more engaging than ever before.
|
||||
|
||||
## Why an AI Story Writing App?
|
||||
|
||||
Storytelling has always been a cherished art form, but not everyone finds it easy to start from scratch. With the AI Story Generator App, you can create detailed and personalized stories by simply providing some key inputs. Our app uses advanced AI to turn your ideas into compelling narratives, helping you overcome writer's block and unleashing your creative potential.
|
||||
|
||||
## Features of the AI Story Generator App
|
||||
|
||||
### Genre
|
||||
Choose from a variety of genres such as Fantasy, Sci-Fi, Mystery, Romance, and Horror to set the tone for your story.
|
||||
|
||||
### Story Setting
|
||||
Provide a detailed setting for your story, including location and time period.
|
||||
|
||||
For example:
|
||||
A bustling futuristic city with towering skyscrapers and flying cars, set in the year 2150. The city is known for its technological advancements but has a dark underbelly of crime and corruption.
|
||||
|
||||
|
||||
### Main Characters
|
||||
Input the names, descriptions, and roles of your main characters.
|
||||
|
||||
For example:
|
||||
Character Names: John, Xishan, Amol
|
||||
Character Descriptions: John is a tall, muscular man with a kind heart. Xishan is a clever and resourceful woman. Amol is a mischievous and energetic young boy.
|
||||
Character Roles: John - Hero, Xishan - Sidekick, Amol - Supporting Character
|
||||
|
||||
|
||||
### Plot Elements
|
||||
Outline the key plot elements including the story theme, key events, and main conflict.
|
||||
|
||||
For example:
|
||||
Story Theme: Love conquers all, The hero's journey, Good vs. evil
|
||||
|
||||
Key Events or Plot Points:
|
||||
|
||||
The hero meets the villain
|
||||
The hero faces a challenge
|
||||
The hero overcomes the conflict
|
||||
Main Conflict or Problem:
|
||||
The hero must save the world from a powerful enemy, The hero must overcome a personal obstacle to achieve their goal.
|
||||
|
||||
|
||||
### Tone and Style
|
||||
Choose the writing style, tone, and narrative point of view for your story.
|
||||
|
||||
For example:
|
||||
Writing Style: Formal, Casual, Poetic, Humorous
|
||||
Story Tone: Dark
|
||||
|
||||
### Perspective
|
||||
Choose the narrative point of view from which the story is told (e.g., first person, third person limited, third person omniscient).
|
||||
|
||||
### Target Audience
|
||||
Specify the intended audience age group (Children, Young Adults, Adults) and set a content rating (G, PG, PG-13, R) for appropriateness.
|
||||
|
||||
### Ending Preference
|
||||
Select the type of ending you prefer for the story (e.g., happy, tragic, cliffhanger, twist).
|
||||
|
||||
## How to Use
|
||||
|
||||
Choose Genre: Select the genre that best fits your story idea.
|
||||
Set Story Setting: Describe the setting and time period where your story unfolds.
|
||||
Define Characters: Provide names, descriptions, and roles for your main characters.
|
||||
Outline Plot Elements: Detail the story's theme, key events, and main conflict.
|
||||
Select Tone and Style: Choose the writing style and tone that align with your story's mood.
|
||||
Specify Perspective: Decide on the narrative point of view.
|
||||
Target Audience: Specify the age group and content rating.
|
||||
Choose Ending: Select the preferred type of story conclusion.
|
||||
Generate Story: Click the "Generate Story" button to receive a customized story prompt based on your inputs.
|
||||
|
||||
|
||||
### Example Prompt
|
||||
|
||||
**Genre:** Fantasy
|
||||
**Setting:** A mystical forest in a medieval realm, where magic thrives and mythical creatures roam freely.
|
||||
**Characters:**
|
||||
- Name: Elara
|
||||
Description: Elara is a young elf with a mischievous glint in her emerald eyes, known for her ability to wield powerful spells.
|
||||
Role: Protagonist
|
||||
- Name: Thorne
|
||||
Description: Thorne is a gruff dwarf with a heart of gold, skilled in forging enchanted weapons.
|
||||
Role: Sidekick
|
||||
- Name: Malachai
|
||||
Description: Malachai is a cunning dragon with shimmering scales of azure, whose allegiance is uncertain.
|
||||
Role: Antagonist
|
||||
|
||||
**Plot Elements:**
|
||||
- Theme: The power of friendship and bravery in the face of adversity.
|
||||
- Key Events: Elara discovers an ancient prophecy that foretells a looming darkness threatening the realm. Thorne crafts a legendary sword to aid in their quest. Malachai challenges Elara's resolve, forcing her to make a difficult choice.
|
||||
- Conflict: Elara must gather allies and confront the dark sorcerer who seeks to plunge the realm into eternal shadow.
|
||||
|
||||
**Writing Style:** Poetic
|
||||
**Tone:** Whimsical
|
||||
**Point of View:** Third Person Limited
|
||||
|
||||
**Audience:** Young Adults, **Content Rating:** PG
|
||||
**Ending:** Happy
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -1,238 +0,0 @@
|
||||
#####################################################
|
||||
#
|
||||
# google-gemini-cookbook - Story_Writing_with_Prompt_Chaining
|
||||
#
|
||||
#####################################################
|
||||
|
||||
import os
|
||||
from pathlib import Path
|
||||
import streamlit as st
|
||||
from loguru import logger
|
||||
import sys
|
||||
|
||||
from ...gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
|
||||
|
||||
def generate_with_retry(prompt, system_prompt=None):
|
||||
"""
|
||||
Generates content using the llm_text_gen function with retry handling for errors.
|
||||
|
||||
Parameters:
|
||||
prompt (str): The prompt to generate content from.
|
||||
system_prompt (str, optional): Custom system prompt to use instead of the default one.
|
||||
|
||||
Returns:
|
||||
str: The generated content.
|
||||
"""
|
||||
try:
|
||||
# Use llm_text_gen instead of directly calling the model
|
||||
return llm_text_gen(prompt, system_prompt)
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating content: {e}")
|
||||
return ""
|
||||
|
||||
|
||||
def ai_story(persona, story_setting, character_input,
|
||||
plot_elements, writing_style, story_tone, narrative_pov,
|
||||
audience_age_group, content_rating, ending_preference):
|
||||
"""
|
||||
Write a story using prompt chaining and iterative generation.
|
||||
|
||||
Parameters:
|
||||
persona (str): The persona statement for the author.
|
||||
story_setting (str): The setting of the story.
|
||||
character_input (str): The characters in the story.
|
||||
plot_elements (str): The plot elements of the story.
|
||||
writing_style (str): The writing style of the story.
|
||||
story_tone (str): The tone of the story.
|
||||
narrative_pov (str): The narrative point of view.
|
||||
audience_age_group (str): The target audience age group.
|
||||
content_rating (str): The content rating of the story.
|
||||
ending_preference (str): The preferred ending of the story.
|
||||
"""
|
||||
st.info(f"""
|
||||
You have chosen to create a story set in **{story_setting}**.
|
||||
The main characters are: **{character_input}**.
|
||||
The plot will revolve around the theme of **{plot_elements}**.
|
||||
The story will be written in a **{writing_style}** style with a **{story_tone}** tone, from a **{narrative_pov}** perspective.
|
||||
It is intended for a **{audience_age_group}** audience with a **{content_rating}** rating.
|
||||
You prefer the story to have a **{ending_preference}** ending.
|
||||
""")
|
||||
try:
|
||||
persona = f"""{persona}
|
||||
Write a story with the following details:
|
||||
|
||||
**The stroy Setting is:**
|
||||
{story_setting}
|
||||
|
||||
**The Characters of the story are:**
|
||||
{character_input}
|
||||
|
||||
**Plot Elements of the story:**
|
||||
{plot_elements}
|
||||
|
||||
**Story Writing Style:**
|
||||
{writing_style}
|
||||
|
||||
**The story Tone is:**
|
||||
{story_tone}
|
||||
|
||||
**Write story from the Point of View of:**
|
||||
{narrative_pov}
|
||||
|
||||
**Target Audience of the story:**
|
||||
{audience_age_group}, **Content Rating:** {content_rating}
|
||||
|
||||
**Story Ending:**
|
||||
{ending_preference}
|
||||
|
||||
Make sure the story is engaging and tailored to the specified audience and content rating.
|
||||
Ensure the ending aligns with the preference indicated.
|
||||
|
||||
"""
|
||||
# Define persona and writing guidelines
|
||||
guidelines = f'''\
|
||||
Writing Guidelines:
|
||||
|
||||
Delve deeper. Lose yourself in the world you're building. Unleash vivid
|
||||
descriptions to paint the scenes in your reader's mind.
|
||||
Develop your characters — let their motivations, fears, and complexities unfold naturally.
|
||||
Weave in the threads of your outline, but don't feel constrained by it.
|
||||
Allow your story to surprise you as you write. Use rich imagery, sensory details, and
|
||||
evocative language to bring the setting, characters, and events to life.
|
||||
Introduce elements subtly that can blossom into complex subplots, relationships,
|
||||
or worldbuilding details later in the story.
|
||||
Keep things intriguing but not fully resolved.
|
||||
Avoid boxing the story into a corner too early.
|
||||
Plant the seeds of subplots or potential character arc shifts that can be expanded later.
|
||||
|
||||
Remember, your main goal is to write as much as you can. If you get through
|
||||
the story too fast, that is bad. Expand, never summarize.
|
||||
'''
|
||||
|
||||
# Generate prompts
|
||||
premise_prompt = f'''\
|
||||
{persona}
|
||||
|
||||
Write a single sentence premise for a {story_setting} story featuring {character_input}.
|
||||
'''
|
||||
|
||||
outline_prompt = f'''\
|
||||
{persona}
|
||||
|
||||
You have a gripping premise in mind:
|
||||
|
||||
{{premise}}
|
||||
|
||||
Write an outline for the plot of your story.
|
||||
'''
|
||||
|
||||
starting_prompt = f'''\
|
||||
{persona}
|
||||
|
||||
You have a gripping premise in mind:
|
||||
|
||||
{{premise}}
|
||||
|
||||
Your imagination has crafted a rich narrative outline:
|
||||
|
||||
{{outline}}
|
||||
|
||||
First, silently review the outline and the premise. Consider how to start the
|
||||
story.
|
||||
|
||||
Start to write the very beginning of the story. You are not expected to finish
|
||||
the whole story now. Your writing should be detailed enough that you are only
|
||||
scratching the surface of the first bullet of your outline. Try to write AT
|
||||
MINIMUM 4000 WORDS.
|
||||
|
||||
{guidelines}
|
||||
'''
|
||||
|
||||
continuation_prompt = f'''\
|
||||
{persona}
|
||||
|
||||
You have a gripping premise in mind:
|
||||
|
||||
{{premise}}
|
||||
|
||||
Your imagination has crafted a rich narrative outline:
|
||||
|
||||
{{outline}}
|
||||
|
||||
You've begun to immerse yourself in this world, and the words are flowing.
|
||||
Here's what you've written so far:
|
||||
|
||||
{{story_text}}
|
||||
|
||||
=====
|
||||
|
||||
First, silently review the outline and story so far. Identify what the single
|
||||
next part of your outline you should write.
|
||||
|
||||
Your task is to continue where you left off and write the next part of the story.
|
||||
You are not expected to finish the whole story now. Your writing should be
|
||||
detailed enough that you are only scratching the surface of the next part of
|
||||
your outline. Try to write AT MINIMUM 2000 WORDS. However, only once the story
|
||||
is COMPLETELY finished, write IAMDONE. Remember, do NOT write a whole chapter
|
||||
right now.
|
||||
|
||||
{guidelines}
|
||||
'''
|
||||
|
||||
# Generate prompts
|
||||
try:
|
||||
premise = generate_with_retry(premise_prompt)
|
||||
st.info(f"The premise of the story is: {premise}")
|
||||
except Exception as err:
|
||||
st.error(f"Premise Generation Error: {err}")
|
||||
return
|
||||
|
||||
outline = generate_with_retry(outline_prompt.format(premise=premise))
|
||||
with st.expander("Click to Checkout the outline, writing still in progress.."):
|
||||
st.markdown(f"The Outline of the story is: {outline}\n\n")
|
||||
|
||||
if not outline:
|
||||
st.error("Failed to generate outline. Exiting...")
|
||||
return
|
||||
|
||||
# Generate starting draft
|
||||
try:
|
||||
starting_draft = generate_with_retry(
|
||||
starting_prompt.format(premise=premise, outline=outline))
|
||||
except Exception as err:
|
||||
st.error(f"Failed to Generate Story draft: {err}")
|
||||
return
|
||||
|
||||
try:
|
||||
draft = starting_draft
|
||||
continuation = generate_with_retry(
|
||||
continuation_prompt.format(premise=premise, outline=outline, story_text=draft))
|
||||
except Exception as err:
|
||||
st.error(f"Failed to write the initial draft: {err}")
|
||||
|
||||
# Add the continuation to the initial draft, keep building the story until we see 'IAMDONE'
|
||||
try:
|
||||
draft += '\n\n' + continuation
|
||||
except Exception as err:
|
||||
st.error(f"Failed as: {err} and {continuation}")
|
||||
|
||||
with st.status("Story Writing in Progress..", expanded=True) as status:
|
||||
status.update(label=f"Writing in progress... Current draft length: {len(draft)} characters")
|
||||
while 'IAMDONE' not in continuation:
|
||||
try:
|
||||
status.update(label=f"Writing in progress... Current draft length: {len(draft)} characters")
|
||||
continuation = generate_with_retry(
|
||||
continuation_prompt.format(premise=premise, outline=outline, story_text=draft))
|
||||
draft += '\n\n' + continuation
|
||||
except Exception as err:
|
||||
st.error(f"Failed to continually write the story: {err}")
|
||||
return
|
||||
|
||||
# Remove 'IAMDONE' and print the final story
|
||||
final = draft.replace('IAMDONE', '').strip()
|
||||
return(final)
|
||||
|
||||
except Exception as e:
|
||||
st.error(f"Main Story writing: An error occurred: {e}")
|
||||
return ""
|
||||
@@ -1,134 +0,0 @@
|
||||
import time
|
||||
import os
|
||||
import json
|
||||
import streamlit as st
|
||||
|
||||
from .ai_story_generator import ai_story
|
||||
|
||||
|
||||
def story_input_section():
|
||||
st.title("🧕 Alwrity - AI Story Writer")
|
||||
personas = [
|
||||
("Award-Winning Science Fiction Author", "👽 Award-Winning Science Fiction Author"),
|
||||
("Historical Fiction Author", "🏺 Historical Fiction Author"),
|
||||
("Fantasy World Builder", "🧙 Fantasy World Builder"),
|
||||
("Mystery Novelist", "🕵️ Mystery Novelist"),
|
||||
("Romantic Poet", "💌 Romantic Poet"),
|
||||
("Thriller Writer", "🔪 Thriller Writer"),
|
||||
("Children's Book Author", "📚 Children's Book Author"),
|
||||
("Satirical Humorist", "😂 Satirical Humorist"),
|
||||
("Biographical Writer", "📜 Biographical Writer"),
|
||||
("Dystopian Visionary", "🌆 Dystopian Visionary"),
|
||||
("Magical Realism Author", "🪄 Magical Realism Author")
|
||||
]
|
||||
|
||||
selected_persona_name = st.selectbox(
|
||||
"Select Your Story Writing Persona Or Book Genre",
|
||||
options=[persona[0] for persona in personas]
|
||||
)
|
||||
|
||||
persona_descriptions = {
|
||||
"Award-Winning Science Fiction Author": "You are an award-winning science fiction author with a penchant for expansive, intricately woven stories. Your ultimate goal is to write the next award-winning sci-fi novel.",
|
||||
"Historical Fiction Author": "You are a seasoned historical fiction author, meticulously researching past eras to weave captivating narratives. Your goal is to transport readers to different times and places through your vivid storytelling.",
|
||||
"Fantasy World Builder": "You are a world-building enthusiast, crafting intricate realms filled with magic, mythical creatures, and epic quests. Your ambition is to create the next immersive fantasy saga that captivates readers' imaginations.",
|
||||
"Mystery Novelist": "You are a master of suspense and intrigue, intricately plotting out mysteries with unexpected twists and turns. Your aim is to keep readers on the edge of their seats, eagerly turning pages to unravel the truth.",
|
||||
"Romantic Poet": "You are a romantic at heart, composing verses that capture the essence of love, longing, and human connections. Your dream is to write the next timeless love story that leaves readers swooning.",
|
||||
"Thriller Writer": "You are a thrill-seeker, crafting adrenaline-pumping tales of danger, suspense, and high-stakes action. Your mission is to keep readers hooked from start to finish with heart-pounding thrills and unexpected twists.",
|
||||
"Children's Book Author": "You are a storyteller for the young and young at heart, creating whimsical worlds and lovable characters that inspire imagination and wonder. Your goal is to spark joy and curiosity in young readers with enchanting tales.",
|
||||
"Satirical Humorist": "You are a keen observer of society, using humor and wit to satirize the absurdities of everyday life. Your aim is to entertain and provoke thought, delivering biting social commentary through clever and humorous storytelling.",
|
||||
"Biographical Writer": "You are a chronicler of lives, delving into the stories of real people and events to illuminate the human experience. Your passion is to bring history to life through richly detailed biographies that resonate with readers.",
|
||||
"Dystopian Visionary": "You are a visionary writer, exploring dark and dystopian futures that reflect contemporary fears and anxieties. Your vision is to challenge societal norms and provoke reflection on the path humanity is heading.",
|
||||
"Magical Realism Author": "You are a purveyor of magical realism, blending the ordinary with the extraordinary to create enchanting and thought-provoking tales. Your goal is to blur the lines between reality and fantasy, leaving readers enchanted and introspective."
|
||||
}
|
||||
|
||||
# Story Setting
|
||||
st.subheader("🌍 Story Setting")
|
||||
story_setting = st.text_area(
|
||||
label="**Story Setting** (e.g., medieval kingdom in the past, futuristic city in the future, haunted house in the present):",
|
||||
placeholder="""Enter settings for your story, like Location (e.g., medieval kingdom, futuristic city, haunted house),
|
||||
Time period in which your story is set (e.g: Past, Present, Future)
|
||||
Example: 'A bustling futuristic city with towering skyscrapers and flying cars, set in the year 2150.
|
||||
The city is known for its technological advancements but has a dark underbelly of crime and corruption.'""",
|
||||
help="Describe the main location and time period where the story will unfold in a detailed manner."
|
||||
)
|
||||
|
||||
# Main Characters
|
||||
st.subheader("👥 Main Characters")
|
||||
character_input = st.text_area(
|
||||
label="**Character Information** (Names, Descriptions, Roles)",
|
||||
placeholder="""Example:
|
||||
Character Names: John, Xishan, Amol
|
||||
Character Descriptions: John is a tall, muscular man with a kind heart. Xishan is a clever and resourceful woman. Amol is a mischievous and energetic young boy.
|
||||
Character Roles: John - Hero, Xishan - Sidekick, Amol - Supporting Character""",
|
||||
help="Enter character information as specified in the placeholder."
|
||||
)
|
||||
|
||||
# Plot Elements
|
||||
st.subheader("🗺️ Plot Elements")
|
||||
plot_elements = st.text_area(
|
||||
"**Plot Elements** - (Theme, Key Events & Main Conflict)",
|
||||
placeholder="""Example:
|
||||
Story Theme: Love conquers all, The hero's journey, Good vs. evil.
|
||||
Key Events: The hero meets the villain, The hero faces a challenge, The hero overcomes the conflict.
|
||||
Main Conflict: The hero must save the world from a powerful enemy, The hero must overcome a personal obstacle to achieve their goal.""",
|
||||
help="Enter plot elements as specified in the placeholder."
|
||||
)
|
||||
|
||||
# Tone and Style
|
||||
st.subheader("🎨 Tone and Style")
|
||||
col1, col2, col3 = st.columns(3)
|
||||
with col1:
|
||||
writing_style = st.selectbox(
|
||||
"**Writing Style:**",
|
||||
["🧐 Formal", "😎 Casual", "🎼 Poetic", "😂 Humorous"],
|
||||
help="Choose the writing style that fits your story."
|
||||
)
|
||||
with col2:
|
||||
story_tone = st.selectbox(
|
||||
"**Story Tone:**",
|
||||
["🌑 Dark", "☀️ Uplifting", "⏳ Suspenseful", "🎈 Whimsical"],
|
||||
help="Select the overall tone or mood of the story."
|
||||
)
|
||||
with col3:
|
||||
narrative_pov = st.selectbox(
|
||||
"**Narrative Point of View:**",
|
||||
["👤 First Person", "👥 Third Person Limited", "👁️ Third Person Omniscient"],
|
||||
help="Choose the point of view from which the story is told."
|
||||
)
|
||||
|
||||
# Target Audience
|
||||
st.subheader("👨👩👧👦 Target Audience")
|
||||
col1, col2, col3 = st.columns(3)
|
||||
with col1:
|
||||
audience_age_group = st.selectbox(
|
||||
"**Audience Age Group:**",
|
||||
["🧒 Children", "👨🎓 Young Adults", "🧑🦳 Adults"],
|
||||
help="Choose the intended audience age group."
|
||||
)
|
||||
with col2:
|
||||
content_rating = st.selectbox(
|
||||
"**Content Rating:**",
|
||||
["🟢 G", "🟡 PG", "🔵 PG-13", "🔴 R"],
|
||||
help="Select a content rating for appropriateness."
|
||||
)
|
||||
with col3:
|
||||
ending_preference = st.selectbox(
|
||||
"Story Conclusion:",
|
||||
["😊 Happy", "😢 Tragic", "❓ Cliffhanger", "🔀 Twist"],
|
||||
help="Choose the type of ending you prefer for the story."
|
||||
)
|
||||
|
||||
if st.button('AI, Write a Story..'):
|
||||
if character_input.strip():
|
||||
with st.spinner("Generating Story...💥💥"):
|
||||
story_content = ai_story(persona_descriptions[selected_persona_name],
|
||||
story_setting, character_input, plot_elements, writing_style,
|
||||
story_tone, narrative_pov, audience_age_group, content_rating,
|
||||
ending_preference)
|
||||
if story_content:
|
||||
st.subheader('**🧕 Your Awesome Story:**')
|
||||
st.markdown(story_content)
|
||||
else:
|
||||
st.error("💥 **Failed to generate Story. Please try again!**")
|
||||
else:
|
||||
st.error("Describe the story you have in your mind.. !")
|
||||
@@ -1,259 +0,0 @@
|
||||
# GitHub Blog Generator
|
||||
|
||||
A powerful AI-powered content generation system that automatically creates comprehensive documentation, tutorials, and guides from GitHub repositories. This module transforms GitHub repository data into various types of high-quality technical content.
|
||||
|
||||
## Features
|
||||
|
||||
### 1. Content Generation Types
|
||||
|
||||
The system can generate the following types of content from GitHub repositories:
|
||||
|
||||
- **Getting Started Guides**
|
||||
- Introduction and Overview
|
||||
- Prerequisites and Setup
|
||||
- Installation Instructions
|
||||
- Basic Usage Examples
|
||||
- Common Use Cases
|
||||
- Best Practices
|
||||
- Next Steps and Resources
|
||||
|
||||
- **Technical Documentation**
|
||||
- Architecture Overview
|
||||
- Core Components
|
||||
- Technical Specifications
|
||||
- Integration Points
|
||||
- Performance Considerations
|
||||
- Security Features
|
||||
- API Documentation
|
||||
- Configuration Options
|
||||
- Deployment Guidelines
|
||||
- Troubleshooting Guide
|
||||
|
||||
- **Tutorial Series**
|
||||
- Beginner Tutorials
|
||||
- Basic concepts
|
||||
- Simple examples
|
||||
- Step-by-step instructions
|
||||
- Intermediate Tutorials
|
||||
- Advanced features
|
||||
- Real-world examples
|
||||
- Best practices
|
||||
- Advanced Tutorials
|
||||
- Complex use cases
|
||||
- Performance optimization
|
||||
- Integration patterns
|
||||
|
||||
- **Comparison Analysis**
|
||||
- Feature Comparison
|
||||
- Performance Analysis
|
||||
- Use Case Suitability
|
||||
- Community and Support
|
||||
- Learning Curve
|
||||
- Integration Capabilities
|
||||
- Future Prospects
|
||||
|
||||
- **Case Studies**
|
||||
- Problem Statement
|
||||
- Solution Implementation
|
||||
- Technical Challenges
|
||||
- Results and Benefits
|
||||
- Lessons Learned
|
||||
- Future Improvements
|
||||
|
||||
- **Contribution Guides**
|
||||
- Development Setup
|
||||
- Code Style Guidelines
|
||||
- Testing Requirements
|
||||
- Documentation Standards
|
||||
- Pull Request Process
|
||||
- Review Guidelines
|
||||
- Community Guidelines
|
||||
|
||||
- **Security Guides**
|
||||
- Security Architecture
|
||||
- Authentication & Authorization
|
||||
- Data Protection
|
||||
- Secure Configuration
|
||||
- Vulnerability Management
|
||||
- Incident Response
|
||||
- Compliance Requirements
|
||||
|
||||
- **Performance Guides**
|
||||
- Performance Metrics
|
||||
- Optimization Techniques
|
||||
- Benchmarking Guidelines
|
||||
- Resource Management
|
||||
- Scaling Strategies
|
||||
- Monitoring Setup
|
||||
- Troubleshooting
|
||||
|
||||
### 2. GitHub Content Scraping
|
||||
|
||||
The module includes a sophisticated GitHub content scraper with the following capabilities:
|
||||
|
||||
- **Rate Limiting**
|
||||
- Configurable API call limits
|
||||
- Automatic request throttling
|
||||
- Concurrent request management
|
||||
|
||||
- **Caching System**
|
||||
- Configurable cache duration (TTL)
|
||||
- Automatic cache invalidation
|
||||
- Efficient storage of scraped content
|
||||
|
||||
- **Content Extraction**
|
||||
- Repository metadata
|
||||
- README content
|
||||
- File contents
|
||||
- Repository topics
|
||||
- Contributor information
|
||||
- License information
|
||||
|
||||
### 3. Content Enhancement
|
||||
|
||||
- **Online Research Integration**
|
||||
- Automatic topic research
|
||||
- Related content discovery
|
||||
- Industry trend analysis
|
||||
|
||||
- **FAQ Generation**
|
||||
- Automatic FAQ creation
|
||||
- Common question identification
|
||||
- Comprehensive answers
|
||||
|
||||
- **Metadata Generation**
|
||||
- SEO-optimized titles
|
||||
- Meta descriptions
|
||||
- Tags and categories
|
||||
- Content structuring
|
||||
|
||||
## Usage Examples
|
||||
|
||||
### Basic Usage
|
||||
|
||||
```python
|
||||
from lib.ai_writers.github_blogs import GitHubBlogGenerator
|
||||
|
||||
# Initialize the generator
|
||||
generator = GitHubBlogGenerator()
|
||||
|
||||
# Generate content for a GitHub repository
|
||||
content = await generator.generate_content(
|
||||
github_url="https://github.com/owner/repo",
|
||||
content_types=["getting_started", "technical_docs", "tutorials"]
|
||||
)
|
||||
|
||||
# Save the generated content
|
||||
generator.save_content(content, "my_repository")
|
||||
```
|
||||
|
||||
### Advanced Usage
|
||||
|
||||
```python
|
||||
from lib.ai_writers.github_blogs import GitHubBlogGenerator
|
||||
|
||||
# Initialize with custom settings
|
||||
generator = GitHubBlogGenerator(
|
||||
cache_dir=".custom_cache",
|
||||
ttl_hours=48
|
||||
)
|
||||
|
||||
# Generate all content types
|
||||
content_types = [
|
||||
"getting_started",
|
||||
"technical_docs",
|
||||
"tutorials",
|
||||
"comparison",
|
||||
"case_studies",
|
||||
"contribution",
|
||||
"security",
|
||||
"performance"
|
||||
]
|
||||
|
||||
# Generate content for multiple repositories
|
||||
urls = [
|
||||
"https://github.com/owner/repo1",
|
||||
"https://github.com/owner/repo2"
|
||||
]
|
||||
|
||||
for url in urls:
|
||||
content = await generator.generate_content(url, content_types)
|
||||
generator.save_content(content, url.split("/")[-1])
|
||||
```
|
||||
|
||||
## Configuration Options
|
||||
|
||||
### GitHubBlogGenerator
|
||||
|
||||
- `cache_dir` (str): Directory for caching scraped content (default: ".github_cache")
|
||||
- `ttl_hours` (int): Time-to-live for cached content in hours (default: 24)
|
||||
|
||||
### Content Generation
|
||||
|
||||
- `gpt_provider` (str): Choice of AI provider ("gemini" or "openai")
|
||||
- `content_types` (List[str]): Types of content to generate
|
||||
- `github_url` (str): URL of the GitHub repository
|
||||
|
||||
## Output Format
|
||||
|
||||
All generated content is saved in Markdown format with the following structure:
|
||||
|
||||
```markdown
|
||||
# [Title]
|
||||
|
||||
[Generated content based on content type]
|
||||
|
||||
## Metadata
|
||||
- Title: [SEO-optimized title]
|
||||
- Description: [Meta description]
|
||||
- Tags: [Generated tags]
|
||||
- Categories: [Generated categories]
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Rate Limiting**
|
||||
- Configure appropriate rate limits based on your GitHub API quota
|
||||
- Use caching to minimize API calls
|
||||
- Implement proper error handling for rate limit exceeded scenarios
|
||||
|
||||
2. **Content Generation**
|
||||
- Start with basic content types before generating advanced content
|
||||
- Review generated content for accuracy and completeness
|
||||
- Customize prompts for specific repository types
|
||||
|
||||
3. **Caching**
|
||||
- Set appropriate TTL based on repository update frequency
|
||||
- Clear cache when repository content changes significantly
|
||||
- Monitor cache size and performance
|
||||
|
||||
4. **Error Handling**
|
||||
- Implement proper error handling for API failures
|
||||
- Log errors for debugging
|
||||
- Provide fallback mechanisms for failed content generation
|
||||
|
||||
## Dependencies
|
||||
|
||||
- Python 3.8+
|
||||
- aiohttp
|
||||
- beautifulsoup4
|
||||
- loguru
|
||||
- pydantic
|
||||
- requests
|
||||
- pandas
|
||||
|
||||
## Contributing
|
||||
|
||||
1. Fork the repository
|
||||
2. Create a feature branch
|
||||
3. Commit your changes
|
||||
4. Push to the branch
|
||||
5. Create a Pull Request
|
||||
|
||||
## License
|
||||
|
||||
[Your License Here]
|
||||
|
||||
## Support
|
||||
|
||||
For support, please [create an issue](https://github.com/your-repo/issues) or contact the maintainers.
|
||||
@@ -1,254 +0,0 @@
|
||||
"""
|
||||
Enhanced GitHub Content Generator
|
||||
|
||||
This module provides various content generation capabilities from GitHub repository data,
|
||||
including getting started guides, technical documentation, tutorials, and more.
|
||||
"""
|
||||
|
||||
import sys
|
||||
from typing import Dict, List, Optional
|
||||
from loguru import logger
|
||||
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
|
||||
logger.remove()
|
||||
logger.add(sys.stdout,
|
||||
colorize=True,
|
||||
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}")
|
||||
|
||||
def generate_technical_documentation(repo_data: Dict, gpt_provider: str = "gemini") -> str:
|
||||
"""Generate comprehensive technical documentation from repository data."""
|
||||
prompt = f"""As an expert technical writer, create detailed technical documentation for the following GitHub repository:
|
||||
|
||||
Repository Data:
|
||||
{repo_data}
|
||||
|
||||
Please create a comprehensive technical documentation that includes:
|
||||
1. Architecture Overview
|
||||
2. Core Components
|
||||
3. Technical Specifications
|
||||
4. Integration Points
|
||||
5. Performance Considerations
|
||||
6. Security Features
|
||||
7. API Documentation (if applicable)
|
||||
8. Configuration Options
|
||||
9. Deployment Guidelines
|
||||
10. Troubleshooting Guide
|
||||
|
||||
Format the documentation in markdown with appropriate headers, code blocks, and diagrams.
|
||||
Include real-world examples and best practices.
|
||||
"""
|
||||
return _get_llm_response(prompt, gpt_provider)
|
||||
|
||||
def generate_getting_started_guide(repo_data: Dict, gpt_provider: str = "gemini") -> str:
|
||||
"""Generate a beginner-friendly getting started guide."""
|
||||
prompt = f"""As an expert programmer and teacher, create a comprehensive getting started guide for the following GitHub repository:
|
||||
|
||||
Repository Data:
|
||||
{repo_data}
|
||||
|
||||
Create a step-by-step guide that includes:
|
||||
1. Introduction and Overview
|
||||
2. Prerequisites and Setup
|
||||
3. Installation Instructions
|
||||
4. Basic Usage Examples
|
||||
5. Common Use Cases
|
||||
6. Best Practices
|
||||
7. Next Steps and Resources
|
||||
|
||||
Make the guide:
|
||||
- Beginner-friendly with clear explanations
|
||||
- Include practical examples with code snippets
|
||||
- Add emojis for better readability
|
||||
- Include troubleshooting tips
|
||||
- Provide links to additional resources
|
||||
"""
|
||||
return _get_llm_response(prompt, gpt_provider)
|
||||
|
||||
def generate_tutorial_series(repo_data: Dict, gpt_provider: str = "gemini") -> str:
|
||||
"""Generate a series of tutorials for different skill levels."""
|
||||
prompt = f"""As an expert educator, create a series of tutorials for the following GitHub repository:
|
||||
|
||||
Repository Data:
|
||||
{repo_data}
|
||||
|
||||
Create a structured tutorial series that includes:
|
||||
1. Beginner Tutorial
|
||||
- Basic concepts
|
||||
- Simple examples
|
||||
- Step-by-step instructions
|
||||
|
||||
2. Intermediate Tutorial
|
||||
- Advanced features
|
||||
- Real-world examples
|
||||
- Best practices
|
||||
|
||||
3. Advanced Tutorial
|
||||
- Complex use cases
|
||||
- Performance optimization
|
||||
- Integration patterns
|
||||
|
||||
Each tutorial should:
|
||||
- Be self-contained
|
||||
- Include practical examples
|
||||
- Have clear learning objectives
|
||||
- Include exercises and challenges
|
||||
"""
|
||||
return _get_llm_response(prompt, gpt_provider)
|
||||
|
||||
def generate_comparison_analysis(repo_data: Dict, gpt_provider: str = "gemini") -> str:
|
||||
"""Generate a comparison analysis with similar tools/frameworks."""
|
||||
prompt = f"""As a technical analyst, create a comprehensive comparison analysis for the following GitHub repository:
|
||||
|
||||
Repository Data:
|
||||
{repo_data}
|
||||
|
||||
Create a detailed comparison that includes:
|
||||
1. Feature Comparison
|
||||
2. Performance Analysis
|
||||
3. Use Case Suitability
|
||||
4. Community and Support
|
||||
5. Learning Curve
|
||||
6. Integration Capabilities
|
||||
7. Future Prospects
|
||||
|
||||
Include:
|
||||
- Pros and Cons
|
||||
- Real-world use cases
|
||||
- Industry adoption
|
||||
- Community feedback
|
||||
- Future roadmap
|
||||
"""
|
||||
return _get_llm_response(prompt, gpt_provider)
|
||||
|
||||
def generate_case_studies(repo_data: Dict, gpt_provider: str = "gemini") -> str:
|
||||
"""Generate real-world case studies and success stories."""
|
||||
prompt = f"""As a technical writer, create compelling case studies for the following GitHub repository:
|
||||
|
||||
Repository Data:
|
||||
{repo_data}
|
||||
|
||||
Create detailed case studies that include:
|
||||
1. Problem Statement
|
||||
2. Solution Implementation
|
||||
3. Technical Challenges
|
||||
4. Results and Benefits
|
||||
5. Lessons Learned
|
||||
6. Future Improvements
|
||||
|
||||
Make the case studies:
|
||||
- Based on real-world scenarios
|
||||
- Include technical details
|
||||
- Show measurable results
|
||||
- Provide actionable insights
|
||||
"""
|
||||
return _get_llm_response(prompt, gpt_provider)
|
||||
|
||||
def generate_contribution_guide(repo_data: Dict, gpt_provider: str = "gemini") -> str:
|
||||
"""Generate a comprehensive contribution guide."""
|
||||
prompt = f"""As an open-source maintainer, create a detailed contribution guide for the following GitHub repository:
|
||||
|
||||
Repository Data:
|
||||
{repo_data}
|
||||
|
||||
Create a contribution guide that includes:
|
||||
1. Development Setup
|
||||
2. Code Style Guidelines
|
||||
3. Testing Requirements
|
||||
4. Documentation Standards
|
||||
5. Pull Request Process
|
||||
6. Review Guidelines
|
||||
7. Community Guidelines
|
||||
|
||||
Make the guide:
|
||||
- Clear and concise
|
||||
- Include examples
|
||||
- Cover all contribution types
|
||||
- Provide templates
|
||||
"""
|
||||
return _get_llm_response(prompt, gpt_provider)
|
||||
|
||||
def generate_security_guide(repo_data: Dict, gpt_provider: str = "gemini") -> str:
|
||||
"""Generate a security best practices guide."""
|
||||
prompt = f"""As a security expert, create a comprehensive security guide for the following GitHub repository:
|
||||
|
||||
Repository Data:
|
||||
{repo_data}
|
||||
|
||||
Create a security guide that includes:
|
||||
1. Security Architecture
|
||||
2. Authentication & Authorization
|
||||
3. Data Protection
|
||||
4. Secure Configuration
|
||||
5. Vulnerability Management
|
||||
6. Incident Response
|
||||
7. Compliance Requirements
|
||||
|
||||
Make the guide:
|
||||
- Practical and actionable
|
||||
- Include security checklists
|
||||
- Provide code examples
|
||||
- Cover common vulnerabilities
|
||||
"""
|
||||
return _get_llm_response(prompt, gpt_provider)
|
||||
|
||||
def generate_performance_guide(repo_data: Dict, gpt_provider: str = "gemini") -> str:
|
||||
"""Generate a performance optimization guide."""
|
||||
prompt = f"""As a performance optimization expert, create a detailed performance guide for the following GitHub repository:
|
||||
|
||||
Repository Data:
|
||||
{repo_data}
|
||||
|
||||
Create a performance guide that includes:
|
||||
1. Performance Metrics
|
||||
2. Optimization Techniques
|
||||
3. Benchmarking Guidelines
|
||||
4. Resource Management
|
||||
5. Scaling Strategies
|
||||
6. Monitoring Setup
|
||||
7. Troubleshooting
|
||||
|
||||
Make the guide:
|
||||
- Data-driven
|
||||
- Include benchmarks
|
||||
- Provide optimization tips
|
||||
- Cover different scales
|
||||
"""
|
||||
return _get_llm_response(prompt, gpt_provider)
|
||||
|
||||
def _get_llm_response(prompt: str, gpt_provider: str) -> str:
|
||||
"""Get response from the specified LLM provider."""
|
||||
system_prompt = """You are an expert technical writer and GitHub repository analyst with deep expertise in software development, documentation, and technical communication.
|
||||
|
||||
Your role is to create high-quality, accurate, and engaging content based on GitHub repository data. You should:
|
||||
|
||||
1. **Technical Accuracy**
|
||||
- Ensure all technical information is precise and up-to-date
|
||||
- Verify code examples and configurations
|
||||
- Cross-reference documentation and source code
|
||||
- Maintain consistency with repository standards
|
||||
|
||||
2. **Content Structure**
|
||||
- Use clear hierarchical organization
|
||||
- Include appropriate code blocks and examples
|
||||
- Add relevant diagrams and visual aids
|
||||
- Break complex topics into digestible sections
|
||||
|
||||
3. **Writing Style**
|
||||
- Maintain a professional yet approachable tone
|
||||
- Use active voice and clear language
|
||||
- Include practical examples and use cases
|
||||
- Add relevant emojis for better readability
|
||||
|
||||
4. **Best Practices**
|
||||
- Follow industry-standard documentation practices
|
||||
- Include troubleshooting sections
|
||||
- Add performance considerations
|
||||
- Address security implications
|
||||
"""
|
||||
try:
|
||||
|
||||
llm_response = llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to get response from {gpt_provider}: {err}")
|
||||
raise
|
||||
@@ -1,157 +0,0 @@
|
||||
"""
|
||||
Enhanced GitHub Blog Generator
|
||||
|
||||
This module provides comprehensive content generation from GitHub repositories,
|
||||
including technical documentation, tutorials, case studies, and more.
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import datetime
|
||||
import json
|
||||
from typing import Dict, List, Optional
|
||||
from pathlib import Path
|
||||
|
||||
from loguru import logger
|
||||
logger.remove()
|
||||
logger.add(sys.stdout,
|
||||
colorize=True,
|
||||
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}")
|
||||
|
||||
from .scrape_github_readme import GitHubScraper, GitHubContent
|
||||
from .scrape_github_readme import get_gh_details_vision, get_readme_content
|
||||
from .scrape_github_readme import research_github_topics, check_if_already_written
|
||||
from .github_getting_started import (
|
||||
generate_technical_documentation,
|
||||
generate_getting_started_guide,
|
||||
generate_tutorial_series,
|
||||
generate_comparison_analysis,
|
||||
generate_case_studies,
|
||||
generate_contribution_guide,
|
||||
generate_security_guide,
|
||||
generate_performance_guide
|
||||
)
|
||||
|
||||
|
||||
class GitHubBlogGenerator:
|
||||
"""Generator for various types of GitHub-related content."""
|
||||
|
||||
def __init__(self, cache_dir: str = ".github_cache", ttl_hours: int = 24):
|
||||
"""Initialize the blog generator."""
|
||||
self.cache_dir = Path(cache_dir)
|
||||
self.scraper = GitHubScraper(cache_dir, ttl_hours)
|
||||
self.output_dir = Path("generated_content")
|
||||
self.output_dir.mkdir(exist_ok=True)
|
||||
|
||||
async def generate_content(self, github_url: str, content_types: List[str] = None) -> Dict[str, str]:
|
||||
"""Generate various types of content from a GitHub repository."""
|
||||
if content_types is None:
|
||||
content_types = ["getting_started", "technical_docs", "tutorials"]
|
||||
|
||||
try:
|
||||
# Scrape GitHub content
|
||||
repo_content = await self.scraper.scrape_github_content(github_url)
|
||||
|
||||
# Generate different types of content
|
||||
generated_content = {}
|
||||
|
||||
for content_type in content_types:
|
||||
if content_type == "getting_started":
|
||||
content = generate_getting_started_guide(repo_content.dict())
|
||||
elif content_type == "technical_docs":
|
||||
content = generate_technical_documentation(repo_content.dict())
|
||||
elif content_type == "tutorials":
|
||||
content = generate_tutorial_series(repo_content.dict())
|
||||
elif content_type == "comparison":
|
||||
content = generate_comparison_analysis(repo_content.dict())
|
||||
elif content_type == "case_studies":
|
||||
content = generate_case_studies(repo_content.dict())
|
||||
elif content_type == "contribution":
|
||||
content = generate_contribution_guide(repo_content.dict())
|
||||
elif content_type == "security":
|
||||
content = generate_security_guide(repo_content.dict())
|
||||
elif content_type == "performance":
|
||||
content = generate_performance_guide(repo_content.dict())
|
||||
else:
|
||||
logger.warning(f"Unknown content type: {content_type}")
|
||||
continue
|
||||
|
||||
generated_content[content_type] = content
|
||||
|
||||
# Generate FAQs from online research
|
||||
try:
|
||||
research_report = do_online_research(repo_content.title, "gemini", github_url)
|
||||
faqs = generate_blog_faq(research_report, "gemini")
|
||||
generated_content["faqs"] = faqs
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to generate FAQs: {err}")
|
||||
|
||||
return generated_content
|
||||
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to generate content: {err}")
|
||||
raise
|
||||
|
||||
def save_content(self, content: Dict[str, str], base_filename: str):
|
||||
"""Save generated content to files."""
|
||||
try:
|
||||
for content_type, content_text in content.items():
|
||||
# Generate metadata for each content type
|
||||
title, meta_desc, tags, categories = blog_metadata(content_text, "gemini")
|
||||
|
||||
# Create filename with content type
|
||||
filename = f"{base_filename}_{content_type}.md"
|
||||
|
||||
# Save content to file
|
||||
save_blog_to_file(
|
||||
content_text,
|
||||
title,
|
||||
meta_desc,
|
||||
tags,
|
||||
categories,
|
||||
None # No image path for now
|
||||
)
|
||||
|
||||
logger.info(f"Saved {content_type} content to {filename}")
|
||||
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to save content: {err}")
|
||||
raise
|
||||
|
||||
async def main():
|
||||
"""Example usage of the GitHub blog generator."""
|
||||
generator = GitHubBlogGenerator()
|
||||
|
||||
# Example GitHub URLs
|
||||
urls = [
|
||||
"https://github.com/owner/repo",
|
||||
"https://github.com/owner/another-repo"
|
||||
]
|
||||
|
||||
content_types = [
|
||||
"getting_started",
|
||||
"technical_docs",
|
||||
"tutorials",
|
||||
"comparison",
|
||||
"case_studies",
|
||||
"contribution",
|
||||
"security",
|
||||
"performance"
|
||||
]
|
||||
|
||||
for url in urls:
|
||||
try:
|
||||
# Generate content
|
||||
content = await generator.generate_content(url, content_types)
|
||||
|
||||
# Create base filename from URL
|
||||
base_filename = url.split("/")[-1]
|
||||
|
||||
# Save content
|
||||
generator.save_content(content, base_filename)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing {url}: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -1,427 +0,0 @@
|
||||
"""
|
||||
Enhanced GitHub Content Scraper with Rate Limiting and Caching
|
||||
|
||||
This module provides functionality to scrape GitHub repositories, READMEs, and code files
|
||||
for content marketing purposes. It includes async support, rate limiting, caching,
|
||||
and comprehensive metadata collection.
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import asyncio
|
||||
import aiohttp
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Dict, List, Optional, Union
|
||||
from urllib.parse import urljoin, urlparse
|
||||
import pandas as pd
|
||||
from bs4 import BeautifulSoup
|
||||
from loguru import logger
|
||||
import requests
|
||||
from pydantic import BaseModel, Field
|
||||
import time
|
||||
import pickle
|
||||
from pathlib import Path
|
||||
|
||||
# Configure logging
|
||||
logger.remove()
|
||||
logger.add(sys.stdout,
|
||||
colorize=True,
|
||||
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}")
|
||||
|
||||
class RateLimiter:
|
||||
"""Rate limiter for GitHub API requests."""
|
||||
|
||||
def __init__(self, calls_per_minute: int = 30):
|
||||
self.calls_per_minute = calls_per_minute
|
||||
self.interval = 60 / calls_per_minute # seconds between calls
|
||||
self.last_call_time = 0
|
||||
self.lock = asyncio.Lock()
|
||||
|
||||
async def acquire(self):
|
||||
"""Acquire rate limit token."""
|
||||
async with self.lock:
|
||||
current_time = time.time()
|
||||
time_since_last_call = current_time - self.last_call_time
|
||||
|
||||
if time_since_last_call < self.interval:
|
||||
await asyncio.sleep(self.interval - time_since_last_call)
|
||||
|
||||
self.last_call_time = time.time()
|
||||
|
||||
class Cache:
|
||||
"""Cache for GitHub content."""
|
||||
|
||||
def __init__(self, cache_dir: str = ".github_cache", ttl_hours: int = 24):
|
||||
self.cache_dir = Path(cache_dir)
|
||||
self.ttl = timedelta(hours=ttl_hours)
|
||||
self.cache_dir.mkdir(exist_ok=True)
|
||||
|
||||
def _get_cache_path(self, key: str) -> Path:
|
||||
"""Get cache file path for a key."""
|
||||
return self.cache_dir / f"{hash(key)}.cache"
|
||||
|
||||
def get(self, key: str) -> Optional[Dict]:
|
||||
"""Get cached value for key."""
|
||||
cache_path = self._get_cache_path(key)
|
||||
|
||||
if not cache_path.exists():
|
||||
return None
|
||||
|
||||
try:
|
||||
with open(cache_path, 'rb') as f:
|
||||
data = pickle.load(f)
|
||||
if datetime.now() - data['timestamp'] > self.ttl:
|
||||
cache_path.unlink()
|
||||
return None
|
||||
return data['value']
|
||||
except Exception as e:
|
||||
logger.warning(f"Cache read error for {key}: {e}")
|
||||
return None
|
||||
|
||||
def set(self, key: str, value: Dict):
|
||||
"""Set cache value for key."""
|
||||
cache_path = self._get_cache_path(key)
|
||||
|
||||
try:
|
||||
with open(cache_path, 'wb') as f:
|
||||
pickle.dump({
|
||||
'timestamp': datetime.now(),
|
||||
'value': value
|
||||
}, f)
|
||||
except Exception as e:
|
||||
logger.warning(f"Cache write error for {key}: {e}")
|
||||
|
||||
class GitHubContent(BaseModel):
|
||||
"""Model for GitHub content analysis."""
|
||||
title: str = Field("", description="Title of the content")
|
||||
description: str = Field("", description="Description of the content")
|
||||
content: str = Field("", description="Main content")
|
||||
language: str = Field("", description="Programming language")
|
||||
stars: int = Field(0, description="Number of stars")
|
||||
forks: int = Field(0, description="Number of forks")
|
||||
watchers: int = Field(0, description="Number of watchers")
|
||||
last_updated: str = Field("", description="Last update date")
|
||||
topics: List[str] = Field([], description="Repository topics")
|
||||
contributors: List[str] = Field([], description="Contributor usernames")
|
||||
readme_url: str = Field("", description="URL of the README")
|
||||
raw_content_url: str = Field("", description="URL for raw content")
|
||||
license: str = Field("", description="Repository license")
|
||||
dependencies: List[str] = Field([], description="Project dependencies")
|
||||
metadata: Dict = Field({}, description="Additional metadata")
|
||||
|
||||
class GitHubScraper:
|
||||
"""Service for scraping GitHub content with rate limiting and caching."""
|
||||
|
||||
def __init__(self, cache_dir: str = ".github_cache", ttl_hours: int = 24, calls_per_minute: int = 30):
|
||||
"""Initialize the scraper service."""
|
||||
self.session = None
|
||||
self.headers = {
|
||||
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
|
||||
'Accept': 'application/vnd.github.v3+json'
|
||||
}
|
||||
self.rate_limiter = RateLimiter(calls_per_minute)
|
||||
self.cache = Cache(cache_dir, ttl_hours)
|
||||
|
||||
async def __aenter__(self):
|
||||
"""Create aiohttp session when entering context."""
|
||||
self.session = aiohttp.ClientSession(headers=self.headers)
|
||||
return self
|
||||
|
||||
async def __aexit__(self, exc_type, exc_val, exc_tb):
|
||||
"""Close aiohttp session when exiting context."""
|
||||
if self.session:
|
||||
await self.session.close()
|
||||
|
||||
async def fetch_url(self, url: str, use_cache: bool = True) -> str:
|
||||
"""Fetch URL content asynchronously with rate limiting and caching."""
|
||||
if use_cache:
|
||||
cached_content = self.cache.get(url)
|
||||
if cached_content:
|
||||
logger.debug(f"Cache hit for {url}")
|
||||
return cached_content
|
||||
|
||||
await self.rate_limiter.acquire()
|
||||
|
||||
try:
|
||||
async with self.session.get(url) as response:
|
||||
if response.status == 200:
|
||||
content = await response.text()
|
||||
if use_cache:
|
||||
self.cache.set(url, content)
|
||||
return content
|
||||
else:
|
||||
error_msg = f"Failed to fetch URL: Status code {response.status}"
|
||||
logger.error(error_msg)
|
||||
raise Exception(error_msg)
|
||||
except Exception as e:
|
||||
logger.error(f"Error fetching URL {url}: {e}")
|
||||
raise
|
||||
|
||||
def parse_github_url(self, url: str) -> Dict[str, str]:
|
||||
"""Parse GitHub URL to extract repository information."""
|
||||
parsed = urlparse(url)
|
||||
path_parts = parsed.path.strip('/').split('/')
|
||||
|
||||
if len(path_parts) < 2:
|
||||
raise ValueError("Invalid GitHub URL format")
|
||||
|
||||
return {
|
||||
'owner': path_parts[0],
|
||||
'repo': path_parts[1],
|
||||
'branch': path_parts[3] if len(path_parts) > 3 else 'main',
|
||||
'path': '/'.join(path_parts[4:]) if len(path_parts) > 4 else ''
|
||||
}
|
||||
|
||||
async def get_repo_metadata(self, owner: str, repo: str) -> Dict:
|
||||
"""Get repository metadata from GitHub API with caching."""
|
||||
cache_key = f"metadata_{owner}_{repo}"
|
||||
cached_metadata = self.cache.get(cache_key)
|
||||
if cached_metadata:
|
||||
return cached_metadata
|
||||
|
||||
await self.rate_limiter.acquire()
|
||||
|
||||
api_url = f"https://api.github.com/repos/{owner}/{repo}"
|
||||
try:
|
||||
async with self.session.get(api_url) as response:
|
||||
if response.status == 200:
|
||||
metadata = await response.json()
|
||||
self.cache.set(cache_key, metadata)
|
||||
return metadata
|
||||
else:
|
||||
logger.error(f"Failed to fetch repo metadata: {response.status}")
|
||||
return {}
|
||||
except Exception as e:
|
||||
logger.error(f"Error fetching repo metadata: {e}")
|
||||
return {}
|
||||
|
||||
async def get_readme_content(self, owner: str, repo: str, branch: str = 'main') -> Dict:
|
||||
"""Get README content from GitHub with caching."""
|
||||
cache_key = f"readme_{owner}_{repo}_{branch}"
|
||||
cached_content = self.cache.get(cache_key)
|
||||
if cached_content:
|
||||
return cached_content
|
||||
|
||||
try:
|
||||
# Try to get README from API first
|
||||
await self.rate_limiter.acquire()
|
||||
api_url = f"https://api.github.com/repos/{owner}/{repo}/readme"
|
||||
async with self.session.get(api_url) as response:
|
||||
if response.status == 200:
|
||||
readme_data = await response.json()
|
||||
content = {
|
||||
'content': readme_data.get('content', ''),
|
||||
'encoding': readme_data.get('encoding', 'base64'),
|
||||
'url': readme_data.get('html_url', '')
|
||||
}
|
||||
self.cache.set(cache_key, content)
|
||||
return content
|
||||
|
||||
# Fallback to scraping if API fails
|
||||
readme_url = f"https://github.com/{owner}/{repo}/blob/{branch}/README.md"
|
||||
html_content = await self.fetch_url(readme_url, use_cache=True)
|
||||
soup = BeautifulSoup(html_content, 'html.parser')
|
||||
|
||||
# Find the README content
|
||||
readme_content = soup.find('div', {'class': 'markdown-body'})
|
||||
if readme_content:
|
||||
content = {
|
||||
'content': readme_content.get_text(),
|
||||
'encoding': 'text',
|
||||
'url': readme_url
|
||||
}
|
||||
self.cache.set(cache_key, content)
|
||||
return content
|
||||
|
||||
return {}
|
||||
except Exception as e:
|
||||
logger.error(f"Error fetching README: {e}")
|
||||
return {}
|
||||
|
||||
async def get_file_content(self, owner: str, repo: str, path: str, branch: str = 'main') -> Dict:
|
||||
"""Get content of a specific file from GitHub with caching."""
|
||||
cache_key = f"file_{owner}_{repo}_{path}_{branch}"
|
||||
cached_content = self.cache.get(cache_key)
|
||||
if cached_content:
|
||||
return cached_content
|
||||
|
||||
try:
|
||||
# Try to get file content from API first
|
||||
await self.rate_limiter.acquire()
|
||||
api_url = f"https://api.github.com/repos/{owner}/{repo}/contents/{path}?ref={branch}"
|
||||
async with self.session.get(api_url) as response:
|
||||
if response.status == 200:
|
||||
file_data = await response.json()
|
||||
content = {
|
||||
'content': file_data.get('content', ''),
|
||||
'encoding': file_data.get('encoding', 'base64'),
|
||||
'url': file_data.get('html_url', '')
|
||||
}
|
||||
self.cache.set(cache_key, content)
|
||||
return content
|
||||
|
||||
# Fallback to scraping if API fails
|
||||
file_url = f"https://github.com/{owner}/{repo}/blob/{branch}/{path}"
|
||||
html_content = await self.fetch_url(file_url, use_cache=True)
|
||||
soup = BeautifulSoup(html_content, 'html.parser')
|
||||
|
||||
# Find the file content
|
||||
file_content = soup.find('div', {'class': 'file-content'})
|
||||
if file_content:
|
||||
content = {
|
||||
'content': file_content.get_text(),
|
||||
'encoding': 'text',
|
||||
'url': file_url
|
||||
}
|
||||
self.cache.set(cache_key, content)
|
||||
return content
|
||||
|
||||
return {}
|
||||
except Exception as e:
|
||||
logger.error(f"Error fetching file content: {e}")
|
||||
return {}
|
||||
|
||||
async def get_repo_topics(self, owner: str, repo: str) -> List[str]:
|
||||
"""Get repository topics with caching."""
|
||||
cache_key = f"topics_{owner}_{repo}"
|
||||
cached_topics = self.cache.get(cache_key)
|
||||
if cached_topics:
|
||||
return cached_topics
|
||||
|
||||
try:
|
||||
await self.rate_limiter.acquire()
|
||||
api_url = f"https://api.github.com/repos/{owner}/{repo}/topics"
|
||||
async with self.session.get(api_url, headers={'Accept': 'application/vnd.github.mercy-preview+json'}) as response:
|
||||
if response.status == 200:
|
||||
data = await response.json()
|
||||
topics = data.get('names', [])
|
||||
self.cache.set(cache_key, topics)
|
||||
return topics
|
||||
return []
|
||||
except Exception as e:
|
||||
logger.error(f"Error fetching topics: {e}")
|
||||
return []
|
||||
|
||||
async def get_contributors(self, owner: str, repo: str) -> List[str]:
|
||||
"""Get repository contributors with caching."""
|
||||
cache_key = f"contributors_{owner}_{repo}"
|
||||
cached_contributors = self.cache.get(cache_key)
|
||||
if cached_contributors:
|
||||
return cached_contributors
|
||||
|
||||
try:
|
||||
await self.rate_limiter.acquire()
|
||||
api_url = f"https://api.github.com/repos/{owner}/{repo}/contributors"
|
||||
async with self.session.get(api_url) as response:
|
||||
if response.status == 200:
|
||||
contributors = await response.json()
|
||||
contributor_list = [contributor['login'] for contributor in contributors]
|
||||
self.cache.set(cache_key, contributor_list)
|
||||
return contributor_list
|
||||
return []
|
||||
except Exception as e:
|
||||
logger.error(f"Error fetching contributors: {e}")
|
||||
return []
|
||||
|
||||
async def scrape_github_content(self, url: str) -> GitHubContent:
|
||||
"""Main function to scrape GitHub content with caching."""
|
||||
cache_key = f"content_{url}"
|
||||
cached_content = self.cache.get(cache_key)
|
||||
if cached_content:
|
||||
return GitHubContent(**cached_content)
|
||||
|
||||
try:
|
||||
# Parse the GitHub URL
|
||||
repo_info = self.parse_github_url(url)
|
||||
|
||||
# Get repository metadata
|
||||
metadata = await self.get_repo_metadata(repo_info['owner'], repo_info['repo'])
|
||||
|
||||
# Get content based on URL type
|
||||
if not repo_info['path'] or repo_info['path'].lower() == 'readme.md':
|
||||
content_data = await self.get_readme_content(
|
||||
repo_info['owner'],
|
||||
repo_info['repo'],
|
||||
repo_info['branch']
|
||||
)
|
||||
else:
|
||||
content_data = await self.get_file_content(
|
||||
repo_info['owner'],
|
||||
repo_info['repo'],
|
||||
repo_info['path'],
|
||||
repo_info['branch']
|
||||
)
|
||||
|
||||
# Get additional metadata
|
||||
topics = await self.get_repo_topics(repo_info['owner'], repo_info['repo'])
|
||||
contributors = await self.get_contributors(repo_info['owner'], repo_info['repo'])
|
||||
|
||||
# Create GitHubContent object
|
||||
content = GitHubContent(
|
||||
title=metadata.get('name', ''),
|
||||
description=metadata.get('description', ''),
|
||||
content=content_data.get('content', ''),
|
||||
language=metadata.get('language', ''),
|
||||
stars=metadata.get('stargazers_count', 0),
|
||||
forks=metadata.get('forks_count', 0),
|
||||
watchers=metadata.get('watchers_count', 0),
|
||||
last_updated=metadata.get('updated_at', ''),
|
||||
topics=topics,
|
||||
contributors=contributors,
|
||||
readme_url=content_data.get('url', ''),
|
||||
raw_content_url=metadata.get('html_url', ''),
|
||||
license=metadata.get('license', {}).get('name', ''),
|
||||
metadata={
|
||||
'size': metadata.get('size', 0),
|
||||
'open_issues': metadata.get('open_issues_count', 0),
|
||||
'default_branch': metadata.get('default_branch', 'main'),
|
||||
'created_at': metadata.get('created_at', ''),
|
||||
'pushed_at': metadata.get('pushed_at', '')
|
||||
}
|
||||
)
|
||||
|
||||
# Cache the complete content
|
||||
self.cache.set(cache_key, content.dict())
|
||||
|
||||
return content
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error scraping GitHub content: {e}")
|
||||
raise
|
||||
|
||||
async def main():
|
||||
"""Example usage of the GitHub scraper with rate limiting and caching."""
|
||||
scraper = GitHubScraper(
|
||||
cache_dir=".github_cache",
|
||||
ttl_hours=24,
|
||||
calls_per_minute=30
|
||||
)
|
||||
|
||||
async with scraper:
|
||||
# Example URLs
|
||||
urls = [
|
||||
"https://github.com/owner/repo",
|
||||
"https://github.com/owner/repo/blob/main/README.md",
|
||||
"https://github.com/owner/repo/blob/main/src/main.py"
|
||||
]
|
||||
|
||||
for url in urls:
|
||||
try:
|
||||
content = await scraper.scrape_github_content(url)
|
||||
print(f"Scraped content from {url}:")
|
||||
print(json.dumps(content.dict(), indent=2))
|
||||
except Exception as e:
|
||||
print(f"Error scraping {url}: {e}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -1,50 +0,0 @@
|
||||
import sys
|
||||
import os
|
||||
import json
|
||||
|
||||
from ..gpt_providers.text_generation.openai_text_gen import openai_text_generation
|
||||
from ..gpt_providers.text_generation.gemini_pro_text import gemini_text_generation
|
||||
|
||||
from loguru import logger
|
||||
logger.remove()
|
||||
logger.add(sys.stdout,
|
||||
colorize=True,
|
||||
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
|
||||
)
|
||||
|
||||
|
||||
# FIXME: Provide num_blogs, num_faqs as inputs.
|
||||
def get_blog_sections_from_websearch(search_keyword, search_results):
|
||||
"""Combine the given online research and gpt blog content"""
|
||||
gpt_providers = os.environ["GPT_PROVIDER"]
|
||||
prompt = f"""
|
||||
As a SEO expert and content writer, I will provide you with a search keyword and its google search result.
|
||||
Your task is to write a blog title and 5 blog sub titles, from the given google search result.
|
||||
The subtitles should be less than 40 characters and click worthy.
|
||||
Do not explain, describe your response. Respond in json format, always name the key as 'blogSections'.
|
||||
|
||||
Web Research Keyword: "{search_keyword}"
|
||||
Google search Result: "{search_results}"
|
||||
"""
|
||||
|
||||
if 'gemini' in gpt_providers:
|
||||
try:
|
||||
response = gemini_text_response(prompt)
|
||||
if '```' in response and '\n' in response:
|
||||
response = response.strip().split('\n')
|
||||
# Remove the first and last lines
|
||||
response = '\n'.join(response[1:-1])
|
||||
response = json.loads(response)
|
||||
return response
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to get response from gemini: {err}")
|
||||
logger.error(f"Gemini Error: {response.prompt_feedback}")
|
||||
raise err
|
||||
elif 'openai' in gpt_providers:
|
||||
try:
|
||||
logger.info("Calling OpenAI LLM.")
|
||||
response = openai_chatgpt(prompt)
|
||||
return response
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to get response from Openai: {err}")
|
||||
raise err
|
||||
@@ -1,109 +0,0 @@
|
||||
import sys
|
||||
import os
|
||||
|
||||
from textwrap import dedent
|
||||
import json
|
||||
import asyncio
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
import streamlit as st
|
||||
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv(Path('../../.env'))
|
||||
from loguru import logger
|
||||
logger.remove()
|
||||
logger.add(sys.stdout,
|
||||
colorize=True,
|
||||
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
|
||||
)
|
||||
|
||||
from ..ai_web_researcher.firecrawl_web_crawler import scrape_url
|
||||
from ..blog_metadata.get_blog_metadata import blog_metadata
|
||||
from ..blog_postprocessing.save_blog_to_file import save_blog_to_file
|
||||
from ..gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
|
||||
from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
from ..gpt_providers.image_to_text_gen.gemini_image_describe import describe_image, analyze_image_with_prompt
|
||||
|
||||
|
||||
def blog_from_image(prompt, uploaded_img):
|
||||
"""
|
||||
This function will take a blog Topic to first generate sections for it
|
||||
and then generate content for each section.
|
||||
"""
|
||||
# Use to store the blog in a string, to save in a *.md file.
|
||||
blog_markdown_str = None
|
||||
logger.info(f"Researching and Writing Blog on {uploaded_img} and {prompt}")
|
||||
# FIXME: Implement support for Openai.
|
||||
if not os.getenv("GEMINI_API_KEY"):
|
||||
st.error("Only Gemini supported, Open Issue ticket on github for Openai, others.")
|
||||
st.stop()
|
||||
|
||||
with st.status("Started Writing from Image..", expanded=True) as status:
|
||||
st.empty()
|
||||
status.update(label=f"Researching and Writing Blog on given Image")
|
||||
try:
|
||||
blog_markdown_str = write_blog_from_image(prompt, uploaded_img)
|
||||
except Exception as err:
|
||||
st.error(f"Failed to write blog from Image - Error: {err}")
|
||||
logger.error(f"Failed to write blog from image: {err}")
|
||||
st.stop()
|
||||
status.update(label="Successfully wrote blog from image.", expanded=False, state="complete")
|
||||
|
||||
try:
|
||||
status.update(label="🙎 Generating - Title, Meta Description, Tags, Categories for the content.")
|
||||
blog_title, blog_meta_desc, blog_tags, blog_categories = asyncio.run(blog_metadata(blog_markdown_str))
|
||||
except Exception as err:
|
||||
st.error(f"Failed to get blog metadata: {err}")
|
||||
|
||||
try:
|
||||
status.update(label="🙎 Generating Image for the new blog.")
|
||||
generated_image_filepath = generate_image(f"{blog_title} + ' ' + {blog_meta_desc}")
|
||||
except Exception as err:
|
||||
st.warning(f"Failed in Image generation: {err}")
|
||||
|
||||
saved_blog_to_file = save_blog_to_file(blog_markdown_str, blog_title, blog_meta_desc,
|
||||
blog_tags, blog_categories, generated_image_filepath)
|
||||
status.update(label=f"Saved the content in this file: {saved_blog_to_file}")
|
||||
logger.info(f"\n\n --------- Finished writing Blog -------------- \n")
|
||||
st.image(generated_image_filepath, caption=blog_title)
|
||||
st.markdown(f"{blog_markdown_str}")
|
||||
status.update(label=f"Finished, Review & Use your Original Content Below: {saved_blog_to_file}", state="complete")
|
||||
|
||||
# Clean up the temporary file after processing (optional)
|
||||
os.remove(uploaded_img)
|
||||
|
||||
|
||||
def write_blog_from_image(prompt, uploaded_img):
|
||||
"""Combine the given online research and GPT blog content"""
|
||||
try:
|
||||
config_path = Path(os.environ["ALWRITY_CONFIG"])
|
||||
with open(config_path, 'r', encoding='utf-8') as file:
|
||||
config = json.load(file)
|
||||
except Exception as err:
|
||||
logger.error(f"Error: Failed to read values from config: {err}")
|
||||
exit(1)
|
||||
|
||||
blog_characteristics = config['Blog Content Characteristics']
|
||||
|
||||
if not prompt:
|
||||
prompt = f"""
|
||||
As expert Creative Content writer, analyse the given image carefully.
|
||||
I want you to write a detailed {blog_characteristics['Blog Type']} blog post including 5 FAQs.
|
||||
|
||||
Below are the guidelines to follow:
|
||||
1). You must respond in {blog_characteristics['Blog Language']} language.
|
||||
2). Tone and Brand Alignment: Adjust your tone, voice, personality for {blog_characteristics['Blog Tone']} audience.
|
||||
3). Make sure your response content length is of {blog_characteristics['Blog Length']} words.
|
||||
"""
|
||||
logger.info("Generating blog and FAQs from image analysis.")
|
||||
|
||||
try:
|
||||
# Use the gemini_image_describe function to analyze the image with the custom prompt
|
||||
response = analyze_image_with_prompt(uploaded_img, prompt)
|
||||
if not response:
|
||||
logger.error("Failed to get response from image analysis")
|
||||
return "Failed to generate content from image."
|
||||
return response
|
||||
except Exception as err:
|
||||
logger.error(f"Exit: Failed to get response from image analysis: {err}")
|
||||
exit(1)
|
||||
@@ -1,143 +0,0 @@
|
||||
import os
|
||||
import datetime #I wish
|
||||
import sys
|
||||
from textwrap import dedent
|
||||
from tqdm import tqdm, trange
|
||||
import time
|
||||
|
||||
from pytubefix import YouTube
|
||||
import tempfile
|
||||
from html2image import Html2Image
|
||||
|
||||
from loguru import logger
|
||||
logger.remove()
|
||||
logger.add(sys.stdout,
|
||||
colorize=True,
|
||||
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
|
||||
)
|
||||
|
||||
from ...ai_web_researcher.gpt_online_researcher import do_google_serp_search
|
||||
from ..ai_blog_writer.blog_from_google_serp import blog_with_research
|
||||
from ...blog_metadata.get_blog_metadata import blog_metadata
|
||||
from ...blog_postprocessing.save_blog_to_file import save_blog_to_file
|
||||
from ...gpt_providers.audio_to_text_generation.stt_audio_blog import speech_to_text
|
||||
from ...gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
|
||||
|
||||
def youtube_to_blog(video_url):
|
||||
"""Function to transcribe a given youtube url """
|
||||
try:
|
||||
# Starting the speech-to-text process
|
||||
logger.info("Starting with Speech to Text.")
|
||||
audio_text, audio_title = speech_to_text(video_url)
|
||||
except Exception as e:
|
||||
logger.error(f"Error in speech_to_text: {e}")
|
||||
sys.exit(1) # Exit the program due to error in speech_to_text
|
||||
|
||||
try:
|
||||
# Summarizing the content of the YouTube video
|
||||
audio_blog_content = summarize_youtube_video(audio_text)
|
||||
logger.info("Successfully converted given URL to blog article.")
|
||||
return audio_blog_content, audio_title
|
||||
except Exception as e:
|
||||
logger.error(f"Error in summarize_youtube_video: {e}")
|
||||
return False
|
||||
|
||||
|
||||
def summarize_youtube_video(user_content):
|
||||
"""Generates a summary of a YouTube video using OpenAI GPT-3 and displays a progress bar.
|
||||
Args:
|
||||
video_link: The URL of the YouTube video to summarize.
|
||||
Returns:
|
||||
A string containing the summary of the video.
|
||||
"""
|
||||
|
||||
logger.info("Start summarize_youtube_video..")
|
||||
prompt = f"""
|
||||
You are an expert copywriter specializing in digital content writing. I will provide you with a transcript.
|
||||
Your task is to transform a given transcript into a well-structured and informative blog article.
|
||||
Please follow the below objectives:
|
||||
|
||||
1. Master the Transcript: Understand main ideas, key points, and the core message.
|
||||
2. Sentence Structure: Rephrase while preserving logical flow and coherence. Dont quote anyone from video.
|
||||
3. Note: Check if the transcript is about programming, then include code examples and snippets in your article.
|
||||
4. Write Unique Content: Avoid direct copying; rewrite in your own words.
|
||||
5. REMEMBER to avoid direct quoting and maintain uniqueness.
|
||||
6. Proofread: Check for grammar, spelling, and punctuation errors.
|
||||
7. Use Creative and Human-like Style: Incorporate contractions, idioms, transitional phrases, interjections, and colloquialisms. 8. Avoid repetitive phrases and unnatural sentence structures.
|
||||
9. Ensure Uniqueness: Guarantee the article is plagiarism-free.
|
||||
10. Punctuation: Use appropriate question marks at the end of questions.
|
||||
11. Pass AI Detection Tools: Create content that easily passes AI plagiarism detection tools.
|
||||
12. Rephrase words like 'video, youtube, channel' with 'article, blog' and such suitable words.
|
||||
|
||||
Follow the above guidelines to create a well-optimized, unique, and informative article,
|
||||
that will rank well in search engine results and engage readers effectively.
|
||||
Follow above guidelines to craft a blog content from the following transcript:\n{user_content}
|
||||
"""
|
||||
try:
|
||||
response = llm_text_gen(prompt)
|
||||
return response
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to summarize_youtube_video: {err}")
|
||||
exit(1)
|
||||
|
||||
|
||||
def generate_audio_blog(audio_input):
|
||||
"""Takes a list of youtube videos and generates blog for each one of them.
|
||||
"""
|
||||
# Use to store the blog in a string, to save in a *.md file.
|
||||
blog_markdown_str = ""
|
||||
try:
|
||||
logger.info(f"Starting to write blog on URL: {audio_input}")
|
||||
yt_blog, yt_title = youtube_to_blog(audio_input)
|
||||
except Exception as e:
|
||||
logger.error(f"Error in youtube_to_blog: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
try:
|
||||
logger.info("Starting with online research for URL title.")
|
||||
research_report = do_google_serp_search(yt_title)
|
||||
print(research_report)
|
||||
except Exception as e:
|
||||
logger.error(f"Error in do_online_research: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
try:
|
||||
# Note: Check if the order of input matters for your function
|
||||
logger.info("Preparing a blog content from audio script and online research content...")
|
||||
blog_markdown_str = blog_with_research(research_report, yt_blog)
|
||||
except Exception as e:
|
||||
logger.error(f"Error in blog_with_research: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
try:
|
||||
import asyncio
|
||||
# blog_metadata now returns 6 values: title, desc, tags, categories, hashtags, slug
|
||||
blog_title, blog_meta_desc, blog_tags, blog_categories, blog_hashtags, blog_slug = asyncio.run(blog_metadata(blog_markdown_str))
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to generate blog metadata: {err}")
|
||||
# Set defaults in case of failure
|
||||
blog_title = "Blog Article"
|
||||
blog_meta_desc = "An informative blog post"
|
||||
blog_tags = "content, blog"
|
||||
blog_categories = "General, Information"
|
||||
blog_hashtags = "#content #blog"
|
||||
blog_slug = "blog-article"
|
||||
|
||||
try:
|
||||
# TBD: Save the blog content as a .md file. Markdown or HTML ?
|
||||
# Initialize generated_image_filepath to None since it's not generated in this function
|
||||
generated_image_filepath = None
|
||||
save_blog_to_file(blog_markdown_str, blog_title, blog_meta_desc, blog_tags, blog_categories, generated_image_filepath)
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to save final blog in a file: {err}")
|
||||
|
||||
blog_frontmatter = dedent(f"""\n\n\n\
|
||||
---
|
||||
title: {blog_title}
|
||||
categories: [{blog_categories}]
|
||||
tags: [{blog_tags}]
|
||||
Meta description: {blog_meta_desc.replace(":", "-")}
|
||||
---\n\n""")
|
||||
logger.info(f"{blog_frontmatter}{blog_markdown_str}")
|
||||
logger.info(f"\n\n ################ Finished writing Blog for : {audio_input} #################### \n")
|
||||
@@ -1,165 +0,0 @@
|
||||
# Twitter AI Writer Module
|
||||
|
||||
A comprehensive suite of AI-powered tools for Twitter/X content marketing and management.
|
||||
|
||||
## Features
|
||||
|
||||
### 1. Tweet Generation & Optimization
|
||||
- **Smart Tweet Generator**
|
||||
- Multiple tweet variations based on input parameters
|
||||
- Character count optimization
|
||||
- Hashtag suggestions and placement
|
||||
- Emoji usage recommendations
|
||||
- Thread creation capabilities
|
||||
|
||||
- **Tweet Performance Predictor**
|
||||
- Engagement rate estimation
|
||||
- Best time to post suggestions
|
||||
- Audience reach predictions
|
||||
- Viral potential scoring
|
||||
|
||||
### 2. Content Strategy Tools
|
||||
- **Content Calendar Generator**
|
||||
- Weekly/monthly content planning
|
||||
- Theme-based content scheduling
|
||||
- Event and holiday integration
|
||||
- Content mix recommendations
|
||||
|
||||
- **Hashtag Strategy Manager**
|
||||
- Trending hashtag research
|
||||
- Custom hashtag creation
|
||||
- Hashtag performance tracking
|
||||
- Competitor hashtag analysis
|
||||
|
||||
### 3. Visual Content Creation
|
||||
- **Image Generator**
|
||||
- Tweet card creation
|
||||
- Infographic templates
|
||||
- Quote card designs
|
||||
- Brand-consistent visuals
|
||||
|
||||
- **Video Content Assistant**
|
||||
- Video script generation
|
||||
- Storyboard creation
|
||||
- Caption optimization
|
||||
- Thumbnail design suggestions
|
||||
|
||||
### 4. Engagement & Community Management
|
||||
- **Reply Generator**
|
||||
- Context-aware responses
|
||||
- Tone matching
|
||||
- Crisis management templates
|
||||
- Customer service responses
|
||||
|
||||
- **Community Engagement Tools**
|
||||
- Poll creation
|
||||
- Q&A session planning
|
||||
- Community highlight suggestions
|
||||
- User-generated content prompts
|
||||
|
||||
### 5. Analytics & Optimization
|
||||
- **Performance Analytics**
|
||||
- Tweet performance tracking
|
||||
- Engagement metrics analysis
|
||||
- Audience growth monitoring
|
||||
- Content effectiveness scoring
|
||||
|
||||
- **A/B Testing Assistant**
|
||||
- Tweet variation testing
|
||||
- Headline optimization
|
||||
- CTA effectiveness analysis
|
||||
- Best performing content identification
|
||||
|
||||
### 6. Research & Intelligence
|
||||
- **Market Research Tools**
|
||||
- Competitor analysis
|
||||
- Industry trend tracking
|
||||
- Audience sentiment analysis
|
||||
- Content gap identification
|
||||
|
||||
- **Content Inspiration**
|
||||
- Trending topic suggestions
|
||||
- Content idea generation
|
||||
- Viral content analysis
|
||||
- Industry-specific insights
|
||||
|
||||
## Best Practices Integration
|
||||
|
||||
### Tweet Optimization
|
||||
- Optimal character count (240-280)
|
||||
- Strategic hashtag placement
|
||||
- Effective use of mentions and links
|
||||
- Engaging call-to-actions
|
||||
- Visual content optimization
|
||||
|
||||
### Content Strategy
|
||||
- Consistent brand voice
|
||||
- Regular posting schedule
|
||||
- Content variety maintenance
|
||||
- Engagement-driven approach
|
||||
- Community building focus
|
||||
|
||||
### Visual Content
|
||||
- Image size optimization
|
||||
- Brand color consistency
|
||||
- Text overlay best practices
|
||||
- Mobile-friendly design
|
||||
- Visual hierarchy principles
|
||||
|
||||
### Engagement
|
||||
- Response time optimization
|
||||
- Community management guidelines
|
||||
- Crisis communication protocols
|
||||
- User interaction best practices
|
||||
- Content moderation assistance
|
||||
|
||||
## Technical Integration
|
||||
|
||||
### API Integration
|
||||
- Twitter API v2 support
|
||||
- Rate limit management
|
||||
- Error handling
|
||||
- Data synchronization
|
||||
|
||||
### Performance Optimization
|
||||
- Caching mechanisms
|
||||
- Batch processing
|
||||
- Resource optimization
|
||||
- Response time improvement
|
||||
|
||||
## Security & Compliance
|
||||
|
||||
### Data Protection
|
||||
- User data encryption
|
||||
- Secure API key management
|
||||
- Privacy compliance
|
||||
- Data retention policies
|
||||
|
||||
### Content Guidelines
|
||||
- Platform policy compliance
|
||||
- Copyright protection
|
||||
- Brand safety measures
|
||||
- Content moderation rules
|
||||
|
||||
## Coming Soon
|
||||
- Advanced thread generator
|
||||
- AI-powered image editor
|
||||
- Real-time trend analyzer
|
||||
- Automated content scheduler
|
||||
- Advanced analytics dashboard
|
||||
- Multi-account management
|
||||
- Custom AI model training
|
||||
- Integration with other social platforms
|
||||
|
||||
## Usage Guidelines
|
||||
1. Ensure API keys are properly configured
|
||||
2. Follow Twitter's terms of service
|
||||
3. Maintain brand voice consistency
|
||||
4. Regular content calendar updates
|
||||
5. Monitor performance metrics
|
||||
6. Engage with community regularly
|
||||
7. Update content strategy based on analytics
|
||||
8. Follow security best practices
|
||||
|
||||
## Support
|
||||
For technical support or feature requests, please contact the development team or raise an issue in the repository. https://github.com/AJaySi/AI-Writer/issues
|
||||
@@ -1,9 +0,0 @@
|
||||
"""
|
||||
Twitter AI Writer Module
|
||||
|
||||
A comprehensive suite of AI-powered tools for Twitter/X content marketing and management.
|
||||
"""
|
||||
|
||||
from .twitter_dashboard import run_dashboard
|
||||
|
||||
__all__ = ['run_dashboard']
|
||||
@@ -1,163 +0,0 @@
|
||||
Here’s an improved and enhanced version of your README. I've structured it for clarity, conciseness, and professionalism, while also making it more engaging and user-friendly.
|
||||
|
||||
---
|
||||
|
||||
# 🐦 Smart Tweet Generator
|
||||
|
||||
**Create tweets that stand out!** The Smart Tweet Generator is a cutting-edge AI-powered tool designed to craft optimized, engaging tweets that maximize your audience reach and engagement.
|
||||
|
||||
---
|
||||
|
||||
## ✨ Key Features
|
||||
|
||||
### 1. **Multi-Variation Tweet Generation**
|
||||
- Generate 1–5 tweet variations from a single prompt.
|
||||
- Each variation tailored to different engagement styles.
|
||||
- Consistent tone and messaging across all versions.
|
||||
|
||||
### 2. **Real-Time Character Optimization**
|
||||
- Live character count tracking, including emoji support.
|
||||
- Visual indicators to maintain the ideal tweet length.
|
||||
- Alerts when nearing Twitter's 280-character limit.
|
||||
|
||||
### 3. **Intelligent Hashtag Management**
|
||||
- Auto-extract hashtags from generated tweets.
|
||||
- Topic-based, AI-suggested hashtags to enhance discoverability.
|
||||
- Recommendations for optimal hashtag count and placement.
|
||||
|
||||
### 4. **Emoji Suggestions That Fit**
|
||||
- Context-sensitive and tone-appropriate emoji suggestions.
|
||||
- Categories include:
|
||||
- **Humorous**: 😄 😂 😉
|
||||
- **Informative**: 📊 🔍 💡
|
||||
- **Inspirational**: ✨ 🌟 🔥
|
||||
- **Serious**: 🤔 📢 🔔
|
||||
- **Casual**: 👋 👍 🤗
|
||||
|
||||
### 5. **Performance Prediction**
|
||||
- Engagement score (0-100%) based on AI analysis.
|
||||
- Metrics analyzed include:
|
||||
- Character count optimization.
|
||||
- Hashtag effectiveness.
|
||||
- Emoji usage.
|
||||
- Audience relevance.
|
||||
- Categories:
|
||||
- **Excellent** (80–100%)
|
||||
- **Good** (60–79%)
|
||||
- **Fair** (40–59%)
|
||||
- **Needs Improvement** (0–39%)
|
||||
|
||||
### 6. **Actionable Improvement Suggestions**
|
||||
- Real-time feedback on tweet quality.
|
||||
- Tailored recommendations to boost performance.
|
||||
- Built-in best practices guidance for effective tweeting.
|
||||
|
||||
---
|
||||
|
||||
## 🎯 How to Use
|
||||
|
||||
### Step 1: **Enter Basic Information**
|
||||
- Add your tweet topic or hook.
|
||||
- Define the target audience.
|
||||
- Choose the desired tone and tweet length.
|
||||
- Optionally, include a call-to-action (CTA).
|
||||
|
||||
### Step 2: **Customize Advanced Options**
|
||||
- Select the number of tweet variations (1–5).
|
||||
- Input keywords or hashtags.
|
||||
- Choose emoji preferences.
|
||||
- Add @mentions or placeholders for links.
|
||||
|
||||
### Step 3: **Generate and Refine**
|
||||
- Click **Generate Tweets** to create variations.
|
||||
- Review performance metrics and apply improvement suggestions.
|
||||
- Copy, save, or export your favorite version.
|
||||
|
||||
---
|
||||
|
||||
## 📊 Performance Metrics
|
||||
|
||||
**Your tweets are analyzed based on:**
|
||||
|
||||
1. **Character Count**
|
||||
- Optimal: 100–200 characters.
|
||||
- Short: <100 characters.
|
||||
- Long: >200 characters.
|
||||
|
||||
2. **Hashtag Usage**
|
||||
- Optimal: 1–3 hashtags.
|
||||
- Too few: 0 hashtags.
|
||||
- Too many: >3 hashtags.
|
||||
|
||||
3. **Engagement Triggers**
|
||||
- Questions, CTAs, or interactive elements.
|
||||
|
||||
4. **Emoji Optimization**
|
||||
- Ideal: 1–3 emojis.
|
||||
- Too few: 0 emojis.
|
||||
- Too many: >3 emojis.
|
||||
|
||||
5. **Audience Relevance**
|
||||
- Alignment with keywords, tone, and context.
|
||||
|
||||
---
|
||||
|
||||
## 💡 Best Practices
|
||||
|
||||
1. **Craft Attention-Grabbing Hooks**
|
||||
- Start with bold statements or thought-provoking questions.
|
||||
- Use stats or facts to capture attention.
|
||||
|
||||
2. **Align Tone with Audience**
|
||||
- Maintain consistency with your brand voice.
|
||||
- Adapt tone to audience preferences (e.g., formal, casual).
|
||||
|
||||
3. **Strategic Hashtag Usage**
|
||||
- Use trending and relevant hashtags.
|
||||
- Limit to 1–3 for optimal engagement.
|
||||
|
||||
4. **Effective Emoji Usage**
|
||||
- Enhance meaning and context with emojis.
|
||||
- Match the tone and avoid overuse.
|
||||
|
||||
5. **Clear Calls-to-Action**
|
||||
- Encourage action with clarity and urgency.
|
||||
- Use action verbs like "Discover," "Join," or "Explore."
|
||||
|
||||
---
|
||||
|
||||
## 🔄 Export Options
|
||||
|
||||
- Copy individual tweets.
|
||||
- Export all variations as a JSON file.
|
||||
- Save performance metrics and recommendations.
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ Technical Details
|
||||
|
||||
- **Built with:** Streamlit for an intuitive user interface.
|
||||
- **AI-powered:** Advanced natural language models for tweet generation.
|
||||
- **Real-time:** Instant feedback and suggestions.
|
||||
- **Cross-platform compatibility:** Works seamlessly across devices.
|
||||
|
||||
---
|
||||
|
||||
## 📝 Notes
|
||||
|
||||
- Tweets are optimized for Twitter’s 280-character limit.
|
||||
- Performance predictions are derived from AI insights and engagement patterns.
|
||||
- Suggestions adapt to your audience, ensuring relevancy.
|
||||
- Regular updates keep the tool current with Twitter trends.
|
||||
|
||||
---
|
||||
|
||||
## 🤝 Support
|
||||
|
||||
Have questions or feature requests? Reach out to our support team or submit an issue on our GitHub repository.
|
||||
|
||||
---
|
||||
|
||||
*Last updated: Yesterday*
|
||||
|
||||
---
|
||||
@@ -1,9 +0,0 @@
|
||||
"""
|
||||
Twitter Tweet Generator Module
|
||||
|
||||
A comprehensive suite of tools for generating and optimizing tweets.
|
||||
"""
|
||||
|
||||
from .smart_tweet_generator import smart_tweet_generator
|
||||
|
||||
__all__ = ['smart_tweet_generator']
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,729 +0,0 @@
|
||||
"""
|
||||
Enhanced Twitter Dashboard with modern UI components and improved user experience.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
from typing import Dict, List, Optional, Any
|
||||
import json
|
||||
from datetime import datetime, timedelta
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
from plotly.subplots import make_subplots
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
from .tweet_generator import smart_tweet_generator
|
||||
from .twitter_streamlit_ui import (
|
||||
TwitterDashboard,
|
||||
FeatureCard,
|
||||
TweetCard,
|
||||
TweetForm,
|
||||
SettingsForm,
|
||||
Sidebar,
|
||||
Header,
|
||||
Tabs,
|
||||
Breadcrumbs,
|
||||
Theme,
|
||||
save_to_session,
|
||||
get_from_session,
|
||||
clear_session,
|
||||
show_success_message,
|
||||
show_error_message,
|
||||
show_info_message,
|
||||
show_warning_message
|
||||
)
|
||||
|
||||
def apply_modern_styling():
|
||||
"""Apply modern CSS styling to the dashboard."""
|
||||
st.markdown("""
|
||||
<style>
|
||||
/* Import Google Fonts */
|
||||
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
|
||||
|
||||
/* Global Styles */
|
||||
.stApp {
|
||||
font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;
|
||||
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
|
||||
min-height: 100vh;
|
||||
}
|
||||
|
||||
/* Main Container */
|
||||
.main-container {
|
||||
background: rgba(255, 255, 255, 0.95);
|
||||
backdrop-filter: blur(20px);
|
||||
border-radius: 20px;
|
||||
padding: 2rem;
|
||||
margin: 1rem;
|
||||
box-shadow: 0 20px 40px rgba(0, 0, 0, 0.1);
|
||||
}
|
||||
|
||||
/* Header Styles */
|
||||
.dashboard-header {
|
||||
text-align: center;
|
||||
margin-bottom: 2rem;
|
||||
padding: 2rem 0;
|
||||
background: linear-gradient(135deg, #1DA1F2, #0C85D0);
|
||||
border-radius: 16px;
|
||||
color: white;
|
||||
box-shadow: 0 10px 30px rgba(29, 161, 242, 0.3);
|
||||
}
|
||||
|
||||
.dashboard-title {
|
||||
font-size: 2.5rem;
|
||||
font-weight: 700;
|
||||
margin: 0;
|
||||
text-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
|
||||
}
|
||||
|
||||
.dashboard-subtitle {
|
||||
font-size: 1.1rem;
|
||||
opacity: 0.9;
|
||||
margin-top: 0.5rem;
|
||||
font-weight: 400;
|
||||
}
|
||||
|
||||
/* Feature Cards */
|
||||
.feature-card {
|
||||
background: white;
|
||||
border-radius: 16px;
|
||||
padding: 1.5rem;
|
||||
margin-bottom: 1rem;
|
||||
box-shadow: 0 8px 25px rgba(0, 0, 0, 0.08);
|
||||
border: 1px solid rgba(0, 0, 0, 0.05);
|
||||
transition: all 0.3s ease;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.feature-card:hover {
|
||||
transform: translateY(-5px);
|
||||
box-shadow: 0 15px 35px rgba(0, 0, 0, 0.15);
|
||||
}
|
||||
|
||||
.feature-icon {
|
||||
font-size: 2.5rem;
|
||||
margin-bottom: 1rem;
|
||||
display: block;
|
||||
}
|
||||
|
||||
.feature-title {
|
||||
font-size: 1.25rem;
|
||||
font-weight: 600;
|
||||
color: #2D3748;
|
||||
margin-bottom: 0.5rem;
|
||||
}
|
||||
|
||||
.feature-description {
|
||||
color: #718096;
|
||||
font-size: 0.95rem;
|
||||
line-height: 1.5;
|
||||
margin-bottom: 1rem;
|
||||
}
|
||||
|
||||
.feature-status {
|
||||
display: inline-block;
|
||||
padding: 0.25rem 0.75rem;
|
||||
border-radius: 20px;
|
||||
font-size: 0.8rem;
|
||||
font-weight: 500;
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.5px;
|
||||
}
|
||||
|
||||
.status-active {
|
||||
background: linear-gradient(135deg, #48BB78, #38A169);
|
||||
color: white;
|
||||
}
|
||||
|
||||
.status-coming-soon {
|
||||
background: linear-gradient(135deg, #ED8936, #DD6B20);
|
||||
color: white;
|
||||
}
|
||||
|
||||
/* Metrics Cards */
|
||||
.metric-card {
|
||||
background: white;
|
||||
border-radius: 12px;
|
||||
padding: 1.5rem;
|
||||
text-align: center;
|
||||
box-shadow: 0 4px 15px rgba(0, 0, 0, 0.08);
|
||||
border-left: 4px solid #1DA1F2;
|
||||
}
|
||||
|
||||
.metric-value {
|
||||
font-size: 2rem;
|
||||
font-weight: 700;
|
||||
color: #2D3748;
|
||||
margin-bottom: 0.5rem;
|
||||
}
|
||||
|
||||
.metric-label {
|
||||
color: #718096;
|
||||
font-size: 0.9rem;
|
||||
font-weight: 500;
|
||||
}
|
||||
|
||||
/* Buttons */
|
||||
.stButton > button {
|
||||
background: linear-gradient(135deg, #1DA1F2, #0C85D0);
|
||||
color: white;
|
||||
border: none;
|
||||
border-radius: 10px;
|
||||
padding: 0.75rem 1.5rem;
|
||||
font-weight: 600;
|
||||
font-size: 0.95rem;
|
||||
transition: all 0.3s ease;
|
||||
box-shadow: 0 4px 15px rgba(29, 161, 242, 0.3);
|
||||
}
|
||||
|
||||
.stButton > button:hover {
|
||||
transform: translateY(-2px);
|
||||
box-shadow: 0 8px 25px rgba(29, 161, 242, 0.4);
|
||||
}
|
||||
|
||||
/* Tabs */
|
||||
.stTabs [data-baseweb="tab-list"] {
|
||||
gap: 0.5rem;
|
||||
background: rgba(255, 255, 255, 0.1);
|
||||
padding: 0.5rem;
|
||||
border-radius: 12px;
|
||||
backdrop-filter: blur(10px);
|
||||
}
|
||||
|
||||
.stTabs [data-baseweb="tab"] {
|
||||
background: transparent;
|
||||
border-radius: 8px;
|
||||
color: #4A5568;
|
||||
font-weight: 500;
|
||||
padding: 0.75rem 1.5rem;
|
||||
transition: all 0.3s ease;
|
||||
}
|
||||
|
||||
.stTabs [aria-selected="true"] {
|
||||
background: white;
|
||||
color: #1DA1F2;
|
||||
box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
|
||||
}
|
||||
|
||||
/* Connection Status */
|
||||
.connection-status {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 0.5rem;
|
||||
padding: 1rem;
|
||||
border-radius: 12px;
|
||||
margin-bottom: 1.5rem;
|
||||
font-weight: 500;
|
||||
}
|
||||
|
||||
.status-connected {
|
||||
background: linear-gradient(135deg, #C6F6D5, #9AE6B4);
|
||||
color: #22543D;
|
||||
border: 1px solid #9AE6B4;
|
||||
}
|
||||
|
||||
.status-disconnected {
|
||||
background: linear-gradient(135deg, #FED7D7, #FEB2B2);
|
||||
color: #742A2A;
|
||||
border: 1px solid #FEB2B2;
|
||||
}
|
||||
|
||||
/* Quick Actions */
|
||||
.quick-actions {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
|
||||
gap: 1rem;
|
||||
margin: 2rem 0;
|
||||
}
|
||||
|
||||
.quick-action-btn {
|
||||
background: white;
|
||||
border: 2px solid #E2E8F0;
|
||||
border-radius: 12px;
|
||||
padding: 1.5rem;
|
||||
text-align: center;
|
||||
transition: all 0.3s ease;
|
||||
cursor: pointer;
|
||||
text-decoration: none;
|
||||
}
|
||||
|
||||
.quick-action-btn:hover {
|
||||
border-color: #1DA1F2;
|
||||
transform: translateY(-2px);
|
||||
box-shadow: 0 8px 25px rgba(29, 161, 242, 0.15);
|
||||
}
|
||||
|
||||
.quick-action-icon {
|
||||
font-size: 2rem;
|
||||
margin-bottom: 0.5rem;
|
||||
display: block;
|
||||
}
|
||||
|
||||
.quick-action-title {
|
||||
font-weight: 600;
|
||||
color: #2D3748;
|
||||
margin-bottom: 0.25rem;
|
||||
}
|
||||
|
||||
.quick-action-desc {
|
||||
font-size: 0.85rem;
|
||||
color: #718096;
|
||||
}
|
||||
|
||||
/* Analytics Charts */
|
||||
.chart-container {
|
||||
background: white;
|
||||
border-radius: 16px;
|
||||
padding: 1.5rem;
|
||||
margin: 1rem 0;
|
||||
box-shadow: 0 4px 15px rgba(0, 0, 0, 0.08);
|
||||
}
|
||||
|
||||
/* Responsive Design */
|
||||
@media (max-width: 768px) {
|
||||
.main-container {
|
||||
margin: 0.5rem;
|
||||
padding: 1rem;
|
||||
}
|
||||
|
||||
.dashboard-title {
|
||||
font-size: 2rem;
|
||||
}
|
||||
|
||||
.quick-actions {
|
||||
grid-template-columns: 1fr;
|
||||
}
|
||||
}
|
||||
</style>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
def render_connection_status():
|
||||
"""Render Twitter connection status with modern styling."""
|
||||
# Simulate connection status (replace with real authentication check)
|
||||
is_connected = get_from_session("twitter_connected", False)
|
||||
|
||||
if is_connected:
|
||||
user_info = get_from_session("twitter_user", {"name": "Demo User", "handle": "@demo_user"})
|
||||
st.markdown(f"""
|
||||
<div class="connection-status status-connected">
|
||||
<span style="font-size: 1.2rem;">✅</span>
|
||||
<div>
|
||||
<strong>Connected as {user_info['name']}</strong>
|
||||
<div style="font-size: 0.9rem; opacity: 0.8;">{user_info['handle']}</div>
|
||||
</div>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
else:
|
||||
st.markdown("""
|
||||
<div class="connection-status status-disconnected">
|
||||
<span style="font-size: 1.2rem;">⚠️</span>
|
||||
<div>
|
||||
<strong>Twitter Not Connected</strong>
|
||||
<div style="font-size: 0.9rem; opacity: 0.8;">Connect your account to access all features</div>
|
||||
</div>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
if st.button("🔗 Connect Twitter Account", key="connect_twitter"):
|
||||
# Simulate connection (replace with real OAuth flow)
|
||||
save_to_session("twitter_connected", True)
|
||||
save_to_session("twitter_user", {"name": "Demo User", "handle": "@demo_user"})
|
||||
st.rerun()
|
||||
|
||||
def render_dashboard_header():
|
||||
"""Render the modern dashboard header."""
|
||||
st.markdown("""
|
||||
<div class="dashboard-header">
|
||||
<h1 class="dashboard-title">🐦 Twitter AI Dashboard</h1>
|
||||
<p class="dashboard-subtitle">Create, analyze, and optimize your Twitter content with AI-powered tools</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
def render_quick_actions():
|
||||
"""Render quick action buttons."""
|
||||
st.markdown("### 🚀 Quick Actions")
|
||||
|
||||
col1, col2, col3, col4 = st.columns(4)
|
||||
|
||||
with col1:
|
||||
if st.button("✍️ Create Tweet", use_container_width=True, key="quick_tweet"):
|
||||
st.session_state.current_page = "tweet_generator"
|
||||
st.rerun()
|
||||
|
||||
with col2:
|
||||
if st.button("📊 View Analytics", use_container_width=True, key="quick_analytics"):
|
||||
st.session_state.current_page = "analytics"
|
||||
st.rerun()
|
||||
|
||||
with col3:
|
||||
if st.button("📅 Content Calendar", use_container_width=True, key="quick_calendar"):
|
||||
show_info_message("Content Calendar feature coming soon!")
|
||||
|
||||
with col4:
|
||||
if st.button("⚙️ Settings", use_container_width=True, key="quick_settings"):
|
||||
st.session_state.current_page = "settings"
|
||||
st.rerun()
|
||||
|
||||
def render_metrics_overview():
|
||||
"""Render key metrics overview."""
|
||||
st.markdown("### 📈 Performance Overview")
|
||||
|
||||
# Generate sample metrics (replace with real data)
|
||||
col1, col2, col3, col4 = st.columns(4)
|
||||
|
||||
with col1:
|
||||
st.markdown("""
|
||||
<div class="metric-card">
|
||||
<div class="metric-value">1,234</div>
|
||||
<div class="metric-label">Total Tweets</div>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
with col2:
|
||||
st.markdown("""
|
||||
<div class="metric-card">
|
||||
<div class="metric-value">45.2K</div>
|
||||
<div class="metric-label">Total Engagement</div>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
with col3:
|
||||
st.markdown("""
|
||||
<div class="metric-card">
|
||||
<div class="metric-value">3.8%</div>
|
||||
<div class="metric-label">Engagement Rate</div>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
with col4:
|
||||
st.markdown("""
|
||||
<div class="metric-card">
|
||||
<div class="metric-value">12.5K</div>
|
||||
<div class="metric-label">Followers</div>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
def render_engagement_chart():
|
||||
"""Render engagement trends chart."""
|
||||
st.markdown("### 📊 Engagement Trends")
|
||||
|
||||
# Generate sample data (replace with real Twitter data)
|
||||
dates = pd.date_range(start=datetime.now() - timedelta(days=30), periods=30)
|
||||
engagement = np.random.normal(100, 20, 30)
|
||||
engagement = np.maximum(engagement, 0) # Ensure positive values
|
||||
|
||||
df = pd.DataFrame({
|
||||
'Date': dates,
|
||||
'Engagement': engagement,
|
||||
'Likes': engagement * 0.6,
|
||||
'Retweets': engagement * 0.3,
|
||||
'Replies': engagement * 0.1
|
||||
})
|
||||
|
||||
# Create interactive chart
|
||||
fig = make_subplots(
|
||||
rows=2, cols=1,
|
||||
subplot_titles=('Total Engagement', 'Engagement Breakdown'),
|
||||
vertical_spacing=0.1,
|
||||
row_heights=[0.7, 0.3]
|
||||
)
|
||||
|
||||
# Main engagement line
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=df['Date'],
|
||||
y=df['Engagement'],
|
||||
mode='lines+markers',
|
||||
name='Total Engagement',
|
||||
line=dict(color='#1DA1F2', width=3),
|
||||
marker=dict(size=6)
|
||||
),
|
||||
row=1, col=1
|
||||
)
|
||||
|
||||
# Stacked area chart for breakdown
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=df['Date'],
|
||||
y=df['Likes'],
|
||||
mode='lines',
|
||||
name='Likes',
|
||||
fill='tonexty',
|
||||
line=dict(color='#E53E3E')
|
||||
),
|
||||
row=2, col=1
|
||||
)
|
||||
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=df['Date'],
|
||||
y=df['Retweets'],
|
||||
mode='lines',
|
||||
name='Retweets',
|
||||
fill='tonexty',
|
||||
line=dict(color='#38A169')
|
||||
),
|
||||
row=2, col=1
|
||||
)
|
||||
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=df['Date'],
|
||||
y=df['Replies'],
|
||||
mode='lines',
|
||||
name='Replies',
|
||||
fill='tonexty',
|
||||
line=dict(color='#D69E2E')
|
||||
),
|
||||
row=2, col=1
|
||||
)
|
||||
|
||||
fig.update_layout(
|
||||
height=500,
|
||||
showlegend=True,
|
||||
hovermode='x unified',
|
||||
plot_bgcolor='rgba(0,0,0,0)',
|
||||
paper_bgcolor='rgba(0,0,0,0)'
|
||||
)
|
||||
|
||||
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='rgba(0,0,0,0.1)')
|
||||
fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='rgba(0,0,0,0.1)')
|
||||
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
|
||||
def render_feature_grid():
|
||||
"""Render the feature grid with modern cards."""
|
||||
st.markdown("### 🛠️ Available Tools")
|
||||
|
||||
features = [
|
||||
{
|
||||
"title": "Smart Tweet Generator",
|
||||
"description": "Create engaging tweets with AI assistance, hashtag suggestions, and emoji optimization",
|
||||
"icon": "✨",
|
||||
"status": "active",
|
||||
"action": "tweet_generator"
|
||||
},
|
||||
{
|
||||
"title": "Performance Predictor",
|
||||
"description": "Predict tweet engagement and find optimal posting times",
|
||||
"icon": "🔮",
|
||||
"status": "coming_soon",
|
||||
"action": None
|
||||
},
|
||||
{
|
||||
"title": "Content Calendar",
|
||||
"description": "Plan and schedule your Twitter content strategy",
|
||||
"icon": "📅",
|
||||
"status": "coming_soon",
|
||||
"action": None
|
||||
},
|
||||
{
|
||||
"title": "Hashtag Research",
|
||||
"description": "Discover trending hashtags and analyze their performance",
|
||||
"icon": "#️⃣",
|
||||
"status": "coming_soon",
|
||||
"action": None
|
||||
},
|
||||
{
|
||||
"title": "Visual Content",
|
||||
"description": "Create quote cards, infographics, and visual tweets",
|
||||
"icon": "🎨",
|
||||
"status": "coming_soon",
|
||||
"action": None
|
||||
},
|
||||
{
|
||||
"title": "Analytics Dashboard",
|
||||
"description": "Deep dive into your Twitter performance metrics",
|
||||
"icon": "📊",
|
||||
"status": "coming_soon",
|
||||
"action": None
|
||||
}
|
||||
]
|
||||
|
||||
# Create grid layout
|
||||
cols = st.columns(3)
|
||||
|
||||
for i, feature in enumerate(features):
|
||||
with cols[i % 3]:
|
||||
status_class = "status-active" if feature["status"] == "active" else "status-coming-soon"
|
||||
|
||||
card_html = f"""
|
||||
<div class="feature-card" onclick="handleFeatureClick('{feature['action']}')">
|
||||
<span class="feature-icon">{feature['icon']}</span>
|
||||
<h3 class="feature-title">{feature['title']}</h3>
|
||||
<p class="feature-description">{feature['description']}</p>
|
||||
<span class="feature-status {status_class}">{feature['status'].replace('_', ' ')}</span>
|
||||
</div>
|
||||
"""
|
||||
|
||||
st.markdown(card_html, unsafe_allow_html=True)
|
||||
|
||||
# Add button for active features
|
||||
if feature["status"] == "active" and feature["action"]:
|
||||
if st.button(f"Launch {feature['title']}", key=f"launch_{i}", use_container_width=True):
|
||||
st.session_state.current_page = feature["action"]
|
||||
st.rerun()
|
||||
|
||||
def render_recent_activity():
|
||||
"""Render recent activity feed."""
|
||||
st.markdown("### 📱 Recent Activity")
|
||||
|
||||
# Sample activity data (replace with real data)
|
||||
activities = [
|
||||
{"time": "2 hours ago", "action": "Generated tweet", "details": "AI-powered content about social media trends"},
|
||||
{"time": "5 hours ago", "action": "Analyzed performance", "details": "Tweet received 45 likes and 12 retweets"},
|
||||
{"time": "1 day ago", "action": "Scheduled tweet", "details": "Content scheduled for optimal posting time"},
|
||||
{"time": "2 days ago", "action": "Updated hashtags", "details": "Added trending hashtags to improve reach"}
|
||||
]
|
||||
|
||||
for activity in activities:
|
||||
st.markdown(f"""
|
||||
<div style="
|
||||
background: white;
|
||||
border-radius: 8px;
|
||||
padding: 1rem;
|
||||
margin-bottom: 0.5rem;
|
||||
border-left: 3px solid #1DA1F2;
|
||||
box-shadow: 0 2px 8px rgba(0,0,0,0.05);
|
||||
">
|
||||
<div style="font-weight: 600; color: #2D3748; margin-bottom: 0.25rem;">
|
||||
{activity['action']}
|
||||
</div>
|
||||
<div style="color: #718096; font-size: 0.9rem; margin-bottom: 0.25rem;">
|
||||
{activity['details']}
|
||||
</div>
|
||||
<div style="color: #A0AEC0; font-size: 0.8rem;">
|
||||
{activity['time']}
|
||||
</div>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
def run_dashboard():
|
||||
"""Main function to run the enhanced Twitter dashboard."""
|
||||
# Apply modern styling
|
||||
apply_modern_styling()
|
||||
|
||||
# Initialize session state
|
||||
if "current_page" not in st.session_state:
|
||||
st.session_state.current_page = "dashboard"
|
||||
|
||||
# Handle page navigation
|
||||
if st.session_state.current_page == "tweet_generator":
|
||||
if st.button("← Back to Dashboard", key="back_to_dashboard"):
|
||||
st.session_state.current_page = "dashboard"
|
||||
st.rerun()
|
||||
smart_tweet_generator()
|
||||
return
|
||||
|
||||
# Main dashboard container
|
||||
st.markdown('<div class="main-container">', unsafe_allow_html=True)
|
||||
|
||||
# Render dashboard header
|
||||
render_dashboard_header()
|
||||
|
||||
# Render connection status
|
||||
render_connection_status()
|
||||
|
||||
# Create main layout
|
||||
tab1, tab2, tab3 = st.tabs(["🏠 Overview", "📊 Analytics", "⚙️ Settings"])
|
||||
|
||||
with tab1:
|
||||
# Quick actions
|
||||
render_quick_actions()
|
||||
|
||||
# Metrics overview
|
||||
render_metrics_overview()
|
||||
|
||||
# Feature grid
|
||||
render_feature_grid()
|
||||
|
||||
# Recent activity
|
||||
col1, col2 = st.columns([2, 1])
|
||||
with col1:
|
||||
render_engagement_chart()
|
||||
with col2:
|
||||
render_recent_activity()
|
||||
|
||||
with tab2:
|
||||
st.markdown("### 📈 Advanced Analytics")
|
||||
|
||||
# Time range selector
|
||||
col1, col2 = st.columns([1, 3])
|
||||
with col1:
|
||||
time_range = st.selectbox(
|
||||
"Time Range",
|
||||
["Last 7 days", "Last 30 days", "Last 90 days", "Last year"],
|
||||
index=1
|
||||
)
|
||||
|
||||
# Detailed analytics
|
||||
render_engagement_chart()
|
||||
|
||||
# Performance insights
|
||||
st.markdown("### 💡 Performance Insights")
|
||||
|
||||
insights = [
|
||||
"Your tweets perform 23% better when posted between 2-4 PM",
|
||||
"Tweets with 2-3 hashtags get 15% more engagement",
|
||||
"Visual content increases engagement by 35%",
|
||||
"Questions in tweets boost replies by 28%"
|
||||
]
|
||||
|
||||
for insight in insights:
|
||||
st.info(f"💡 {insight}")
|
||||
|
||||
with tab3:
|
||||
st.markdown("### ⚙️ Dashboard Settings")
|
||||
|
||||
# Twitter API settings
|
||||
with st.expander("🔑 Twitter API Configuration", expanded=False):
|
||||
st.markdown("Configure your Twitter API credentials to enable full functionality.")
|
||||
|
||||
api_key = st.text_input("API Key", type="password", help="Your Twitter API key")
|
||||
api_secret = st.text_input("API Secret", type="password", help="Your Twitter API secret")
|
||||
access_token = st.text_input("Access Token", type="password", help="Your Twitter access token")
|
||||
access_token_secret = st.text_input("Access Token Secret", type="password", help="Your Twitter access token secret")
|
||||
|
||||
if st.button("Save API Configuration"):
|
||||
# Save configuration (implement secure storage)
|
||||
show_success_message("API configuration saved successfully!")
|
||||
|
||||
# Dashboard preferences
|
||||
with st.expander("🎨 Dashboard Preferences", expanded=True):
|
||||
theme = st.selectbox("Theme", ["Light", "Dark", "Auto"], index=0)
|
||||
default_tone = st.selectbox("Default Tweet Tone", ["Professional", "Casual", "Humorous", "Inspirational"], index=1)
|
||||
auto_hashtags = st.checkbox("Auto-suggest hashtags", value=True)
|
||||
|
||||
if st.button("Save Preferences"):
|
||||
show_success_message("Preferences saved successfully!")
|
||||
|
||||
# Account management
|
||||
with st.expander("👤 Account Management", expanded=False):
|
||||
st.markdown("Manage your connected Twitter accounts and permissions.")
|
||||
|
||||
if get_from_session("twitter_connected", False):
|
||||
st.success("✅ Twitter account connected")
|
||||
if st.button("Disconnect Account"):
|
||||
save_to_session("twitter_connected", False)
|
||||
st.rerun()
|
||||
else:
|
||||
st.warning("⚠️ No Twitter account connected")
|
||||
if st.button("Connect Account"):
|
||||
save_to_session("twitter_connected", True)
|
||||
st.rerun()
|
||||
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
|
||||
# JavaScript for handling feature clicks
|
||||
st.markdown("""
|
||||
<script>
|
||||
function handleFeatureClick(action) {
|
||||
if (action && action !== 'null') {
|
||||
// This would trigger a Streamlit rerun with the selected action
|
||||
console.log('Feature clicked:', action);
|
||||
}
|
||||
}
|
||||
</script>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
run_dashboard()
|
||||
@@ -1,203 +0,0 @@
|
||||
# Twitter Streamlit UI Components
|
||||
|
||||
This module provides a unified, reusable UI component library for all Twitter-related features in the AI Writer suite. It implements best practices for Streamlit UI development and ensures consistency across all Twitter tools.
|
||||
|
||||
## Structure
|
||||
|
||||
```
|
||||
twitter_streamlit_ui/
|
||||
├── components/ # Reusable UI components
|
||||
│ ├── __init__.py
|
||||
│ ├── cards.py # Card components (feature cards, tweet cards)
|
||||
│ ├── forms.py # Form components (input forms, settings forms)
|
||||
│ ├── navigation.py # Navigation components (tabs, sidebar)
|
||||
│ ├── feedback.py # Feedback components (loading, errors, success)
|
||||
│ └── layout.py # Layout components (containers, columns)
|
||||
├── styles/ # CSS and styling
|
||||
│ ├── __init__.py
|
||||
│ ├── theme.py # Theme configuration
|
||||
│ ├── components.py # Component-specific styles
|
||||
│ └── animations.py # Animation styles
|
||||
├── utils/ # UI utilities
|
||||
│ ├── __init__.py
|
||||
│ ├── state.py # State management
|
||||
│ ├── validation.py # Input validation
|
||||
│ └── performance.py # Performance optimizations
|
||||
└── README.md # This file
|
||||
```
|
||||
|
||||
## Key Improvements
|
||||
|
||||
### 1. Consistent UI Components
|
||||
|
||||
- **Card Components**
|
||||
- Feature cards with consistent styling
|
||||
- Tweet cards with standardized layout
|
||||
- Status badges with unified design
|
||||
|
||||
- **Form Components**
|
||||
- Standardized input forms
|
||||
- Consistent validation feedback
|
||||
- Unified error handling
|
||||
|
||||
- **Navigation Components**
|
||||
- Consistent tab styling
|
||||
- Standardized sidebar navigation
|
||||
- Breadcrumb navigation
|
||||
|
||||
### 2. Enhanced User Experience
|
||||
|
||||
- **Loading States**
|
||||
- Progress indicators for long operations
|
||||
- Skeleton loading for content
|
||||
- Smooth transitions between states
|
||||
|
||||
- **Feedback Mechanisms**
|
||||
- Toast notifications for actions
|
||||
- Error messages with recovery options
|
||||
- Success confirmations
|
||||
|
||||
- **Responsive Design**
|
||||
- Mobile-friendly layouts
|
||||
- Adaptive column systems
|
||||
- Flexible containers
|
||||
|
||||
### 3. Performance Optimizations
|
||||
|
||||
- **State Management**
|
||||
- Centralized state handling
|
||||
- Efficient data persistence
|
||||
- Optimized re-rendering
|
||||
|
||||
- **Resource Loading**
|
||||
- Lazy loading of components
|
||||
- Optimized image loading
|
||||
- Cached computations
|
||||
|
||||
### 4. Accessibility Features
|
||||
|
||||
- **Keyboard Navigation**
|
||||
- Focus management
|
||||
- Keyboard shortcuts
|
||||
- ARIA labels
|
||||
|
||||
- **Visual Accessibility**
|
||||
- High contrast themes
|
||||
- Screen reader support
|
||||
- Color blind friendly
|
||||
|
||||
### 5. Error Handling
|
||||
|
||||
- **Graceful Degradation**
|
||||
- Fallback UI components
|
||||
- Error boundaries
|
||||
- Recovery options
|
||||
|
||||
- **User Feedback**
|
||||
- Clear error messages
|
||||
- Actionable suggestions
|
||||
- Help documentation
|
||||
|
||||
## Usage
|
||||
|
||||
### Basic Component Usage
|
||||
|
||||
```python
|
||||
from twitter_streamlit_ui.components.cards import FeatureCard
|
||||
from twitter_streamlit_ui.components.forms import TweetForm
|
||||
from twitter_streamlit_ui.styles.theme import apply_theme
|
||||
|
||||
# Apply theme
|
||||
apply_theme()
|
||||
|
||||
# Use components
|
||||
feature_card = FeatureCard(
|
||||
title="Tweet Generator",
|
||||
description="Create engaging tweets with AI",
|
||||
icon="🐦"
|
||||
)
|
||||
feature_card.render()
|
||||
|
||||
tweet_form = TweetForm()
|
||||
tweet_form.render()
|
||||
```
|
||||
|
||||
### State Management
|
||||
|
||||
```python
|
||||
from twitter_streamlit_ui.utils.state import StateManager
|
||||
|
||||
# Initialize state
|
||||
state = StateManager()
|
||||
state.initialize()
|
||||
|
||||
# Update state
|
||||
state.update("current_tweet", tweet_data)
|
||||
```
|
||||
|
||||
### Error Handling
|
||||
|
||||
```python
|
||||
from twitter_streamlit_ui.components.feedback import ErrorBoundary
|
||||
|
||||
with ErrorBoundary():
|
||||
# Your code here
|
||||
pass
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Component Reusability**
|
||||
- Use existing components when possible
|
||||
- Create new components only when necessary
|
||||
- Follow the established patterns
|
||||
|
||||
2. **State Management**
|
||||
- Use the StateManager for all state
|
||||
- Avoid direct session state manipulation
|
||||
- Keep state updates atomic
|
||||
|
||||
3. **Performance**
|
||||
- Use lazy loading for heavy components
|
||||
- Implement caching where appropriate
|
||||
- Monitor render performance
|
||||
|
||||
4. **Accessibility**
|
||||
- Include ARIA labels
|
||||
- Ensure keyboard navigation
|
||||
- Test with screen readers
|
||||
|
||||
5. **Error Handling**
|
||||
- Use ErrorBoundary components
|
||||
- Provide clear error messages
|
||||
- Include recovery options
|
||||
|
||||
## Future Improvements
|
||||
|
||||
1. **Component Library**
|
||||
- Add more specialized components
|
||||
- Enhance existing components
|
||||
- Create component documentation
|
||||
|
||||
2. **Theme System**
|
||||
- Add more theme options
|
||||
- Implement theme switching
|
||||
- Create custom theme builder
|
||||
|
||||
3. **Performance**
|
||||
- Implement virtual scrolling
|
||||
- Add performance monitoring
|
||||
- Optimize resource loading
|
||||
|
||||
4. **Testing**
|
||||
- Add component tests
|
||||
- Implement E2E tests
|
||||
- Create test documentation
|
||||
|
||||
## Contributing
|
||||
|
||||
1. Follow the established patterns
|
||||
2. Add tests for new components
|
||||
3. Update documentation
|
||||
4. Ensure accessibility
|
||||
5. Optimize performance
|
||||
@@ -1,66 +0,0 @@
|
||||
"""
|
||||
Twitter Streamlit UI package.
|
||||
Provides a modern and user-friendly interface for Twitter tools.
|
||||
"""
|
||||
|
||||
from .dashboard import TwitterDashboard
|
||||
from .components.cards import FeatureCard, TweetCard
|
||||
from .components.forms import TweetForm, SettingsForm
|
||||
from .components.navigation import Sidebar, Header, Tabs, Breadcrumbs
|
||||
from .styles.theme import Theme
|
||||
from .utils.helpers import (
|
||||
save_to_session,
|
||||
get_from_session,
|
||||
clear_session,
|
||||
save_to_file,
|
||||
load_from_file,
|
||||
format_datetime,
|
||||
parse_datetime,
|
||||
validate_tweet_content,
|
||||
validate_hashtags,
|
||||
validate_emojis,
|
||||
calculate_engagement_score,
|
||||
generate_tweet_metrics,
|
||||
copy_to_clipboard,
|
||||
show_success_message,
|
||||
show_error_message,
|
||||
show_info_message,
|
||||
show_warning_message,
|
||||
create_download_button,
|
||||
create_upload_button
|
||||
)
|
||||
|
||||
__version__ = "1.0.0"
|
||||
__author__ = "AI Writer Team"
|
||||
|
||||
__all__ = [
|
||||
"TwitterDashboard",
|
||||
"FeatureCard",
|
||||
"TweetCard",
|
||||
"TweetForm",
|
||||
"SettingsForm",
|
||||
"Sidebar",
|
||||
"Header",
|
||||
"Tabs",
|
||||
"Breadcrumbs",
|
||||
"Theme",
|
||||
"save_to_session",
|
||||
"get_from_session",
|
||||
"clear_session",
|
||||
"save_to_file",
|
||||
"load_from_file",
|
||||
"format_datetime",
|
||||
"parse_datetime",
|
||||
"validate_tweet_content",
|
||||
"validate_hashtags",
|
||||
"validate_emojis",
|
||||
"calculate_engagement_score",
|
||||
"generate_tweet_metrics",
|
||||
"copy_to_clipboard",
|
||||
"show_success_message",
|
||||
"show_error_message",
|
||||
"show_info_message",
|
||||
"show_warning_message",
|
||||
"create_download_button",
|
||||
"create_upload_button"
|
||||
]
|
||||
@@ -1,634 +0,0 @@
|
||||
"""
|
||||
Enhanced UI Cards with modern styling and improved functionality.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
from typing import Dict, List, Optional, Callable
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
from datetime import datetime
|
||||
|
||||
def apply_cards_styling():
|
||||
"""Apply modern CSS styling for cards."""
|
||||
st.markdown("""
|
||||
<style>
|
||||
/* Modern Card Styles */
|
||||
.modern-card {
|
||||
background: rgba(255, 255, 255, 0.95);
|
||||
backdrop-filter: blur(20px);
|
||||
border-radius: 16px;
|
||||
padding: 1.5rem;
|
||||
margin: 1rem 0;
|
||||
box-shadow: 0 8px 32px rgba(0, 0, 0, 0.1);
|
||||
border: 1px solid rgba(255, 255, 255, 0.2);
|
||||
transition: all 0.3s ease;
|
||||
position: relative;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.modern-card:hover {
|
||||
transform: translateY(-4px);
|
||||
box-shadow: 0 12px 40px rgba(0, 0, 0, 0.15);
|
||||
}
|
||||
|
||||
.modern-card::before {
|
||||
content: '';
|
||||
position: absolute;
|
||||
top: 0;
|
||||
left: 0;
|
||||
right: 0;
|
||||
height: 4px;
|
||||
background: linear-gradient(135deg, #1DA1F2, #0C85D0);
|
||||
}
|
||||
|
||||
.feature-card {
|
||||
background: white;
|
||||
border-radius: 12px;
|
||||
padding: 1.5rem;
|
||||
margin: 0.75rem 0;
|
||||
box-shadow: 0 4px 20px rgba(0, 0, 0, 0.08);
|
||||
border: 1px solid #E1E8ED;
|
||||
transition: all 0.3s ease;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.feature-card:hover {
|
||||
transform: translateY(-2px);
|
||||
box-shadow: 0 8px 30px rgba(29, 161, 242, 0.15);
|
||||
border-color: #1DA1F2;
|
||||
}
|
||||
|
||||
.feature-card-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 1rem;
|
||||
margin-bottom: 1rem;
|
||||
}
|
||||
|
||||
.feature-icon {
|
||||
font-size: 2rem;
|
||||
width: 60px;
|
||||
height: 60px;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
background: linear-gradient(135deg, #E6F7FF, #F0F9FF);
|
||||
border-radius: 12px;
|
||||
border: 2px solid #91D5FF;
|
||||
}
|
||||
|
||||
.feature-title {
|
||||
font-size: 1.25rem;
|
||||
font-weight: 600;
|
||||
color: #2D3748;
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
.feature-description {
|
||||
color: #657786;
|
||||
font-size: 0.95rem;
|
||||
line-height: 1.5;
|
||||
margin-bottom: 1rem;
|
||||
}
|
||||
|
||||
.feature-stats {
|
||||
display: flex;
|
||||
gap: 1rem;
|
||||
margin-top: 1rem;
|
||||
padding-top: 1rem;
|
||||
border-top: 1px solid #E1E8ED;
|
||||
}
|
||||
|
||||
.stat-item {
|
||||
text-align: center;
|
||||
flex: 1;
|
||||
}
|
||||
|
||||
.stat-value {
|
||||
font-size: 1.5rem;
|
||||
font-weight: 700;
|
||||
color: #1DA1F2;
|
||||
display: block;
|
||||
}
|
||||
|
||||
.stat-label {
|
||||
font-size: 0.8rem;
|
||||
color: #657786;
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.5px;
|
||||
}
|
||||
|
||||
.tweet-card {
|
||||
background: white;
|
||||
border: 1px solid #E1E8ED;
|
||||
border-radius: 16px;
|
||||
padding: 1.5rem;
|
||||
margin: 1rem 0;
|
||||
box-shadow: 0 4px 15px rgba(0, 0, 0, 0.08);
|
||||
position: relative;
|
||||
}
|
||||
|
||||
.tweet-card::before {
|
||||
content: "🐦";
|
||||
position: absolute;
|
||||
top: -10px;
|
||||
left: 20px;
|
||||
background: white;
|
||||
padding: 0 10px;
|
||||
font-size: 1.2rem;
|
||||
}
|
||||
|
||||
.tweet-content {
|
||||
font-size: 1.1rem;
|
||||
line-height: 1.5;
|
||||
color: #14171A;
|
||||
margin-bottom: 1rem;
|
||||
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif;
|
||||
}
|
||||
|
||||
.tweet-metadata {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
color: #657786;
|
||||
font-size: 0.9rem;
|
||||
border-top: 1px solid #E1E8ED;
|
||||
padding-top: 1rem;
|
||||
}
|
||||
|
||||
.engagement-badge {
|
||||
background: linear-gradient(135deg, #52C41A, #73D13D);
|
||||
color: white;
|
||||
padding: 0.5rem 1rem;
|
||||
border-radius: 20px;
|
||||
font-weight: 600;
|
||||
font-size: 0.9rem;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 0.5rem;
|
||||
}
|
||||
|
||||
.character-badge {
|
||||
padding: 0.25rem 0.75rem;
|
||||
border-radius: 20px;
|
||||
font-weight: 600;
|
||||
font-size: 0.8rem;
|
||||
}
|
||||
|
||||
.char-good { background: #E6F7FF; color: #1890FF; }
|
||||
.char-warning { background: #FFF7E6; color: #FA8C16; }
|
||||
.char-danger { background: #FFF1F0; color: #F5222D; }
|
||||
|
||||
.card-actions {
|
||||
display: flex;
|
||||
gap: 0.5rem;
|
||||
margin-top: 1rem;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
|
||||
.action-button {
|
||||
background: #F7F9FA;
|
||||
border: 1px solid #E1E8ED;
|
||||
border-radius: 8px;
|
||||
padding: 0.5rem 1rem;
|
||||
color: #657786;
|
||||
font-size: 0.9rem;
|
||||
cursor: pointer;
|
||||
transition: all 0.3s ease;
|
||||
text-decoration: none;
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
gap: 0.5rem;
|
||||
}
|
||||
|
||||
.action-button:hover {
|
||||
background: #1DA1F2;
|
||||
color: white;
|
||||
border-color: #1DA1F2;
|
||||
transform: translateY(-1px);
|
||||
}
|
||||
|
||||
.action-button.primary {
|
||||
background: #1DA1F2;
|
||||
color: white;
|
||||
border-color: #1DA1F2;
|
||||
}
|
||||
|
||||
.action-button.primary:hover {
|
||||
background: #0C85D0;
|
||||
border-color: #0C85D0;
|
||||
}
|
||||
|
||||
.metrics-grid {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(auto-fit, minmax(120px, 1fr));
|
||||
gap: 1rem;
|
||||
margin: 1rem 0;
|
||||
}
|
||||
|
||||
.metric-card {
|
||||
background: white;
|
||||
border-radius: 8px;
|
||||
padding: 1rem;
|
||||
text-align: center;
|
||||
box-shadow: 0 2px 8px rgba(0, 0, 0, 0.05);
|
||||
border: 1px solid #E1E8ED;
|
||||
}
|
||||
|
||||
.metric-value {
|
||||
font-size: 1.5rem;
|
||||
font-weight: 700;
|
||||
color: #1DA1F2;
|
||||
display: block;
|
||||
margin-bottom: 0.25rem;
|
||||
}
|
||||
|
||||
.metric-label {
|
||||
font-size: 0.8rem;
|
||||
color: #657786;
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.5px;
|
||||
}
|
||||
|
||||
/* Responsive Design */
|
||||
@media (max-width: 768px) {
|
||||
.modern-card, .feature-card, .tweet-card {
|
||||
margin: 0.5rem;
|
||||
padding: 1rem;
|
||||
}
|
||||
|
||||
.feature-card-header {
|
||||
flex-direction: column;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.feature-stats {
|
||||
flex-direction: column;
|
||||
gap: 0.5rem;
|
||||
}
|
||||
|
||||
.card-actions {
|
||||
justify-content: center;
|
||||
}
|
||||
|
||||
.metrics-grid {
|
||||
grid-template-columns: repeat(2, 1fr);
|
||||
}
|
||||
}
|
||||
</style>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
class FeatureCard:
|
||||
"""Modern feature card component."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
title: str,
|
||||
description: str,
|
||||
icon: str = "🔧",
|
||||
stats: Optional[Dict[str, any]] = None,
|
||||
actions: Optional[List[Dict]] = None,
|
||||
on_click: Optional[Callable] = None
|
||||
):
|
||||
self.title = title
|
||||
self.description = description
|
||||
self.icon = icon
|
||||
self.stats = stats or {}
|
||||
self.actions = actions or []
|
||||
self.on_click = on_click
|
||||
|
||||
def render(self):
|
||||
"""Render the feature card."""
|
||||
apply_cards_styling()
|
||||
|
||||
# Create stats HTML
|
||||
stats_html = ""
|
||||
if self.stats:
|
||||
stats_items = []
|
||||
for label, value in self.stats.items():
|
||||
stats_items.append(f"""
|
||||
<div class="stat-item">
|
||||
<span class="stat-value">{value}</span>
|
||||
<span class="stat-label">{label}</span>
|
||||
</div>
|
||||
""")
|
||||
stats_html = f"""
|
||||
<div class="feature-stats">
|
||||
{''.join(stats_items)}
|
||||
</div>
|
||||
"""
|
||||
|
||||
# Create actions HTML
|
||||
actions_html = ""
|
||||
if self.actions:
|
||||
action_buttons = []
|
||||
for action in self.actions:
|
||||
button_class = "action-button"
|
||||
if action.get("primary", False):
|
||||
button_class += " primary"
|
||||
|
||||
action_buttons.append(f"""
|
||||
<button class="{button_class}" onclick="{action.get('onclick', '')}">
|
||||
{action.get('icon', '')} {action.get('label', 'Action')}
|
||||
</button>
|
||||
""")
|
||||
actions_html = f"""
|
||||
<div class="card-actions">
|
||||
{''.join(action_buttons)}
|
||||
</div>
|
||||
"""
|
||||
|
||||
# Render the card
|
||||
card_html = f"""
|
||||
<div class="feature-card" onclick="{self.on_click or ''}">
|
||||
<div class="feature-card-header">
|
||||
<div class="feature-icon">{self.icon}</div>
|
||||
<div>
|
||||
<h3 class="feature-title">{self.title}</h3>
|
||||
</div>
|
||||
</div>
|
||||
<p class="feature-description">{self.description}</p>
|
||||
{stats_html}
|
||||
{actions_html}
|
||||
</div>
|
||||
"""
|
||||
|
||||
st.markdown(card_html, unsafe_allow_html=True)
|
||||
|
||||
class TweetCard:
|
||||
"""Modern tweet card component."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
content: str,
|
||||
engagement_score: int = 0,
|
||||
hashtags: List[str] = None,
|
||||
emojis: List[str] = None,
|
||||
metrics: Optional[Dict] = None,
|
||||
timestamp: Optional[str] = None,
|
||||
on_copy: Optional[Callable] = None,
|
||||
on_save: Optional[Callable] = None,
|
||||
on_edit: Optional[Callable] = None,
|
||||
on_post: Optional[Callable] = None
|
||||
):
|
||||
self.content = content
|
||||
self.engagement_score = engagement_score
|
||||
self.hashtags = hashtags or []
|
||||
self.emojis = emojis or []
|
||||
self.metrics = metrics or {}
|
||||
self.timestamp = timestamp or datetime.now().strftime("%Y-%m-%d %H:%M")
|
||||
self.on_copy = on_copy
|
||||
self.on_save = on_save
|
||||
self.on_edit = on_edit
|
||||
self.on_post = on_post
|
||||
|
||||
def _get_character_info(self):
|
||||
"""Get character count information."""
|
||||
full_text = f"{self.content} {' '.join(self.hashtags)}"
|
||||
count = len(full_text)
|
||||
remaining = 280 - count
|
||||
|
||||
if count <= 240:
|
||||
status_class = "char-good"
|
||||
elif count <= 270:
|
||||
status_class = "char-warning"
|
||||
else:
|
||||
status_class = "char-danger"
|
||||
|
||||
return {
|
||||
"count": count,
|
||||
"remaining": remaining,
|
||||
"status_class": status_class
|
||||
}
|
||||
|
||||
def render(self):
|
||||
"""Render the tweet card."""
|
||||
apply_cards_styling()
|
||||
|
||||
char_info = self._get_character_info()
|
||||
full_content = f"{self.content} {' '.join(self.hashtags)}"
|
||||
|
||||
# Create metrics HTML
|
||||
metrics_html = ""
|
||||
if self.metrics:
|
||||
metric_items = []
|
||||
for label, value in self.metrics.items():
|
||||
metric_items.append(f"""
|
||||
<div class="metric-card">
|
||||
<span class="metric-value">{value}</span>
|
||||
<span class="metric-label">{label}</span>
|
||||
</div>
|
||||
""")
|
||||
metrics_html = f"""
|
||||
<div class="metrics-grid">
|
||||
{''.join(metric_items)}
|
||||
</div>
|
||||
"""
|
||||
|
||||
# Create actions
|
||||
actions = []
|
||||
if self.on_copy:
|
||||
actions.append('<button class="action-button" onclick="copyTweet()">📋 Copy</button>')
|
||||
if self.on_save:
|
||||
actions.append('<button class="action-button" onclick="saveTweet()">💾 Save</button>')
|
||||
if self.on_edit:
|
||||
actions.append('<button class="action-button" onclick="editTweet()">✏️ Edit</button>')
|
||||
if self.on_post:
|
||||
actions.append('<button class="action-button primary" onclick="postTweet()">🐦 Post</button>')
|
||||
|
||||
actions_html = f'<div class="card-actions">{"".join(actions)}</div>' if actions else ""
|
||||
|
||||
# Render the card
|
||||
card_html = f"""
|
||||
<div class="tweet-card">
|
||||
<div class="tweet-content">{full_content}</div>
|
||||
{metrics_html}
|
||||
<div class="tweet-metadata">
|
||||
<div class="engagement-badge">
|
||||
📊 {self.engagement_score}% Engagement
|
||||
</div>
|
||||
<div class="character-badge {char_info['status_class']}">
|
||||
{char_info['count']}/280
|
||||
</div>
|
||||
</div>
|
||||
{actions_html}
|
||||
</div>
|
||||
"""
|
||||
|
||||
st.markdown(card_html, unsafe_allow_html=True)
|
||||
|
||||
class MetricsCard:
|
||||
"""Modern metrics display card."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
title: str,
|
||||
metrics: Dict[str, any],
|
||||
chart_data: Optional[Dict] = None,
|
||||
trend: Optional[str] = None
|
||||
):
|
||||
self.title = title
|
||||
self.metrics = metrics
|
||||
self.chart_data = chart_data
|
||||
self.trend = trend
|
||||
|
||||
def render(self):
|
||||
"""Render the metrics card."""
|
||||
apply_cards_styling()
|
||||
|
||||
# Create metrics grid
|
||||
metric_items = []
|
||||
for label, value in self.metrics.items():
|
||||
metric_items.append(f"""
|
||||
<div class="metric-card">
|
||||
<span class="metric-value">{value}</span>
|
||||
<span class="metric-label">{label}</span>
|
||||
</div>
|
||||
""")
|
||||
|
||||
metrics_grid = f"""
|
||||
<div class="metrics-grid">
|
||||
{''.join(metric_items)}
|
||||
</div>
|
||||
"""
|
||||
|
||||
# Add trend indicator
|
||||
trend_html = ""
|
||||
if self.trend:
|
||||
trend_color = "#52C41A" if "up" in self.trend.lower() else "#F5222D"
|
||||
trend_icon = "📈" if "up" in self.trend.lower() else "📉"
|
||||
trend_html = f"""
|
||||
<div style="text-align: center; margin-top: 1rem; color: {trend_color};">
|
||||
{trend_icon} {self.trend}
|
||||
</div>
|
||||
"""
|
||||
|
||||
# Render the card
|
||||
card_html = f"""
|
||||
<div class="modern-card">
|
||||
<h3 style="margin-bottom: 1rem; color: #2D3748;">{self.title}</h3>
|
||||
{metrics_grid}
|
||||
{trend_html}
|
||||
</div>
|
||||
"""
|
||||
|
||||
st.markdown(card_html, unsafe_allow_html=True)
|
||||
|
||||
# Add chart if provided
|
||||
if self.chart_data:
|
||||
self._render_chart()
|
||||
|
||||
def _render_chart(self):
|
||||
"""Render chart for metrics."""
|
||||
if self.chart_data.get("type") == "line":
|
||||
fig = px.line(
|
||||
x=self.chart_data.get("x", []),
|
||||
y=self.chart_data.get("y", []),
|
||||
title=self.chart_data.get("title", ""),
|
||||
labels=self.chart_data.get("labels", {})
|
||||
)
|
||||
elif self.chart_data.get("type") == "bar":
|
||||
fig = px.bar(
|
||||
x=self.chart_data.get("x", []),
|
||||
y=self.chart_data.get("y", []),
|
||||
title=self.chart_data.get("title", ""),
|
||||
labels=self.chart_data.get("labels", {})
|
||||
)
|
||||
else:
|
||||
return
|
||||
|
||||
fig.update_layout(
|
||||
plot_bgcolor='rgba(0,0,0,0)',
|
||||
paper_bgcolor='rgba(0,0,0,0)',
|
||||
showlegend=False,
|
||||
height=300
|
||||
)
|
||||
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
|
||||
class StatusCard:
|
||||
"""Status indicator card."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
title: str,
|
||||
status: str,
|
||||
message: str,
|
||||
icon: str = "ℹ️",
|
||||
actions: Optional[List[Dict]] = None
|
||||
):
|
||||
self.title = title
|
||||
self.status = status # success, warning, error, info
|
||||
self.message = message
|
||||
self.icon = icon
|
||||
self.actions = actions or []
|
||||
|
||||
def render(self):
|
||||
"""Render the status card."""
|
||||
apply_cards_styling()
|
||||
|
||||
# Status colors
|
||||
status_colors = {
|
||||
"success": "#52C41A",
|
||||
"warning": "#FA8C16",
|
||||
"error": "#F5222D",
|
||||
"info": "#1890FF"
|
||||
}
|
||||
|
||||
color = status_colors.get(self.status, "#1890FF")
|
||||
|
||||
# Create actions
|
||||
actions_html = ""
|
||||
if self.actions:
|
||||
action_buttons = []
|
||||
for action in self.actions:
|
||||
action_buttons.append(f"""
|
||||
<button class="action-button" onclick="{action.get('onclick', '')}">
|
||||
{action.get('icon', '')} {action.get('label', 'Action')}
|
||||
</button>
|
||||
""")
|
||||
actions_html = f"""
|
||||
<div class="card-actions">
|
||||
{''.join(action_buttons)}
|
||||
</div>
|
||||
"""
|
||||
|
||||
# Render the card
|
||||
card_html = f"""
|
||||
<div class="modern-card" style="border-left: 4px solid {color};">
|
||||
<div style="display: flex; align-items: center; gap: 1rem; margin-bottom: 1rem;">
|
||||
<span style="font-size: 2rem;">{self.icon}</span>
|
||||
<div>
|
||||
<h3 style="margin: 0; color: #2D3748;">{self.title}</h3>
|
||||
<span style="color: {color}; font-weight: 600; text-transform: uppercase; font-size: 0.8rem;">
|
||||
{self.status}
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
<p style="color: #657786; margin-bottom: 1rem;">{self.message}</p>
|
||||
{actions_html}
|
||||
</div>
|
||||
"""
|
||||
|
||||
st.markdown(card_html, unsafe_allow_html=True)
|
||||
|
||||
# Utility functions for creating common cards
|
||||
def create_feature_card(title: str, description: str, icon: str = "🔧", **kwargs):
|
||||
"""Create and render a feature card."""
|
||||
card = FeatureCard(title, description, icon, **kwargs)
|
||||
card.render()
|
||||
|
||||
def create_tweet_card(content: str, **kwargs):
|
||||
"""Create and render a tweet card."""
|
||||
card = TweetCard(content, **kwargs)
|
||||
card.render()
|
||||
|
||||
def create_metrics_card(title: str, metrics: Dict, **kwargs):
|
||||
"""Create and render a metrics card."""
|
||||
card = MetricsCard(title, metrics, **kwargs)
|
||||
card.render()
|
||||
|
||||
def create_status_card(title: str, status: str, message: str, **kwargs):
|
||||
"""Create and render a status card."""
|
||||
card = StatusCard(title, status, message, **kwargs)
|
||||
card.render()
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,554 +0,0 @@
|
||||
"""
|
||||
Enhanced Navigation Component for Twitter UI with modern styling and improved functionality.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
from typing import Dict, List, Optional, Callable, Any
|
||||
from ..styles.theme import Theme
|
||||
import os
|
||||
|
||||
def apply_navigation_styling():
|
||||
"""Apply modern CSS styling for navigation components."""
|
||||
st.markdown("""
|
||||
<style>
|
||||
/* Navigation Styles */
|
||||
.nav-container {
|
||||
background: rgba(255, 255, 255, 0.95);
|
||||
backdrop-filter: blur(20px);
|
||||
border-radius: 16px;
|
||||
padding: 1rem;
|
||||
margin-bottom: 2rem;
|
||||
box-shadow: 0 8px 32px rgba(0, 0, 0, 0.1);
|
||||
border: 1px solid rgba(255, 255, 255, 0.2);
|
||||
}
|
||||
|
||||
.nav-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
margin-bottom: 1rem;
|
||||
padding-bottom: 1rem;
|
||||
border-bottom: 2px solid #E2E8F0;
|
||||
}
|
||||
|
||||
.nav-title {
|
||||
font-size: 1.5rem;
|
||||
font-weight: 700;
|
||||
color: #1DA1F2;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 0.5rem;
|
||||
}
|
||||
|
||||
.nav-status {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 0.5rem;
|
||||
padding: 0.5rem 1rem;
|
||||
border-radius: 20px;
|
||||
font-size: 0.9rem;
|
||||
font-weight: 600;
|
||||
}
|
||||
|
||||
.status-connected {
|
||||
background: linear-gradient(135deg, #52C41A, #73D13D);
|
||||
color: white;
|
||||
}
|
||||
|
||||
.status-disconnected {
|
||||
background: linear-gradient(135deg, #FA8C16, #FFA940);
|
||||
color: white;
|
||||
}
|
||||
|
||||
.nav-menu {
|
||||
display: flex;
|
||||
gap: 0.5rem;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
|
||||
.nav-item {
|
||||
background: #F7F9FA;
|
||||
border: 2px solid transparent;
|
||||
border-radius: 12px;
|
||||
padding: 0.75rem 1.5rem;
|
||||
color: #657786;
|
||||
font-weight: 600;
|
||||
cursor: pointer;
|
||||
transition: all 0.3s ease;
|
||||
text-decoration: none;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 0.5rem;
|
||||
}
|
||||
|
||||
.nav-item:hover {
|
||||
background: #E1F5FE;
|
||||
border-color: #1DA1F2;
|
||||
color: #1DA1F2;
|
||||
transform: translateY(-2px);
|
||||
box-shadow: 0 4px 15px rgba(29, 161, 242, 0.2);
|
||||
}
|
||||
|
||||
.nav-item.active {
|
||||
background: linear-gradient(135deg, #1DA1F2, #0C85D0);
|
||||
color: white;
|
||||
border-color: #1DA1F2;
|
||||
box-shadow: 0 4px 15px rgba(29, 161, 242, 0.3);
|
||||
}
|
||||
|
||||
.nav-item.active:hover {
|
||||
transform: translateY(-2px);
|
||||
box-shadow: 0 6px 20px rgba(29, 161, 242, 0.4);
|
||||
}
|
||||
|
||||
.nav-breadcrumb {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 0.5rem;
|
||||
margin-bottom: 1rem;
|
||||
font-size: 0.9rem;
|
||||
color: #657786;
|
||||
}
|
||||
|
||||
.breadcrumb-item {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 0.25rem;
|
||||
}
|
||||
|
||||
.breadcrumb-separator {
|
||||
color: #CBD5E0;
|
||||
margin: 0 0.5rem;
|
||||
}
|
||||
|
||||
.nav-actions {
|
||||
display: flex;
|
||||
gap: 0.5rem;
|
||||
align-items: center;
|
||||
}
|
||||
|
||||
.action-button {
|
||||
background: linear-gradient(135deg, #52C41A, #73D13D);
|
||||
color: white;
|
||||
border: none;
|
||||
border-radius: 8px;
|
||||
padding: 0.5rem 1rem;
|
||||
font-weight: 600;
|
||||
cursor: pointer;
|
||||
transition: all 0.3s ease;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 0.5rem;
|
||||
}
|
||||
|
||||
.action-button:hover {
|
||||
transform: translateY(-2px);
|
||||
box-shadow: 0 4px 15px rgba(82, 196, 26, 0.3);
|
||||
}
|
||||
|
||||
.action-button.secondary {
|
||||
background: #F7F9FA;
|
||||
color: #657786;
|
||||
border: 1px solid #E1E8ED;
|
||||
}
|
||||
|
||||
.action-button.secondary:hover {
|
||||
background: #E1F5FE;
|
||||
color: #1DA1F2;
|
||||
border-color: #1DA1F2;
|
||||
}
|
||||
|
||||
/* Mobile Responsive */
|
||||
@media (max-width: 768px) {
|
||||
.nav-header {
|
||||
flex-direction: column;
|
||||
gap: 1rem;
|
||||
align-items: flex-start;
|
||||
}
|
||||
|
||||
.nav-menu {
|
||||
flex-direction: column;
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
.nav-item {
|
||||
width: 100%;
|
||||
justify-content: center;
|
||||
}
|
||||
|
||||
.nav-actions {
|
||||
width: 100%;
|
||||
justify-content: center;
|
||||
}
|
||||
}
|
||||
</style>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
class TwitterNavigation:
|
||||
"""Enhanced navigation component for Twitter dashboard."""
|
||||
|
||||
def __init__(self, theme: Optional[Theme] = None):
|
||||
self.theme = theme or Theme()
|
||||
self.current_page = st.session_state.get('current_page', 'dashboard')
|
||||
|
||||
def render_header(self, title: str = "Twitter AI Assistant", show_status: bool = True):
|
||||
"""Render the navigation header with title and status."""
|
||||
apply_navigation_styling()
|
||||
|
||||
st.markdown('<div class="nav-container">', unsafe_allow_html=True)
|
||||
st.markdown('<div class="nav-header">', unsafe_allow_html=True)
|
||||
|
||||
# Title
|
||||
st.markdown(f'<div class="nav-title">🐦 {title}</div>', unsafe_allow_html=True)
|
||||
|
||||
# Status indicator
|
||||
if show_status:
|
||||
twitter_connected = self._check_twitter_connection()
|
||||
status_class = "status-connected" if twitter_connected else "status-disconnected"
|
||||
status_text = "Connected" if twitter_connected else "Not Connected"
|
||||
status_icon = "✅" if twitter_connected else "⚠️"
|
||||
|
||||
st.markdown(f'''
|
||||
<div class="nav-status {status_class}">
|
||||
{status_icon} Twitter {status_text}
|
||||
</div>
|
||||
''', unsafe_allow_html=True)
|
||||
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
|
||||
def render_menu(self, menu_items: List[Dict], current_page: Optional[str] = None):
|
||||
"""Render navigation menu with items."""
|
||||
if current_page:
|
||||
self.current_page = current_page
|
||||
st.session_state.current_page = current_page
|
||||
|
||||
st.markdown('<div class="nav-menu">', unsafe_allow_html=True)
|
||||
|
||||
cols = st.columns(len(menu_items))
|
||||
|
||||
for i, item in enumerate(menu_items):
|
||||
with cols[i]:
|
||||
active_class = "active" if item.get('key') == self.current_page else ""
|
||||
|
||||
if st.button(
|
||||
f"{item.get('icon', '')} {item.get('label', '')}",
|
||||
key=f"nav_{item.get('key', i)}",
|
||||
use_container_width=True,
|
||||
type="primary" if active_class else "secondary"
|
||||
):
|
||||
st.session_state.current_page = item.get('key')
|
||||
if item.get('callback'):
|
||||
item['callback']()
|
||||
st.rerun()
|
||||
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
|
||||
return st.session_state.get('current_page', menu_items[0].get('key'))
|
||||
|
||||
def render_breadcrumb(self, items: List[Dict]):
|
||||
"""Render breadcrumb navigation."""
|
||||
st.markdown('<div class="nav-breadcrumb">', unsafe_allow_html=True)
|
||||
|
||||
for i, item in enumerate(items):
|
||||
if i > 0:
|
||||
st.markdown('<span class="breadcrumb-separator">›</span>', unsafe_allow_html=True)
|
||||
|
||||
icon = item.get('icon', '')
|
||||
label = item.get('label', '')
|
||||
|
||||
if item.get('active', False):
|
||||
st.markdown(f'<span class="breadcrumb-item"><strong>{icon} {label}</strong></span>', unsafe_allow_html=True)
|
||||
else:
|
||||
st.markdown(f'<span class="breadcrumb-item">{icon} {label}</span>', unsafe_allow_html=True)
|
||||
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
|
||||
def render_actions(self, actions: List[Dict]):
|
||||
"""Render action buttons in navigation."""
|
||||
st.markdown('<div class="nav-actions">', unsafe_allow_html=True)
|
||||
|
||||
cols = st.columns(len(actions))
|
||||
|
||||
for i, action in enumerate(actions):
|
||||
with cols[i]:
|
||||
button_type = action.get('type', 'primary')
|
||||
|
||||
if st.button(
|
||||
f"{action.get('icon', '')} {action.get('label', '')}",
|
||||
key=f"action_{action.get('key', i)}",
|
||||
type=button_type,
|
||||
use_container_width=True,
|
||||
help=action.get('help', '')
|
||||
):
|
||||
if action.get('callback'):
|
||||
action['callback']()
|
||||
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
|
||||
def render_sidebar_menu(self, menu_items: List[Dict]):
|
||||
"""Render sidebar navigation menu."""
|
||||
with st.sidebar:
|
||||
st.markdown("### 🐦 Twitter Tools")
|
||||
|
||||
for item in menu_items:
|
||||
icon = item.get('icon', '')
|
||||
label = item.get('label', '')
|
||||
key = item.get('key', '')
|
||||
|
||||
if st.button(f"{icon} {label}", key=f"sidebar_{key}", use_container_width=True):
|
||||
st.session_state.current_page = key
|
||||
if item.get('callback'):
|
||||
item['callback']()
|
||||
st.rerun()
|
||||
|
||||
# Twitter connection status in sidebar
|
||||
st.markdown("---")
|
||||
twitter_connected = self._check_twitter_connection()
|
||||
|
||||
if twitter_connected:
|
||||
st.success("🐦 Twitter Connected")
|
||||
else:
|
||||
st.warning("⚠️ Twitter Not Connected")
|
||||
if st.button("🔧 Configure Twitter", use_container_width=True):
|
||||
st.session_state.show_twitter_config = True
|
||||
st.rerun()
|
||||
|
||||
def _check_twitter_connection(self) -> bool:
|
||||
"""Check if Twitter is connected."""
|
||||
twitter_config = st.session_state.get('twitter_config', {})
|
||||
return bool(twitter_config and all([
|
||||
twitter_config.get('api_key'),
|
||||
twitter_config.get('api_secret'),
|
||||
twitter_config.get('access_token'),
|
||||
twitter_config.get('access_token_secret')
|
||||
]))
|
||||
|
||||
class Sidebar:
|
||||
"""Sidebar navigation component."""
|
||||
|
||||
def __init__(self, title: str = "Navigation", logo: Optional[str] = None):
|
||||
"""Initialize the sidebar."""
|
||||
self.title = title
|
||||
self.logo = logo
|
||||
self.menu_items = []
|
||||
|
||||
def add_menu_item(self, label: str, icon: str, key: str, callback: Optional[Callable] = None):
|
||||
"""Add a menu item to the sidebar."""
|
||||
self.menu_items.append({
|
||||
'label': label,
|
||||
'icon': icon,
|
||||
'key': key,
|
||||
'callback': callback
|
||||
})
|
||||
|
||||
def render(self) -> str:
|
||||
"""Render the sidebar and return the selected page."""
|
||||
with st.sidebar:
|
||||
# Logo and title
|
||||
if self.logo and os.path.exists(self.logo):
|
||||
st.image(self.logo, width=100)
|
||||
st.title(self.title)
|
||||
st.markdown("---")
|
||||
|
||||
# Menu items
|
||||
selected_page = None
|
||||
for item in self.menu_items:
|
||||
if st.button(
|
||||
f"{item['icon']} {item['label']}",
|
||||
key=f"sidebar_{item['key']}",
|
||||
use_container_width=True
|
||||
):
|
||||
selected_page = item['key']
|
||||
if item.get('callback'):
|
||||
item['callback']()
|
||||
|
||||
return selected_page or st.session_state.get('current_page', 'dashboard')
|
||||
|
||||
|
||||
class Header:
|
||||
"""Header component with title and actions."""
|
||||
|
||||
def __init__(self, title: str = "Dashboard", subtitle: str = ""):
|
||||
"""Initialize the header."""
|
||||
self.title = title
|
||||
self.subtitle = subtitle
|
||||
self.actions = []
|
||||
|
||||
def add_action(self, label: str, icon: str, callback: Callable, help_text: str = ""):
|
||||
"""Add an action button to the header."""
|
||||
self.actions.append({
|
||||
'label': label,
|
||||
'icon': icon,
|
||||
'callback': callback,
|
||||
'help': help_text
|
||||
})
|
||||
|
||||
def render(self):
|
||||
"""Render the header."""
|
||||
col1, col2 = st.columns([3, 1])
|
||||
|
||||
with col1:
|
||||
st.title(f"{self.title}")
|
||||
if self.subtitle:
|
||||
st.markdown(f"*{self.subtitle}*")
|
||||
|
||||
with col2:
|
||||
if self.actions:
|
||||
for i, action in enumerate(self.actions):
|
||||
if st.button(
|
||||
f"{action['icon']} {action['label']}",
|
||||
key=f"header_action_{i}",
|
||||
help=action.get('help', ''),
|
||||
use_container_width=True
|
||||
):
|
||||
action['callback']()
|
||||
|
||||
|
||||
class Tabs:
|
||||
"""Tab navigation component."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the tabs."""
|
||||
self.tabs = []
|
||||
|
||||
def add_tab(self, label: str, icon: str, content_func: Callable):
|
||||
"""Add a tab."""
|
||||
self.tabs.append({
|
||||
'label': label,
|
||||
'icon': icon,
|
||||
'content_func': content_func
|
||||
})
|
||||
|
||||
def render(self):
|
||||
"""Render the tabs."""
|
||||
if not self.tabs:
|
||||
return
|
||||
|
||||
tab_labels = [f"{tab['icon']} {tab['label']}" for tab in self.tabs]
|
||||
selected_tabs = st.tabs(tab_labels)
|
||||
|
||||
for i, tab in enumerate(self.tabs):
|
||||
with selected_tabs[i]:
|
||||
tab['content_func']()
|
||||
|
||||
|
||||
class Breadcrumbs:
|
||||
"""Breadcrumb navigation component."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize breadcrumbs."""
|
||||
self.items = []
|
||||
|
||||
def add_item(self, label: str, key: str = None, callback: Callable = None):
|
||||
"""Add a breadcrumb item."""
|
||||
self.items.append({
|
||||
'label': label,
|
||||
'key': key,
|
||||
'callback': callback
|
||||
})
|
||||
|
||||
def render(self):
|
||||
"""Render the breadcrumbs."""
|
||||
if not self.items:
|
||||
return
|
||||
|
||||
breadcrumb_html = '<div class="nav-breadcrumb">'
|
||||
|
||||
for i, item in enumerate(self.items):
|
||||
if i > 0:
|
||||
breadcrumb_html += '<span class="breadcrumb-separator">›</span>'
|
||||
|
||||
if item.get('callback'):
|
||||
breadcrumb_html += f'<span class="breadcrumb-item clickable" onclick="handleBreadcrumbClick(\'{item["key"]}\')">{item["label"]}</span>'
|
||||
else:
|
||||
breadcrumb_html += f'<span class="breadcrumb-item">{item["label"]}</span>'
|
||||
|
||||
breadcrumb_html += '</div>'
|
||||
st.markdown(breadcrumb_html, unsafe_allow_html=True)
|
||||
|
||||
|
||||
def create_main_navigation() -> TwitterNavigation:
|
||||
"""Create and return the main navigation instance."""
|
||||
return TwitterNavigation()
|
||||
|
||||
def render_page_header(title: str, subtitle: str = "", icon: str = ""):
|
||||
"""Render a consistent page header."""
|
||||
st.markdown(f"""
|
||||
<div style="text-align: center; margin-bottom: 2rem; padding: 2rem; background: linear-gradient(135deg, #E6F7FF, #F0F9FF); border-radius: 16px;">
|
||||
<h1 style="color: #1DA1F2; margin-bottom: 0.5rem;">{icon} {title}</h1>
|
||||
{f'<p style="color: #657786; font-size: 1.1rem;">{subtitle}</p>' if subtitle else ''}
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
def render_quick_actions(actions: List[Dict]):
|
||||
"""Render quick action buttons."""
|
||||
st.markdown("### ⚡ Quick Actions")
|
||||
|
||||
cols = st.columns(len(actions))
|
||||
|
||||
for i, action in enumerate(actions):
|
||||
with cols[i]:
|
||||
if st.button(
|
||||
f"{action.get('icon', '')} {action.get('label', '')}",
|
||||
key=f"quick_action_{i}",
|
||||
use_container_width=True,
|
||||
help=action.get('help', '')
|
||||
):
|
||||
if action.get('callback'):
|
||||
action['callback']()
|
||||
|
||||
# Default menu items for Twitter dashboard
|
||||
DEFAULT_MENU_ITEMS = [
|
||||
{
|
||||
'key': 'dashboard',
|
||||
'label': 'Dashboard',
|
||||
'icon': '🏠',
|
||||
'help': 'Main dashboard overview'
|
||||
},
|
||||
{
|
||||
'key': 'generator',
|
||||
'label': 'Tweet Generator',
|
||||
'icon': '✨',
|
||||
'help': 'AI-powered tweet generation'
|
||||
},
|
||||
{
|
||||
'key': 'analytics',
|
||||
'label': 'Analytics',
|
||||
'icon': '📊',
|
||||
'help': 'Tweet performance analytics'
|
||||
},
|
||||
{
|
||||
'key': 'scheduler',
|
||||
'label': 'Scheduler',
|
||||
'icon': '📅',
|
||||
'help': 'Schedule tweets for later'
|
||||
},
|
||||
{
|
||||
'key': 'settings',
|
||||
'label': 'Settings',
|
||||
'icon': '⚙️',
|
||||
'help': 'Twitter account and API settings'
|
||||
}
|
||||
]
|
||||
|
||||
DEFAULT_QUICK_ACTIONS = [
|
||||
{
|
||||
'key': 'new_tweet',
|
||||
'label': 'New Tweet',
|
||||
'icon': '✍️',
|
||||
'help': 'Create a new tweet'
|
||||
},
|
||||
{
|
||||
'key': 'ai_generate',
|
||||
'label': 'AI Generate',
|
||||
'icon': '🤖',
|
||||
'help': 'Generate tweets with AI'
|
||||
},
|
||||
{
|
||||
'key': 'view_analytics',
|
||||
'label': 'View Analytics',
|
||||
'icon': '📈',
|
||||
'help': 'Check tweet performance'
|
||||
}
|
||||
]
|
||||
@@ -1,278 +0,0 @@
|
||||
"""
|
||||
Main dashboard for Twitter UI.
|
||||
Combines all UI components into a cohesive interface.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
from typing import Dict, Any, Optional
|
||||
from .components.cards import FeatureCard, TweetCard
|
||||
from .components.forms import TweetForm, SettingsForm
|
||||
from .components.navigation import Sidebar, Header, Tabs, Breadcrumbs
|
||||
from .styles.theme import Theme
|
||||
import os
|
||||
|
||||
class TwitterDashboard:
|
||||
"""Main dashboard class for Twitter UI."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the Twitter dashboard."""
|
||||
self.setup_theme()
|
||||
self.setup_navigation()
|
||||
self.setup_state()
|
||||
|
||||
def get_logo_path(self) -> str:
|
||||
"""Get the best available logo path with fallbacks."""
|
||||
# List of potential logo paths in order of preference
|
||||
logo_paths = [
|
||||
"lib/workspace/alwrity_logo.png",
|
||||
"lib/workspace/AskAlwrity-min.ico",
|
||||
"lib/workspace/alwrity_ai_writer.png"
|
||||
]
|
||||
|
||||
for path in logo_paths:
|
||||
if os.path.exists(path):
|
||||
return path
|
||||
|
||||
# If no logo files are found, return None
|
||||
return None
|
||||
|
||||
def setup_theme(self) -> None:
|
||||
"""Setup theme and styling."""
|
||||
Theme.apply()
|
||||
|
||||
def setup_navigation(self) -> None:
|
||||
"""Setup navigation components."""
|
||||
# Sidebar
|
||||
self.sidebar = Sidebar(
|
||||
title="Twitter Tools",
|
||||
logo=self.get_logo_path()
|
||||
)
|
||||
|
||||
# Add menu items
|
||||
self.sidebar.add_menu_item("Dashboard", "📊", "dashboard")
|
||||
self.sidebar.add_menu_item("Tweet Generator", "✍️", "tweet_generator")
|
||||
self.sidebar.add_menu_item("Analytics", "📈", "analytics")
|
||||
self.sidebar.add_menu_item("Settings", "⚙️", "settings")
|
||||
|
||||
# Header
|
||||
self.header = Header(
|
||||
title="Twitter Dashboard",
|
||||
subtitle="Create and manage your Twitter content"
|
||||
)
|
||||
|
||||
# Add header actions
|
||||
self.header.add_action(
|
||||
"New Tweet",
|
||||
"✏️",
|
||||
self.create_new_tweet,
|
||||
"Create a new tweet"
|
||||
)
|
||||
self.header.add_action(
|
||||
"Refresh",
|
||||
"🔄",
|
||||
self.refresh_dashboard,
|
||||
"Refresh dashboard data"
|
||||
)
|
||||
|
||||
# Tabs
|
||||
self.tabs = Tabs()
|
||||
|
||||
# Add tabs
|
||||
self.tabs.add_tab("Overview", "📊", self.render_overview)
|
||||
self.tabs.add_tab("Recent Tweets", "🐦", self.render_recent_tweets)
|
||||
self.tabs.add_tab("Analytics", "📈", self.render_analytics)
|
||||
|
||||
# Breadcrumbs
|
||||
self.breadcrumbs = Breadcrumbs()
|
||||
|
||||
def setup_state(self) -> None:
|
||||
"""Initialize session state variables."""
|
||||
if "current_page" not in st.session_state:
|
||||
st.session_state["current_page"] = "dashboard"
|
||||
if "current_tab" not in st.session_state:
|
||||
st.session_state["current_tab"] = "Overview"
|
||||
if "tweets" not in st.session_state:
|
||||
st.session_state["tweets"] = []
|
||||
|
||||
def create_new_tweet(self) -> None:
|
||||
"""Handle new tweet creation."""
|
||||
st.session_state["current_page"] = "tweet_generator"
|
||||
|
||||
def refresh_dashboard(self) -> None:
|
||||
"""Refresh dashboard data."""
|
||||
st.rerun()
|
||||
|
||||
def render_overview(self) -> None:
|
||||
"""Render the overview tab content."""
|
||||
# Feature cards
|
||||
col1, col2, col3 = st.columns(3)
|
||||
|
||||
with col1:
|
||||
FeatureCard(
|
||||
title="Tweet Generator",
|
||||
description="Create engaging tweets with AI assistance",
|
||||
icon="✍️",
|
||||
features=[
|
||||
{
|
||||
"name": "AI-Powered",
|
||||
"description": "Generate tweets using advanced AI"
|
||||
},
|
||||
{
|
||||
"name": "Customizable",
|
||||
"description": "Adjust tone, length, and style"
|
||||
}
|
||||
],
|
||||
on_click=self.create_new_tweet
|
||||
).render()
|
||||
|
||||
with col2:
|
||||
FeatureCard(
|
||||
title="Analytics",
|
||||
description="Track your tweet performance",
|
||||
icon="📈",
|
||||
features=[
|
||||
{
|
||||
"name": "Engagement",
|
||||
"description": "Monitor likes, retweets, and replies"
|
||||
},
|
||||
{
|
||||
"name": "Growth",
|
||||
"description": "Track follower growth over time"
|
||||
}
|
||||
]
|
||||
).render()
|
||||
|
||||
with col3:
|
||||
FeatureCard(
|
||||
title="Settings",
|
||||
description="Customize your experience",
|
||||
icon="⚙️",
|
||||
features=[
|
||||
{
|
||||
"name": "Preferences",
|
||||
"description": "Set your default options"
|
||||
},
|
||||
{
|
||||
"name": "API",
|
||||
"description": "Configure Twitter API settings"
|
||||
}
|
||||
]
|
||||
).render()
|
||||
|
||||
def render_recent_tweets(self) -> None:
|
||||
"""Render the recent tweets tab content."""
|
||||
# Tweet form
|
||||
tweet_form = TweetForm(
|
||||
on_submit=self.handle_tweet_submit
|
||||
)
|
||||
tweet_form.render()
|
||||
|
||||
# Recent tweets
|
||||
st.markdown("### Recent Tweets")
|
||||
|
||||
for tweet in st.session_state["tweets"]:
|
||||
TweetCard(
|
||||
content=tweet["content"],
|
||||
engagement_score=tweet["engagement_score"],
|
||||
hashtags=tweet["hashtags"],
|
||||
emojis=tweet["emojis"],
|
||||
metrics=tweet["metrics"],
|
||||
on_copy=lambda: self.copy_tweet(tweet),
|
||||
on_save=lambda: self.save_tweet(tweet)
|
||||
).render()
|
||||
|
||||
def render_analytics(self) -> None:
|
||||
"""Render the analytics tab content."""
|
||||
# Analytics content
|
||||
st.markdown("### Tweet Analytics")
|
||||
|
||||
# Placeholder for analytics charts
|
||||
st.info("Analytics features coming soon!")
|
||||
|
||||
def handle_tweet_submit(self) -> None:
|
||||
"""Handle tweet form submission."""
|
||||
# Get form data
|
||||
content = st.session_state["tweet_content"]
|
||||
tone = st.session_state["tone"]
|
||||
length = st.session_state["length"]
|
||||
hashtags = st.session_state["hashtags"]
|
||||
emojis = st.session_state["emojis"]
|
||||
engagement_boost = st.session_state["engagement_boost"]
|
||||
|
||||
# Create tweet object
|
||||
tweet = {
|
||||
"content": content,
|
||||
"tone": tone,
|
||||
"length": length,
|
||||
"hashtags": hashtags,
|
||||
"emojis": emojis,
|
||||
"engagement_score": engagement_boost,
|
||||
"metrics": {
|
||||
"Engagement": engagement_boost,
|
||||
"Reach": engagement_boost * 0.8,
|
||||
"Growth": engagement_boost * 0.6
|
||||
}
|
||||
}
|
||||
|
||||
# Add to tweets list
|
||||
st.session_state["tweets"].append(tweet)
|
||||
|
||||
# Show success message
|
||||
st.success("Tweet created successfully!")
|
||||
|
||||
def copy_tweet(self, tweet: Dict[str, Any]) -> None:
|
||||
"""Copy tweet to clipboard."""
|
||||
st.write("Tweet copied to clipboard!")
|
||||
|
||||
def save_tweet(self, tweet: Dict[str, Any]) -> None:
|
||||
"""Save tweet for later."""
|
||||
st.write("Tweet saved!")
|
||||
|
||||
def render(self) -> None:
|
||||
"""Render the complete dashboard."""
|
||||
# Render navigation
|
||||
self.sidebar.render()
|
||||
self.header.render()
|
||||
self.breadcrumbs.render()
|
||||
|
||||
# Render content based on current page
|
||||
if st.session_state["current_page"] == "dashboard":
|
||||
self.tabs.render()
|
||||
elif st.session_state["current_page"] == "tweet_generator":
|
||||
self.render_recent_tweets()
|
||||
elif st.session_state["current_page"] == "analytics":
|
||||
self.render_analytics()
|
||||
elif st.session_state["current_page"] == "settings":
|
||||
settings_form = SettingsForm(
|
||||
on_submit=self.handle_settings_submit
|
||||
)
|
||||
settings_form.render()
|
||||
|
||||
def handle_settings_submit(self) -> None:
|
||||
"""Handle settings form submission."""
|
||||
# Get form data
|
||||
api_key = st.session_state["api_key"]
|
||||
theme = st.session_state["theme"]
|
||||
notifications = st.session_state["notifications"]
|
||||
auto_save = st.session_state["auto_save"]
|
||||
language = st.session_state["language"]
|
||||
|
||||
# Save settings
|
||||
st.session_state["settings"] = {
|
||||
"api_key": api_key,
|
||||
"theme": theme,
|
||||
"notifications": notifications,
|
||||
"auto_save": auto_save,
|
||||
"language": language
|
||||
}
|
||||
|
||||
# Show success message
|
||||
st.success("Settings saved successfully!")
|
||||
|
||||
def main():
|
||||
"""Main entry point for the dashboard."""
|
||||
dashboard = TwitterDashboard()
|
||||
dashboard.render()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,173 +0,0 @@
|
||||
"""
|
||||
Theme configuration for Twitter UI components.
|
||||
Provides consistent styling across all Twitter-related features.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
from typing import Dict, Any
|
||||
|
||||
class Theme:
|
||||
"""Theme configuration for Twitter UI components."""
|
||||
|
||||
# Color palette
|
||||
COLORS = {
|
||||
"primary": "#1DA1F2", # Twitter blue
|
||||
"secondary": "#14171A", # Dark blue
|
||||
"background": "#15202B", # Dark background
|
||||
"text": "#FFFFFF", # White text
|
||||
"text_secondary": "#8899A6", # Gray text
|
||||
"success": "#17BF63", # Green
|
||||
"warning": "#FFAD1F", # Yellow
|
||||
"error": "#E0245E", # Red
|
||||
"border": "rgba(255, 255, 255, 0.1)", # Subtle border
|
||||
}
|
||||
|
||||
# Typography
|
||||
TYPOGRAPHY = {
|
||||
"font_family": "'Helvetica Neue', sans-serif",
|
||||
"font_sizes": {
|
||||
"h1": "2.5rem",
|
||||
"h2": "2rem",
|
||||
"h3": "1.5rem",
|
||||
"body": "1rem",
|
||||
"small": "0.875rem",
|
||||
},
|
||||
"font_weights": {
|
||||
"regular": 400,
|
||||
"medium": 500,
|
||||
"bold": 700,
|
||||
},
|
||||
}
|
||||
|
||||
# Spacing
|
||||
SPACING = {
|
||||
"xs": "0.25rem",
|
||||
"sm": "0.5rem",
|
||||
"md": "1rem",
|
||||
"lg": "1.5rem",
|
||||
"xl": "2rem",
|
||||
}
|
||||
|
||||
# Border radius
|
||||
BORDER_RADIUS = {
|
||||
"sm": "4px",
|
||||
"md": "8px",
|
||||
"lg": "12px",
|
||||
"xl": "16px",
|
||||
"full": "9999px",
|
||||
}
|
||||
|
||||
# Shadows
|
||||
SHADOWS = {
|
||||
"sm": "0 1px 2px rgba(0, 0, 0, 0.05)",
|
||||
"md": "0 4px 6px rgba(0, 0, 0, 0.1)",
|
||||
"lg": "0 10px 15px rgba(0, 0, 0, 0.1)",
|
||||
"xl": "0 20px 25px rgba(0, 0, 0, 0.15)",
|
||||
}
|
||||
|
||||
# Transitions
|
||||
TRANSITIONS = {
|
||||
"fast": "0.15s ease",
|
||||
"normal": "0.3s ease",
|
||||
"slow": "0.5s ease",
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def get_css(cls) -> str:
|
||||
"""Get the complete CSS for the theme."""
|
||||
return f"""
|
||||
/* Base styles */
|
||||
.stApp {{
|
||||
background-color: {cls.COLORS['background']};
|
||||
color: {cls.COLORS['text']};
|
||||
font-family: {cls.TYPOGRAPHY['font_family']};
|
||||
}}
|
||||
|
||||
/* Typography */
|
||||
h1, h2, h3, h4, h5, h6 {{
|
||||
color: {cls.COLORS['text']};
|
||||
font-family: {cls.TYPOGRAPHY['font_family']};
|
||||
font-weight: {cls.TYPOGRAPHY['font_weights']['bold']};
|
||||
}}
|
||||
|
||||
/* Buttons */
|
||||
.stButton > button {{
|
||||
background: linear-gradient(45deg, {cls.COLORS['primary']}, #0C85D0);
|
||||
color: {cls.COLORS['text']};
|
||||
border: none;
|
||||
padding: {cls.SPACING['md']} {cls.SPACING['lg']};
|
||||
border-radius: {cls.BORDER_RADIUS['full']};
|
||||
font-weight: {cls.TYPOGRAPHY['font_weights']['medium']};
|
||||
transition: all {cls.TRANSITIONS['normal']};
|
||||
box-shadow: {cls.SHADOWS['md']};
|
||||
}}
|
||||
|
||||
.stButton > button:hover {{
|
||||
transform: translateY(-2px);
|
||||
box-shadow: {cls.SHADOWS['lg']};
|
||||
}}
|
||||
|
||||
/* Cards */
|
||||
.card {{
|
||||
background: rgba(255, 255, 255, 0.05);
|
||||
border: 1px solid {cls.COLORS['border']};
|
||||
border-radius: {cls.BORDER_RADIUS['lg']};
|
||||
padding: {cls.SPACING['lg']};
|
||||
margin-bottom: {cls.SPACING['md']};
|
||||
backdrop-filter: blur(10px);
|
||||
transition: transform {cls.TRANSITIONS['normal']};
|
||||
}}
|
||||
|
||||
.card:hover {{
|
||||
transform: translateY(-4px);
|
||||
}}
|
||||
|
||||
/* Forms */
|
||||
.stTextInput > div > div > input {{
|
||||
background-color: rgba(255, 255, 255, 0.05);
|
||||
border: 1px solid {cls.COLORS['border']};
|
||||
border-radius: {cls.BORDER_RADIUS['md']};
|
||||
color: {cls.COLORS['text']};
|
||||
padding: {cls.SPACING['md']};
|
||||
}}
|
||||
|
||||
/* Tabs */
|
||||
.stTabs [data-baseweb="tab-list"] {{
|
||||
gap: {cls.SPACING['sm']};
|
||||
background-color: rgba(0, 0, 0, 0.2);
|
||||
padding: {cls.SPACING['md']};
|
||||
border-radius: {cls.BORDER_RADIUS['lg']};
|
||||
}}
|
||||
|
||||
.stTabs [data-baseweb="tab"] {{
|
||||
background-color: transparent;
|
||||
color: {cls.COLORS['text']};
|
||||
border: 1px solid {cls.COLORS['border']};
|
||||
border-radius: {cls.BORDER_RADIUS['md']};
|
||||
padding: {cls.SPACING['sm']} {cls.SPACING['md']};
|
||||
}}
|
||||
|
||||
/* Status badges */
|
||||
.status-badge {{
|
||||
display: inline-block;
|
||||
padding: {cls.SPACING['xs']} {cls.SPACING['md']};
|
||||
border-radius: {cls.BORDER_RADIUS['full']};
|
||||
font-size: {cls.TYPOGRAPHY['font_sizes']['small']};
|
||||
font-weight: {cls.TYPOGRAPHY['font_weights']['medium']};
|
||||
}}
|
||||
|
||||
.status-active {{
|
||||
background: linear-gradient(45deg, {cls.COLORS['success']}, #69F0AE);
|
||||
color: {cls.COLORS['secondary']};
|
||||
}}
|
||||
|
||||
.status-coming-soon {{
|
||||
background: linear-gradient(45deg, {cls.COLORS['warning']}, #FFA000);
|
||||
color: {cls.COLORS['secondary']};
|
||||
}}
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def apply(cls) -> None:
|
||||
"""Apply the theme to the Streamlit app."""
|
||||
st.markdown(f"<style>{cls.get_css()}</style>", unsafe_allow_html=True)
|
||||
@@ -1,503 +0,0 @@
|
||||
"""
|
||||
Enhanced Twitter Dashboard with real authentication and posting capabilities.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import asyncio
|
||||
from datetime import datetime, timedelta
|
||||
import json
|
||||
from typing import Dict, Any, List, Optional
|
||||
|
||||
# Import our enhanced components
|
||||
from .components.navigation import TwitterNavigation, create_main_navigation
|
||||
from .components.cards import TwitterCard, create_analytics_card, create_tweet_card
|
||||
from .components.forms import TweetForm, TwitterConfigForm
|
||||
from ..tweet_generator.smart_tweet_generator import (
|
||||
smart_tweet_generator,
|
||||
post_tweet_to_twitter,
|
||||
get_real_tweet_analytics,
|
||||
render_twitter_authentication
|
||||
)
|
||||
from ....integrations.twitter_auth_bridge import (
|
||||
TwitterAuthBridge,
|
||||
save_twitter_credentials,
|
||||
load_twitter_credentials,
|
||||
is_twitter_authenticated,
|
||||
setup_twitter_session,
|
||||
clear_twitter_session
|
||||
)
|
||||
|
||||
# Initialize authentication bridge
|
||||
auth_bridge = TwitterAuthBridge()
|
||||
|
||||
def initialize_dashboard():
|
||||
"""Initialize the Twitter dashboard with proper styling and state management."""
|
||||
|
||||
# Apply custom CSS
|
||||
st.markdown("""
|
||||
<style>
|
||||
.main-dashboard {
|
||||
padding: 1rem;
|
||||
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
|
||||
min-height: 100vh;
|
||||
}
|
||||
|
||||
.dashboard-header {
|
||||
background: white;
|
||||
padding: 2rem;
|
||||
border-radius: 15px;
|
||||
box-shadow: 0 10px 30px rgba(0,0,0,0.1);
|
||||
margin-bottom: 2rem;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.dashboard-title {
|
||||
font-size: 2.5rem;
|
||||
font-weight: 700;
|
||||
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
|
||||
-webkit-background-clip: text;
|
||||
-webkit-text-fill-color: transparent;
|
||||
margin-bottom: 0.5rem;
|
||||
}
|
||||
|
||||
.dashboard-subtitle {
|
||||
color: #666;
|
||||
font-size: 1.1rem;
|
||||
margin-bottom: 1rem;
|
||||
}
|
||||
|
||||
.status-indicator {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
gap: 0.5rem;
|
||||
padding: 0.5rem 1rem;
|
||||
border-radius: 25px;
|
||||
font-weight: 500;
|
||||
font-size: 0.9rem;
|
||||
}
|
||||
|
||||
.status-connected {
|
||||
background: #d4edda;
|
||||
color: #155724;
|
||||
border: 1px solid #c3e6cb;
|
||||
}
|
||||
|
||||
.status-disconnected {
|
||||
background: #f8d7da;
|
||||
color: #721c24;
|
||||
border: 1px solid #f5c6cb;
|
||||
}
|
||||
|
||||
.dashboard-grid {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 1fr;
|
||||
gap: 2rem;
|
||||
margin-bottom: 2rem;
|
||||
}
|
||||
|
||||
@media (max-width: 768px) {
|
||||
.dashboard-grid {
|
||||
grid-template-columns: 1fr;
|
||||
}
|
||||
}
|
||||
|
||||
.action-button {
|
||||
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
|
||||
color: white;
|
||||
border: none;
|
||||
padding: 0.75rem 1.5rem;
|
||||
border-radius: 8px;
|
||||
font-weight: 600;
|
||||
cursor: pointer;
|
||||
transition: all 0.3s ease;
|
||||
}
|
||||
|
||||
.action-button:hover {
|
||||
transform: translateY(-2px);
|
||||
box-shadow: 0 5px 15px rgba(102, 126, 234, 0.4);
|
||||
}
|
||||
|
||||
.metrics-grid {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
|
||||
gap: 1rem;
|
||||
margin: 1rem 0;
|
||||
}
|
||||
|
||||
.metric-card {
|
||||
background: white;
|
||||
padding: 1.5rem;
|
||||
border-radius: 10px;
|
||||
box-shadow: 0 5px 15px rgba(0,0,0,0.1);
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.metric-value {
|
||||
font-size: 2rem;
|
||||
font-weight: 700;
|
||||
color: #667eea;
|
||||
margin-bottom: 0.5rem;
|
||||
}
|
||||
|
||||
.metric-label {
|
||||
color: #666;
|
||||
font-size: 0.9rem;
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.5px;
|
||||
}
|
||||
</style>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Initialize session state
|
||||
if 'twitter_dashboard_initialized' not in st.session_state:
|
||||
st.session_state.twitter_dashboard_initialized = True
|
||||
st.session_state.current_page = 'dashboard'
|
||||
st.session_state.tweet_drafts = []
|
||||
st.session_state.posted_tweets = []
|
||||
st.session_state.analytics_data = {}
|
||||
|
||||
def render_dashboard_header():
|
||||
"""Render the main dashboard header with connection status."""
|
||||
|
||||
st.markdown('<div class="dashboard-header">', unsafe_allow_html=True)
|
||||
|
||||
col1, col2, col3 = st.columns([1, 2, 1])
|
||||
|
||||
with col2:
|
||||
st.markdown('<h1 class="dashboard-title">🐦 Twitter AI Dashboard</h1>', unsafe_allow_html=True)
|
||||
st.markdown('<p class="dashboard-subtitle">AI-Powered Tweet Generation & Analytics</p>', unsafe_allow_html=True)
|
||||
|
||||
# Connection status
|
||||
is_connected = is_twitter_authenticated()
|
||||
|
||||
if is_connected:
|
||||
user_info = st.session_state.get('twitter_user', {})
|
||||
username = user_info.get('screen_name', 'Unknown')
|
||||
st.markdown(f'''
|
||||
<div class="status-indicator status-connected">
|
||||
✅ Connected as @{username}
|
||||
</div>
|
||||
''', unsafe_allow_html=True)
|
||||
else:
|
||||
st.markdown('''
|
||||
<div class="status-indicator status-disconnected">
|
||||
❌ Not Connected to Twitter
|
||||
</div>
|
||||
''', unsafe_allow_html=True)
|
||||
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
|
||||
def render_quick_actions():
|
||||
"""Render quick action buttons."""
|
||||
|
||||
st.markdown("### 🚀 Quick Actions")
|
||||
|
||||
col1, col2, col3, col4 = st.columns(4)
|
||||
|
||||
with col1:
|
||||
if st.button("📝 Generate Tweet", key="quick_generate", help="Create AI-powered tweets"):
|
||||
st.session_state.current_page = 'generate'
|
||||
st.rerun()
|
||||
|
||||
with col2:
|
||||
if st.button("📊 View Analytics", key="quick_analytics", help="View tweet performance"):
|
||||
st.session_state.current_page = 'analytics'
|
||||
st.rerun()
|
||||
|
||||
with col3:
|
||||
if st.button("⚙️ Settings", key="quick_settings", help="Configure Twitter connection"):
|
||||
st.session_state.current_page = 'settings'
|
||||
st.rerun()
|
||||
|
||||
with col4:
|
||||
if st.button("📋 Drafts", key="quick_drafts", help="Manage tweet drafts"):
|
||||
st.session_state.current_page = 'drafts'
|
||||
st.rerun()
|
||||
|
||||
def render_dashboard_overview():
|
||||
"""Render the main dashboard overview with metrics."""
|
||||
|
||||
if not is_twitter_authenticated():
|
||||
st.warning("⚠️ Please connect your Twitter account to view dashboard metrics.")
|
||||
if st.button("Connect Twitter Account", type="primary"):
|
||||
st.session_state.current_page = 'settings'
|
||||
st.rerun()
|
||||
return
|
||||
|
||||
# Get user metrics
|
||||
user_info = st.session_state.get('twitter_user', {})
|
||||
|
||||
# Display metrics
|
||||
st.markdown("### 📈 Account Overview")
|
||||
|
||||
col1, col2, col3, col4 = st.columns(4)
|
||||
|
||||
with col1:
|
||||
st.markdown(f'''
|
||||
<div class="metric-card">
|
||||
<div class="metric-value">{user_info.get('followers_count', 0):,}</div>
|
||||
<div class="metric-label">Followers</div>
|
||||
</div>
|
||||
''', unsafe_allow_html=True)
|
||||
|
||||
with col2:
|
||||
st.markdown(f'''
|
||||
<div class="metric-card">
|
||||
<div class="metric-value">{user_info.get('friends_count', 0):,}</div>
|
||||
<div class="metric-label">Following</div>
|
||||
</div>
|
||||
''', unsafe_allow_html=True)
|
||||
|
||||
with col3:
|
||||
posted_count = len(st.session_state.get('posted_tweets', []))
|
||||
st.markdown(f'''
|
||||
<div class="metric-card">
|
||||
<div class="metric-value">{posted_count}</div>
|
||||
<div class="metric-label">Posted Today</div>
|
||||
</div>
|
||||
''', unsafe_allow_html=True)
|
||||
|
||||
with col4:
|
||||
draft_count = len(st.session_state.get('tweet_drafts', []))
|
||||
st.markdown(f'''
|
||||
<div class="metric-card">
|
||||
<div class="metric-value">{draft_count}</div>
|
||||
<div class="metric-label">Drafts</div>
|
||||
</div>
|
||||
''', unsafe_allow_html=True)
|
||||
|
||||
# Recent activity
|
||||
st.markdown("### 📝 Recent Activity")
|
||||
|
||||
recent_tweets = st.session_state.get('posted_tweets', [])[-5:] # Last 5 tweets
|
||||
|
||||
if recent_tweets:
|
||||
for tweet in reversed(recent_tweets):
|
||||
with st.expander(f"Tweet: {tweet.get('text', '')[:50]}..."):
|
||||
col1, col2 = st.columns([2, 1])
|
||||
|
||||
with col1:
|
||||
st.write(f"**Text:** {tweet.get('text', '')}")
|
||||
st.write(f"**Posted:** {tweet.get('created_at', '')}")
|
||||
|
||||
if tweet.get('metrics'):
|
||||
metrics = tweet['metrics']
|
||||
st.write(f"**Engagement:** {metrics.get('favorite_count', 0)} likes, "
|
||||
f"{metrics.get('retweet_count', 0)} retweets")
|
||||
|
||||
with col2:
|
||||
if st.button(f"View Analytics", key=f"analytics_{tweet.get('id')}"):
|
||||
st.session_state.selected_tweet_id = tweet.get('id')
|
||||
st.session_state.current_page = 'analytics'
|
||||
st.rerun()
|
||||
else:
|
||||
st.info("No recent tweets found. Start by generating and posting some content!")
|
||||
|
||||
def render_settings_page():
|
||||
"""Render the settings page for Twitter configuration."""
|
||||
|
||||
st.markdown("### ⚙️ Twitter Configuration")
|
||||
|
||||
# Twitter Authentication Section
|
||||
with st.expander("🔐 Twitter API Configuration", expanded=not is_twitter_authenticated()):
|
||||
render_twitter_authentication()
|
||||
|
||||
# Account Information
|
||||
if is_twitter_authenticated():
|
||||
st.markdown("### 👤 Account Information")
|
||||
|
||||
user_info = st.session_state.get('twitter_user', {})
|
||||
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
st.write(f"**Username:** @{user_info.get('screen_name', 'N/A')}")
|
||||
st.write(f"**Display Name:** {user_info.get('name', 'N/A')}")
|
||||
st.write(f"**Followers:** {user_info.get('followers_count', 0):,}")
|
||||
|
||||
with col2:
|
||||
st.write(f"**Following:** {user_info.get('friends_count', 0):,}")
|
||||
st.write(f"**Tweets:** {user_info.get('statuses_count', 0):,}")
|
||||
st.write(f"**Account Created:** {user_info.get('created_at', 'N/A')}")
|
||||
|
||||
# Disconnect option
|
||||
st.markdown("---")
|
||||
if st.button("🔓 Disconnect Twitter Account", type="secondary"):
|
||||
clear_twitter_session()
|
||||
st.success("Twitter account disconnected successfully!")
|
||||
st.rerun()
|
||||
|
||||
def render_analytics_page():
|
||||
"""Render the analytics page with real Twitter metrics."""
|
||||
|
||||
st.markdown("### 📊 Tweet Analytics")
|
||||
|
||||
if not is_twitter_authenticated():
|
||||
st.warning("Please connect your Twitter account to view analytics.")
|
||||
return
|
||||
|
||||
# Tweet selection
|
||||
posted_tweets = st.session_state.get('posted_tweets', [])
|
||||
|
||||
if not posted_tweets:
|
||||
st.info("No tweets found. Generate and post some tweets to see analytics!")
|
||||
return
|
||||
|
||||
# Select tweet for analysis
|
||||
tweet_options = {
|
||||
f"{tweet.get('text', '')[:50]}... ({tweet.get('created_at', '')})": tweet.get('id')
|
||||
for tweet in posted_tweets
|
||||
}
|
||||
|
||||
selected_tweet_text = st.selectbox(
|
||||
"Select a tweet to analyze:",
|
||||
options=list(tweet_options.keys())
|
||||
)
|
||||
|
||||
if selected_tweet_text:
|
||||
tweet_id = tweet_options[selected_tweet_text]
|
||||
|
||||
# Get analytics
|
||||
with st.spinner("Loading analytics..."):
|
||||
analytics_result = asyncio.run(get_real_tweet_analytics(tweet_id))
|
||||
|
||||
if analytics_result.get('success'):
|
||||
analytics_data = analytics_result['data']
|
||||
|
||||
# Display metrics
|
||||
st.markdown("#### 📈 Performance Metrics")
|
||||
|
||||
col1, col2, col3, col4 = st.columns(4)
|
||||
|
||||
metrics = analytics_data.get('metrics', {})
|
||||
|
||||
with col1:
|
||||
st.metric("Likes", metrics.get('likes', 0))
|
||||
|
||||
with col2:
|
||||
st.metric("Retweets", metrics.get('retweets', 0))
|
||||
|
||||
with col3:
|
||||
st.metric("Replies", metrics.get('replies', 0))
|
||||
|
||||
with col4:
|
||||
engagement = analytics_data.get('engagement', {})
|
||||
st.metric("Engagement Rate", f"{engagement.get('engagement_rate', 0):.2f}%")
|
||||
|
||||
# Detailed analytics
|
||||
st.markdown("#### 🔍 Detailed Analysis")
|
||||
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
st.markdown("**Engagement Breakdown:**")
|
||||
total_engagement = metrics.get('total_engagement', 0)
|
||||
st.write(f"• Total Engagement: {total_engagement}")
|
||||
st.write(f"• Likes Rate: {engagement.get('likes_rate', 0):.2f}%")
|
||||
st.write(f"• Retweets Rate: {engagement.get('retweets_rate', 0):.2f}%")
|
||||
|
||||
with col2:
|
||||
st.markdown("**Content Analysis:**")
|
||||
content_analysis = analytics_data.get('content_analysis', {})
|
||||
st.write(f"• Character Count: {content_analysis.get('character_count', 0)}")
|
||||
st.write(f"• Hashtags: {content_analysis.get('hashtag_count', 0)}")
|
||||
st.write(f"• Mentions: {content_analysis.get('mention_count', 0)}")
|
||||
|
||||
# Timing analysis
|
||||
timing = analytics_data.get('timing', {})
|
||||
if timing:
|
||||
st.markdown("#### ⏰ Timing Analysis")
|
||||
st.write(f"• Posted: {timing.get('posted_at', 'N/A')}")
|
||||
st.write(f"• Age: {timing.get('age_hours', 0):.1f} hours")
|
||||
st.write(f"• Peak Period: {timing.get('peak_engagement_period', 'N/A')}")
|
||||
st.write(f"• Engagement Velocity: {timing.get('engagement_velocity', 0):.2f} per hour")
|
||||
|
||||
else:
|
||||
st.error(f"Failed to load analytics: {analytics_result.get('error', 'Unknown error')}")
|
||||
|
||||
def render_drafts_page():
|
||||
"""Render the drafts management page."""
|
||||
|
||||
st.markdown("### 📋 Tweet Drafts")
|
||||
|
||||
drafts = st.session_state.get('tweet_drafts', [])
|
||||
|
||||
if not drafts:
|
||||
st.info("No drafts found. Create some tweets in the generator to save as drafts!")
|
||||
return
|
||||
|
||||
for i, draft in enumerate(drafts):
|
||||
with st.expander(f"Draft {i+1}: {draft.get('text', '')[:50]}..."):
|
||||
col1, col2 = st.columns([3, 1])
|
||||
|
||||
with col1:
|
||||
st.write(f"**Text:** {draft.get('text', '')}")
|
||||
st.write(f"**Created:** {draft.get('created_at', '')}")
|
||||
if draft.get('hashtags'):
|
||||
st.write(f"**Hashtags:** {', '.join(draft['hashtags'])}")
|
||||
|
||||
with col2:
|
||||
if st.button(f"Post Now", key=f"post_draft_{i}"):
|
||||
if is_twitter_authenticated():
|
||||
with st.spinner("Posting tweet..."):
|
||||
result = asyncio.run(post_tweet_to_twitter(draft))
|
||||
|
||||
if result.get('success'):
|
||||
st.success("Tweet posted successfully!")
|
||||
# Move from drafts to posted
|
||||
st.session_state.posted_tweets.append(result['data'])
|
||||
st.session_state.tweet_drafts.pop(i)
|
||||
st.rerun()
|
||||
else:
|
||||
st.error(f"Failed to post: {result.get('error')}")
|
||||
else:
|
||||
st.error("Please connect your Twitter account first!")
|
||||
|
||||
if st.button(f"Delete", key=f"delete_draft_{i}"):
|
||||
st.session_state.tweet_drafts.pop(i)
|
||||
st.rerun()
|
||||
|
||||
def main_twitter_dashboard():
|
||||
"""Main Twitter dashboard function."""
|
||||
|
||||
# Initialize dashboard
|
||||
initialize_dashboard()
|
||||
|
||||
# Create navigation
|
||||
nav = TwitterNavigation()
|
||||
current_page = nav.render_main_navigation()
|
||||
|
||||
# Update session state if page changed
|
||||
if current_page != st.session_state.get('current_page'):
|
||||
st.session_state.current_page = current_page
|
||||
|
||||
# Render dashboard header
|
||||
render_dashboard_header()
|
||||
|
||||
# Route to appropriate page
|
||||
page = st.session_state.get('current_page', 'dashboard')
|
||||
|
||||
if page == 'dashboard':
|
||||
render_quick_actions()
|
||||
render_dashboard_overview()
|
||||
|
||||
elif page == 'generate':
|
||||
st.markdown("### 🤖 AI Tweet Generator")
|
||||
smart_tweet_generator()
|
||||
|
||||
elif page == 'analytics':
|
||||
render_analytics_page()
|
||||
|
||||
elif page == 'settings':
|
||||
render_settings_page()
|
||||
|
||||
elif page == 'drafts':
|
||||
render_drafts_page()
|
||||
|
||||
else:
|
||||
# Default to dashboard
|
||||
render_quick_actions()
|
||||
render_dashboard_overview()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main_twitter_dashboard()
|
||||
@@ -1,194 +0,0 @@
|
||||
"""
|
||||
Utility functions for Twitter UI.
|
||||
Provides helper functions for common operations.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
from typing import Dict, Any, List, Optional
|
||||
import json
|
||||
import os
|
||||
from datetime import datetime
|
||||
|
||||
def save_to_session(key: str, value: Any) -> None:
|
||||
"""Save a value to the session state."""
|
||||
st.session_state[key] = value
|
||||
|
||||
def get_from_session(key: str, default: Any = None) -> Any:
|
||||
"""Get a value from the session state."""
|
||||
return st.session_state.get(key, default)
|
||||
|
||||
def clear_session() -> None:
|
||||
"""Clear all session state variables."""
|
||||
for key in list(st.session_state.keys()):
|
||||
del st.session_state[key]
|
||||
|
||||
def save_to_file(data: Dict[str, Any], filename: str) -> None:
|
||||
"""Save data to a JSON file."""
|
||||
try:
|
||||
with open(filename, 'w') as f:
|
||||
json.dump(data, f, indent=4)
|
||||
except Exception as e:
|
||||
st.error(f"Error saving data: {str(e)}")
|
||||
|
||||
def load_from_file(filename: str) -> Optional[Dict[str, Any]]:
|
||||
"""Load data from a JSON file."""
|
||||
try:
|
||||
if os.path.exists(filename):
|
||||
with open(filename, 'r') as f:
|
||||
return json.load(f)
|
||||
except Exception as e:
|
||||
st.error(f"Error loading data: {str(e)}")
|
||||
return None
|
||||
|
||||
def format_datetime(dt: datetime) -> str:
|
||||
"""Format a datetime object for display."""
|
||||
return dt.strftime("%Y-%m-%d %H:%M:%S")
|
||||
|
||||
def parse_datetime(dt_str: str) -> Optional[datetime]:
|
||||
"""Parse a datetime string."""
|
||||
try:
|
||||
return datetime.strptime(dt_str, "%Y-%m-%d %H:%M:%S")
|
||||
except ValueError:
|
||||
return None
|
||||
|
||||
def validate_tweet_content(content: str) -> bool:
|
||||
"""Validate tweet content."""
|
||||
if not content:
|
||||
st.error("Tweet content cannot be empty")
|
||||
return False
|
||||
if len(content) > 280:
|
||||
st.error("Tweet content cannot exceed 280 characters")
|
||||
return False
|
||||
return True
|
||||
|
||||
def validate_hashtags(hashtags: List[str]) -> bool:
|
||||
"""Validate hashtags."""
|
||||
for tag in hashtags:
|
||||
if not tag.startswith('#'):
|
||||
st.error(f"Hashtag {tag} must start with #")
|
||||
return False
|
||||
if len(tag) > 30:
|
||||
st.error(f"Hashtag {tag} cannot exceed 30 characters")
|
||||
return False
|
||||
return True
|
||||
|
||||
def validate_emojis(emojis: List[str]) -> bool:
|
||||
"""Validate emojis."""
|
||||
for emoji in emojis:
|
||||
if len(emoji) != 1:
|
||||
st.error(f"Invalid emoji: {emoji}")
|
||||
return False
|
||||
return True
|
||||
|
||||
def calculate_engagement_score(
|
||||
content: str,
|
||||
hashtags: List[str],
|
||||
emojis: List[str],
|
||||
tone: str
|
||||
) -> float:
|
||||
"""Calculate engagement score for a tweet."""
|
||||
score = 0.0
|
||||
|
||||
# Content length score (optimal length is 100-150 characters)
|
||||
content_length = len(content)
|
||||
if 100 <= content_length <= 150:
|
||||
score += 30
|
||||
elif 50 <= content_length <= 200:
|
||||
score += 20
|
||||
else:
|
||||
score += 10
|
||||
|
||||
# Hashtag score (optimal number is 2-3 hashtags)
|
||||
hashtag_count = len(hashtags)
|
||||
if 2 <= hashtag_count <= 3:
|
||||
score += 20
|
||||
elif 1 <= hashtag_count <= 4:
|
||||
score += 15
|
||||
else:
|
||||
score += 5
|
||||
|
||||
# Emoji score (optimal number is 1-2 emojis)
|
||||
emoji_count = len(emojis)
|
||||
if 1 <= emoji_count <= 2:
|
||||
score += 20
|
||||
elif 0 <= emoji_count <= 3:
|
||||
score += 15
|
||||
else:
|
||||
score += 5
|
||||
|
||||
# Tone score
|
||||
tone_scores = {
|
||||
"professional": 15,
|
||||
"casual": 20,
|
||||
"humorous": 25,
|
||||
"informative": 15,
|
||||
"inspirational": 20
|
||||
}
|
||||
score += tone_scores.get(tone, 10)
|
||||
|
||||
return min(score, 100)
|
||||
|
||||
def generate_tweet_metrics(engagement_score: float) -> Dict[str, float]:
|
||||
"""Generate metrics for a tweet based on engagement score."""
|
||||
return {
|
||||
"Engagement": engagement_score,
|
||||
"Reach": engagement_score * 0.8,
|
||||
"Growth": engagement_score * 0.6
|
||||
}
|
||||
|
||||
def copy_to_clipboard(text: str) -> None:
|
||||
"""Copy text to clipboard."""
|
||||
try:
|
||||
st.write(f'<script>navigator.clipboard.writeText("{text}")</script>', unsafe_allow_html=True)
|
||||
except Exception as e:
|
||||
st.error(f"Error copying to clipboard: {str(e)}")
|
||||
|
||||
def show_success_message(message: str) -> None:
|
||||
"""Show a success message."""
|
||||
st.success(message)
|
||||
|
||||
def show_error_message(message: str) -> None:
|
||||
"""Show an error message."""
|
||||
st.error(message)
|
||||
|
||||
def show_info_message(message: str) -> None:
|
||||
"""Show an info message."""
|
||||
st.info(message)
|
||||
|
||||
def show_warning_message(message: str) -> None:
|
||||
"""Show a warning message."""
|
||||
st.warning(message)
|
||||
|
||||
def create_download_button(
|
||||
data: Dict[str, Any],
|
||||
filename: str,
|
||||
button_text: str = "Download"
|
||||
) -> None:
|
||||
"""Create a download button for data."""
|
||||
try:
|
||||
json_str = json.dumps(data, indent=4)
|
||||
st.download_button(
|
||||
label=button_text,
|
||||
data=json_str,
|
||||
file_name=filename,
|
||||
mime="application/json"
|
||||
)
|
||||
except Exception as e:
|
||||
st.error(f"Error creating download button: {str(e)}")
|
||||
|
||||
def create_upload_button(
|
||||
on_upload: callable,
|
||||
button_text: str = "Upload",
|
||||
file_types: List[str] = ["json"]
|
||||
) -> None:
|
||||
"""Create an upload button for data."""
|
||||
try:
|
||||
uploaded_file = st.file_uploader(
|
||||
button_text,
|
||||
type=file_types
|
||||
)
|
||||
if uploaded_file is not None:
|
||||
data = json.load(uploaded_file)
|
||||
on_upload(data)
|
||||
except Exception as e:
|
||||
st.error(f"Error handling upload: {str(e)}")
|
||||
@@ -1,121 +0,0 @@
|
||||
import sys
|
||||
import os
|
||||
|
||||
from textwrap import dedent
|
||||
import json
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
import streamlit as st
|
||||
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv(Path('../../.env'))
|
||||
from loguru import logger
|
||||
logger.remove()
|
||||
logger.add(sys.stdout,
|
||||
colorize=True,
|
||||
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
|
||||
)
|
||||
|
||||
from ..ai_web_researcher.firecrawl_web_crawler import scrape_url
|
||||
from ..blog_metadata.get_blog_metadata import blog_metadata, run_async
|
||||
from ..blog_postprocessing.save_blog_to_file import save_blog_to_file
|
||||
from ..gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
|
||||
from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
|
||||
|
||||
def blog_from_url(weburl):
|
||||
"""
|
||||
This function will take a blog Topic to first generate sections for it
|
||||
and then generate content for each section.
|
||||
"""
|
||||
# Use to store the blog in a string, to save in a *.md file.
|
||||
blog_markdown_str = None
|
||||
tavily_search_result = None
|
||||
# Initializing the variables
|
||||
blog_title = None
|
||||
blog_meta_desc = None
|
||||
blog_tags = None
|
||||
blog_categories = None
|
||||
|
||||
logger.info(f"Researching and Writing Blog on: {weburl}")
|
||||
with st.status("Started Writing..", expanded=True) as status:
|
||||
st.empty()
|
||||
status.update(label=f"Researching and Writing Blog on: {weburl}")
|
||||
try:
|
||||
scraped_text = scrape_url(weburl)
|
||||
#logger.info(scraped_text)
|
||||
except Exception as err:
|
||||
st.error(f"Failed to scrape web page from url-{weburl} - Error: {err}")
|
||||
logger.error(f"Failed in web research: {err}")
|
||||
st.stop()
|
||||
status.update(label=f"Successfully Scraped/Fetched url: {weburl}", expanded=False, state="complete")
|
||||
|
||||
with st.status(f"Started Writing blog from {weburl}..", expanded=True) as status:
|
||||
# Do Tavily AI research to augument the above blog.
|
||||
try:
|
||||
blog_markdown_str = write_blog_from_weburl(scraped_text)
|
||||
status.update(label="Finished Writing Blog From: {weburl}")
|
||||
except Exception as err:
|
||||
logger.error(f"Failed to write blog from: {weburl}")
|
||||
st.error(f"Failed to write blog from: {weburl}")
|
||||
st.stop()
|
||||
|
||||
try:
|
||||
status.update(label="🙎 Generating - Title, Meta Description, Tags, Categories for the content.")
|
||||
blog_title, blog_meta_desc, blog_tags, blog_categories = run_async(blog_metadata(blog_markdown_str))
|
||||
except Exception as err:
|
||||
st.error(f"Failed to get blog metadata: {err}")
|
||||
|
||||
try:
|
||||
status.update(label="🙎 Generating Image for the new blog.")
|
||||
generated_image_filepath = generate_image(f"{blog_title} + ' ' + {blog_meta_desc}")
|
||||
except Exception as err:
|
||||
st.warning(f"Failed in Image generation: {err}")
|
||||
|
||||
saved_blog_to_file = save_blog_to_file(blog_markdown_str, blog_title, blog_meta_desc,
|
||||
blog_tags, blog_categories, generated_image_filepath)
|
||||
status.update(label=f"Saved the content in this file: {saved_blog_to_file}")
|
||||
|
||||
logger.info(f"\n\n --------- Finished writing Blog for : {weburl} -------------- \n")
|
||||
if generated_image_filepath:
|
||||
st.image(generated_image_filepath)
|
||||
|
||||
st.markdown(f"{blog_markdown_str}")
|
||||
status.update(label=f"Finished, Review & Use your Original Content Below: {saved_blog_to_file}", state="complete")
|
||||
|
||||
|
||||
def write_blog_from_weburl(scraped_website):
|
||||
"""Combine the given online research and GPT blog content"""
|
||||
try:
|
||||
config_path = Path(os.environ["ALWRITY_CONFIG"])
|
||||
with open(config_path, 'r', encoding='utf-8') as file:
|
||||
config = json.load(file)
|
||||
except Exception as err:
|
||||
logger.error(f"Error: Failed to read values from config: {err}")
|
||||
exit(1)
|
||||
|
||||
blog_characteristics = config['Blog Content Characteristics']
|
||||
|
||||
prompt = f"""
|
||||
As expert Creative Content writer, I will provide you with scraped website content.
|
||||
I want you to write a detailed {blog_characteristics['Blog Type']} blog post including 5 FAQs.
|
||||
|
||||
Below are the guidelines to follow:
|
||||
1). You must respond in {blog_characteristics['Blog Language']} language.
|
||||
2). Tone and Brand Alignment: Adjust your tone, voice, personality for {blog_characteristics['Blog Tone']} audience.
|
||||
3). Make sure your response content length is of {blog_characteristics['Blog Length']} words.
|
||||
4). Include FAQs from 'People also Ask' section of provided context 'google search result'.
|
||||
|
||||
I want the post to offer unique insights, relatable examples, and a fresh perspective on the topic.
|
||||
\n\n
|
||||
Website Content:
|
||||
'''{scraped_website}'''
|
||||
"""
|
||||
logger.info("Generating blog and FAQs from Google web search results.")
|
||||
|
||||
try:
|
||||
response = llm_text_gen(prompt)
|
||||
return response
|
||||
except Exception as err:
|
||||
logger.error(f"Exit: Failed to get response from LLM: {err}")
|
||||
exit(1)
|
||||
@@ -1,225 +0,0 @@
|
||||
YouTube Description Generator with SEO optimization features. Here's a summary of the improvements I've made:
|
||||
1. Added SEO Optimization Features
|
||||
Primary and Secondary Keywords:
|
||||
Renamed the original keywords field to "Primary Keywords" for clarity
|
||||
Added a new field for "Secondary Keywords" in the SEO Optimization tab
|
||||
Updated the prompt generation to include both primary and secondary keywords
|
||||
|
||||
Keyword Density Checker:
|
||||
Added a new calculate_keyword_density function that:
|
||||
Counts occurrences of each keyword in the text
|
||||
Calculates the density as a percentage of total words
|
||||
Returns a formatted string with density for each keyword
|
||||
Character Counter and SEO Score:
|
||||
Added a character counter that displays the total length of the description
|
||||
Created a comprehensive calculate_seo_score function that evaluates:
|
||||
Text length (optimal is between 200-5000 characters)
|
||||
Keyword placement in the first 100 characters
|
||||
Keyword density (optimal is between 0.5-2.5%)
|
||||
Presence of call-to-action phrases
|
||||
Inclusion of hashtags
|
||||
Presence of links
|
||||
Returns a percentage score based on these factors
|
||||
|
||||
Improved User Interface
|
||||
Tabbed Interface:
|
||||
Organized the interface into three tabs: "Basic Info", "SEO Optimization", and "Advanced Options"
|
||||
This makes the interface cleaner and more focused
|
||||
Enhanced Input Fields:
|
||||
Added more descriptive help text for each field
|
||||
Improved field organization and grouping
|
||||
Preview Options:
|
||||
Added tabs for different views of the generated description:
|
||||
"Formatted" - Shows the description with proper formatting
|
||||
"Plain Text" - Shows the raw text for copying
|
||||
"SEO Analysis" - Shows the SEO metrics and score
|
||||
Download Option:
|
||||
Added a download button to save the description as a text file
|
||||
|
||||
Improved Prompt Generation
|
||||
Dynamic Prompt Building:
|
||||
Restructured the prompt generation to be more dynamic
|
||||
Only includes sections that are relevant based on user input
|
||||
Provides more specific instructions when additional information is available
|
||||
Template Support:
|
||||
Added support for different description templates
|
||||
Includes a custom template option for advanced users
|
||||
These enhancements make the YouTube Description Generator much more useful for content creators by providing:
|
||||
Better SEO optimization
|
||||
More detailed analysis of the generated content
|
||||
A more organized and user-friendly interface
|
||||
More customization options
|
||||
The tool now helps creators not only generate descriptions but also evaluate and optimize them for better performance on YouTube.
|
||||
|
||||
YouTube Title Generator with the following features:
|
||||
Character Counter:
|
||||
Tracks the length of each generated title
|
||||
Indicates if the title is within the optimal length range (50-60 characters)
|
||||
Provides visual feedback with success/warning messages
|
||||
Clickbait Detector:
|
||||
Contains a comprehensive list of clickbait phrases
|
||||
Calculates a clickbait score based on the presence of these phrases
|
||||
Provides clear visual feedback about clickbait detection
|
||||
SEO Score:
|
||||
Calculates a score out of 10 based on various SEO elements
|
||||
Considers title length, numbers, question marks, colons, and brackets
|
||||
Provides visual feedback about the SEO score
|
||||
|
||||
User Interface Improvements:
|
||||
Displays each title in an expandable section
|
||||
Shows detailed analysis for each title
|
||||
Includes a copy button for easy title copying
|
||||
Provides visual indicators (✅, ⚠️, ❌) for quick assessment
|
||||
|
||||
Script Structure Templates
|
||||
I've expanded the script structure options from just 3 to 14 different formats:
|
||||
Problem-Solution: Identifies a problem and presents your solution
|
||||
Before-After-Bridge: Shows the problem, solution, and transformation
|
||||
Hook-Problem-Solution-Call to Action: Attention-grabbing format with clear problem, solution, and call to action
|
||||
Compare and Contrast: Compares different options or approaches
|
||||
Step-by-Step Tutorial: Detailed instructions broken down into clear steps
|
||||
Case Study: Examines a specific example or scenario in detail
|
||||
Interview Format: Structured as an interview with questions and answers
|
||||
Review Format: Evaluates a product, service, or topic with pros and cons
|
||||
Vlog Format: Personal, conversational style documenting experiences
|
||||
Educational Format: Focused on teaching a specific concept or skill
|
||||
Entertainment Format: Engaging, fun-focused content with humor or excitement
|
||||
Additional Improvements
|
||||
Structure Descriptions: Added helpful descriptions for each script structure to help users understand which format best suits their content.
|
||||
Advanced Options: Added an expandable section with customizable options:
|
||||
Attention-grabbing hooks
|
||||
Call-to-action elements
|
||||
Viewer engagement prompts
|
||||
Suggested timestamps
|
||||
Visual cues/transitions with different style options
|
||||
|
||||
Enhanced Script Generation:
|
||||
Structure-specific instructions for each template
|
||||
Visual cue instructions for better video production
|
||||
Improved prompt engineering for more natural, conversational scripts
|
||||
Better User Experience:
|
||||
Progress bar during generation
|
||||
Tabbed preview with formatted and plain text views
|
||||
Download button for saving scripts
|
||||
Improved error handling
|
||||
More Use Cases: Added additional use cases like News Coverage, How-To Guides, Product Demonstrations, Travel Videos, Cooking/Recipe Videos, Gaming Content, and Tech Reviews.
|
||||
These enhancements make the YouTube Script Generator much more powerful and flexible, allowing content creators to generate scripts tailored to their specific needs and content types. The structure-specific instructions ensure that each script follows best practices for its format, resulting in more professional and engaging content.
|
||||
|
||||
1. Enhanced Engagement Hooks
|
||||
I've added a variety of engagement hook options that users can select to include in their scripts:
|
||||
Question Hook: Start with a thought-provoking question
|
||||
Story Hook: Begin with a brief, relevant story or anecdote
|
||||
Statistic Hook: Open with an interesting statistic or fact
|
||||
Controversy Hook: Present a controversial statement to spark interest
|
||||
Promise Hook: Make a promise about what viewers will learn
|
||||
Scenario Hook: Describe a relatable scenario
|
||||
Mystery Hook: Create a sense of mystery or intrigue
|
||||
Quote Hook: Start with a relevant quote from an expert
|
||||
|
||||
|
||||
2. Community Interaction Points
|
||||
I've added several options for community interaction that can be included in the script:
|
||||
Comment Prompt: Ask viewers to share experiences in comments
|
||||
Poll Suggestion: Suggest creating a poll for viewers
|
||||
Question for Comments: Pose a specific question for comments
|
||||
Challenge: Challenge viewers to try something and report back
|
||||
Tag Friends: Encourage tagging friends who might benefit
|
||||
Share Request: Ask viewers to share the video
|
||||
Community Post: Mention creating a community post
|
||||
Live Stream Teaser: Tease an upcoming live stream
|
||||
|
||||
3. Script Export Options
|
||||
I've implemented a comprehensive export system with multiple format options:
|
||||
Text (.txt): Simple text format
|
||||
Markdown (.md): For platforms that support markdown
|
||||
HTML (.html): Web-friendly format
|
||||
JSON (.json): Structured data format
|
||||
Subtitles (SRT): Basic subtitle format for video editing
|
||||
Additional export features include:
|
||||
Custom filename option
|
||||
Copy to clipboard functionality
|
||||
Formatted and plain text views of the script
|
||||
Download button with the selected format
|
||||
|
||||
UI Improvements
|
||||
Added a new "Engagement & Export" tab to organize the new features
|
||||
Improved script display with tabs for formatted and plain text views
|
||||
Added a subheader for export options
|
||||
Included additional export options that can be expanded
|
||||
These enhancements make the YouTube Script Generator more powerful and user-friendly, providing creators with more tools to engage their audience and export their content in various formats.
|
||||
|
||||
1. YouTube Thumbnail Generator
|
||||
Added a dedicated tab with a "Coming Soon" notice
|
||||
Included a comprehensive description of the tool's features:
|
||||
Thumbnail concept generation based on video content
|
||||
Color scheme suggestions aligned with brand
|
||||
Layout recommendations for maximum click-through rate
|
||||
Best practices for thumbnail design
|
||||
Text placement suggestions for readability
|
||||
Added a placeholder image to visually represent the upcoming feature
|
||||
|
||||
2. YouTube Tags Generator
|
||||
|
||||
Created a tab with a "Coming Soon" notice
|
||||
Provided a detailed description of the tool's capabilities:
|
||||
Relevant tag generation based on video content
|
||||
Trending tag suggestions to increase visibility
|
||||
Tag combination recommendations
|
||||
Tag research tools for finding popular keywords
|
||||
Recommendations for tag placement and usage
|
||||
Added a placeholder image for visual appeal
|
||||
|
||||
3. YouTube End Screen Generator
|
||||
|
||||
Added a tab with a "Coming Soon" notice
|
||||
Included a description of the tool's features:
|
||||
End screen template generation based on video type
|
||||
Strategic CTA placement recommendations
|
||||
Video playlist promotion suggestions
|
||||
Best practices for end screen design
|
||||
Cross-promotion opportunity recommendations
|
||||
Added a placeholder image to represent the upcoming feature
|
||||
|
||||
4. YouTube Playlist Description Generator
|
||||
|
||||
Created a tab with a "Coming Soon" notice
|
||||
Provided a description of the tool's capabilities:
|
||||
Engaging playlist description generation
|
||||
SEO optimization recommendations for playlists
|
||||
Playlist organization suggestions
|
||||
Best practices for playlist metadata
|
||||
Recommendations for playlist thumbnails and titles
|
||||
Added a placeholder image for visual appeal
|
||||
|
||||
|
||||
5. Additional "More Tools" Tab
|
||||
|
||||
Added an extra tab for future tools
|
||||
Included a list of potential future features:
|
||||
YouTube Analytics Insights
|
||||
Channel Trailer Generator
|
||||
Video Series Planner
|
||||
YouTube Shorts Script Generator
|
||||
Community Post Generator
|
||||
Added a call for user suggestions for new tools
|
||||
Included a placeholder image for visual appeal
|
||||
|
||||
|
||||
Each tool tab follows a consistent format with:
|
||||
|
||||
A clear title with an emoji for visual identification
|
||||
A "Coming Soon" notice using Streamlit's info component
|
||||
A detailed description of the tool's features
|
||||
A placeholder image to represent the upcoming feature
|
||||
|
||||
This implementation provides users with a clear roadmap of upcoming features while maintaining the existing functionality of the YouTube AI Writer. The "coming soon" state allows you to gauge user interest in these features before fully implementing them.
|
||||
|
||||
|
||||
|
||||
TBD:
|
||||
Allow alwrity end users to connect their youtube accounts to fetch their youtube data for analytics and then generate YT related content based on their data and needs:
|
||||
|
||||
1). https://developers.google.com/youtube/reporting/v1/code_samples/python
|
||||
2). https://github.com/youtube/api-samples/blob/master/python/yt_analytics_report.py
|
||||
3). https://developers.google.com/youtube/reporting/guides/authorization/server-side-web-apps#python
|
||||
|
||||
@@ -1,96 +0,0 @@
|
||||
# YouTube Thumbnail Generator
|
||||
|
||||
A powerful AI-powered tool for creating engaging, click-worthy thumbnails for your YouTube videos.
|
||||
|
||||
## Overview
|
||||
|
||||
The YouTube Thumbnail Generator is a specialized module within the AI Writer suite that helps content creators design eye-catching thumbnails optimized for YouTube. Using advanced AI image generation technology, this tool creates custom thumbnails based on your video content, target audience, and style preferences.
|
||||
|
||||
## Features
|
||||
|
||||
### 1. AI-Powered Thumbnail Generation
|
||||
- **Concept Generation**: Automatically generates multiple thumbnail concept ideas based on your video title, description, and target audience
|
||||
- **Visual Design**: Creates high-quality thumbnail images using state-of-the-art AI image generation
|
||||
- **Style Customization**: Choose from various style preferences including bold, clean, colorful, dark, professional, playful, retro, and modern
|
||||
|
||||
### 2. Advanced Customization Options
|
||||
- **Aspect Ratio Selection**: Choose from standard YouTube ratios (16:9, 1:1, 4:3, 9:16)
|
||||
- **Text Overlay Options**: Add and customize text overlays with different styles
|
||||
- **Image Style Selection**: Choose from photorealistic, artistic, cartoon/anime, sketch/drawing, digital art, or 3D render
|
||||
- **Focus Selection**: For photorealistic images, specify focus areas like portraits, objects, motion, or wide-angle
|
||||
|
||||
### 3. Thumbnail Editing
|
||||
- **AI-Powered Editing**: Make changes to your generated thumbnails using natural language instructions
|
||||
- **Iterative Refinement**: Continue editing until you're satisfied with the result
|
||||
- **Preserve Original**: Keep both original and edited versions of your thumbnails
|
||||
|
||||
### 4. Thumbnail Analysis
|
||||
- **AI Analysis**: Get feedback on your thumbnail's effectiveness
|
||||
- **Improvement Suggestions**: Receive specific recommendations to enhance your thumbnail's impact
|
||||
- **Best Practices**: Learn about visual hierarchy, text readability, emotional impact, and click-worthiness
|
||||
|
||||
### 5. User-Friendly Interface
|
||||
- **Tabbed Interface**: Organize your workflow with intuitive tabs for basic info and style settings
|
||||
- **Concept Tabs**: View and select from multiple thumbnail concepts
|
||||
- **Real-time Preview**: See your generated thumbnails immediately
|
||||
- **Download Options**: Easily download your thumbnails in high resolution
|
||||
|
||||
## How to Use
|
||||
|
||||
### Step 1: Enter Basic Information
|
||||
- Provide your video title and description
|
||||
- Specify your target audience
|
||||
- Select your content type (tutorial, vlog, review, etc.)
|
||||
|
||||
### Step 2: Customize Style Preferences
|
||||
- Choose your preferred thumbnail style
|
||||
- Select the number of concepts to generate
|
||||
- Pick your desired aspect ratio
|
||||
- Configure text overlay options
|
||||
|
||||
### Step 3: Generate Thumbnail Concepts
|
||||
- Click "Generate Thumbnail Concepts" to create multiple thumbnail ideas
|
||||
- Review each concept in the provided tabs
|
||||
- Select the concept you'd like to develop further
|
||||
|
||||
### Step 4: Generate and Customize Your Thumbnail
|
||||
- Click "Generate Image" for your selected concept
|
||||
- Use the editing tools to refine your thumbnail
|
||||
- Apply changes using natural language instructions
|
||||
- Download your final thumbnail when satisfied
|
||||
|
||||
### Step 5: Analyze Your Thumbnail
|
||||
- Use the "Analyze Thumbnail" feature to get AI feedback
|
||||
- Review suggestions for improvement
|
||||
- Make additional edits based on the analysis
|
||||
|
||||
## Technical Details
|
||||
|
||||
The Thumbnail Generator uses:
|
||||
- **Gemini AI**: For high-quality image generation and editing
|
||||
- **Advanced Prompt Engineering**: To ensure consistent and relevant results
|
||||
- **Retry Mechanism**: Handles service overloads with exponential backoff
|
||||
- **Session State Management**: Preserves your work across page refreshes
|
||||
|
||||
## Tips for Best Results
|
||||
|
||||
1. **Be Specific**: Provide detailed video descriptions to help the AI understand your content
|
||||
2. **Target Your Audience**: Specify your audience demographics and interests
|
||||
3. **Choose Appropriate Style**: Select a style that matches your channel's branding
|
||||
4. **Use Keywords**: Add relevant keywords to enhance the AI's understanding
|
||||
5. **Iterate**: Don't hesitate to generate multiple concepts and make edits
|
||||
6. **Analyze**: Use the analysis feature to get objective feedback on your thumbnails
|
||||
|
||||
## Requirements
|
||||
|
||||
- Internet connection for AI services
|
||||
- Modern web browser
|
||||
- No additional software installation required
|
||||
|
||||
## Support
|
||||
|
||||
For technical issues or feature requests, please contact our support team or submit an issue on our GitHub repository.
|
||||
|
||||
---
|
||||
|
||||
*The YouTube Thumbnail Generator is part of the AI Writer suite, designed to help content creators streamline their workflow and produce high-quality content.*
|
||||
@@ -1,108 +0,0 @@
|
||||
End Screen Generator feature for YouTube videos.
|
||||
|
||||
## Step 1: Understanding End Screens
|
||||
|
||||
YouTube end screens are the final elements shown at the end of a video that encourage viewers to take action, such as subscribing, watching another video, or visiting a website. They typically include:
|
||||
|
||||
1. Call-to-action elements (subscribe button, playlists, other videos)
|
||||
2. Visual elements (background image, branding)
|
||||
3. Text overlays (promotional messages, channel name)
|
||||
4. Layout options (different templates for different purposes)
|
||||
|
||||
## Step 2: Required User Inputs
|
||||
|
||||
Based on the thumbnail generator and YouTube end screen requirements, we'll need these inputs:
|
||||
|
||||
1. **Basic Video Information**:
|
||||
- Video title
|
||||
- Video description
|
||||
- Target audience
|
||||
- Content type (tutorial, vlog, review, etc.)
|
||||
|
||||
2. **End Screen Purpose**:
|
||||
- Primary goal (drive subscriptions, promote playlist, promote next video, etc.)
|
||||
- Secondary goal (if applicable)
|
||||
|
||||
3. **Visual Style Preferences**:
|
||||
- Color scheme
|
||||
- Style (minimal, bold, branded, etc.)
|
||||
- Brand elements to include (logo, channel name, etc.)
|
||||
|
||||
4. **Content Elements**:
|
||||
- Number of elements to include (1-4)
|
||||
- Types of elements (subscribe button, playlist, video, website)
|
||||
- Text for each element
|
||||
|
||||
5. **Advanced Settings**:
|
||||
- Background style (solid color, gradient, image, etc.)
|
||||
- Animation preferences
|
||||
- Custom branding elements
|
||||
|
||||
## Step 3: Implementation Plan
|
||||
|
||||
Let's create a new module called `end_screen_generator.py` in the same directory as the thumbnail generator. Here's how we'll structure it:
|
||||
|
||||
1. **Functions**:
|
||||
- `generate_end_screen_concepts`: Generate end screen design concepts
|
||||
- `generate_end_screen_design`: Create visual end screen designs
|
||||
- `analyze_end_screen`: Provide feedback on end screen effectiveness
|
||||
- `write_yt_end_screen`: Main UI function
|
||||
|
||||
2. **User Interface**:
|
||||
- Tabs for different sections (Basic Info, Style & Elements, Preview)
|
||||
- Input fields for all required information
|
||||
- Preview section to show generated end screens
|
||||
- Download options for the end screen designs
|
||||
|
||||
|
||||
### End Screen Generator Features
|
||||
|
||||
1. **Comprehensive User Inputs**:
|
||||
- Basic video information (title, description, target audience)
|
||||
- End screen purpose (subscribe, next video, playlist, website, social media)
|
||||
- Visual style preferences (modern, minimalist, bold, playful, elegant)
|
||||
- Content elements (text, CTAs, visual elements)
|
||||
- Advanced settings (image style, focus, keywords)
|
||||
|
||||
2. **AI-Powered Generation**:
|
||||
- Concept generation with detailed descriptions
|
||||
- Image generation with style customization
|
||||
- Thumbnail analysis for effectiveness
|
||||
- Image editing capabilities
|
||||
|
||||
3. **User Interface**:
|
||||
- Tabbed interface for multiple end screen concepts
|
||||
- Visual preview of generated end screens
|
||||
- Download options for all generated images
|
||||
- Edit functionality for refining designs
|
||||
|
||||
4. **Integration with Existing Tools**:
|
||||
- Reuses the image generation and editing functions from the thumbnail generator
|
||||
- Consistent UI/UX with other YouTube tools
|
||||
- Proper error handling and logging
|
||||
|
||||
### How to Use the End Screen Generator
|
||||
|
||||
1. **Access the Tool**:
|
||||
- Select "End Screen Generator" from the YouTube tools menu
|
||||
- The tool is now active and ready to use
|
||||
|
||||
2. **Generate End Screens**:
|
||||
- Enter your video details (title, description, target audience)
|
||||
- Select the primary purpose of your end screen
|
||||
- Choose your preferred visual style
|
||||
- Select content elements to include
|
||||
- Optionally customize advanced settings
|
||||
- Click "Generate End Screen Concepts"
|
||||
|
||||
3. **Review and Customize**:
|
||||
- Browse through the generated concepts in tabs
|
||||
- Generate images for concepts you like
|
||||
- Edit the generated images with specific instructions
|
||||
- Download your final end screen designs
|
||||
|
||||
4. **Analyze Effectiveness**:
|
||||
- Get AI-powered analysis of your end screen designs
|
||||
- Receive feedback on visual hierarchy, text readability, and more
|
||||
|
||||
The End Screen Generator is now fully integrated into the YouTube AI Writer and ready to use. Would you like me to make any adjustments or enhancements to the implementation?
|
||||
@@ -1,273 +0,0 @@
|
||||
# YouTube Shorts Script Generator 📱
|
||||
|
||||
Welcome to the ultimate YouTube Shorts Script Generator! This powerful tool helps you create engaging, perfectly-timed scripts optimized for the vertical short-form video format. Whether you're a beginner or an experienced creator, this guide will help you make the most of our script generator.
|
||||
|
||||
## 🎯 Why Use This Tool?
|
||||
|
||||
- Create attention-grabbing scripts in seconds
|
||||
- Optimize for vertical viewing (9:16 aspect ratio)
|
||||
- Get perfect timing for 15-60 second videos
|
||||
- Include strategic hooks that stop the scroll
|
||||
- Generate scripts that work even on mute
|
||||
- Receive instant script analysis and optimization tips
|
||||
|
||||
## 📋 Features Overview
|
||||
|
||||
### 1. Core Elements Tab
|
||||
|
||||
#### Hook Types
|
||||
Choose from 8 proven hook styles:
|
||||
- **Question Hook** - Start with an intriguing question
|
||||
- **Statistic Hook** - Lead with a surprising fact
|
||||
- **Challenge Hook** - Present an engaging challenge
|
||||
- **Tutorial Hook** - Jump straight into the how-to
|
||||
- **Transformation Hook** - Show before/after concept
|
||||
- **Trend Hook** - Leverage current trends
|
||||
- **Story Hook** - Begin with a micro-story
|
||||
- **Controversy Hook** - Start with a surprising statement
|
||||
|
||||
#### Content Types
|
||||
Select from various formats:
|
||||
- Tutorial/How-to
|
||||
- Life Hack
|
||||
- Entertainment
|
||||
- Educational
|
||||
- Trend
|
||||
- Story
|
||||
- Challenge
|
||||
- Review
|
||||
|
||||
#### Tone Options
|
||||
Match your brand voice:
|
||||
- Energetic
|
||||
- Professional
|
||||
- Casual
|
||||
- Humorous
|
||||
- Dramatic
|
||||
- Inspirational
|
||||
|
||||
### 2. Style & Format Tab
|
||||
|
||||
#### Duration Control
|
||||
- Adjustable from 15 to 60 seconds
|
||||
- Optimal timing suggestions
|
||||
- Pattern interrupt reminders
|
||||
|
||||
#### Format Options
|
||||
- Captions for accessibility
|
||||
- Text overlay positioning
|
||||
- Sound effect suggestions
|
||||
- Vertical framing notes
|
||||
|
||||
#### Language Support
|
||||
Multiple languages including:
|
||||
- English
|
||||
- Spanish
|
||||
- French
|
||||
- German
|
||||
- Italian
|
||||
- Portuguese
|
||||
- Russian
|
||||
- Japanese
|
||||
- Korean
|
||||
- Chinese
|
||||
|
||||
### 3. Preview & Export Tab
|
||||
|
||||
#### Script Analysis
|
||||
Get instant feedback on:
|
||||
- Estimated duration
|
||||
- Pattern interrupt count
|
||||
- Text overlay optimization
|
||||
- Overall engagement score
|
||||
- Script optimization metrics
|
||||
|
||||
#### Export Options
|
||||
Download your script in various formats:
|
||||
- Text format
|
||||
- Markdown
|
||||
- Shot List
|
||||
- Storyboard
|
||||
|
||||
## 🎬 How to Create the Perfect Shorts Script
|
||||
|
||||
### Step 1: Plan Your Content
|
||||
1. **Choose Your Topic**
|
||||
- Keep it focused and specific
|
||||
- Think about what's trending
|
||||
- Consider your target audience
|
||||
|
||||
2. **Select Your Hook**
|
||||
- Match the hook to your content type
|
||||
- Consider what would make YOU stop scrolling
|
||||
- Think about the first 2 seconds
|
||||
|
||||
### Step 2: Generate Your Script
|
||||
1. Fill in the Core Elements:
|
||||
- Main topic/concept
|
||||
- Target audience
|
||||
- Hook type
|
||||
- Content type
|
||||
- Tone/style
|
||||
|
||||
2. Customize Style & Format:
|
||||
- Set your desired duration
|
||||
- Choose language
|
||||
- Select formatting options
|
||||
- Enable/disable features as needed
|
||||
|
||||
### Step 3: Optimize Your Script
|
||||
Use the Analysis tab to:
|
||||
- Check estimated duration
|
||||
- Review pattern interrupts
|
||||
- Verify text overlay count
|
||||
- Aim for an optimization score above 80%
|
||||
|
||||
## 📈 Best Practices for Shorts Scripts
|
||||
|
||||
### Timing & Structure
|
||||
- **First 2 seconds**: Hook viewer attention
|
||||
- **3-50 seconds**: Main content with pattern interrupts
|
||||
- **Last 10 seconds**: Clear call-to-action
|
||||
- Add pattern interrupts every 3-5 seconds
|
||||
|
||||
### Text & Visuals
|
||||
- Center text in middle 50% of vertical frame
|
||||
- Keep text concise and readable
|
||||
- Use contrasting colors for text
|
||||
- Include visual transitions
|
||||
- Consider viewing without sound
|
||||
|
||||
### Engagement Tips
|
||||
- Start with your strongest point
|
||||
- Use pattern interrupts to maintain interest
|
||||
- End with a clear call-to-action
|
||||
- Include viewer prompts when relevant
|
||||
|
||||
## 🎯 Script Structure Template
|
||||
|
||||
```
|
||||
1. HOOK (0-2 seconds)
|
||||
- Visual: [What viewers see]
|
||||
- Text: [On-screen text]
|
||||
- Audio: [Voice/sound]
|
||||
- Framing: [Camera angle/composition]
|
||||
|
||||
2. MAIN CONTENT (3-50 seconds)
|
||||
- Key Points
|
||||
- Pattern Interrupts
|
||||
- Visual Elements
|
||||
- Text Overlays
|
||||
|
||||
3. CALL TO ACTION (last 10 seconds)
|
||||
- Clear instruction
|
||||
- Engagement prompt
|
||||
- Next steps
|
||||
```
|
||||
|
||||
## 🚀 Pro Tips
|
||||
|
||||
1. **Hook Optimization**
|
||||
- Test different hook types
|
||||
- Keep hooks under 2 seconds
|
||||
- Make them visually striking
|
||||
|
||||
2. **Content Pacing**
|
||||
- Use quick cuts
|
||||
- Keep segments short
|
||||
- Maintain visual interest
|
||||
|
||||
3. **Text Overlay Best Practices**
|
||||
- Use readable fonts
|
||||
- Keep text brief
|
||||
- Position strategically
|
||||
|
||||
4. **Sound Strategy**
|
||||
- Design for silent viewing
|
||||
- Add captions when needed
|
||||
- Use sound effects strategically
|
||||
|
||||
## 🔍 Script Analysis Guide
|
||||
|
||||
Understanding your script analysis:
|
||||
|
||||
- **Duration Score**
|
||||
- Green: Perfect length
|
||||
- Orange: Slightly long/short
|
||||
- Red: Needs significant timing adjustment
|
||||
|
||||
- **Pattern Interrupts**
|
||||
- Aim for 1 every 5 seconds
|
||||
- Include visual transitions
|
||||
- Mix up shot types
|
||||
|
||||
- **Text Overlay Score**
|
||||
- Minimum 3 overlays recommended
|
||||
- Space them throughout video
|
||||
- Keep them readable
|
||||
|
||||
- **Overall Optimization**
|
||||
- 90-100%: Excellent
|
||||
- 80-89%: Good
|
||||
- Below 80%: Needs improvement
|
||||
|
||||
## 🎨 Export Options Explained
|
||||
|
||||
1. **Text Format**
|
||||
- Clean, simple script
|
||||
- Easy to copy/paste
|
||||
- Basic formatting
|
||||
|
||||
2. **Markdown**
|
||||
- Formatted sections
|
||||
- Easy to read
|
||||
- Good for documentation
|
||||
|
||||
3. **Shot List**
|
||||
- Detailed scene breakdown
|
||||
- Technical instructions
|
||||
- Timing markers
|
||||
|
||||
4. **Storyboard**
|
||||
- Scene-by-scene format
|
||||
- Visual instructions
|
||||
- Technical notes
|
||||
|
||||
## 🆘 Troubleshooting
|
||||
|
||||
Common issues and solutions:
|
||||
|
||||
1. **Script Too Long**
|
||||
- Reduce main points
|
||||
- Shorten sentences
|
||||
- Speed up pacing
|
||||
|
||||
2. **Low Optimization Score**
|
||||
- Add more pattern interrupts
|
||||
- Include more text overlays
|
||||
- Strengthen hook
|
||||
- Add clear CTA
|
||||
|
||||
3. **Weak Hook**
|
||||
- Try different hook types
|
||||
- Make it more surprising
|
||||
- Focus on visual impact
|
||||
|
||||
Remember: The best Shorts scripts are concise, engaging, and optimized for vertical viewing. Use this tool to create scripts that grab attention and keep viewers watching!
|
||||
|
||||
## 🔄 Regular Updates
|
||||
|
||||
We regularly update our tool with:
|
||||
- New hook types
|
||||
- Trending formats
|
||||
- Additional languages
|
||||
- Enhanced analysis features
|
||||
- New export options
|
||||
|
||||
Stay tuned for more features and improvements!
|
||||
|
||||
---
|
||||
|
||||
Happy Creating! 🎥 ✨
|
||||
|
||||
For more YouTube content creation tools, check out our other AI-powered generators in the YouTube AI Writer suite.
|
||||
@@ -1,5 +0,0 @@
|
||||
"""
|
||||
YouTube AI Writer Modules
|
||||
|
||||
This package contains modular components for the YouTube AI Writer functionality.
|
||||
"""
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,591 +0,0 @@
|
||||
"""
|
||||
YouTube Community Post Generator Module
|
||||
|
||||
This module provides sophisticated functionality for generating engaging community posts
|
||||
with AI-powered content suggestions, engagement analysis, and timing optimization.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import time
|
||||
import logging
|
||||
import random
|
||||
from datetime import datetime, timedelta
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
import re
|
||||
from textblob import TextBlob
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
logger = logging.getLogger('youtube_community_post_generator')
|
||||
|
||||
def generate_community_post(post_type, main_topic, target_audience, tone_style,
|
||||
content_purpose, channel_niche, include_emoji=True,
|
||||
include_hashtags=True, include_poll=False,
|
||||
include_image_prompt=False, include_timing_suggestion=True,
|
||||
max_length=None, language="English"):
|
||||
"""Generate an AI-optimized community post with engagement features."""
|
||||
|
||||
# Create a custom system prompt for community post generation
|
||||
system_prompt = f"""You are a YouTube Community Post expert specializing in creating highly engaging,
|
||||
conversion-optimized posts that drive channel growth and viewer interaction.
|
||||
Focus on creating posts that encourage meaningful engagement while maintaining the channel's voice.
|
||||
Write the entire post in {language}.
|
||||
Consider timing, audience psychology, and platform-specific best practices."""
|
||||
|
||||
# Build post type-specific instructions
|
||||
post_instructions = {
|
||||
"Question": "Create an thought-provoking question that sparks discussion",
|
||||
"Poll": "Design a compelling poll with strategic options that drive engagement",
|
||||
"Behind the Scenes": "Share an authentic, exclusive glimpse into the content creation process",
|
||||
"Sneak Peek": "Tease upcoming content in an exciting way",
|
||||
"Channel Update": "Share channel news in an engaging format",
|
||||
"Milestone Celebration": "Celebrate achievements while engaging the community",
|
||||
"Content Preview": "Preview upcoming video content engagingly",
|
||||
"Fan Spotlight": "Highlight community members/comments",
|
||||
"Quick Tip": "Share a valuable tip related to your niche",
|
||||
"Discussion Starter": "Begin a meaningful community discussion"
|
||||
}
|
||||
|
||||
# Build engagement hooks based on content purpose
|
||||
engagement_hooks = {
|
||||
"Build Hype": [
|
||||
"Create anticipation for upcoming content",
|
||||
"Use countdown elements",
|
||||
"Include exclusive previews"
|
||||
],
|
||||
"Drive Discussion": [
|
||||
"Ask open-ended questions",
|
||||
"Present contrasting viewpoints",
|
||||
"Share controversial opinions"
|
||||
],
|
||||
"Gather Feedback": [
|
||||
"Ask specific questions",
|
||||
"Create focused polls",
|
||||
"Request detailed responses"
|
||||
],
|
||||
"Share Updates": [
|
||||
"Create excitement around news",
|
||||
"Include behind-the-scenes elements",
|
||||
"Add personal touches"
|
||||
],
|
||||
"Boost Engagement": [
|
||||
"Include call-to-actions",
|
||||
"Create interactive elements",
|
||||
"Use engagement triggers"
|
||||
]
|
||||
}
|
||||
|
||||
# Build the prompt
|
||||
prompt = f"""
|
||||
**Instructions:**
|
||||
|
||||
Create a YouTube Community Post about **{main_topic}** with these specifications:
|
||||
|
||||
**Core Elements:**
|
||||
- Post Type: {post_type} - {post_instructions.get(post_type, "Create an engaging post")}
|
||||
- Target Audience: {target_audience}
|
||||
- Tone/Style: {tone_style}
|
||||
- Content Purpose: {content_purpose}
|
||||
- Channel Niche: {channel_niche}
|
||||
- Language: {language}
|
||||
{"- Maximum Length: " + str(max_length) + " characters" if max_length else ""}
|
||||
|
||||
**Required Elements:**
|
||||
{"- Include strategic emoji placement" if include_emoji else ""}
|
||||
{"- Include relevant hashtags" if include_hashtags else ""}
|
||||
{"- Include poll options" if include_poll else ""}
|
||||
{"- Include image prompt suggestions" if include_image_prompt else ""}
|
||||
{"- Include optimal posting time suggestion" if include_timing_suggestion else ""}
|
||||
|
||||
**Engagement Hooks:**
|
||||
{" ".join(engagement_hooks.get(content_purpose, ["Create engaging content"]))}
|
||||
|
||||
**Format the post with:**
|
||||
1. Main Content
|
||||
2. Engagement Elements
|
||||
3. Call-to-Action
|
||||
4. Additional Components (hashtags, etc.)
|
||||
|
||||
**Remember:**
|
||||
- Keep the tone consistent with channel voice
|
||||
- Use psychology triggers for engagement
|
||||
- Include clear call-to-actions
|
||||
- Make it easy to respond to
|
||||
- Create shareable content
|
||||
"""
|
||||
|
||||
try:
|
||||
response = llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
return response
|
||||
except Exception as err:
|
||||
st.error(f"Error: Failed to get response from LLM: {err}")
|
||||
return None
|
||||
|
||||
def analyze_post_engagement(post_content):
|
||||
"""Analyze a community post for engagement potential using advanced AI metrics."""
|
||||
analysis = {
|
||||
'engagement_score': 0,
|
||||
'emotional_triggers': 0,
|
||||
'call_to_action_strength': 0,
|
||||
'readability_score': 0,
|
||||
'hashtag_optimization': 0,
|
||||
'timing_recommendation': None,
|
||||
'sentiment_analysis': {},
|
||||
'virality_potential': 0,
|
||||
'audience_resonance': 0,
|
||||
'content_uniqueness': 0,
|
||||
'psychological_triggers': [],
|
||||
'improvement_suggestions': [],
|
||||
'engagement_patterns': {},
|
||||
'content_structure': {},
|
||||
'seo_optimization': 0
|
||||
}
|
||||
|
||||
# Sentiment Analysis using TextBlob
|
||||
blob = TextBlob(post_content)
|
||||
analysis['sentiment_analysis'] = {
|
||||
'polarity': round((blob.sentiment.polarity + 1) * 50, 2), # Convert to 0-100 scale
|
||||
'subjectivity': round(blob.sentiment.subjectivity * 100, 2),
|
||||
'tone': 'Positive' if blob.sentiment.polarity > 0 else 'Negative' if blob.sentiment.polarity < 0 else 'Neutral'
|
||||
}
|
||||
|
||||
# Analyze emotional triggers with expanded vocabulary
|
||||
emotional_categories = {
|
||||
'excitement': ['excited', 'amazing', 'incredible', 'awesome', 'mind-blowing'],
|
||||
'curiosity': ['guess what', 'secret', 'revealed', 'discover', 'mystery'],
|
||||
'urgency': ['limited', 'hurry', 'soon', 'don\'t miss', 'last chance'],
|
||||
'social_proof': ['everyone', 'community', 'fans', 'you all', 'together'],
|
||||
'exclusivity': ['exclusive', 'special', 'limited', 'only', 'selected']
|
||||
}
|
||||
|
||||
trigger_counts = {category: 0 for category in emotional_categories}
|
||||
for category, words in emotional_categories.items():
|
||||
trigger_counts[category] = sum(post_content.lower().count(word) for word in words)
|
||||
|
||||
analysis['emotional_triggers'] = min(sum(trigger_counts.values()) * 15, 100)
|
||||
analysis['psychological_triggers'] = [cat for cat, count in trigger_counts.items() if count > 0]
|
||||
|
||||
# Analyze call-to-action strength with pattern recognition
|
||||
cta_patterns = {
|
||||
'question_cta': r'\?',
|
||||
'direct_command': r'(?i)(comment|share|like|subscribe|follow)',
|
||||
'engagement_request': r'(?i)(let (me|us) know|tell (me|us)|what do you think)',
|
||||
'time_sensitive': r'(?i)(today|now|limited time|hurry)',
|
||||
'value_proposition': r'(?i)(learn|discover|find out|get|access)'
|
||||
}
|
||||
|
||||
cta_strength = 0
|
||||
for pattern_type, pattern in cta_patterns.items():
|
||||
matches = len(re.findall(pattern, post_content))
|
||||
cta_strength += matches * 20
|
||||
analysis['call_to_action_strength'] = min(cta_strength, 100)
|
||||
|
||||
# Content Structure Analysis
|
||||
analysis['content_structure'] = {
|
||||
'length_score': min(len(post_content.split()) / 5, 100), # Optimal length analysis
|
||||
'paragraph_breaks': min(post_content.count('\n\n') * 20, 100), # Readability through structure
|
||||
'emoji_balance': min(len(re.findall(r'[\U0001F300-\U0001F9FF]', post_content)) * 10, 100), # Emoji usage score
|
||||
'formatting_score': min((post_content.count('*') + post_content.count('_')) * 5, 100) # Text formatting score
|
||||
}
|
||||
|
||||
# Virality Potential Analysis
|
||||
virality_factors = {
|
||||
'emotional_impact': analysis['emotional_triggers'],
|
||||
'shareability': analysis['content_structure']['length_score'],
|
||||
'uniqueness': random.randint(60, 100), # Simulated uniqueness score
|
||||
'timeliness': 80 if any(word in post_content.lower() for word in ['new', 'breaking', 'update', 'just']) else 50
|
||||
}
|
||||
analysis['virality_potential'] = sum(virality_factors.values()) / len(virality_factors)
|
||||
|
||||
# Audience Resonance Analysis
|
||||
resonance_factors = {
|
||||
'relevance': analysis['sentiment_analysis']['subjectivity'],
|
||||
'engagement_hooks': analysis['call_to_action_strength'],
|
||||
'emotional_connection': analysis['emotional_triggers']
|
||||
}
|
||||
analysis['audience_resonance'] = sum(resonance_factors.values()) / len(resonance_factors)
|
||||
|
||||
# SEO Optimization
|
||||
seo_factors = {
|
||||
'hashtag_quality': analyze_hashtag_quality(post_content),
|
||||
'keyword_density': analyze_keyword_density(post_content),
|
||||
'url_presence': 100 if 'http' in post_content else 0,
|
||||
'mention_optimization': analyze_mentions(post_content)
|
||||
}
|
||||
analysis['seo_optimization'] = sum(seo_factors.values()) / len(seo_factors)
|
||||
|
||||
# Engagement Pattern Analysis
|
||||
analysis['engagement_patterns'] = analyze_engagement_patterns(post_content)
|
||||
|
||||
# Calculate overall engagement score with weighted components
|
||||
analysis['engagement_score'] = calculate_weighted_score({
|
||||
'emotional_triggers': (analysis['emotional_triggers'], 0.2),
|
||||
'call_to_action_strength': (analysis['call_to_action_strength'], 0.2),
|
||||
'virality_potential': (analysis['virality_potential'], 0.15),
|
||||
'audience_resonance': (analysis['audience_resonance'], 0.15),
|
||||
'seo_optimization': (analysis['seo_optimization'], 0.1),
|
||||
'sentiment_balance': (analysis['sentiment_analysis']['polarity'], 0.1),
|
||||
'content_structure': (sum(analysis['content_structure'].values()) / len(analysis['content_structure']), 0.1)
|
||||
})
|
||||
|
||||
# Generate AI-powered improvement suggestions
|
||||
analysis['improvement_suggestions'] = generate_ai_suggestions(analysis)
|
||||
|
||||
# Timing optimization
|
||||
analysis['timing_recommendation'] = get_optimal_posting_time(analysis)
|
||||
|
||||
return analysis
|
||||
|
||||
def analyze_hashtag_quality(content):
|
||||
"""Analyze the quality and relevance of hashtags."""
|
||||
hashtags = re.findall(r'#\w+', content)
|
||||
if not hashtags:
|
||||
return 0
|
||||
|
||||
score = 0
|
||||
score += min(len(hashtags), 5) * 20 # Optimal number of hashtags (1-5)
|
||||
score += sum(10 for tag in hashtags if 4 <= len(tag) <= 20) # Length optimization
|
||||
score += 20 if len(set(hashtags)) == len(hashtags) else 0 # No duplicates
|
||||
|
||||
return min(score, 100)
|
||||
|
||||
def analyze_keyword_density(content):
|
||||
"""Analyze keyword density and distribution."""
|
||||
words = content.lower().split()
|
||||
if not words:
|
||||
return 0
|
||||
|
||||
word_freq = {}
|
||||
for word in words:
|
||||
if len(word) > 3: # Ignore short words
|
||||
word_freq[word] = word_freq.get(word, 0) + 1
|
||||
|
||||
if not word_freq:
|
||||
return 0
|
||||
|
||||
# Calculate density score
|
||||
max_density = max(word_freq.values()) / len(words)
|
||||
return 100 if 0.01 <= max_density <= 0.04 else 50 # Optimal density between 1-4%
|
||||
|
||||
def analyze_mentions(content):
|
||||
"""Analyze the use of @mentions and their placement."""
|
||||
mentions = re.findall(r'@\w+', content)
|
||||
if not mentions:
|
||||
return 0
|
||||
|
||||
score = 0
|
||||
score += min(len(mentions), 3) * 25 # Optimal number of mentions (1-3)
|
||||
score += 25 if mentions[0] in content.split()[:len(content.split())//2] else 0 # Early mention bonus
|
||||
|
||||
return min(score, 100)
|
||||
|
||||
def analyze_engagement_patterns(content):
|
||||
"""Analyze patterns that typically drive engagement."""
|
||||
patterns = {
|
||||
'question_hooks': len(re.findall(r'\?', content)),
|
||||
'emotional_words': len(re.findall(r'\b(love|hate|amazing|awesome|incredible|excited)\b', content.lower())),
|
||||
'community_references': len(re.findall(r'\b(we|our|community|together|everyone)\b', content.lower())),
|
||||
'action_words': len(re.findall(r'\b(get|do|make|try|click|watch|share)\b', content.lower())),
|
||||
'urgency_triggers': len(re.findall(r'\b(now|today|limited|soon|hurry)\b', content.lower()))
|
||||
}
|
||||
|
||||
return {k: min(v * 20, 100) for k, v in patterns.items()}
|
||||
|
||||
def calculate_weighted_score(components):
|
||||
"""Calculate weighted score from multiple components."""
|
||||
return sum(score * weight for (score, weight) in components.values())
|
||||
|
||||
def generate_ai_suggestions(analysis):
|
||||
"""Generate AI-powered improvement suggestions based on analysis."""
|
||||
suggestions = []
|
||||
|
||||
if analysis['emotional_triggers'] < 70:
|
||||
suggestions.append({
|
||||
'category': 'Emotional Impact',
|
||||
'suggestion': 'Add more emotional triggers to increase engagement',
|
||||
'examples': ['amazing', 'incredible', 'exciting']
|
||||
})
|
||||
|
||||
if analysis['call_to_action_strength'] < 70:
|
||||
suggestions.append({
|
||||
'category': 'Call-to-Action',
|
||||
'suggestion': 'Strengthen your call-to-action',
|
||||
'examples': ['Comment below', 'Share your thoughts', 'Let me know']
|
||||
})
|
||||
|
||||
if analysis['virality_potential'] < 70:
|
||||
suggestions.append({
|
||||
'category': 'Virality',
|
||||
'suggestion': 'Increase viral potential by adding trending elements',
|
||||
'examples': ['Current trends', 'Popular hashtags', 'Timely topics']
|
||||
})
|
||||
|
||||
if analysis['seo_optimization'] < 70:
|
||||
suggestions.append({
|
||||
'category': 'SEO',
|
||||
'suggestion': 'Optimize for better discovery',
|
||||
'examples': ['Strategic hashtags', 'Relevant keywords', 'Proper mentions']
|
||||
})
|
||||
|
||||
return suggestions
|
||||
|
||||
def get_optimal_posting_time(analysis):
|
||||
"""Determine optimal posting time based on content analysis."""
|
||||
current_hour = datetime.now().hour
|
||||
|
||||
# Factor in content type and engagement patterns
|
||||
if analysis['sentiment_analysis']['tone'] == 'Positive' and analysis['virality_potential'] > 70:
|
||||
prime_times = {
|
||||
'Morning Rush': (8, 10),
|
||||
'Lunch Break': (12, 14),
|
||||
'Evening Prime': (18, 21)
|
||||
}
|
||||
else:
|
||||
prime_times = {
|
||||
'Mid-Morning': (10, 12),
|
||||
'Afternoon': (14, 16),
|
||||
'Late Evening': (20, 22)
|
||||
}
|
||||
|
||||
# Find next available prime time
|
||||
for time_slot, (start, end) in prime_times.items():
|
||||
if start <= current_hour <= end:
|
||||
return f"Post now ({time_slot})"
|
||||
elif current_hour < start:
|
||||
return f"Schedule for {time_slot} ({start}:00 - {end}:00)"
|
||||
|
||||
return "Schedule for tomorrow morning (8:00 - 10:00)"
|
||||
|
||||
def write_yt_community_post():
|
||||
"""Create a user interface for YouTube Community Post Generator."""
|
||||
st.write("Generate engaging community posts that drive interaction and channel growth.")
|
||||
|
||||
# Initialize session state
|
||||
if "generated_post" not in st.session_state:
|
||||
st.session_state.generated_post = None
|
||||
if "post_history" not in st.session_state:
|
||||
st.session_state.post_history = []
|
||||
|
||||
# Create tabs for different sections
|
||||
tab1, tab2, tab3 = st.tabs(["Post Creation", "Engagement Strategy", "Preview & Analytics"])
|
||||
|
||||
with tab1:
|
||||
# Core elements
|
||||
main_topic = st.text_area("Main Topic/Message",
|
||||
placeholder="e.g., New video announcement, Channel update, Question for viewers")
|
||||
|
||||
col1, col2 = st.columns(2)
|
||||
with col1:
|
||||
post_type = st.selectbox("Post Type", [
|
||||
"Question",
|
||||
"Poll",
|
||||
"Behind the Scenes",
|
||||
"Sneak Peek",
|
||||
"Channel Update",
|
||||
"Milestone Celebration",
|
||||
"Content Preview",
|
||||
"Fan Spotlight",
|
||||
"Quick Tip",
|
||||
"Discussion Starter"
|
||||
])
|
||||
|
||||
target_audience = st.text_input("Target Audience",
|
||||
placeholder="e.g., Tech enthusiasts, Gamers, DIY lovers")
|
||||
|
||||
with col2:
|
||||
content_purpose = st.selectbox("Content Purpose", [
|
||||
"Build Hype",
|
||||
"Drive Discussion",
|
||||
"Gather Feedback",
|
||||
"Share Updates",
|
||||
"Boost Engagement"
|
||||
])
|
||||
|
||||
tone_style = st.selectbox("Tone/Style", [
|
||||
"Casual",
|
||||
"Professional",
|
||||
"Excited",
|
||||
"Mysterious",
|
||||
"Humorous",
|
||||
"Informative"
|
||||
])
|
||||
|
||||
channel_niche = st.text_input("Channel Niche",
|
||||
placeholder="e.g., Tech Reviews, Gaming, Education")
|
||||
|
||||
with tab2:
|
||||
# Engagement options
|
||||
st.subheader("Engagement Elements")
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
include_emoji = st.checkbox("Include Emojis", value=True)
|
||||
include_hashtags = st.checkbox("Include Hashtags", value=True)
|
||||
max_length = st.number_input("Maximum Length (characters)",
|
||||
min_value=100, max_value=2000, value=500)
|
||||
|
||||
with col2:
|
||||
include_poll = st.checkbox("Include Poll", value=False)
|
||||
include_image_prompt = st.checkbox("Include Image Suggestions", value=True)
|
||||
include_timing_suggestion = st.checkbox("Include Timing Suggestion", value=True)
|
||||
|
||||
# Advanced options
|
||||
st.subheader("Advanced Options")
|
||||
language = st.selectbox("Language", [
|
||||
"English",
|
||||
"Spanish",
|
||||
"French",
|
||||
"German",
|
||||
"Italian",
|
||||
"Portuguese",
|
||||
"Russian",
|
||||
"Japanese",
|
||||
"Korean",
|
||||
"Chinese"
|
||||
])
|
||||
|
||||
with tab3:
|
||||
if st.session_state.generated_post:
|
||||
# Display the generated post
|
||||
st.subheader("Generated Community Post")
|
||||
|
||||
# Create tabs for different views
|
||||
post_tab1, post_tab2, post_tab3 = st.tabs(["Preview", "Analytics", "History"])
|
||||
|
||||
with post_tab1:
|
||||
st.markdown(st.session_state.generated_post)
|
||||
|
||||
# Quick actions
|
||||
col1, col2 = st.columns(2)
|
||||
with col1:
|
||||
if st.button("Copy to Clipboard"):
|
||||
st.code(st.session_state.generated_post)
|
||||
st.success("Post copied to clipboard!")
|
||||
|
||||
with col2:
|
||||
if st.button("Save to History"):
|
||||
st.session_state.post_history.append({
|
||||
'post': st.session_state.generated_post,
|
||||
'timestamp': datetime.now(),
|
||||
'type': post_type
|
||||
})
|
||||
st.success("Post saved to history!")
|
||||
|
||||
with post_tab2:
|
||||
# Analyze the post
|
||||
analysis = analyze_post_engagement(st.session_state.generated_post)
|
||||
|
||||
# Create expandable sections for different analysis categories
|
||||
with st.expander("📊 Overall Performance Metrics", expanded=True):
|
||||
cols = st.columns(3)
|
||||
|
||||
with cols[0]:
|
||||
score = analysis['engagement_score']
|
||||
color = "red" if score < 60 else "orange" if score < 80 else "green"
|
||||
st.markdown(f"### Overall Score: <span style='color: {color}'>{score:.1f}%</span>",
|
||||
unsafe_allow_html=True)
|
||||
|
||||
# Sentiment Analysis
|
||||
st.markdown("#### Sentiment Analysis")
|
||||
st.metric("Polarity", f"{analysis['sentiment_analysis']['polarity']}%")
|
||||
st.metric("Subjectivity", f"{analysis['sentiment_analysis']['subjectivity']}%")
|
||||
st.info(f"Tone: {analysis['sentiment_analysis']['tone']}")
|
||||
|
||||
with cols[1]:
|
||||
st.markdown("#### Engagement Metrics")
|
||||
st.metric("Emotional Impact", f"{analysis['emotional_triggers']}%")
|
||||
st.metric("CTA Strength", f"{analysis['call_to_action_strength']}%")
|
||||
st.metric("Virality Potential", f"{analysis['virality_potential']:.1f}%")
|
||||
|
||||
with cols[2]:
|
||||
st.markdown("#### Content Quality")
|
||||
st.metric("Audience Resonance", f"{analysis['audience_resonance']:.1f}%")
|
||||
st.metric("SEO Score", f"{analysis['seo_optimization']:.1f}%")
|
||||
if analysis['timing_recommendation']:
|
||||
st.success(f"📅 {analysis['timing_recommendation']}")
|
||||
|
||||
with st.expander("🎯 Psychological Triggers & Patterns"):
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
st.markdown("#### Active Psychological Triggers")
|
||||
if analysis['psychological_triggers']:
|
||||
for trigger in analysis['psychological_triggers']:
|
||||
st.markdown(f"✓ {trigger.title()}")
|
||||
else:
|
||||
st.info("No strong psychological triggers detected")
|
||||
|
||||
with col2:
|
||||
st.markdown("#### Engagement Patterns")
|
||||
patterns = analysis['engagement_patterns']
|
||||
for pattern, score in patterns.items():
|
||||
st.metric(pattern.replace('_', ' ').title(), f"{score}%")
|
||||
|
||||
with st.expander("📝 Content Structure Analysis"):
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
structure = analysis['content_structure']
|
||||
st.markdown("#### Structure Metrics")
|
||||
for metric, score in structure.items():
|
||||
st.metric(
|
||||
metric.replace('_', ' ').title(),
|
||||
f"{score:.1f}%"
|
||||
)
|
||||
|
||||
with col2:
|
||||
st.markdown("#### SEO Analysis")
|
||||
st.metric("Hashtag Quality", f"{analyze_hashtag_quality(st.session_state.generated_post)}%")
|
||||
st.metric("Keyword Density", f"{analyze_keyword_density(st.session_state.generated_post)}%")
|
||||
st.metric("Mention Optimization", f"{analyze_mentions(st.session_state.generated_post)}%")
|
||||
|
||||
# Show improvement suggestions
|
||||
if analysis['improvement_suggestions']:
|
||||
with st.expander("💡 AI-Powered Suggestions", expanded=True):
|
||||
for suggestion in analysis['improvement_suggestions']:
|
||||
with st.container():
|
||||
st.markdown(f"#### {suggestion['category']}")
|
||||
st.info(suggestion['suggestion'])
|
||||
if suggestion['examples']:
|
||||
st.markdown("**Examples:**")
|
||||
for example in suggestion['examples']:
|
||||
st.markdown(f"- {example}")
|
||||
|
||||
# Add a refresh button for analysis
|
||||
if st.button("🔄 Refresh Analysis"):
|
||||
st.rerun()
|
||||
|
||||
with post_tab3:
|
||||
if st.session_state.post_history:
|
||||
st.subheader("Previous Posts")
|
||||
for i, post in enumerate(reversed(st.session_state.post_history)):
|
||||
with st.expander(f"Post {len(st.session_state.post_history)-i}: "
|
||||
f"{post['type']} - "
|
||||
f"{post['timestamp'].strftime('%Y-%m-%d %H:%M')}"):
|
||||
st.write(post['post'])
|
||||
else:
|
||||
st.info("No post history yet. Save posts to see them here!")
|
||||
|
||||
# Generate button
|
||||
if st.button("Generate Community Post"):
|
||||
if not main_topic:
|
||||
st.error("Please enter a main topic/message.")
|
||||
return
|
||||
|
||||
with st.spinner("Generating community post..."):
|
||||
post = generate_community_post(
|
||||
post_type, main_topic, target_audience, tone_style,
|
||||
content_purpose, channel_niche, include_emoji,
|
||||
include_hashtags, include_poll, include_image_prompt,
|
||||
include_timing_suggestion, max_length, language
|
||||
)
|
||||
|
||||
if post:
|
||||
st.session_state.generated_post = post
|
||||
st.success("✨ Post generated successfully! Check the 'Preview & Analytics' tab to view, analyze, and save your post.")
|
||||
st.rerun()
|
||||
else:
|
||||
st.error("Failed to generate post. Please try again.")
|
||||
@@ -1,404 +0,0 @@
|
||||
"""
|
||||
YouTube Description Generator Module
|
||||
|
||||
This module provides functionality for generating YouTube video descriptions.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import time
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
|
||||
|
||||
def calculate_keyword_density(text, keywords):
|
||||
"""Calculate the density of keywords in the text."""
|
||||
if not text or not keywords:
|
||||
return 0
|
||||
|
||||
text = text.lower()
|
||||
keywords = [k.lower() for k in keywords]
|
||||
|
||||
total_words = len(text.split())
|
||||
keyword_count = sum(text.count(k) for k in keywords)
|
||||
|
||||
return (keyword_count / total_words) * 100 if total_words > 0 else 0
|
||||
|
||||
|
||||
def calculate_seo_score(text, keywords):
|
||||
"""Calculate the SEO score of the description."""
|
||||
score = 0
|
||||
|
||||
# Text length (optimal: 250-300 words)
|
||||
word_count = len(text.split())
|
||||
if 250 <= word_count <= 300:
|
||||
score += 3
|
||||
elif 200 <= word_count <= 350:
|
||||
score += 2
|
||||
elif 150 <= word_count <= 400:
|
||||
score += 1
|
||||
|
||||
# Keyword presence
|
||||
text_lower = text.lower()
|
||||
keywords_lower = [k.lower() for k in keywords]
|
||||
keyword_count = sum(text_lower.count(k) for k in keywords_lower)
|
||||
if keyword_count >= 3:
|
||||
score += 3
|
||||
elif keyword_count >= 2:
|
||||
score += 2
|
||||
elif keyword_count >= 1:
|
||||
score += 1
|
||||
|
||||
# Call to action phrases
|
||||
cta_phrases = ["subscribe", "like", "comment", "share", "follow", "check out", "visit", "learn more"]
|
||||
cta_count = sum(text_lower.count(phrase) for phrase in cta_phrases)
|
||||
if cta_count >= 2:
|
||||
score += 2
|
||||
elif cta_count >= 1:
|
||||
score += 1
|
||||
|
||||
# Hashtags
|
||||
hashtag_count = text.count("#")
|
||||
if 3 <= hashtag_count <= 5:
|
||||
score += 2
|
||||
elif 1 <= hashtag_count <= 8:
|
||||
score += 1
|
||||
|
||||
# Links
|
||||
link_count = text.count("http")
|
||||
if 1 <= link_count <= 3:
|
||||
score += 2
|
||||
elif link_count > 3:
|
||||
score += 1
|
||||
|
||||
return min(score, 10) # Cap at 10
|
||||
|
||||
|
||||
def generate_youtube_description(target_audience, main_points, tone_style, use_case, primary_keywords,
|
||||
secondary_keywords, language, seo_goals, include_timestamps=False,
|
||||
include_hashtags=False, include_social_handles=False):
|
||||
"""Generate a YouTube description based on the provided parameters."""
|
||||
|
||||
# Create a custom system prompt for YouTube description generation
|
||||
system_prompt = """You are a YouTube description expert specializing in creating engaging, SEO-optimized video descriptions.
|
||||
Your task is to generate YouTube video descriptions based on the provided information.
|
||||
Focus ONLY on creating descriptions that are optimized for YouTube, with proper formatting, keywords, and calls to action.
|
||||
Return ONLY the description text, without any additional commentary or explanations."""
|
||||
|
||||
# Build the prompt
|
||||
prompt = f"""
|
||||
**Instructions:**
|
||||
|
||||
Please generate a YouTube description for a video about **{main_points}** based on the following information:
|
||||
|
||||
**Target Audience:** {target_audience}
|
||||
**Tone and Style:** {tone_style}
|
||||
**Use Case:** {use_case}
|
||||
**Language:** {language}
|
||||
**Primary Keywords:** {primary_keywords}
|
||||
**Secondary Keywords:** {secondary_keywords}
|
||||
**SEO Goals:** {seo_goals}
|
||||
|
||||
**Additional Elements:**
|
||||
{"- Include timestamps for key sections." if include_timestamps else ""}
|
||||
{"- Include relevant hashtags." if include_hashtags else ""}
|
||||
{"- Include social media handles." if include_social_handles else ""}
|
||||
|
||||
**Specific Instructions:**
|
||||
* Keep the description informative and engaging.
|
||||
* Use a conversational tone that matches the target audience.
|
||||
* Include relevant keywords naturally.
|
||||
* Add a call to action.
|
||||
* Keep the length between 250-300 words for optimal SEO.
|
||||
"""
|
||||
|
||||
try:
|
||||
response = llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
return response
|
||||
except Exception as err:
|
||||
st.error(f"Error: Failed to get response from LLM: {err}")
|
||||
return None
|
||||
|
||||
|
||||
def write_yt_description():
|
||||
"""Create a user interface for YouTube Description Generator."""
|
||||
st.write("Generate SEO-optimized YouTube video descriptions that drive engagement.")
|
||||
|
||||
# Initialize session state for generated description if it doesn't exist
|
||||
if "generated_description" not in st.session_state:
|
||||
st.session_state.generated_description = None
|
||||
|
||||
# Create tabs for different sections
|
||||
tab1, tab2, tab3 = st.tabs(["Basic Info", "SEO Optimization", "Advanced Options"])
|
||||
|
||||
with tab1:
|
||||
# Basic information inputs
|
||||
main_points = st.text_area("Main Points/Keywords (comma-separated)",
|
||||
placeholder="e.g., cooking tips, healthy recipes, quick meals")
|
||||
|
||||
# Create columns for the other inputs
|
||||
col1, col2, col3, col4 = st.columns(4)
|
||||
|
||||
with col1:
|
||||
tone_style = st.selectbox("Tone/Style",
|
||||
["Professional", "Casual", "Humorous", "Educational", "Entertaining", "Inspirational"])
|
||||
|
||||
with col2:
|
||||
target_audience = st.text_input("Target Audience",
|
||||
placeholder="e.g., beginners, professionals, parents")
|
||||
|
||||
with col3:
|
||||
use_case = st.selectbox("Use Case",
|
||||
["How-to/Tutorial", "Vlog", "Review", "Educational", "Entertainment", "News"])
|
||||
|
||||
with col4:
|
||||
language = st.selectbox("Language", ["English", "Spanish", "French", "German", "Italian", "Portuguese"])
|
||||
|
||||
with tab2:
|
||||
# SEO optimization inputs
|
||||
primary_keywords = st.text_input("Primary Keywords (comma-separated)",
|
||||
placeholder="e.g., cooking, recipes, healthy food")
|
||||
secondary_keywords = st.text_input("Secondary Keywords (comma-separated)",
|
||||
placeholder="e.g., quick meals, budget cooking")
|
||||
seo_goals = st.multiselect("SEO Goals",
|
||||
["Increase Views", "Drive Engagement", "Build Subscribers", "Promote Products/Services"])
|
||||
|
||||
with tab3:
|
||||
# Advanced options
|
||||
st.subheader("Additional Elements")
|
||||
include_timestamps = st.checkbox("Include Timestamps", value=True)
|
||||
include_hashtags = st.checkbox("Include Hashtags", value=True)
|
||||
include_social_handles = st.checkbox("Include Social Media Handles", value=True)
|
||||
|
||||
if st.button("Generate Description"):
|
||||
if not main_points:
|
||||
st.error("Please enter main points/keywords.")
|
||||
return
|
||||
|
||||
with st.spinner("Generating description..."):
|
||||
description = generate_youtube_description(
|
||||
target_audience, main_points, tone_style, use_case, primary_keywords,
|
||||
secondary_keywords, language, seo_goals, include_timestamps,
|
||||
include_hashtags, include_social_handles
|
||||
)
|
||||
|
||||
if description:
|
||||
# Store the description in session state
|
||||
st.session_state.generated_description = description
|
||||
|
||||
# Store other parameters in session state for regeneration
|
||||
st.session_state.description_params = {
|
||||
"target_audience": target_audience,
|
||||
"main_points": main_points,
|
||||
"tone_style": tone_style,
|
||||
"use_case": use_case,
|
||||
"primary_keywords": primary_keywords,
|
||||
"secondary_keywords": secondary_keywords,
|
||||
"language": language,
|
||||
"seo_goals": seo_goals,
|
||||
"include_timestamps": include_timestamps,
|
||||
"include_hashtags": include_hashtags,
|
||||
"include_social_handles": include_social_handles
|
||||
}
|
||||
|
||||
st.subheader("Generated Description")
|
||||
|
||||
# Display description with analysis
|
||||
st.text_area("Description", description, height=200)
|
||||
|
||||
# Calculate and display metrics
|
||||
all_keywords = primary_keywords.split(",") + secondary_keywords.split(",")
|
||||
keyword_density = calculate_keyword_density(description, all_keywords)
|
||||
seo_score = calculate_seo_score(description, all_keywords)
|
||||
|
||||
col1, col2 = st.columns(2)
|
||||
with col1:
|
||||
st.metric("Keyword Density", f"{keyword_density:.1f}%")
|
||||
with col2:
|
||||
st.metric("SEO Score", f"{seo_score}/10")
|
||||
|
||||
# Create columns for the buttons
|
||||
btn_col1, btn_col2 = st.columns(2)
|
||||
|
||||
with btn_col1:
|
||||
# Download button
|
||||
st.download_button(
|
||||
label="Download Description",
|
||||
data=description,
|
||||
file_name="youtube_description.txt",
|
||||
mime="text/plain"
|
||||
)
|
||||
|
||||
with btn_col2:
|
||||
# Regenerate button
|
||||
if st.button("Regenerate"):
|
||||
st.session_state.show_regenerate_popover = True
|
||||
|
||||
# Regenerate popover
|
||||
if st.session_state.get("show_regenerate_popover", False):
|
||||
with st.form("regenerate_form"):
|
||||
st.subheader("Regenerate Description")
|
||||
st.write("Specify changes you'd like to make to the description:")
|
||||
changes = st.text_area("Changes to make",
|
||||
placeholder="e.g., Make it more casual, add more call-to-actions, focus on product benefits")
|
||||
|
||||
submitted = st.form_submit_button("Regenerate with Changes")
|
||||
|
||||
if submitted and changes:
|
||||
with st.spinner("Regenerating description..."):
|
||||
# Get the stored parameters
|
||||
params = st.session_state.description_params
|
||||
|
||||
# Add the changes to the prompt
|
||||
params["changes"] = changes
|
||||
|
||||
# Generate a new description with the changes
|
||||
new_description = generate_youtube_description_with_changes(
|
||||
params["target_audience"],
|
||||
params["main_points"],
|
||||
params["tone_style"],
|
||||
params["use_case"],
|
||||
params["primary_keywords"],
|
||||
params["secondary_keywords"],
|
||||
params["language"],
|
||||
params["seo_goals"],
|
||||
params["include_timestamps"],
|
||||
params["include_hashtags"],
|
||||
params["include_social_handles"],
|
||||
changes
|
||||
)
|
||||
|
||||
if new_description:
|
||||
# Update the stored description
|
||||
st.session_state.generated_description = new_description
|
||||
st.session_state.show_regenerate_popover = False
|
||||
st.rerun()
|
||||
else:
|
||||
st.error("Failed to regenerate description. Please try again.")
|
||||
else:
|
||||
st.error("Failed to generate description. Please try again.")
|
||||
|
||||
# Display previously generated description if it exists in session state
|
||||
elif st.session_state.generated_description:
|
||||
description = st.session_state.generated_description
|
||||
params = st.session_state.description_params
|
||||
|
||||
st.subheader("Generated Description")
|
||||
|
||||
# Display description with analysis
|
||||
st.text_area("Description", description, height=200)
|
||||
|
||||
# Calculate and display metrics
|
||||
all_keywords = params["primary_keywords"].split(",") + params["secondary_keywords"].split(",")
|
||||
keyword_density = calculate_keyword_density(description, all_keywords)
|
||||
seo_score = calculate_seo_score(description, all_keywords)
|
||||
|
||||
col1, col2 = st.columns(2)
|
||||
with col1:
|
||||
st.metric("Keyword Density", f"{keyword_density:.1f}%")
|
||||
with col2:
|
||||
st.metric("SEO Score", f"{seo_score}/10")
|
||||
|
||||
# Create columns for the buttons
|
||||
btn_col1, btn_col2 = st.columns(2)
|
||||
|
||||
with btn_col1:
|
||||
# Download button
|
||||
st.download_button(
|
||||
label="Download Description",
|
||||
data=description,
|
||||
file_name="youtube_description.txt",
|
||||
mime="text/plain"
|
||||
)
|
||||
|
||||
with btn_col2:
|
||||
# Regenerate button
|
||||
if st.button("Regenerate"):
|
||||
st.session_state.show_regenerate_popover = True
|
||||
|
||||
# Regenerate popover
|
||||
if st.session_state.get("show_regenerate_popover", False):
|
||||
with st.form("regenerate_form"):
|
||||
st.subheader("Regenerate Description")
|
||||
st.write("Specify changes you'd like to make to the description:")
|
||||
changes = st.text_area("Changes to make",
|
||||
placeholder="e.g., Make it more casual, add more call-to-actions, focus on product benefits")
|
||||
|
||||
submitted = st.form_submit_button("Regenerate with Changes")
|
||||
|
||||
if submitted and changes:
|
||||
with st.spinner("Regenerating description..."):
|
||||
# Add the changes to the prompt
|
||||
params["changes"] = changes
|
||||
|
||||
# Generate a new description with the changes
|
||||
new_description = generate_youtube_description_with_changes(
|
||||
params["target_audience"],
|
||||
params["main_points"],
|
||||
params["tone_style"],
|
||||
params["use_case"],
|
||||
params["primary_keywords"],
|
||||
params["secondary_keywords"],
|
||||
params["language"],
|
||||
params["seo_goals"],
|
||||
params["include_timestamps"],
|
||||
params["include_hashtags"],
|
||||
params["include_social_handles"],
|
||||
changes
|
||||
)
|
||||
|
||||
if new_description:
|
||||
# Update the stored description
|
||||
st.session_state.generated_description = new_description
|
||||
st.session_state.show_regenerate_popover = False
|
||||
st.rerun()
|
||||
else:
|
||||
st.error("Failed to regenerate description. Please try again.")
|
||||
|
||||
|
||||
def generate_youtube_description_with_changes(target_audience, main_points, tone_style, use_case, primary_keywords,
|
||||
secondary_keywords, language, seo_goals, include_timestamps=False,
|
||||
include_hashtags=False, include_social_handles=False, changes=""):
|
||||
"""Generate a YouTube description based on the provided parameters and requested changes."""
|
||||
|
||||
# Create a custom system prompt for YouTube description generation
|
||||
system_prompt = """You are a YouTube description expert specializing in creating engaging, SEO-optimized video descriptions.
|
||||
Your task is to generate YouTube video descriptions based on the provided information.
|
||||
Focus ONLY on creating descriptions that are optimized for YouTube, with proper formatting, keywords, and calls to action.
|
||||
Return ONLY the description text, without any additional commentary or explanations."""
|
||||
|
||||
# Build the prompt
|
||||
prompt = f"""
|
||||
**Instructions:**
|
||||
|
||||
Please generate a YouTube description for a video about **{main_points}** based on the following information:
|
||||
|
||||
**Target Audience:** {target_audience}
|
||||
**Tone and Style:** {tone_style}
|
||||
**Use Case:** {use_case}
|
||||
**Language:** {language}
|
||||
**Primary Keywords:** {primary_keywords}
|
||||
**Secondary Keywords:** {secondary_keywords}
|
||||
**SEO Goals:** {seo_goals}
|
||||
|
||||
**Additional Elements:**
|
||||
{"- Include timestamps for key sections." if include_timestamps else ""}
|
||||
{"- Include relevant hashtags." if include_hashtags else ""}
|
||||
{"- Include social media handles." if include_social_handles else ""}
|
||||
|
||||
**Requested Changes:**
|
||||
{changes}
|
||||
|
||||
**Specific Instructions:**
|
||||
* Keep the description informative and engaging.
|
||||
* Use a conversational tone that matches the target audience.
|
||||
* Include relevant keywords naturally.
|
||||
* Add a call to action.
|
||||
* Keep the length between 250-300 words for optimal SEO.
|
||||
* Incorporate the requested changes into the description.
|
||||
"""
|
||||
|
||||
try:
|
||||
response = llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
return response
|
||||
except Exception as err:
|
||||
st.error(f"Error: Failed to get response from LLM: {err}")
|
||||
return None
|
||||
@@ -1,740 +0,0 @@
|
||||
"""
|
||||
YouTube End Screen Generator Module
|
||||
|
||||
This module provides functionality for generating YouTube video end screens.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import time
|
||||
import logging
|
||||
import traceback
|
||||
from PIL import Image
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
from lib.gpt_providers.text_to_image_generation.gen_gemini_images import generate_gemini_image, edit_image
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
logger = logging.getLogger('youtube_end_screen_generator')
|
||||
|
||||
|
||||
def generate_end_screen_concepts(video_title, video_description, target_audience, content_type,
|
||||
primary_goal, secondary_goal=None, num_concepts=3):
|
||||
"""Generate end screen concept ideas based on video content."""
|
||||
logger.info(f"Generating end screen concepts for: '{video_title}'")
|
||||
logger.info(f"Parameters: target_audience={target_audience}, content_type={content_type}, "
|
||||
f"primary_goal={primary_goal}, secondary_goal={secondary_goal}, num_concepts={num_concepts}")
|
||||
|
||||
# Create a system prompt for end screen concept generation
|
||||
system_prompt = """You are a YouTube end screen expert specializing in creating engaging, action-driving end screen concepts.
|
||||
Your task is to generate end screen concept ideas based on the provided video information.
|
||||
Focus ONLY on creating end screens that are optimized for YouTube, with proper visual hierarchy, element placement, and call-to-action triggers.
|
||||
Return ONLY the concept descriptions, without any additional commentary or explanations.
|
||||
Each concept should include:
|
||||
1. A main visual element or background
|
||||
2. Element placement and content (subscribe button, playlist, video, website)
|
||||
3. Color scheme suggestions
|
||||
4. Text content for each element
|
||||
5. Brief explanation of why this concept would be effective for the specified goals
|
||||
|
||||
IMPORTANT: Format each concept with a clear numbered heading like "1. [Concept Name]" to ensure proper parsing."""
|
||||
|
||||
# Build the prompt
|
||||
prompt = f"""
|
||||
**Instructions:**
|
||||
|
||||
Please generate {num_concepts} end screen concept ideas for a YouTube video with the following information:
|
||||
|
||||
**Video Title:** {video_title}
|
||||
**Video Description:** {video_description}
|
||||
**Target Audience:** {target_audience}
|
||||
**Content Type:** {content_type}
|
||||
**Primary Goal:** {primary_goal}
|
||||
**Secondary Goal:** {secondary_goal if secondary_goal else "None specified"}
|
||||
|
||||
**Specific Instructions:**
|
||||
* Each concept should be clearly separated and numbered with a heading like "1. [Concept Name]".
|
||||
* Focus on creating end screens that drive the specified goals.
|
||||
* Consider the target audience's interests and preferences.
|
||||
* Include specific details about visual elements, element placement, and color schemes.
|
||||
* Explain why each concept would be effective for this specific video and goals.
|
||||
* Include text suggestions for each element (subscribe button, playlist, video, website).
|
||||
"""
|
||||
|
||||
try:
|
||||
logger.info("Sending request to LLM for end screen concepts")
|
||||
response = llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
logger.info(f"Received response from LLM: {len(response)} characters")
|
||||
return response
|
||||
except Exception as err:
|
||||
logger.error(f"Error generating end screen concepts: {err}")
|
||||
logger.error(traceback.format_exc())
|
||||
st.error(f"Error: Failed to generate end screen concepts: {err}")
|
||||
return None
|
||||
|
||||
|
||||
def generate_end_screen_design(concept_description, style_preference, element_count=2,
|
||||
element_types=None, element_texts=None, aspect_ratio="16:9",
|
||||
keywords=None, style=None, focus=None):
|
||||
"""Generate an end screen image based on the concept description."""
|
||||
logger.info(f"Generating end screen design for concept: '{concept_description[:50]}...'")
|
||||
logger.info(f"Parameters: style_preference={style_preference}, element_count={element_count}, "
|
||||
f"element_types={element_types}, element_texts={element_texts}, aspect_ratio={aspect_ratio}")
|
||||
|
||||
# Extract key elements from the concept description
|
||||
# This helps focus the prompt on the most important aspects
|
||||
concept_lines = concept_description.split('\n')
|
||||
main_visual = ""
|
||||
element_placement = ""
|
||||
color_scheme = ""
|
||||
text_content = ""
|
||||
|
||||
for line in concept_lines:
|
||||
if "visual" in line.lower() or "background" in line.lower():
|
||||
main_visual = line
|
||||
elif "placement" in line.lower() or "layout" in line.lower():
|
||||
element_placement = line
|
||||
elif "color" in line.lower() or "scheme" in line.lower():
|
||||
color_scheme = line
|
||||
elif "text" in line.lower() or "content" in line.lower():
|
||||
text_content = line
|
||||
|
||||
# Create a more focused prompt for the image generation
|
||||
image_prompt = f"""
|
||||
Create a YouTube end screen image with the following specifications:
|
||||
|
||||
MAIN VISUAL: {main_visual if main_visual else "Not specified"}
|
||||
ELEMENT PLACEMENT: {element_placement if element_placement else "Not specified"}
|
||||
COLOR SCHEME: {color_scheme if color_scheme else "Not specified"}
|
||||
TEXT CONTENT: {text_content if text_content else "Not specified"}
|
||||
|
||||
STYLE: {style_preference}
|
||||
ASPECT RATIO: {aspect_ratio}
|
||||
NUMBER OF ELEMENTS: {element_count}
|
||||
|
||||
ELEMENT TYPES: {', '.join(element_types) if element_types else 'Not specified'}
|
||||
ELEMENT TEXTS: {', '.join(element_texts) if element_texts else 'Not specified'}
|
||||
|
||||
IMPORTANT REQUIREMENTS:
|
||||
1. This must be a VISUAL IMAGE of a YouTube end screen, not just a text description
|
||||
2. The image should be high contrast and visually striking
|
||||
3. All text should be large and readable
|
||||
4. Elements should be properly placed for optimal viewer engagement
|
||||
5. The design should follow the specified color scheme
|
||||
6. The image should be optimized for the specified aspect ratio
|
||||
|
||||
PLEASE GENERATE AN ACTUAL IMAGE, NOT JUST A TEXT DESCRIPTION.
|
||||
"""
|
||||
|
||||
try:
|
||||
logger.info("Sending request to Gemini for end screen image")
|
||||
# Generate the image using Gemini with enhanced prompt
|
||||
img_path = generate_gemini_image(
|
||||
image_prompt,
|
||||
keywords=keywords,
|
||||
style=style,
|
||||
focus=focus,
|
||||
enhance_prompt=True
|
||||
)
|
||||
logger.info(f"Received image from Gemini: {img_path}")
|
||||
return img_path
|
||||
except Exception as err:
|
||||
logger.error(f"Error generating end screen image: {err}")
|
||||
logger.error(traceback.format_exc())
|
||||
st.error(f"Error: Failed to generate end screen image: {err}")
|
||||
return None
|
||||
|
||||
|
||||
def edit_end_screen_image(img_path, edit_instructions):
|
||||
"""Edit an end screen image based on user instructions."""
|
||||
logger.info(f"Editing end screen image: '{img_path}'")
|
||||
logger.info(f"Edit instructions: '{edit_instructions}'")
|
||||
|
||||
try:
|
||||
logger.info("Sending request to Gemini for image editing")
|
||||
# Edit the image using Gemini
|
||||
edited_img_path = edit_image(img_path, f"Edit this image according to these instructions: {edit_instructions}. IMPORTANT: Please generate an actual edited image, not just a text description. I need a visual representation of the edited end screen.")
|
||||
logger.info(f"Image editing completed. Edited image path: {edited_img_path}")
|
||||
|
||||
# Return the path to the edited image
|
||||
return edited_img_path
|
||||
except Exception as err:
|
||||
logger.error(f"Error editing end screen image: {err}")
|
||||
logger.error(traceback.format_exc())
|
||||
st.error(f"Error: Failed to edit end screen image: {err}")
|
||||
return None
|
||||
|
||||
|
||||
def analyze_end_screen(end_screen_path):
|
||||
"""Analyze an end screen for effectiveness."""
|
||||
logger.info(f"Analyzing end screen: '{end_screen_path}'")
|
||||
|
||||
# This would typically involve image analysis, but for now we'll use AI to provide feedback
|
||||
system_prompt = """You are a YouTube end screen expert specializing in analyzing and providing feedback on end screen designs.
|
||||
Your task is to analyze the end screen and provide constructive feedback on its effectiveness.
|
||||
Focus on aspects like visual hierarchy, element placement, call-to-action clarity, and overall effectiveness."""
|
||||
|
||||
# For now, we'll just return a placeholder analysis
|
||||
# In a real implementation, we would analyze the actual image
|
||||
logger.info("Generating end screen analysis")
|
||||
return """
|
||||
**End Screen Analysis:**
|
||||
|
||||
- **Visual Hierarchy:** The main elements are well-positioned and stand out against the background.
|
||||
- **Element Placement:** The call-to-action elements are strategically placed for optimal viewer engagement.
|
||||
- **Call-to-Action Clarity:** The text and visual cues clearly communicate the desired actions.
|
||||
- **Overall Effectiveness:** The design is likely to drive the specified goals due to its visual appeal and clear value proposition.
|
||||
|
||||
**Suggestions for Improvement:**
|
||||
- Consider adding a subtle animation hint to draw attention to the most important element.
|
||||
- The text could be slightly larger for better readability on mobile devices.
|
||||
- Adding a small icon or logo could help with brand recognition.
|
||||
"""
|
||||
|
||||
|
||||
def parse_concepts(concepts_text):
|
||||
"""Parse the concepts text into a list of individual concepts."""
|
||||
logger.info("Parsing concepts text into individual concepts")
|
||||
|
||||
# Split the concepts text by main concept headers
|
||||
concepts = []
|
||||
current_concept = ""
|
||||
|
||||
# Look for patterns like numbered headings (e.g., "1.", "2.", "3.") or "Concept 1:", "Concept 2:", etc.
|
||||
concept_patterns = ["1.", "2.", "3.", "4.", "5.", "Concept 1:", "Concept 2:", "Concept 3:", "Concept 4:", "Concept 5:"]
|
||||
|
||||
for line in concepts_text.split('\n'):
|
||||
# Check if line starts with a concept pattern
|
||||
is_new_concept = False
|
||||
for pattern in concept_patterns:
|
||||
if line.strip().startswith(pattern):
|
||||
# If we have a previous concept, add it to the list
|
||||
if current_concept:
|
||||
concepts.append(current_concept.strip())
|
||||
# Start a new concept
|
||||
current_concept = line
|
||||
is_new_concept = True
|
||||
break
|
||||
|
||||
if not is_new_concept:
|
||||
# Add the line to the current concept
|
||||
current_concept += "\n" + line
|
||||
|
||||
# Add the last concept
|
||||
if current_concept:
|
||||
concepts.append(current_concept.strip())
|
||||
|
||||
logger.info(f"Parsed {len(concepts)} concepts from the response")
|
||||
return concepts
|
||||
|
||||
|
||||
def write_yt_end_screen():
|
||||
"""Create a user interface for YouTube End Screen Generator."""
|
||||
logger.info("Initializing YouTube End Screen Generator UI")
|
||||
st.title("YouTube End Screen Generator")
|
||||
st.write("Create engaging, action-driving end screens for your YouTube videos.")
|
||||
|
||||
# Initialize session state for generated end screens if it doesn't exist
|
||||
if "generated_end_screens" not in st.session_state:
|
||||
st.session_state.generated_end_screens = []
|
||||
if "end_screen_concepts" not in st.session_state:
|
||||
st.session_state.end_screen_concepts = None
|
||||
if "current_end_screen_path" not in st.session_state:
|
||||
st.session_state.current_end_screen_path = None
|
||||
if "concept_list" not in st.session_state:
|
||||
st.session_state.concept_list = []
|
||||
if "editing_end_screen" not in st.session_state:
|
||||
st.session_state.editing_end_screen = False
|
||||
if "edit_instructions" not in st.session_state:
|
||||
st.session_state.edit_instructions = ""
|
||||
if "edited_end_screen_path" not in st.session_state:
|
||||
st.session_state.edited_end_screen_path = None
|
||||
if "show_edit_form" not in st.session_state:
|
||||
st.session_state.show_edit_form = False
|
||||
|
||||
# Create tabs for different sections
|
||||
tab1, tab2 = st.tabs(["Basic Info", "Style & Elements"])
|
||||
|
||||
with tab1:
|
||||
# Basic information inputs
|
||||
video_title = st.text_input("Video Title",
|
||||
placeholder="e.g., 10 Tips for Better Photography")
|
||||
video_description = st.text_area("Video Description",
|
||||
placeholder="Brief description of your video content")
|
||||
target_audience = st.text_input("Target Audience",
|
||||
placeholder="e.g., photography enthusiasts, beginners")
|
||||
|
||||
# Content type selection
|
||||
content_type = st.selectbox("Content Type", [
|
||||
"Tutorial/How-to",
|
||||
"Vlog",
|
||||
"Review",
|
||||
"Educational",
|
||||
"Entertainment",
|
||||
"News/Update",
|
||||
"Product Showcase",
|
||||
"Challenge",
|
||||
"Reaction",
|
||||
"Comparison"
|
||||
])
|
||||
|
||||
# End screen goals
|
||||
st.subheader("End Screen Goals")
|
||||
primary_goal = st.selectbox("Primary Goal", [
|
||||
"Drive Subscriptions",
|
||||
"Promote Playlist",
|
||||
"Promote Next Video",
|
||||
"Promote Website",
|
||||
"Promote Social Media",
|
||||
"Promote Product/Service",
|
||||
"Encourage Comments",
|
||||
"Mixed Goals"
|
||||
])
|
||||
|
||||
secondary_goal = st.selectbox("Secondary Goal (Optional)", [
|
||||
"None",
|
||||
"Drive Subscriptions",
|
||||
"Promote Playlist",
|
||||
"Promote Next Video",
|
||||
"Promote Website",
|
||||
"Promote Social Media",
|
||||
"Promote Product/Service",
|
||||
"Encourage Comments"
|
||||
])
|
||||
|
||||
if secondary_goal == "None":
|
||||
secondary_goal = None
|
||||
|
||||
with tab2:
|
||||
# Style preferences
|
||||
st.subheader("Style Preferences")
|
||||
|
||||
# Create columns for style options
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
style_preference = st.selectbox("End Screen Style", [
|
||||
"Bold and Dramatic",
|
||||
"Clean and Minimal",
|
||||
"Colorful and Vibrant",
|
||||
"Dark and Moody",
|
||||
"Professional and Corporate",
|
||||
"Playful and Fun",
|
||||
"Retro/Vintage",
|
||||
"Modern and Sleek"
|
||||
])
|
||||
|
||||
num_concepts = st.slider("Number of Concepts", 1, 5, 3)
|
||||
|
||||
with col2:
|
||||
aspect_ratio = st.selectbox("Aspect Ratio", [
|
||||
"16:9 (Standard)",
|
||||
"1:1 (Square)",
|
||||
"4:3 (Classic)",
|
||||
"9:16 (Vertical)"
|
||||
])
|
||||
|
||||
include_branding = st.checkbox("Include Branding Elements", value=True)
|
||||
if include_branding:
|
||||
branding_elements = st.multiselect("Branding Elements", [
|
||||
"Channel Logo",
|
||||
"Channel Name",
|
||||
"Channel Tagline",
|
||||
"Brand Colors",
|
||||
"Watermark"
|
||||
])
|
||||
|
||||
# Element configuration
|
||||
st.subheader("End Screen Elements")
|
||||
|
||||
# Number of elements
|
||||
element_count = st.slider("Number of Elements", 1, 4, 2)
|
||||
|
||||
# Element types
|
||||
element_types = []
|
||||
element_texts = []
|
||||
|
||||
for i in range(element_count):
|
||||
st.write(f"Element {i+1}")
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
element_type = st.selectbox(
|
||||
f"Type",
|
||||
["Subscribe Button", "Playlist", "Video", "Website", "Social Media"],
|
||||
key=f"element_type_{i}"
|
||||
)
|
||||
element_types.append(element_type)
|
||||
|
||||
with col2:
|
||||
element_text = st.text_input(
|
||||
f"Text",
|
||||
placeholder=f"Text for {element_type}",
|
||||
key=f"element_text_{i}"
|
||||
)
|
||||
element_texts.append(element_text)
|
||||
|
||||
# Advanced AI Prompt Settings
|
||||
st.subheader("Advanced AI Prompt Settings")
|
||||
|
||||
# Create columns for advanced settings
|
||||
col3, col4 = st.columns(2)
|
||||
|
||||
with col3:
|
||||
# Image style selection
|
||||
image_style = st.selectbox("Image Style", [
|
||||
"Auto (AI will choose best style)",
|
||||
"Photorealistic",
|
||||
"Artistic",
|
||||
"Cartoon/Anime",
|
||||
"Sketch/Drawing",
|
||||
"Digital Art",
|
||||
"3D Render"
|
||||
])
|
||||
|
||||
# Extract style for the generate_gemini_image function
|
||||
style = None
|
||||
if image_style == "Photorealistic":
|
||||
style = "photorealistic"
|
||||
elif image_style == "Artistic":
|
||||
style = "artistic"
|
||||
elif image_style == "Cartoon/Anime":
|
||||
style = "cartoon"
|
||||
elif image_style == "Sketch/Drawing":
|
||||
style = "sketch"
|
||||
elif image_style == "Digital Art":
|
||||
style = "digital_art"
|
||||
elif image_style == "3D Render":
|
||||
style = "3d_render"
|
||||
|
||||
with col4:
|
||||
# Focus selection for photorealistic images
|
||||
focus = None
|
||||
if style == "photorealistic":
|
||||
focus = st.selectbox("Image Focus", [
|
||||
"Auto (AI will choose best focus)",
|
||||
"Portraits",
|
||||
"Objects",
|
||||
"Motion",
|
||||
"Wide-angle"
|
||||
])
|
||||
|
||||
# Extract focus for the generate_gemini_image function
|
||||
if focus == "Portraits":
|
||||
focus = "portraits"
|
||||
elif focus == "Objects":
|
||||
focus = "objects"
|
||||
elif focus == "Motion":
|
||||
focus = "motion"
|
||||
elif focus == "Wide-angle":
|
||||
focus = "wide-angle"
|
||||
elif focus == "Auto (AI will choose best focus)":
|
||||
focus = None
|
||||
|
||||
# Keywords for enhanced prompt generation
|
||||
st.subheader("Keywords for Enhanced Prompt")
|
||||
st.write("Add keywords to enhance the AI prompt generation. These will help create more detailed and accurate end screens.")
|
||||
|
||||
# Create a text area for keywords
|
||||
keywords_input = st.text_area(
|
||||
"Keywords (comma-separated)",
|
||||
placeholder="e.g., vibrant, energetic, bold, eye-catching, professional"
|
||||
)
|
||||
|
||||
# Process keywords
|
||||
keywords = None
|
||||
if keywords_input:
|
||||
keywords = [k.strip() for k in keywords_input.split(",") if k.strip()]
|
||||
logger.info(f"User provided keywords: {keywords}")
|
||||
|
||||
# Generate button - placed outside of tabs for better visibility
|
||||
st.markdown("---")
|
||||
st.subheader("Generate End Screen Concepts")
|
||||
st.write("Click the button below to generate end screen concepts based on your inputs.")
|
||||
|
||||
if st.button("Generate End Screen Concepts", type="primary"):
|
||||
if not video_title:
|
||||
st.error("Please enter a video title.")
|
||||
return
|
||||
|
||||
with st.spinner("Generating end screen concepts..."):
|
||||
logger.info("User clicked Generate End Screen Concepts button")
|
||||
concepts = generate_end_screen_concepts(
|
||||
video_title,
|
||||
video_description,
|
||||
target_audience,
|
||||
content_type,
|
||||
primary_goal,
|
||||
secondary_goal,
|
||||
num_concepts
|
||||
)
|
||||
|
||||
if concepts:
|
||||
# Store the concepts in session state
|
||||
st.session_state.end_screen_concepts = concepts
|
||||
# Parse the concepts and store in session state
|
||||
st.session_state.concept_list = parse_concepts(concepts)
|
||||
logger.info("Stored end screen concepts in session state")
|
||||
|
||||
# Display the concepts in tabs
|
||||
st.subheader("End Screen Concepts")
|
||||
|
||||
# Create tabs for each concept
|
||||
concept_tabs = st.tabs([f"Concept {i+1}" for i in range(len(st.session_state.concept_list))])
|
||||
|
||||
for i, tab in enumerate(concept_tabs):
|
||||
with tab:
|
||||
st.markdown(st.session_state.concept_list[i])
|
||||
|
||||
# Add a button to generate image for this concept
|
||||
if st.button(f"Generate Image for Concept {i+1}", key=f"gen_img_{i}"):
|
||||
with st.spinner(f"Generating end screen image for concept {i+1}..."):
|
||||
logger.info(f"User selected concept {i+1} for image generation")
|
||||
# Get the selected concept
|
||||
selected_concept = st.session_state.concept_list[i]
|
||||
|
||||
# Generate the end screen image with enhanced prompt
|
||||
img_path = generate_end_screen_design(
|
||||
selected_concept,
|
||||
style_preference,
|
||||
element_count,
|
||||
element_types,
|
||||
element_texts,
|
||||
aspect_ratio.split()[0], # Extract just the ratio part
|
||||
keywords=keywords,
|
||||
style=style,
|
||||
focus=focus
|
||||
)
|
||||
|
||||
if img_path:
|
||||
# Store the current end screen path in session state
|
||||
st.session_state.current_end_screen_path = img_path
|
||||
logger.info(f"Stored current end screen path in session state: {img_path}")
|
||||
|
||||
# Display the generated image
|
||||
st.subheader("Generated End Screen")
|
||||
st.image(img_path, use_container_width=True)
|
||||
|
||||
# Add download button
|
||||
with open(img_path, "rb") as file:
|
||||
st.download_button(
|
||||
label="Download End Screen",
|
||||
data=file,
|
||||
file_name=f"youtube_end_screen_{int(time.time())}.png",
|
||||
mime="image/png"
|
||||
)
|
||||
|
||||
# Add image editing section
|
||||
st.subheader("Edit End Screen")
|
||||
st.write("Make changes to your end screen by providing instructions below:")
|
||||
|
||||
# Create a text area for edit instructions
|
||||
edit_instructions = st.text_area(
|
||||
"Edit Instructions",
|
||||
placeholder="e.g., Make the background darker, Add a red border, Change the text color to white",
|
||||
key=f"edit_instructions_{i}"
|
||||
)
|
||||
|
||||
# Store edit instructions in session state
|
||||
st.session_state.edit_instructions = edit_instructions
|
||||
|
||||
# Add a button to apply edits
|
||||
if st.button("Apply Edits", key=f"apply_edits_{i}"):
|
||||
if not edit_instructions:
|
||||
st.warning("Please provide edit instructions.")
|
||||
else:
|
||||
# Set editing flag
|
||||
st.session_state.editing_end_screen = True
|
||||
st.session_state.show_edit_form = True
|
||||
|
||||
# Rerun to update the UI
|
||||
st.rerun()
|
||||
|
||||
# Add analysis button
|
||||
if st.button("Analyze End Screen", key=f"analyze_{i}"):
|
||||
logger.info("User clicked Analyze End Screen button")
|
||||
analysis = analyze_end_screen(img_path)
|
||||
st.subheader("End Screen Analysis")
|
||||
st.markdown(analysis)
|
||||
else:
|
||||
st.error("Failed to generate end screen concepts. Please try again.")
|
||||
|
||||
# Display previously generated concepts if they exist in session state
|
||||
elif st.session_state.end_screen_concepts and st.session_state.concept_list:
|
||||
logger.info("Displaying previously generated concepts from session state")
|
||||
st.subheader("End Screen Concepts")
|
||||
|
||||
# Create tabs for each concept
|
||||
concept_tabs = st.tabs([f"Concept {i+1}" for i in range(len(st.session_state.concept_list))])
|
||||
|
||||
for i, tab in enumerate(concept_tabs):
|
||||
with tab:
|
||||
st.markdown(st.session_state.concept_list[i])
|
||||
|
||||
# Add a button to generate image for this concept
|
||||
if st.button(f"Generate Image for Concept {i+1}", key=f"gen_img_existing_{i}"):
|
||||
with st.spinner(f"Generating end screen image for concept {i+1}..."):
|
||||
logger.info(f"User selected concept {i+1} for image generation")
|
||||
# Get the selected concept
|
||||
selected_concept = st.session_state.concept_list[i]
|
||||
|
||||
# Generate the end screen image with enhanced prompt
|
||||
img_path = generate_end_screen_design(
|
||||
selected_concept,
|
||||
style_preference,
|
||||
element_count,
|
||||
element_types,
|
||||
element_texts,
|
||||
aspect_ratio.split()[0], # Extract just the ratio part
|
||||
keywords=keywords,
|
||||
style=style,
|
||||
focus=focus
|
||||
)
|
||||
|
||||
if img_path:
|
||||
# Store the current end screen path in session state
|
||||
st.session_state.current_end_screen_path = img_path
|
||||
logger.info(f"Stored current end screen path in session state: {img_path}")
|
||||
|
||||
# Display the generated image
|
||||
st.subheader("Generated End Screen")
|
||||
st.image(img_path, use_container_width=True)
|
||||
|
||||
# Add download button
|
||||
with open(img_path, "rb") as file:
|
||||
st.download_button(
|
||||
label="Download End Screen",
|
||||
data=file,
|
||||
file_name=f"youtube_end_screen_{int(time.time())}.png",
|
||||
mime="image/png"
|
||||
)
|
||||
|
||||
# Add image editing section
|
||||
st.subheader("Edit End Screen")
|
||||
st.write("Make changes to your end screen by providing instructions below:")
|
||||
|
||||
# Create a text area for edit instructions
|
||||
edit_instructions = st.text_area(
|
||||
"Edit Instructions",
|
||||
placeholder="e.g., Make the background darker, Add a red border, Change the text color to white",
|
||||
key=f"edit_instructions_existing_{i}"
|
||||
)
|
||||
|
||||
# Store edit instructions in session state
|
||||
st.session_state.edit_instructions = edit_instructions
|
||||
|
||||
# Add a button to apply edits
|
||||
if st.button("Apply Edits", key=f"apply_edits_existing_{i}"):
|
||||
if not edit_instructions:
|
||||
st.warning("Please provide edit instructions.")
|
||||
else:
|
||||
# Set editing flag
|
||||
st.session_state.editing_end_screen = True
|
||||
st.session_state.show_edit_form = True
|
||||
|
||||
# Rerun to update the UI
|
||||
st.rerun()
|
||||
|
||||
# Add analysis button
|
||||
if st.button("Analyze End Screen", key=f"analyze_existing_{i}"):
|
||||
logger.info("User clicked Analyze End Screen button")
|
||||
analysis = analyze_end_screen(img_path)
|
||||
st.subheader("End Screen Analysis")
|
||||
st.markdown(analysis)
|
||||
|
||||
# Display current end screen if it exists in session state
|
||||
elif st.session_state.current_end_screen_path:
|
||||
logger.info(f"Displaying current end screen from session state: {st.session_state.current_end_screen_path}")
|
||||
st.subheader("Current End Screen")
|
||||
st.image(st.session_state.current_end_screen_path, use_container_width=True)
|
||||
|
||||
# Add download button
|
||||
with open(st.session_state.current_end_screen_path, "rb") as file:
|
||||
st.download_button(
|
||||
label="Download End Screen",
|
||||
data=file,
|
||||
file_name=f"youtube_end_screen_{int(time.time())}.png",
|
||||
mime="image/png"
|
||||
)
|
||||
|
||||
# Add image editing section
|
||||
st.subheader("Edit End Screen")
|
||||
st.write("Make changes to your end screen by providing instructions below:")
|
||||
|
||||
# Create a text area for edit instructions
|
||||
edit_instructions = st.text_area(
|
||||
"Edit Instructions",
|
||||
placeholder="e.g., Make the background darker, Add a new element, Change the text color to white",
|
||||
key="edit_instructions_current",
|
||||
value=st.session_state.edit_instructions if st.session_state.edit_instructions else ""
|
||||
)
|
||||
|
||||
# Store edit instructions in session state
|
||||
st.session_state.edit_instructions = edit_instructions
|
||||
|
||||
# Add a button to apply edits
|
||||
if st.button("Apply Edits", key="apply_edits_current"):
|
||||
if not edit_instructions:
|
||||
st.warning("Please provide edit instructions.")
|
||||
else:
|
||||
# Set editing flag
|
||||
st.session_state.editing_end_screen = True
|
||||
st.session_state.show_edit_form = True
|
||||
|
||||
# Rerun to update the UI
|
||||
st.rerun()
|
||||
|
||||
# Add analysis button
|
||||
if st.button("Analyze End Screen", key="analyze_current"):
|
||||
logger.info("User clicked Analyze End Screen button")
|
||||
analysis = analyze_end_screen(st.session_state.current_end_screen_path)
|
||||
st.subheader("End Screen Analysis")
|
||||
st.markdown(analysis)
|
||||
|
||||
# Handle the editing process
|
||||
if st.session_state.editing_end_screen and st.session_state.show_edit_form:
|
||||
st.subheader("Editing End Screen")
|
||||
|
||||
# Show a spinner while editing
|
||||
with st.spinner("Editing end screen..."):
|
||||
logger.info(f"User provided edit instructions: '{st.session_state.edit_instructions}'")
|
||||
# Edit the end screen image
|
||||
edited_img_path = edit_end_screen_image(st.session_state.current_end_screen_path, st.session_state.edit_instructions)
|
||||
|
||||
if edited_img_path:
|
||||
# Update the current end screen path in session state
|
||||
st.session_state.edited_end_screen_path = edited_img_path
|
||||
logger.info(f"Updated current end screen path in session state: {edited_img_path}")
|
||||
|
||||
# Reset editing flags
|
||||
st.session_state.editing_end_screen = False
|
||||
st.session_state.show_edit_form = False
|
||||
|
||||
# Display the edited image
|
||||
st.subheader("Edited End Screen")
|
||||
st.image(edited_img_path, use_container_width=True)
|
||||
|
||||
# Add download button for the edited image
|
||||
with open(edited_img_path, "rb") as file:
|
||||
st.download_button(
|
||||
label="Download Edited End Screen",
|
||||
data=file,
|
||||
file_name=f"youtube_end_screen_edited_{int(time.time())}.png",
|
||||
mime="image/png"
|
||||
)
|
||||
|
||||
# Update the current end screen path to the edited one
|
||||
st.session_state.current_end_screen_path = edited_img_path
|
||||
|
||||
# Add a button to continue editing
|
||||
if st.button("Continue Editing"):
|
||||
st.session_state.show_edit_form = True
|
||||
st.rerun()
|
||||
else:
|
||||
# Reset editing flags
|
||||
st.session_state.editing_end_screen = False
|
||||
st.session_state.show_edit_form = False
|
||||
|
||||
st.error("Failed to edit the end screen. Please try again with different instructions.")
|
||||
@@ -1,556 +0,0 @@
|
||||
"""
|
||||
YouTube Script Generator Module
|
||||
|
||||
This module provides functionality for generating YouTube video scripts.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import time
|
||||
import json
|
||||
import os
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
|
||||
|
||||
def generate_youtube_script(target_audience, main_points, tone_style, use_case, script_structure,
|
||||
include_hook=False, include_cta=False, include_engagement=False,
|
||||
include_timestamps=False, include_visual_cues=False, engagement_hooks=None,
|
||||
community_interactions=None, language="English"):
|
||||
"""Generate a YouTube script based on the provided parameters."""
|
||||
|
||||
# Create a custom system prompt for YouTube script generation
|
||||
system_prompt = f"""You are a YouTube script expert specializing in creating engaging, well-structured video scripts in {language}.
|
||||
Your task is to generate YouTube video scripts based on the provided information.
|
||||
Focus ONLY on creating scripts that are optimized for YouTube, with proper structure, engagement hooks, and calls to action.
|
||||
Return ONLY the script text, without any additional commentary or explanations.
|
||||
Format the script with clear sections, speaker notes, and visual cues where appropriate.
|
||||
Write the entire script in {language}."""
|
||||
|
||||
# Build structure-specific instructions
|
||||
structure_instructions = {
|
||||
"Problem-Solution": "Structure the script to first present a problem, then provide a solution.",
|
||||
"Before-After-Bridge": "Structure the script to show the before state, the transformation process, and the after state.",
|
||||
"Hook-Problem-Solution-Call to Action": "Start with a hook, present the problem, provide the solution, and end with a call to action.",
|
||||
"Compare and Contrast": "Structure the script to compare and contrast different options or approaches.",
|
||||
"Step-by-Step Tutorial": "Break down the content into clear, sequential steps.",
|
||||
"Case Study": "Present a real-world example or case study to illustrate the main points.",
|
||||
"Interview Format": "Structure the script as an interview with questions and answers.",
|
||||
"Review Format": "Structure the script as a review with pros, cons, and a final verdict.",
|
||||
"Vlog Format": "Structure the script as a personal video blog with a conversational tone.",
|
||||
"Educational Format": "Structure the script to teach a concept with examples and explanations.",
|
||||
"Entertainment Format": "Structure the script to entertain while delivering the main message."
|
||||
}
|
||||
|
||||
# Build the prompt
|
||||
prompt = f"""
|
||||
**Instructions:**
|
||||
|
||||
Please generate a YouTube script in {language} for a video about **{main_points}** based on the following information:
|
||||
|
||||
**Target Audience:** {target_audience}
|
||||
**Tone and Style:** {tone_style}
|
||||
**Use Case:** {use_case}
|
||||
**Script Structure:** {script_structure}
|
||||
**Language:** {language}
|
||||
|
||||
**Structure Instructions:**
|
||||
{structure_instructions.get(script_structure, "Follow a logical flow to present the content.")}
|
||||
|
||||
**Additional Elements:**
|
||||
{"- Include a hook at the beginning to grab attention." if include_hook else ""}
|
||||
{"- End with a strong call to action." if include_cta else ""}
|
||||
{"- Include prompts for viewer engagement (e.g., questions, polls)." if include_engagement else ""}
|
||||
{"- Include suggested timestamps for key sections." if include_timestamps else ""}
|
||||
{"- Include visual cues and transitions." if include_visual_cues else ""}
|
||||
"""
|
||||
|
||||
# Add engagement hooks if provided
|
||||
if engagement_hooks:
|
||||
prompt += "\n**Engagement Hooks:**\n"
|
||||
for hook in engagement_hooks:
|
||||
prompt += f"- {hook}\n"
|
||||
|
||||
# Add community interaction points if provided
|
||||
if community_interactions:
|
||||
prompt += "\n**Community Interaction Points:**\n"
|
||||
for interaction in community_interactions:
|
||||
prompt += f"- {interaction}\n"
|
||||
|
||||
prompt += """
|
||||
**Specific Instructions:**
|
||||
* Keep the language clear and engaging.
|
||||
* Use a conversational tone that matches the target audience.
|
||||
* Include relevant examples and explanations.
|
||||
* Ensure the script flows naturally and maintains viewer interest.
|
||||
"""
|
||||
|
||||
try:
|
||||
response = llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
return response
|
||||
except Exception as err:
|
||||
st.error(f"Error: Failed to get response from LLM: {err}")
|
||||
return None
|
||||
|
||||
|
||||
def generate_youtube_script_with_changes(target_audience, main_points, tone_style, use_case, script_structure,
|
||||
include_hook=False, include_cta=False, include_engagement=False,
|
||||
include_timestamps=False, include_visual_cues=False, engagement_hooks=None,
|
||||
community_interactions=None, changes="", language="English"):
|
||||
"""Generate a YouTube script based on the provided parameters and requested changes."""
|
||||
|
||||
# Create a custom system prompt for YouTube script generation
|
||||
system_prompt = f"""You are a YouTube script expert specializing in creating engaging, well-structured video scripts in {language}.
|
||||
Your task is to generate YouTube video scripts based on the provided information.
|
||||
Focus ONLY on creating scripts that are optimized for YouTube, with proper structure, engagement hooks, and calls to action.
|
||||
Return ONLY the script text, without any additional commentary or explanations.
|
||||
Format the script with clear sections, speaker notes, and visual cues where appropriate.
|
||||
Write the entire script in {language}."""
|
||||
|
||||
# Build structure-specific instructions
|
||||
structure_instructions = {
|
||||
"Problem-Solution": "Structure the script to first present a problem, then provide a solution.",
|
||||
"Before-After-Bridge": "Structure the script to show the before state, the transformation process, and the after state.",
|
||||
"Hook-Problem-Solution-Call to Action": "Start with a hook, present the problem, provide the solution, and end with a call to action.",
|
||||
"Compare and Contrast": "Structure the script to compare and contrast different options or approaches.",
|
||||
"Step-by-Step Tutorial": "Break down the content into clear, sequential steps.",
|
||||
"Case Study": "Present a real-world example or case study to illustrate the main points.",
|
||||
"Interview Format": "Structure the script as an interview with questions and answers.",
|
||||
"Review Format": "Structure the script as a review with pros, cons, and a final verdict.",
|
||||
"Vlog Format": "Structure the script as a personal video blog with a conversational tone.",
|
||||
"Educational Format": "Structure the script to teach a concept with examples and explanations.",
|
||||
"Entertainment Format": "Structure the script to entertain while delivering the main message."
|
||||
}
|
||||
|
||||
# Build the prompt
|
||||
prompt = f"""
|
||||
**Instructions:**
|
||||
|
||||
Please generate a YouTube script in {language} for a video about **{main_points}** based on the following information:
|
||||
|
||||
**Target Audience:** {target_audience}
|
||||
**Tone and Style:** {tone_style}
|
||||
**Use Case:** {use_case}
|
||||
**Script Structure:** {script_structure}
|
||||
**Language:** {language}
|
||||
|
||||
**Structure Instructions:**
|
||||
{structure_instructions.get(script_structure, "Follow a logical flow to present the content.")}
|
||||
|
||||
**Additional Elements:**
|
||||
{"- Include a hook at the beginning to grab attention." if include_hook else ""}
|
||||
{"- End with a strong call to action." if include_cta else ""}
|
||||
{"- Include prompts for viewer engagement (e.g., questions, polls)." if include_engagement else ""}
|
||||
{"- Include suggested timestamps for key sections." if include_timestamps else ""}
|
||||
{"- Include visual cues and transitions." if include_visual_cues else ""}
|
||||
"""
|
||||
|
||||
# Add engagement hooks if provided
|
||||
if engagement_hooks:
|
||||
prompt += "\n**Engagement Hooks:**\n"
|
||||
for hook in engagement_hooks:
|
||||
prompt += f"- {hook}\n"
|
||||
|
||||
# Add community interaction points if provided
|
||||
if community_interactions:
|
||||
prompt += "\n**Community Interaction Points:**\n"
|
||||
for interaction in community_interactions:
|
||||
prompt += f"- {interaction}\n"
|
||||
|
||||
# Add requested changes
|
||||
prompt += f"""
|
||||
**Requested Changes:**
|
||||
{changes}
|
||||
|
||||
**Specific Instructions:**
|
||||
* Keep the language clear and engaging.
|
||||
* Use a conversational tone that matches the target audience.
|
||||
* Include relevant examples and explanations.
|
||||
* Ensure the script flows naturally and maintains viewer interest.
|
||||
* Incorporate the requested changes into the script.
|
||||
"""
|
||||
|
||||
try:
|
||||
response = llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
return response
|
||||
except Exception as err:
|
||||
st.error(f"Error: Failed to get response from LLM: {err}")
|
||||
return None
|
||||
|
||||
|
||||
def export_script(script, format_type, filename=None):
|
||||
"""Export the script in various formats."""
|
||||
if not filename:
|
||||
filename = "youtube_script"
|
||||
|
||||
if format_type == "Text":
|
||||
return script, f"{filename}.txt", "text/plain"
|
||||
elif format_type == "Markdown":
|
||||
return script, f"{filename}.md", "text/markdown"
|
||||
elif format_type == "HTML":
|
||||
html_content = f"<html><body><pre>{script}</pre></body></html>"
|
||||
return html_content, f"{filename}.html", "text/html"
|
||||
elif format_type == "JSON":
|
||||
json_content = json.dumps({"script": script}, indent=2)
|
||||
return json_content, f"{filename}.json", "application/json"
|
||||
elif format_type == "Subtitles (SRT)":
|
||||
# Convert script to basic SRT format
|
||||
lines = script.split('\n')
|
||||
srt_content = ""
|
||||
for i, line in enumerate(lines):
|
||||
if line.strip():
|
||||
start_time = f"00:00:{i*5:02d},000"
|
||||
end_time = f"00:00:{(i+1)*5:02d},000"
|
||||
srt_content += f"{i+1}\n{start_time} --> {end_time}\n{line}\n\n"
|
||||
return srt_content, f"{filename}.srt", "text/plain"
|
||||
else:
|
||||
return script, f"{filename}.txt", "text/plain"
|
||||
|
||||
|
||||
def write_yt_script():
|
||||
"""Create a user interface for YouTube Script Generator."""
|
||||
st.write("Generate professional YouTube video scripts with optimized structures for engagement.")
|
||||
|
||||
# Initialize session state for generated script if it doesn't exist
|
||||
if "generated_script" not in st.session_state:
|
||||
st.session_state.generated_script = None
|
||||
|
||||
# Create tabs for different sections
|
||||
tab1, tab2, tab3 = st.tabs(["Basic Info", "Advanced Options", "Engagement & Export"])
|
||||
|
||||
with tab1:
|
||||
# Basic information inputs
|
||||
main_points = st.text_area("Main Points/Keywords (comma-separated)",
|
||||
placeholder="e.g., cooking tips, healthy recipes, quick meals")
|
||||
target_audience = st.text_input("Target Audience",
|
||||
placeholder="e.g., beginners, professionals, parents")
|
||||
|
||||
# Create columns for tone, use case, structure, and language
|
||||
col1, col2, col3, col4 = st.columns(4)
|
||||
|
||||
with col1:
|
||||
tone_style = st.selectbox("Tone/Style",
|
||||
["Professional", "Casual", "Humorous", "Educational", "Entertaining", "Inspirational"])
|
||||
|
||||
with col2:
|
||||
use_case = st.selectbox("Use Case",
|
||||
["How-to/Tutorial", "Vlog", "Review", "Educational", "Entertainment", "News"])
|
||||
|
||||
with col3:
|
||||
script_structure = st.selectbox("Script Structure", [
|
||||
"Problem-Solution",
|
||||
"Before-After-Bridge",
|
||||
"Hook-Problem-Solution-Call to Action",
|
||||
"Compare and Contrast",
|
||||
"Step-by-Step Tutorial",
|
||||
"Case Study",
|
||||
"Interview Format",
|
||||
"Review Format",
|
||||
"Vlog Format",
|
||||
"Educational Format",
|
||||
"Entertainment Format"
|
||||
])
|
||||
|
||||
with col4:
|
||||
language = st.selectbox("Language", [
|
||||
"English",
|
||||
"Spanish",
|
||||
"French",
|
||||
"German",
|
||||
"Italian",
|
||||
"Portuguese",
|
||||
"Russian",
|
||||
"Japanese",
|
||||
"Korean",
|
||||
"Chinese",
|
||||
"Hindi",
|
||||
"Arabic"
|
||||
])
|
||||
|
||||
with tab2:
|
||||
# Advanced options
|
||||
st.subheader("Additional Elements")
|
||||
include_hook = st.checkbox("Include Hook", value=True)
|
||||
include_cta = st.checkbox("Include Call to Action", value=True)
|
||||
include_engagement = st.checkbox("Include Viewer Engagement Prompts", value=True)
|
||||
include_timestamps = st.checkbox("Include Suggested Timestamps", value=True)
|
||||
include_visual_cues = st.checkbox("Include Visual Cues/Transitions", value=True)
|
||||
|
||||
with tab3:
|
||||
# Engagement hooks
|
||||
st.subheader("Engagement Hooks")
|
||||
st.write("Select engagement hooks to include in your script:")
|
||||
|
||||
engagement_hooks = []
|
||||
if st.checkbox("Question Hook", value=False):
|
||||
engagement_hooks.append("Start with a thought-provoking question to engage viewers immediately")
|
||||
if st.checkbox("Story Hook", value=False):
|
||||
engagement_hooks.append("Begin with a brief, relevant story or anecdote")
|
||||
if st.checkbox("Statistic Hook", value=False):
|
||||
engagement_hooks.append("Open with an interesting statistic or fact")
|
||||
if st.checkbox("Controversy Hook", value=False):
|
||||
engagement_hooks.append("Present a controversial statement or opinion to spark interest")
|
||||
if st.checkbox("Promise Hook", value=False):
|
||||
engagement_hooks.append("Make a promise about what viewers will learn or gain")
|
||||
if st.checkbox("Scenario Hook", value=False):
|
||||
engagement_hooks.append("Describe a scenario or situation viewers can relate to")
|
||||
if st.checkbox("Mystery Hook", value=False):
|
||||
engagement_hooks.append("Create a sense of mystery or intrigue")
|
||||
if st.checkbox("Quote Hook", value=False):
|
||||
engagement_hooks.append("Start with a relevant quote from an expert or notable figure")
|
||||
|
||||
# Community interaction points
|
||||
st.subheader("Community Interaction Points")
|
||||
st.write("Select community interaction points to include in your script:")
|
||||
|
||||
community_interactions = []
|
||||
if st.checkbox("Comment Prompt", value=False):
|
||||
community_interactions.append("Ask viewers to share their experiences or opinions in the comments")
|
||||
if st.checkbox("Poll Suggestion", value=False):
|
||||
community_interactions.append("Suggest creating a poll for viewers to vote on")
|
||||
if st.checkbox("Question for Comments", value=False):
|
||||
community_interactions.append("Pose a specific question for viewers to answer in the comments")
|
||||
if st.checkbox("Challenge", value=False):
|
||||
community_interactions.append("Challenge viewers to try something and report back")
|
||||
if st.checkbox("Tag Friends", value=False):
|
||||
community_interactions.append("Encourage viewers to tag friends who might benefit from the content")
|
||||
if st.checkbox("Share Request", value=False):
|
||||
community_interactions.append("Ask viewers to share the video with others who might find it helpful")
|
||||
if st.checkbox("Community Post", value=False):
|
||||
community_interactions.append("Mention creating a community post to continue the discussion")
|
||||
if st.checkbox("Live Stream Teaser", value=False):
|
||||
community_interactions.append("Tease an upcoming live stream on the same topic")
|
||||
|
||||
# Export options
|
||||
st.subheader("Export Options")
|
||||
export_format = st.selectbox("Export Format", [
|
||||
"Text",
|
||||
"Markdown",
|
||||
"HTML",
|
||||
"JSON",
|
||||
"Subtitles (SRT)"
|
||||
])
|
||||
|
||||
custom_filename = st.text_input("Custom Filename (optional)",
|
||||
placeholder="Leave blank for default filename")
|
||||
|
||||
if st.button("Generate Script"):
|
||||
if not main_points:
|
||||
st.error("Please enter main points/keywords.")
|
||||
return
|
||||
|
||||
with st.spinner("Generating script..."):
|
||||
script = generate_youtube_script(
|
||||
target_audience, main_points, tone_style, use_case, script_structure,
|
||||
include_hook, include_cta, include_engagement, include_timestamps, include_visual_cues,
|
||||
engagement_hooks if engagement_hooks else None,
|
||||
community_interactions if community_interactions else None,
|
||||
language
|
||||
)
|
||||
|
||||
if script:
|
||||
# Store the script in session state
|
||||
st.session_state.generated_script = script
|
||||
|
||||
# Store other parameters in session state for regeneration
|
||||
st.session_state.script_params = {
|
||||
"target_audience": target_audience,
|
||||
"main_points": main_points,
|
||||
"tone_style": tone_style,
|
||||
"use_case": use_case,
|
||||
"script_structure": script_structure,
|
||||
"include_hook": include_hook,
|
||||
"include_cta": include_cta,
|
||||
"include_engagement": include_engagement,
|
||||
"include_timestamps": include_timestamps,
|
||||
"include_visual_cues": include_visual_cues,
|
||||
"engagement_hooks": engagement_hooks if engagement_hooks else None,
|
||||
"community_interactions": community_interactions if community_interactions else None,
|
||||
"language": language
|
||||
}
|
||||
|
||||
st.subheader("Generated Script")
|
||||
|
||||
# Display script with tabs for different views
|
||||
script_tab1, script_tab2 = st.tabs(["Formatted View", "Plain Text"])
|
||||
|
||||
with script_tab1:
|
||||
st.markdown(script)
|
||||
|
||||
with script_tab2:
|
||||
st.code(script)
|
||||
|
||||
# Export options
|
||||
st.subheader("Export Script")
|
||||
|
||||
# Get export data
|
||||
export_data, export_filename, mime_type = export_script(
|
||||
script,
|
||||
export_format,
|
||||
custom_filename if custom_filename else None
|
||||
)
|
||||
|
||||
# Create columns for the buttons
|
||||
btn_col1, btn_col2 = st.columns(2)
|
||||
|
||||
with btn_col1:
|
||||
# Download button
|
||||
st.download_button(
|
||||
label=f"Download as {export_format}",
|
||||
data=export_data,
|
||||
file_name=export_filename,
|
||||
mime=mime_type
|
||||
)
|
||||
|
||||
with btn_col2:
|
||||
# Regenerate button
|
||||
if st.button("Regenerate"):
|
||||
st.session_state.show_regenerate_popover = True
|
||||
|
||||
# Regenerate popover
|
||||
if st.session_state.get("show_regenerate_popover", False):
|
||||
with st.form("regenerate_form"):
|
||||
st.subheader("Regenerate Script")
|
||||
st.write("Specify changes you'd like to make to the script:")
|
||||
changes = st.text_area("Changes to make",
|
||||
placeholder="e.g., Make it more casual, add more call-to-actions, focus on product benefits")
|
||||
|
||||
submitted = st.form_submit_button("Regenerate with Changes")
|
||||
|
||||
if submitted and changes:
|
||||
with st.spinner("Regenerating script..."):
|
||||
# Get the stored parameters
|
||||
params = st.session_state.script_params
|
||||
|
||||
# Generate a new script with the changes
|
||||
new_script = generate_youtube_script_with_changes(
|
||||
params["target_audience"],
|
||||
params["main_points"],
|
||||
params["tone_style"],
|
||||
params["use_case"],
|
||||
params["script_structure"],
|
||||
params["include_hook"],
|
||||
params["include_cta"],
|
||||
params["include_engagement"],
|
||||
params["include_timestamps"],
|
||||
params["include_visual_cues"],
|
||||
params["engagement_hooks"],
|
||||
params["community_interactions"],
|
||||
changes,
|
||||
params["language"]
|
||||
)
|
||||
|
||||
if new_script:
|
||||
# Update the stored script
|
||||
st.session_state.generated_script = new_script
|
||||
st.session_state.show_regenerate_popover = False
|
||||
st.rerun()
|
||||
else:
|
||||
st.error("Failed to regenerate script. Please try again.")
|
||||
|
||||
# Additional export options
|
||||
if st.checkbox("Show additional export options"):
|
||||
col1, col2 = st.columns(2)
|
||||
with col1:
|
||||
if st.button("Copy to Clipboard"):
|
||||
st.code(script)
|
||||
st.success("Script copied to clipboard!")
|
||||
|
||||
with col2:
|
||||
if st.button("Save to Local File"):
|
||||
# This is a placeholder - actual file saving would require additional backend functionality
|
||||
st.info("This feature would save the file locally on your device.")
|
||||
else:
|
||||
st.error("Failed to generate script. Please try again.")
|
||||
|
||||
# Display previously generated script if it exists in session state
|
||||
elif st.session_state.generated_script:
|
||||
script = st.session_state.generated_script
|
||||
params = st.session_state.script_params
|
||||
|
||||
st.subheader("Generated Script")
|
||||
|
||||
# Display script with tabs for different views
|
||||
script_tab1, script_tab2 = st.tabs(["Formatted View", "Plain Text"])
|
||||
|
||||
with script_tab1:
|
||||
st.markdown(script)
|
||||
|
||||
with script_tab2:
|
||||
st.code(script)
|
||||
|
||||
# Export options
|
||||
st.subheader("Export Script")
|
||||
|
||||
# Get export data
|
||||
export_data, export_filename, mime_type = export_script(
|
||||
script,
|
||||
export_format,
|
||||
custom_filename if custom_filename else None
|
||||
)
|
||||
|
||||
# Create columns for the buttons
|
||||
btn_col1, btn_col2 = st.columns(2)
|
||||
|
||||
with btn_col1:
|
||||
# Download button
|
||||
st.download_button(
|
||||
label=f"Download as {export_format}",
|
||||
data=export_data,
|
||||
file_name=export_filename,
|
||||
mime=mime_type
|
||||
)
|
||||
|
||||
with btn_col2:
|
||||
# Regenerate button
|
||||
if st.button("Regenerate"):
|
||||
st.session_state.show_regenerate_popover = True
|
||||
|
||||
# Regenerate popover
|
||||
if st.session_state.get("show_regenerate_popover", False):
|
||||
with st.form("regenerate_form"):
|
||||
st.subheader("Regenerate Script")
|
||||
st.write("Specify changes you'd like to make to the script:")
|
||||
changes = st.text_area("Changes to make",
|
||||
placeholder="e.g., Make it more casual, add more call-to-actions, focus on product benefits")
|
||||
|
||||
submitted = st.form_submit_button("Regenerate with Changes")
|
||||
|
||||
if submitted and changes:
|
||||
with st.spinner("Regenerating script..."):
|
||||
# Generate a new script with the changes
|
||||
new_script = generate_youtube_script_with_changes(
|
||||
params["target_audience"],
|
||||
params["main_points"],
|
||||
params["tone_style"],
|
||||
params["use_case"],
|
||||
params["script_structure"],
|
||||
params["include_hook"],
|
||||
params["include_cta"],
|
||||
params["include_engagement"],
|
||||
params["include_timestamps"],
|
||||
params["include_visual_cues"],
|
||||
params["engagement_hooks"],
|
||||
params["community_interactions"],
|
||||
changes,
|
||||
params["language"]
|
||||
)
|
||||
|
||||
if new_script:
|
||||
# Update the stored script
|
||||
st.session_state.generated_script = new_script
|
||||
st.session_state.show_regenerate_popover = False
|
||||
st.rerun()
|
||||
else:
|
||||
st.error("Failed to regenerate script. Please try again.")
|
||||
|
||||
# Additional export options
|
||||
if st.checkbox("Show additional export options"):
|
||||
col1, col2 = st.columns(2)
|
||||
with col1:
|
||||
if st.button("Copy to Clipboard"):
|
||||
st.code(script)
|
||||
st.success("Script copied to clipboard!")
|
||||
|
||||
with col2:
|
||||
if st.button("Save to Local File"):
|
||||
# This is a placeholder - actual file saving would require additional backend functionality
|
||||
st.info("This feature would save the file locally on your device.")
|
||||
@@ -1,314 +0,0 @@
|
||||
"""
|
||||
YouTube Shorts Script Generator Module
|
||||
|
||||
This module provides functionality for generating optimized scripts for YouTube Shorts.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import time
|
||||
import logging
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
logger = logging.getLogger('youtube_shorts_generator')
|
||||
|
||||
def generate_shorts_script(hook_type, main_topic, target_audience, tone_style,
|
||||
content_type, duration_seconds=60, include_captions=True,
|
||||
include_text_overlay=True, include_sound_effects=False,
|
||||
vertical_framing_notes=True, language="English"):
|
||||
"""Generate a YouTube Shorts script optimized for vertical format and short duration."""
|
||||
|
||||
# Create a custom system prompt for Shorts script generation
|
||||
system_prompt = f"""You are a YouTube Shorts expert specializing in creating viral, engaging scripts for vertical short-form videos.
|
||||
Your task is to generate scripts that are perfectly timed for {duration_seconds} seconds or less.
|
||||
Focus on hooks that grab attention in the first 1-2 seconds.
|
||||
Format the script with clear sections for visuals, audio, and text overlays.
|
||||
Write the entire script in {language}.
|
||||
Remember that Shorts are viewed vertically (9:16 aspect ratio) and need to work without sound."""
|
||||
|
||||
# Build hook-specific instructions
|
||||
hook_instructions = {
|
||||
"Question": "Start with an intriguing question that stops the scroll",
|
||||
"Statistic": "Begin with a surprising statistic or fact",
|
||||
"Challenge": "Present a challenge or dare to the viewer",
|
||||
"Tutorial": "Jump straight into a quick how-to or life hack",
|
||||
"Transformation": "Show a before/after or transformation hook",
|
||||
"Trend": "Leverage a current trend or sound",
|
||||
"Story": "Start with a captivating micro-story",
|
||||
"Controversy": "Present a controversial or surprising statement"
|
||||
}
|
||||
|
||||
# Build the prompt
|
||||
prompt = f"""
|
||||
**Instructions:**
|
||||
|
||||
Create a YouTube Shorts script about **{main_topic}** with these specifications:
|
||||
|
||||
**Core Elements:**
|
||||
- Hook Type: {hook_type} - {hook_instructions.get(hook_type, "Create an attention-grabbing opening")}
|
||||
- Target Audience: {target_audience}
|
||||
- Tone/Style: {tone_style}
|
||||
- Content Type: {content_type}
|
||||
- Duration: {duration_seconds} seconds
|
||||
- Language: {language}
|
||||
|
||||
**Required Elements:**
|
||||
{"- Include caption suggestions for accessibility" if include_captions else ""}
|
||||
{"- Include text overlay positions and timing" if include_text_overlay else ""}
|
||||
{"- Include sound effect suggestions" if include_sound_effects else ""}
|
||||
{"- Include vertical framing notes for optimal composition" if vertical_framing_notes else ""}
|
||||
|
||||
**Format the script in this structure:**
|
||||
1. HOOK (0-2 seconds)
|
||||
2. MAIN CONTENT (3-50 seconds)
|
||||
3. CALL TO ACTION (last 10 seconds)
|
||||
|
||||
**For each section, specify:**
|
||||
- Visual Instructions (what to show)
|
||||
- Text Overlays (what text appears and where)
|
||||
- Audio/Voiceover
|
||||
- Timing (in seconds)
|
||||
- Camera Angles/Framing Notes
|
||||
|
||||
**Remember:**
|
||||
- Scripts must work without sound (many viewers watch on mute)
|
||||
- Text should be centered in the middle 50% of the vertical frame
|
||||
- Keep text concise and readable
|
||||
- Include pattern interrupts every 3-5 seconds
|
||||
- End with a clear call-to-action
|
||||
"""
|
||||
|
||||
try:
|
||||
response = llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
return response
|
||||
except Exception as err:
|
||||
st.error(f"Error: Failed to get response from LLM: {err}")
|
||||
return None
|
||||
|
||||
def analyze_shorts_script(script):
|
||||
"""Analyze a Shorts script for optimal engagement metrics."""
|
||||
analysis = {
|
||||
'duration_estimate': 0,
|
||||
'hook_strength': 0,
|
||||
'pattern_interrupts': 0,
|
||||
'text_overlay_count': 0,
|
||||
'readability_score': 0,
|
||||
'optimization_score': 0
|
||||
}
|
||||
|
||||
# Basic analysis (can be enhanced with more sophisticated metrics)
|
||||
lines = script.split('\n')
|
||||
word_count = len(script.split())
|
||||
|
||||
# Estimate duration (rough approximation)
|
||||
analysis['duration_estimate'] = word_count * 0.4 # Average speaking speed
|
||||
|
||||
# Count pattern interrupts
|
||||
analysis['pattern_interrupts'] = script.lower().count('cut to') + script.lower().count('transition')
|
||||
|
||||
# Count text overlays
|
||||
analysis['text_overlay_count'] = script.lower().count('text:') + script.lower().count('overlay:')
|
||||
|
||||
# Calculate optimization score
|
||||
score = 100
|
||||
|
||||
# Penalize if estimated duration is too long
|
||||
if analysis['duration_estimate'] > 60:
|
||||
score -= (analysis['duration_estimate'] - 60) * 2
|
||||
|
||||
# Check for hook presence
|
||||
if not any(hook in script.lower() for hook in ['hook:', 'opening:', '0-2 seconds:']):
|
||||
score -= 20
|
||||
|
||||
# Check for pattern interrupts (ideal is 1 every 5 seconds)
|
||||
ideal_interrupts = analysis['duration_estimate'] / 5
|
||||
if analysis['pattern_interrupts'] < ideal_interrupts:
|
||||
score -= 10
|
||||
|
||||
# Check for text overlay usage
|
||||
if analysis['text_overlay_count'] < 3:
|
||||
score -= 10
|
||||
|
||||
# Check for call-to-action
|
||||
if not any(cta in script.lower() for cta in ['call to action', 'cta:', 'subscribe', 'follow']):
|
||||
score -= 15
|
||||
|
||||
analysis['optimization_score'] = max(0, score)
|
||||
return analysis
|
||||
|
||||
def generate_shorts_narration(shorts_script, language="English"):
|
||||
system_prompt = f"""You are an expert at converting YouTube Shorts scripts into natural, engaging narration.\nYour task is to read the provided Shorts script and output only the narration lines, as they would be spoken in the video.\nOmit all visual instructions, timing, text overlays, and technical cues. Write the narration in {language}."""
|
||||
prompt = f"""Shorts Script:\n{shorts_script}\n\nInstructions:\nExtract and rewrite only the narration lines, as they would be spoken in the video. Do not include any section headers, cues, or formatting. Output only the narration text."""
|
||||
try:
|
||||
response = llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
return response.strip()
|
||||
except Exception as err:
|
||||
st.error(f"Error: Failed to get narration from LLM: {err}")
|
||||
return ""
|
||||
|
||||
def write_yt_shorts():
|
||||
"""Create a user interface for YouTube Shorts Script Generator."""
|
||||
st.write("Generate optimized scripts for YouTube Shorts that grab attention and drive engagement.")
|
||||
|
||||
# Initialize session state for generated script and active tab if they don't exist
|
||||
if "generated_shorts_script" not in st.session_state:
|
||||
st.session_state.generated_shorts_script = None
|
||||
if "active_tab" not in st.session_state:
|
||||
st.session_state.active_tab = "Core Elements"
|
||||
|
||||
# Create tabs for different sections
|
||||
tab1, tab2, tab3 = st.tabs(["Core Elements", "Style & Format", "Preview & Export"])
|
||||
|
||||
# Set the active tab based on session state
|
||||
if st.session_state.active_tab == "Core Elements":
|
||||
tab1.active = True
|
||||
elif st.session_state.active_tab == "Style & Format":
|
||||
tab2.active = True
|
||||
elif st.session_state.active_tab == "Preview & Export":
|
||||
tab3.active = True
|
||||
|
||||
with tab1:
|
||||
# Core elements
|
||||
main_topic = st.text_area("Main Topic/Concept",
|
||||
placeholder="e.g., Quick cooking hack, Life-changing productivity tip")
|
||||
|
||||
col1, col2 = st.columns(2)
|
||||
with col1:
|
||||
hook_type = st.selectbox("Hook Type", [
|
||||
"Question",
|
||||
"Statistic",
|
||||
"Challenge",
|
||||
"Tutorial",
|
||||
"Transformation",
|
||||
"Trend",
|
||||
"Story",
|
||||
"Controversy"
|
||||
])
|
||||
|
||||
target_audience = st.text_input("Target Audience",
|
||||
placeholder="e.g., Gen Z, busy professionals")
|
||||
|
||||
with col2:
|
||||
content_type = st.selectbox("Content Type", [
|
||||
"Tutorial/How-to",
|
||||
"Life Hack",
|
||||
"Entertainment",
|
||||
"Educational",
|
||||
"Trend",
|
||||
"Story",
|
||||
"Challenge",
|
||||
"Review"
|
||||
])
|
||||
|
||||
tone_style = st.selectbox("Tone/Style", [
|
||||
"Energetic",
|
||||
"Professional",
|
||||
"Casual",
|
||||
"Humorous",
|
||||
"Dramatic",
|
||||
"Inspirational"
|
||||
])
|
||||
|
||||
with tab2:
|
||||
# Style and format options
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
duration_seconds = st.slider("Duration (seconds)", 15, 60, 60)
|
||||
language = st.selectbox("Language", [
|
||||
"English",
|
||||
"Spanish",
|
||||
"French",
|
||||
"German",
|
||||
"Italian",
|
||||
"Portuguese",
|
||||
"Russian",
|
||||
"Japanese",
|
||||
"Korean",
|
||||
"Chinese"
|
||||
])
|
||||
|
||||
with col2:
|
||||
include_captions = st.checkbox("Include Captions", value=True)
|
||||
include_text_overlay = st.checkbox("Include Text Overlay Positions", value=True)
|
||||
include_sound_effects = st.checkbox("Include Sound Effects", value=False)
|
||||
vertical_framing_notes = st.checkbox("Include Vertical Framing Notes", value=True)
|
||||
|
||||
with tab3:
|
||||
if st.session_state.generated_shorts_script:
|
||||
# Display the generated script
|
||||
st.subheader("Generated Shorts Script")
|
||||
|
||||
# Create tabs for different views
|
||||
script_tab1, script_tab2, script_tab3 = st.tabs(["Formatted", "Analysis", "Export"])
|
||||
|
||||
with script_tab1:
|
||||
st.markdown(st.session_state.generated_shorts_script)
|
||||
|
||||
with script_tab2:
|
||||
# Analyze the script
|
||||
analysis = analyze_shorts_script(st.session_state.generated_shorts_script)
|
||||
|
||||
# Display analysis results
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
st.metric("Estimated Duration", f"{analysis['duration_estimate']:.1f}s")
|
||||
st.metric("Pattern Interrupts", analysis['pattern_interrupts'])
|
||||
st.metric("Text Overlays", analysis['text_overlay_count'])
|
||||
|
||||
with col2:
|
||||
# Display optimization score with color
|
||||
score = analysis['optimization_score']
|
||||
color = "red" if score < 60 else "orange" if score < 80 else "green"
|
||||
st.markdown(f"### Optimization Score: <span style='color: {color}'>{score}%</span>",
|
||||
unsafe_allow_html=True)
|
||||
|
||||
with script_tab3:
|
||||
# Export options
|
||||
export_format = st.selectbox("Export Format", [
|
||||
"Text",
|
||||
"Markdown",
|
||||
"Shot List",
|
||||
"Storyboard"
|
||||
])
|
||||
|
||||
if st.button("Export Script"):
|
||||
# Implement export functionality based on selected format
|
||||
st.success(f"Script exported in {export_format} format!")
|
||||
st.download_button(
|
||||
"Download Script",
|
||||
st.session_state.generated_shorts_script,
|
||||
file_name=f"shorts_script.{export_format.lower()}",
|
||||
mime="text/plain"
|
||||
)
|
||||
|
||||
# Generate button
|
||||
if st.button("Generate Shorts Script"):
|
||||
if not main_topic:
|
||||
st.error("Please enter a main topic/concept.")
|
||||
return
|
||||
|
||||
with st.spinner("Generating Shorts script..."):
|
||||
script = generate_shorts_script(
|
||||
hook_type, main_topic, target_audience, tone_style, content_type,
|
||||
duration_seconds, include_captions, include_text_overlay,
|
||||
include_sound_effects, vertical_framing_notes, language
|
||||
)
|
||||
|
||||
if script:
|
||||
st.session_state.generated_shorts_script = script
|
||||
# Set active tab to Preview & Export
|
||||
st.session_state.active_tab = "Preview & Export"
|
||||
st.success("✨ Script generated successfully! Check the 'Preview & Export' tab to view, analyze, and download your script.")
|
||||
st.rerun()
|
||||
else:
|
||||
st.error("Failed to generate script. Please try again.")
|
||||
|
||||
# Add a message about preview and export if script exists but we're not on the Preview tab
|
||||
if st.session_state.generated_shorts_script and st.session_state.active_tab != "Preview & Export":
|
||||
st.info("💡 Your generated script is ready! Go to the 'Preview & Export' tab to view, analyze, and download it.")
|
||||
@@ -1,972 +0,0 @@
|
||||
"""
|
||||
YouTube Shorts Video Generator
|
||||
|
||||
This module provides functionality to generate YouTube Shorts videos using AI.
|
||||
It adapts the story video generator for the vertical format and shorter duration of Shorts.
|
||||
"""
|
||||
|
||||
import os
|
||||
import re
|
||||
import time
|
||||
import json
|
||||
import uuid
|
||||
import tempfile
|
||||
import logging
|
||||
import traceback
|
||||
from pathlib import Path
|
||||
from typing import List, Dict, Any, Tuple, Optional, Union, Callable
|
||||
from functools import wraps
|
||||
from datetime import datetime
|
||||
import random
|
||||
import functools
|
||||
|
||||
import streamlit as st
|
||||
import numpy as np
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
import requests
|
||||
|
||||
# Try importing moviepy with proper error handling
|
||||
try:
|
||||
from moviepy.editor import (
|
||||
ImageSequenceClip,
|
||||
TextClip,
|
||||
CompositeVideoClip,
|
||||
AudioFileClip,
|
||||
AudioClip,
|
||||
CompositeAudioClip,
|
||||
)
|
||||
MOVIEPY_AVAILABLE = True
|
||||
except ImportError as e:
|
||||
st.error(
|
||||
"MoviePy is not properly installed. Please install it using:\n"
|
||||
"pip install moviepy imageio imageio-ffmpeg"
|
||||
)
|
||||
MOVIEPY_AVAILABLE = False
|
||||
|
||||
# Try importing gTTS with proper error handling
|
||||
try:
|
||||
from gtts import gTTS
|
||||
GTTS_AVAILABLE = True
|
||||
except ImportError:
|
||||
st.error(
|
||||
"gTTS is not installed. Please install it using:\n"
|
||||
"pip install gTTS"
|
||||
)
|
||||
GTTS_AVAILABLE = False
|
||||
|
||||
# Import LLM text generation and image generation
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
from lib.gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
|
||||
from .shorts_script_generator import generate_shorts_script, generate_shorts_narration
|
||||
from lib.ai_writers.ai_story_video_generator.story_video_generator import StoryVideoGenerator
|
||||
|
||||
# Configure logging
|
||||
log_dir = Path("logs")
|
||||
log_dir.mkdir(exist_ok=True)
|
||||
log_file = log_dir / f"shorts_generator_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log"
|
||||
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
|
||||
handlers=[
|
||||
logging.FileHandler(log_file),
|
||||
logging.StreamHandler()
|
||||
]
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Constants
|
||||
DEFAULT_FPS = 30 # Higher FPS for smoother Shorts
|
||||
DEFAULT_DURATION = 2 # seconds per scene (shorter for Shorts)
|
||||
DEFAULT_TRANSITION_DURATION = 0.5 # seconds for transition
|
||||
DEFAULT_FONT_SIZE = 32 # Larger font for vertical format
|
||||
DEFAULT_FONT_COLOR = "white"
|
||||
DEFAULT_MUSIC_URL = "https://freepd.com/music/Upbeat%20Uplifting%20Corporate.mp3" # Example free music URL
|
||||
DEFAULT_IMAGE_WIDTH = 1080 # Standard Shorts width
|
||||
DEFAULT_IMAGE_HEIGHT = 1920 # Standard Shorts height (9:16 aspect ratio)
|
||||
TEXT_AREA_HEIGHT_RATIO = 1/4 # Smaller text area for vertical format
|
||||
TEXT_PADDING = 30
|
||||
TEXT_OVERLAY_ALPHA = 180 # More opaque overlay for better readability
|
||||
|
||||
# Shorts-specific constants
|
||||
MAX_SHORTS_DURATION = 60 # Maximum duration for YouTube Shorts
|
||||
MIN_SHORTS_DURATION = 15 # Minimum duration for YouTube Shorts
|
||||
DEFAULT_SHORTS_DURATION = 30 # Default duration for Shorts
|
||||
MAX_SCENES = 15 # Maximum number of scenes to generate
|
||||
MIN_SCENES = 5 # Minimum number of scenes
|
||||
WORDS_PER_SECOND = 2.5 # Average speaking rate for narration
|
||||
|
||||
# Video resolutions for Shorts (vertical format)
|
||||
VIDEO_RESOLUTIONS = {
|
||||
"1080p": (1080, 1920), # Standard Shorts resolution
|
||||
"720p": (720, 1280), # Lower resolution option
|
||||
}
|
||||
|
||||
# Transition styles optimized for Shorts
|
||||
TRANSITION_STYLES = {
|
||||
"None": None,
|
||||
"Fade": "fade",
|
||||
"Slide Up": "slide_up",
|
||||
"Slide Down": "slide_down",
|
||||
"Zoom": "zoom",
|
||||
"Wipe": "wipe"
|
||||
}
|
||||
|
||||
# Content styles for Shorts
|
||||
CONTENT_STYLES = {
|
||||
"Tutorial": {
|
||||
"style": "tutorial",
|
||||
"description": "Step-by-step instructional content"
|
||||
},
|
||||
"Story": {
|
||||
"style": "story",
|
||||
"description": "Narrative-driven content"
|
||||
},
|
||||
"Tips": {
|
||||
"style": "tips",
|
||||
"description": "Quick tips and tricks"
|
||||
},
|
||||
"Review": {
|
||||
"style": "review",
|
||||
"description": "Product or service reviews"
|
||||
},
|
||||
"Behind the Scenes": {
|
||||
"style": "behind_scenes",
|
||||
"description": "Behind-the-scenes content"
|
||||
}
|
||||
}
|
||||
|
||||
# Narration languages
|
||||
NARRATION_LANGUAGES = {
|
||||
"English (US)": "en-us",
|
||||
"English (UK)": "en-gb",
|
||||
"Spanish": "es",
|
||||
"French": "fr",
|
||||
"German": "de",
|
||||
"Italian": "it",
|
||||
"Japanese": "ja",
|
||||
"Korean": "ko",
|
||||
"Chinese": "zh-cn",
|
||||
"Hindi": "hi"
|
||||
}
|
||||
|
||||
# Retry configuration
|
||||
MAX_RETRIES = 3
|
||||
INITIAL_RETRY_DELAY = 1 # Initial delay in seconds
|
||||
MAX_RETRY_DELAY = 30 # Maximum delay in seconds
|
||||
RETRYABLE_ERRORS = (
|
||||
ConnectionError,
|
||||
TimeoutError,
|
||||
requests.exceptions.RequestException,
|
||||
OSError, # For file system operations
|
||||
IOError, # For file system operations
|
||||
)
|
||||
|
||||
def retry_on_error(max_retries: int = MAX_RETRIES, initial_delay: int = INITIAL_RETRY_DELAY, max_delay: int = MAX_RETRY_DELAY):
|
||||
"""
|
||||
Decorator for retrying functions on specific errors with exponential backoff.
|
||||
|
||||
# ... existing code ...
|
||||
"""
|
||||
|
||||
def extract_narration_from_shorts_script(script: str) -> str:
|
||||
"""
|
||||
Extract and optimize narration from the script for Shorts format.
|
||||
Ensures narration is concise, valuable, and properly timed.
|
||||
"""
|
||||
scenes = re.split(r'\n\n+', script)
|
||||
narration_lines = []
|
||||
total_words = 0
|
||||
max_words = 75 # Target for 30-second video (2.5 words per second)
|
||||
|
||||
# Extract all potential narration lines first
|
||||
potential_lines = []
|
||||
for scene in scenes:
|
||||
match = re.search(r'Audio/Voiceover:\s*(.*)', scene)
|
||||
if match:
|
||||
narration = match.group(1).strip()
|
||||
narration = re.split(r'\n[A-Z][^:]+:', narration)[0].strip()
|
||||
if narration:
|
||||
potential_lines.append(narration)
|
||||
|
||||
# Process lines to create engaging narration
|
||||
if potential_lines:
|
||||
# Start with a hook
|
||||
first_line = potential_lines[0]
|
||||
if not any(word in first_line.lower() for word in ['discover', 'learn', 'find out', 'see how', 'watch']):
|
||||
first_line = f"Discover how to {first_line.lower()}"
|
||||
narration_lines.append(first_line)
|
||||
total_words += len(first_line.split())
|
||||
|
||||
# Process middle lines
|
||||
for line in potential_lines[1:-1]:
|
||||
# Add value-focused phrases
|
||||
if not any(word in line.lower() for word in ['because', 'why', 'how', 'what', 'when', 'where']):
|
||||
line = f"Here's why: {line}"
|
||||
|
||||
# Check word count
|
||||
words = line.split()
|
||||
if total_words + len(words) <= max_words:
|
||||
narration_lines.append(line)
|
||||
total_words += len(words)
|
||||
else:
|
||||
break
|
||||
|
||||
# Add a strong closing
|
||||
if len(potential_lines) > 1:
|
||||
last_line = potential_lines[-1]
|
||||
if not any(phrase in last_line.lower() for phrase in ['try it', 'get started', 'follow for more']):
|
||||
last_line = f"Ready to try it? {last_line}"
|
||||
if total_words + len(last_line.split()) <= max_words:
|
||||
narration_lines.append(last_line)
|
||||
|
||||
# If we have too few words, add a call to action
|
||||
if total_words < 50 and narration_lines:
|
||||
cta = "Follow for more tips like this!"
|
||||
if total_words + len(cta.split()) <= max_words:
|
||||
narration_lines.append(cta)
|
||||
|
||||
# Join with proper pacing and emphasis
|
||||
final_narration = ' '.join(narration_lines)
|
||||
|
||||
# Add emphasis to key points
|
||||
final_narration = re.sub(r'([.!?])\s+', r'\1\n\n', final_narration) # Add pauses
|
||||
|
||||
return final_narration
|
||||
|
||||
def generate_shorts_narration(script: str, language: str = "en-us", target_duration: int = 30) -> str:
|
||||
"""
|
||||
Generate a clean, natural-sounding narration script for YouTube Shorts.
|
||||
Focuses only on what the listener needs to hear, without technical details.
|
||||
"""
|
||||
# Calculate target word count based on duration and user-defined speaking rate
|
||||
words_per_second = getattr(st.session_state, 'svgen_words_per_second', WORDS_PER_SECOND)
|
||||
narration_padding = getattr(st.session_state, 'svgen_narration_padding', 0.5)
|
||||
target_words = int((target_duration - narration_padding) * words_per_second)
|
||||
|
||||
# Extract key information from the script
|
||||
scenes = re.split(r'\n\n+', script)
|
||||
audio_lines = []
|
||||
|
||||
for scene in scenes:
|
||||
# Extract only the audio/voiceover content
|
||||
audio_match = re.search(r'Audio/Voiceover:\s*(.*?)(?=\n|$)', scene)
|
||||
if audio_match:
|
||||
audio_lines.append(audio_match.group(1).strip())
|
||||
|
||||
# Create a specialized prompt for clean narration generation
|
||||
narration_prompt = f"""
|
||||
Create a natural, conversational narration script for a YouTube Shorts video.
|
||||
Focus ONLY on what the listener needs to hear - no technical details, scene descriptions, or timing markers.
|
||||
|
||||
Content Context:
|
||||
{script}
|
||||
|
||||
Requirements:
|
||||
1. Length: {target_duration} seconds (approximately {target_words} words)
|
||||
2. Style: Natural, conversational, and engaging
|
||||
3. Structure:
|
||||
- Start with a hook
|
||||
- Present key points
|
||||
- End with a call to action
|
||||
4. Tone: {st.session_state.svgen_content_style.lower()}
|
||||
|
||||
Important Guidelines:
|
||||
- Write ONLY the spoken words - no descriptions, timing, or technical details
|
||||
- Use natural language that sounds good when spoken
|
||||
- Keep sentences short and clear
|
||||
- Add natural pauses with ellipsis (...)
|
||||
- No scene numbers, timing markers, or technical instructions
|
||||
- No sound effect descriptions or music cues
|
||||
- No formatting markers or special characters
|
||||
- Target word count: {target_words} words (±10%)
|
||||
- Speaking rate: {words_per_second} words per second
|
||||
|
||||
Example of good narration:
|
||||
"Writer's block got you down? Meet your new secret weapon: an AI content writer! This tool helps you write ten times faster. No more blank page terror! Blog posts, social media, even killer emails - all generated in seconds. Ready to unleash your content creation superpowers? Try it free today!"
|
||||
|
||||
Format the narration as a single, flowing script with natural pauses.
|
||||
"""
|
||||
|
||||
try:
|
||||
# Generate narration using LLM
|
||||
narration = llm_text_gen(narration_prompt)
|
||||
if narration:
|
||||
# Clean up the narration
|
||||
narration = re.sub(r'\s+', ' ', narration) # Remove extra spaces
|
||||
narration = re.sub(r'[^\w\s.,!?…-]', '', narration) # Keep only essential punctuation
|
||||
narration = re.sub(r'([.!?])\s+', r'\1\n\n', narration) # Add natural pauses
|
||||
narration = re.sub(r'\*\*.*?\*\*', '', narration) # Remove any markdown
|
||||
narration = re.sub(r'\(.*?\)', '', narration) # Remove any parenthetical notes
|
||||
narration = re.sub(r'\n\s*\n', '\n\n', narration) # Clean up extra line breaks
|
||||
|
||||
# Verify word count
|
||||
word_count = len(narration.split())
|
||||
if word_count < target_words * 0.9 or word_count > target_words * 1.1:
|
||||
print(f'[WARNING] Generated narration word count ({word_count}) is outside target range ({target_words}±10%)')
|
||||
|
||||
return narration.strip()
|
||||
except Exception as e:
|
||||
print(f'[ERROR] Failed to generate narration: {e}')
|
||||
return None
|
||||
|
||||
def write_yt_shorts_video():
|
||||
"""
|
||||
Main function to generate a YouTube Shorts video.
|
||||
This function provides a Streamlit interface for users to generate Shorts videos.
|
||||
"""
|
||||
st.markdown("""
|
||||
<style>
|
||||
.stepper {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
margin-bottom: 2rem;
|
||||
}
|
||||
.step {
|
||||
flex: 1;
|
||||
text-align: center;
|
||||
padding: 0.5rem 0;
|
||||
border-bottom: 4px solid #e0e0e0;
|
||||
color: #888;
|
||||
font-weight: 600;
|
||||
font-size: 1.1rem;
|
||||
}
|
||||
.step.active {
|
||||
color: #2563eb;
|
||||
border-bottom: 4px solid #2563eb;
|
||||
background: #f0f6ff;
|
||||
border-radius: 8px 8px 0 0;
|
||||
}
|
||||
.card {
|
||||
background: #f8fafc;
|
||||
border-radius: 12px;
|
||||
box-shadow: 0 2px 8px rgba(0,0,0,0.04);
|
||||
padding: 2rem 2rem 1.5rem 2rem;
|
||||
margin-bottom: 2rem;
|
||||
}
|
||||
.section-title {
|
||||
font-size: 1.3rem;
|
||||
font-weight: 700;
|
||||
margin-bottom: 1rem;
|
||||
color: #222;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
}
|
||||
.section-title svg {
|
||||
margin-right: 0.5rem;
|
||||
}
|
||||
.primary-btn {
|
||||
background: #2563eb;
|
||||
color: #fff;
|
||||
border-radius: 8px;
|
||||
font-size: 1.1rem;
|
||||
font-weight: 600;
|
||||
padding: 0.75rem 2.5rem;
|
||||
border: none;
|
||||
margin-top: 1.5rem;
|
||||
margin-bottom: 0.5rem;
|
||||
box-shadow: 0 2px 8px rgba(37,99,235,0.08);
|
||||
}
|
||||
</style>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Stepper logic
|
||||
if 'shorts_stage' not in st.session_state:
|
||||
st.session_state.shorts_stage = 1
|
||||
if 'generated_script' not in st.session_state:
|
||||
st.session_state.generated_script = None
|
||||
if 'script_approved' not in st.session_state:
|
||||
st.session_state.script_approved = False
|
||||
|
||||
# Stepper UI
|
||||
st.markdown(f'''
|
||||
<div class="stepper">
|
||||
<div class="step {'active' if st.session_state.shorts_stage == 1 else ''}">1. Input Details</div>
|
||||
<div class="step {'active' if st.session_state.shorts_stage == 2 else ''}">2. Script Review</div>
|
||||
<div class="step {'active' if st.session_state.shorts_stage == 3 else ''}">3. Video Generation</div>
|
||||
</div>
|
||||
''', unsafe_allow_html=True)
|
||||
|
||||
# --- Stage 1: Input Details ---
|
||||
if st.session_state.shorts_stage == 1:
|
||||
print('[DEBUG] Stage 1: Input Details loaded')
|
||||
st.markdown('---')
|
||||
st.markdown('### 1️⃣ Input Video Details')
|
||||
st.info("Fill in all details below, then click **Generate Script** to continue.")
|
||||
with st.container():
|
||||
st.markdown('<div class="card">', unsafe_allow_html=True)
|
||||
st.markdown('<div class="section-title">📝 Video Content</div>', unsafe_allow_html=True)
|
||||
video_topic = st.text_input(
|
||||
"What's your video about?",
|
||||
placeholder="Enter the main topic or theme of your Shorts video",
|
||||
help="Be specific about what you want to create"
|
||||
)
|
||||
style_col, duration_col = st.columns(2)
|
||||
with style_col:
|
||||
content_style = st.selectbox(
|
||||
"Content Style",
|
||||
list(CONTENT_STYLES.keys()),
|
||||
help="Select the style that best fits your content"
|
||||
)
|
||||
with duration_col:
|
||||
video_duration = st.slider(
|
||||
"Duration (seconds)",
|
||||
MIN_SHORTS_DURATION,
|
||||
MAX_SHORTS_DURATION,
|
||||
DEFAULT_SHORTS_DURATION,
|
||||
help=f"Shorts must be between {MIN_SHORTS_DURATION} and {MAX_SHORTS_DURATION} seconds"
|
||||
)
|
||||
|
||||
# Calculate and display scene count based on duration
|
||||
scene_duration = DEFAULT_DURATION # seconds per scene
|
||||
max_possible_scenes = min(MAX_SCENES, int(video_duration / scene_duration))
|
||||
min_possible_scenes = max(MIN_SCENES, int(video_duration / (scene_duration * 2)))
|
||||
|
||||
scene_count = st.slider(
|
||||
"Number of Scenes",
|
||||
min_possible_scenes,
|
||||
max_possible_scenes,
|
||||
min(max_possible_scenes, 10), # Default to 10 or max possible
|
||||
help=f"Based on {scene_duration}s per scene, you can have {min_possible_scenes}-{max_possible_scenes} scenes"
|
||||
)
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
|
||||
with st.container():
|
||||
settings_col = st.columns(1)[0]
|
||||
with settings_col:
|
||||
with st.expander("⚙️ Video Settings", expanded=True):
|
||||
res_col, trans_col = st.columns(2)
|
||||
with res_col:
|
||||
resolution = st.selectbox(
|
||||
"Resolution",
|
||||
list(VIDEO_RESOLUTIONS.keys()),
|
||||
help="Higher resolution = better quality but longer processing time"
|
||||
)
|
||||
with trans_col:
|
||||
transition_style = st.selectbox(
|
||||
"Transition Style",
|
||||
list(TRANSITION_STYLES.keys()),
|
||||
help="How scenes transition between each other"
|
||||
)
|
||||
|
||||
# Add timing controls
|
||||
st.markdown("---")
|
||||
st.markdown("#### ⏱️ Timing Settings")
|
||||
|
||||
# Scene timing controls
|
||||
timing_col1, timing_col2 = st.columns(2)
|
||||
with timing_col1:
|
||||
scene_duration = st.slider(
|
||||
"Seconds per Scene",
|
||||
min_value=1.0,
|
||||
max_value=5.0,
|
||||
value=DEFAULT_DURATION,
|
||||
step=0.5,
|
||||
help="How long each scene should be displayed"
|
||||
)
|
||||
st.session_state.svgen_scene_duration = scene_duration
|
||||
|
||||
with timing_col2:
|
||||
transition_duration = st.slider(
|
||||
"Transition Duration (seconds)",
|
||||
min_value=0.1,
|
||||
max_value=1.0,
|
||||
value=DEFAULT_TRANSITION_DURATION,
|
||||
step=0.1,
|
||||
help="Duration of transitions between scenes"
|
||||
)
|
||||
st.session_state.svgen_transition_duration = transition_duration
|
||||
|
||||
# Narration timing controls
|
||||
narr_timing_col1, narr_timing_col2 = st.columns(2)
|
||||
with narr_timing_col1:
|
||||
words_per_second = st.slider(
|
||||
"Speaking Rate (words/second)",
|
||||
min_value=1.5,
|
||||
max_value=3.5,
|
||||
value=WORDS_PER_SECOND,
|
||||
step=0.1,
|
||||
help="Adjust narration speed (default: 2.5 words/second)"
|
||||
)
|
||||
st.session_state.svgen_words_per_second = words_per_second
|
||||
|
||||
with narr_timing_col2:
|
||||
narration_padding = st.slider(
|
||||
"Narration Padding (seconds)",
|
||||
min_value=0.0,
|
||||
max_value=2.0,
|
||||
value=0.5,
|
||||
step=0.1,
|
||||
help="Extra time to add to narration duration"
|
||||
)
|
||||
st.session_state.svgen_narration_padding = narration_padding
|
||||
|
||||
# Calculate and display timing information
|
||||
total_scene_time = scene_duration * scene_count
|
||||
total_transition_time = transition_duration * (scene_count - 1)
|
||||
total_video_time = total_scene_time + total_transition_time
|
||||
|
||||
st.info(f"""
|
||||
**Timing Summary:**
|
||||
- Total Scene Time: {total_scene_time:.1f}s
|
||||
- Total Transition Time: {total_transition_time:.1f}s
|
||||
- Estimated Video Duration: {total_video_time:.1f}s
|
||||
- Target Narration Length: {int(total_video_time * words_per_second)} words
|
||||
""")
|
||||
with st.expander("🎙️ Narration Settings", expanded=True):
|
||||
narr_col1, narr_col2 = st.columns(2)
|
||||
with narr_col1:
|
||||
narration_language = st.selectbox(
|
||||
"Language",
|
||||
list(NARRATION_LANGUAGES.keys()),
|
||||
help="Select the language for narration"
|
||||
)
|
||||
with narr_col2:
|
||||
include_music = st.checkbox(
|
||||
"Include Background Music",
|
||||
value=True,
|
||||
help="Add background music to enhance the video"
|
||||
)
|
||||
st.markdown('---')
|
||||
can_generate_script = bool(video_topic and content_style and video_duration and resolution and narration_language)
|
||||
if st.button("📝 Generate Script", key="generate_script_btn", help="Generate a script for your Shorts video", use_container_width=True, disabled=not can_generate_script):
|
||||
print(f'[DEBUG] Generate Script button clicked. Topic: {video_topic}, Style: {content_style}, Duration: {video_duration}, Resolution: {resolution}, Language: {narration_language}')
|
||||
try:
|
||||
with st.spinner("Generating script..."):
|
||||
script = generate_shorts_script(
|
||||
hook_type="Question",
|
||||
main_topic=video_topic,
|
||||
target_audience="general",
|
||||
tone_style=content_style,
|
||||
content_type=CONTENT_STYLES[content_style]["style"],
|
||||
duration_seconds=video_duration,
|
||||
include_captions=True,
|
||||
include_text_overlay=True,
|
||||
include_sound_effects=True,
|
||||
vertical_framing_notes=True,
|
||||
language=narration_language
|
||||
)
|
||||
print(f'[DEBUG] Script generated: {bool(script)}')
|
||||
if script:
|
||||
st.session_state.generated_script = script
|
||||
st.session_state.script_approved = False
|
||||
st.session_state.shorts_stage = 2
|
||||
st.session_state.svgen_resolution = resolution
|
||||
st.session_state.svgen_transition_style = transition_style
|
||||
st.session_state.svgen_narration_language = narration_language
|
||||
st.session_state.svgen_include_music = include_music
|
||||
st.session_state.svgen_content_style = content_style
|
||||
st.session_state.svgen_video_duration = video_duration
|
||||
st.session_state.svgen_video_topic = video_topic
|
||||
print('[DEBUG] Script saved to session state and moving to Stage 2')
|
||||
st.success("Script generated! Review and edit below.")
|
||||
else:
|
||||
print('[ERROR] Script generation failed')
|
||||
st.error("Failed to generate script. Please try again.")
|
||||
except Exception as e:
|
||||
print(f'[ERROR] Exception during script generation: {e}')
|
||||
st.error(f"An error occurred while generating the script: {str(e)}")
|
||||
logger.error(f"Error in script generation: {str(e)}")
|
||||
logger.error(traceback.format_exc())
|
||||
if not can_generate_script:
|
||||
st.warning("Please fill in all required fields above to enable script generation.")
|
||||
st.markdown('---')
|
||||
st.info("Next: Review and edit your script.")
|
||||
|
||||
# --- Stage 2: Script Review & Edit ---
|
||||
if st.session_state.shorts_stage == 2:
|
||||
print('[DEBUG] Stage 2: Script Review & Edit loaded')
|
||||
st.markdown('---')
|
||||
st.markdown('### 2️⃣ Script Review & Edit')
|
||||
st.info("Review your generated script. Use the Edit tab to make changes. Approve to continue.")
|
||||
st.markdown('<div class="card">', unsafe_allow_html=True)
|
||||
st.markdown('<div class="section-title">📄 Script Preview & Edit</div>', unsafe_allow_html=True)
|
||||
preview_tab, edit_tab = st.tabs(["Preview", "Edit"])
|
||||
with preview_tab:
|
||||
st.markdown(st.session_state.generated_script)
|
||||
if not st.session_state.script_approved:
|
||||
if st.button("✅ Approve Script", key="approve_script_btn", use_container_width=True):
|
||||
st.session_state.script_approved = True
|
||||
print('[DEBUG] Script approved by user')
|
||||
st.success("Script approved! You can now generate your video.")
|
||||
with edit_tab:
|
||||
edited_script = st.text_area(
|
||||
"Edit Script",
|
||||
value=st.session_state.generated_script,
|
||||
height=400,
|
||||
help="Make any necessary changes to the script. The format should be maintained."
|
||||
)
|
||||
if edited_script != st.session_state.generated_script:
|
||||
print('[DEBUG] Script edited by user')
|
||||
st.session_state.generated_script = edited_script
|
||||
st.session_state.script_approved = False
|
||||
st.info("Script updated. Please review and approve the changes.")
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
st.markdown('---')
|
||||
st.button("⬅️ Back to Details", key="back_to_details_btn", use_container_width=True, on_click=lambda: st.session_state.update({'shorts_stage': 1}))
|
||||
if st.session_state.script_approved:
|
||||
st.success("Script approved! You can now generate your video.")
|
||||
st.button("🎬 Proceed to Video Generation", key="proceed_to_video_btn", use_container_width=True, on_click=lambda: st.session_state.update({'shorts_stage': 3}))
|
||||
else:
|
||||
st.warning("Please approve your script before proceeding.")
|
||||
st.markdown('---')
|
||||
st.info("Next: Review and edit narration, then generate your video.")
|
||||
|
||||
# --- Stage 3: Video Generation ---
|
||||
if st.session_state.shorts_stage == 3:
|
||||
print('[DEBUG] Stage 3: Narration & Video Generation loaded')
|
||||
st.markdown('---')
|
||||
st.markdown('### 3️⃣ Narration & Video Generation')
|
||||
st.info("Edit or generate narration, preview audio, then click **Generate Video**.")
|
||||
st.markdown('<div class="card">', unsafe_allow_html=True)
|
||||
st.markdown('<div class="section-title">🗣️ Narration for Review & Edit</div>', unsafe_allow_html=True)
|
||||
narr_col1, narr_col2 = st.columns([4, 1])
|
||||
with narr_col1:
|
||||
if 'editable_narration' not in st.session_state:
|
||||
st.session_state.editable_narration = generate_shorts_narration(
|
||||
st.session_state.generated_script,
|
||||
language=st.session_state.svgen_narration_language,
|
||||
target_duration=st.session_state.svgen_video_duration
|
||||
)
|
||||
print('[DEBUG] Initial narration generated')
|
||||
|
||||
edited_narration = st.text_area(
|
||||
"Edit narration to be used for TTS:",
|
||||
value=st.session_state.editable_narration,
|
||||
height=120,
|
||||
key="editable_narration_area",
|
||||
help="Edit the narration to sound natural when spoken. No technical details needed."
|
||||
)
|
||||
st.session_state.editable_narration = edited_narration
|
||||
|
||||
# Calculate and display timing information
|
||||
narration_word_count = len(edited_narration.split())
|
||||
words_per_second = 2.5 # Standard speaking rate
|
||||
estimated_duration = narration_word_count / words_per_second
|
||||
|
||||
narration_stats = (
|
||||
f"Words: {narration_word_count} | "
|
||||
f"Est. duration: {estimated_duration:.1f}s | "
|
||||
f"Target: {st.session_state.svgen_video_duration}s"
|
||||
)
|
||||
st.caption(narration_stats)
|
||||
|
||||
# Display timing warnings
|
||||
if estimated_duration < 20:
|
||||
st.warning("⚠️ Narration is too short for a 30-second video. Consider generating a new narration.")
|
||||
elif estimated_duration > 35:
|
||||
st.warning("⚠️ Narration is too long for a 30-second video. Consider generating a new narration.")
|
||||
|
||||
# Narration Tips in an expander
|
||||
with st.expander("💡 Narration Tips", expanded=False):
|
||||
st.markdown("""
|
||||
### Tips for Natural Narration
|
||||
|
||||
- Write only what should be spoken
|
||||
- Keep it conversational and clear
|
||||
- Use natural pauses (...)
|
||||
- Focus on the message, not the technical details
|
||||
- End with a clear call to action
|
||||
""")
|
||||
|
||||
tts_col1, tts_col2 = st.columns(2)
|
||||
with tts_col1:
|
||||
tts_gender = st.selectbox("Voice Gender (affects some TTS engines)", ["Default", "Female", "Male"], key="tts_gender_select")
|
||||
with tts_col2:
|
||||
tts_speed = st.selectbox("Speech Speed", ["Normal", "Slow"], key="tts_speed_select")
|
||||
if st.button("🔊 Preview Narration Audio", key="preview_tts_btn"):
|
||||
print('[DEBUG] TTS preview button clicked')
|
||||
try:
|
||||
tts_kwargs = {"lang": NARRATION_LANGUAGES[st.session_state.svgen_narration_language]}
|
||||
tts_kwargs["slow"] = tts_speed == "Slow"
|
||||
tts = gTTS(text=edited_narration, **tts_kwargs)
|
||||
preview_audio_path = os.path.join(tempfile.gettempdir(), f"tts_preview_{os.getpid()}.mp3")
|
||||
tts.save(preview_audio_path)
|
||||
with open(preview_audio_path, "rb") as audio_file:
|
||||
audio_bytes = audio_file.read()
|
||||
st.audio(audio_bytes, format="audio/mp3")
|
||||
print('[DEBUG] TTS preview audio generated and played')
|
||||
except Exception as tts_err:
|
||||
print(f'[ERROR] Failed to generate TTS preview: {tts_err}')
|
||||
st.error(f"Failed to generate TTS preview: {tts_err}")
|
||||
if narration_word_count < 10:
|
||||
st.warning("Narration is very short. Consider adding more detail.")
|
||||
elif narration_word_count > 120:
|
||||
st.warning("Narration is quite long. Consider shortening for Shorts.")
|
||||
with narr_col2:
|
||||
if st.button("🔄 Generate New Narration", key="generate_narration_btn"):
|
||||
with st.spinner("Generating engaging narration..."):
|
||||
new_narration = generate_shorts_narration(
|
||||
st.session_state.generated_script,
|
||||
language=st.session_state.svgen_narration_language,
|
||||
target_duration=st.session_state.svgen_video_duration
|
||||
)
|
||||
if new_narration:
|
||||
st.session_state.editable_narration = new_narration
|
||||
print('[DEBUG] New narration generated')
|
||||
st.success("New narration generated successfully!")
|
||||
st.rerun()
|
||||
else:
|
||||
st.error("Failed to generate narration. Please try again.")
|
||||
|
||||
if st.button("🤖 Generate AI Narration", key="ai_narration_btn"):
|
||||
with st.spinner("Generating AI-optimized narration..."):
|
||||
ai_narr = generate_shorts_narration(
|
||||
st.session_state.generated_script,
|
||||
language=st.session_state.svgen_narration_language,
|
||||
target_duration=st.session_state.svgen_video_duration
|
||||
)
|
||||
if ai_narr:
|
||||
st.session_state.editable_narration = ai_narr
|
||||
print('[DEBUG] AI-generated narration updated')
|
||||
st.success("AI-generated narration updated.")
|
||||
st.rerun()
|
||||
else:
|
||||
st.error("Failed to generate AI narration. Please try again.")
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
st.markdown('---')
|
||||
st.markdown('### 3️⃣ Video Generation')
|
||||
st.info("Click **Generate Video** to start the final process. This may take a few minutes.")
|
||||
st.markdown('<div class="card">', unsafe_allow_html=True)
|
||||
st.markdown('<div class="section-title"> Video Generation</div>', unsafe_allow_html=True)
|
||||
|
||||
# Video Information in an expander
|
||||
with st.expander("📋 Video Information", expanded=True):
|
||||
st.markdown("""
|
||||
### Video Details
|
||||
| Setting | Value |
|
||||
|---------|--------|
|
||||
| Video Topic | {} |
|
||||
| Content Style | {} |
|
||||
| Duration | {} seconds |
|
||||
| Resolution | {} |
|
||||
| Narration Language | {} |
|
||||
| Background Music | {} |
|
||||
""".format(
|
||||
st.session_state.svgen_video_topic,
|
||||
st.session_state.svgen_content_style,
|
||||
st.session_state.svgen_video_duration,
|
||||
st.session_state.svgen_resolution,
|
||||
st.session_state.svgen_narration_language,
|
||||
"Yes" if st.session_state.svgen_include_music else "No"
|
||||
))
|
||||
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
st.markdown('<div style="text-align:center">', unsafe_allow_html=True)
|
||||
st.button("⬅️ Back to Script Review", key="back_to_script_btn", use_container_width=True, on_click=lambda: st.session_state.update({'shorts_stage': 2}))
|
||||
if st.button("🚀 Generate Video", key="generate_video_btn", use_container_width=True):
|
||||
print('[DEBUG] Generate Video button clicked')
|
||||
try:
|
||||
with st.spinner("Generating your Shorts video..."):
|
||||
st.info("Step 1/3: Generating images...")
|
||||
image_paths = []
|
||||
temp_dir = Path(tempfile.mkdtemp())
|
||||
# Filter out empty scenes and limit to MAX_SCENES
|
||||
scenes = [s.strip() for s in st.session_state.generated_script.split("\n\n") if s.strip()][:MAX_SCENES]
|
||||
resolution = st.session_state.svgen_resolution
|
||||
narration_language = st.session_state.svgen_narration_language
|
||||
scene_count = 0
|
||||
num_scenes_total = len(scenes)
|
||||
progress_bar = st.progress(0.0)
|
||||
status_text = st.empty()
|
||||
|
||||
# Initialize or load image cache
|
||||
if 'generated_image_paths' not in st.session_state:
|
||||
st.session_state.generated_image_paths = {}
|
||||
generated_image_paths = st.session_state.generated_image_paths
|
||||
|
||||
# Clear any invalid cache entries
|
||||
generated_image_paths = {k: v for k, v in generated_image_paths.items()
|
||||
if os.path.exists(v) and k < num_scenes_total}
|
||||
st.session_state.generated_image_paths = generated_image_paths
|
||||
|
||||
preview_container = st.container()
|
||||
preview_thumbnails = []
|
||||
|
||||
def retry_on_error(max_retries=3, initial_delay=1, max_delay=10):
|
||||
def decorator(func):
|
||||
@functools.wraps(func)
|
||||
def wrapper(*args, **kwargs):
|
||||
delay = initial_delay
|
||||
for attempt in range(max_retries):
|
||||
try:
|
||||
return func(*args, **kwargs)
|
||||
except Exception as e:
|
||||
if attempt == max_retries - 1:
|
||||
raise
|
||||
print(f'[WARN] Retry {attempt+1}/{max_retries} for image generation: {e}')
|
||||
time.sleep(delay)
|
||||
delay = min(delay * 2, max_delay)
|
||||
return None
|
||||
return wrapper
|
||||
return decorator
|
||||
|
||||
@retry_on_error(max_retries=3, initial_delay=2, max_delay=10)
|
||||
def safe_generate_image(prompt):
|
||||
return generate_image(prompt)
|
||||
|
||||
for i, scene in enumerate(scenes):
|
||||
print(f'[DEBUG] Processing scene {i+1}/{num_scenes_total}')
|
||||
status_text.text(f"Generating image for scene {i+1}/{num_scenes_total}...")
|
||||
|
||||
# Check cache first
|
||||
if i in generated_image_paths:
|
||||
image_paths.append(generated_image_paths[i])
|
||||
preview_thumbnails.append((generated_image_paths[i], i+1))
|
||||
print(f'[DEBUG] Using cached image for scene {i+1}')
|
||||
scene_count += 1
|
||||
progress_bar.progress(scene_count / num_scenes_total)
|
||||
continue
|
||||
|
||||
# Extract details for a more specific prompt
|
||||
visual_desc = scene.split("Visual Instructions:")[1].split("\n")[0] if "Visual Instructions:" in scene else scene
|
||||
narration_match = re.search(r'Audio/Voiceover:\s*(.*)', scene)
|
||||
narration_line = narration_match.group(1).strip() if narration_match else ""
|
||||
|
||||
# Enhanced prompt with more specific details and style guidance
|
||||
prompt = (
|
||||
f"Create a vertical (9:16) image for YouTube Shorts video.\n"
|
||||
f"Scene {i+1} of {num_scenes_total}:\n"
|
||||
f"Visual Description: {visual_desc}\n"
|
||||
f"Context: {narration_line}\n"
|
||||
f"Style Requirements:\n"
|
||||
f"- High contrast and vibrant colors for better mobile viewing\n"
|
||||
f"- Clear focal point in the center for vertical format\n"
|
||||
f"- Professional quality, cinematic lighting\n"
|
||||
f"- Text-safe areas on top and bottom\n"
|
||||
f"- Visually distinct from other scenes\n"
|
||||
f"- Modern, engaging composition\n"
|
||||
f"- Suitable for {st.session_state.svgen_content_style} style content\n"
|
||||
f"Technical Requirements:\n"
|
||||
f"- Vertical 9:16 aspect ratio\n"
|
||||
f"- High resolution, sharp details\n"
|
||||
f"- No text or watermarks\n"
|
||||
f"- No blurry or low-quality elements"
|
||||
)
|
||||
|
||||
try:
|
||||
image_path = safe_generate_image(prompt)
|
||||
if image_path:
|
||||
img = Image.open(image_path)
|
||||
target_size = VIDEO_RESOLUTIONS[resolution]
|
||||
img = img.resize(target_size, Image.LANCZOS)
|
||||
resized_path = temp_dir / f"scene_{i}.png"
|
||||
img.save(resized_path)
|
||||
image_paths.append(str(resized_path))
|
||||
generated_image_paths[i] = str(resized_path)
|
||||
st.session_state.generated_image_paths = generated_image_paths
|
||||
preview_thumbnails.append((str(resized_path), i+1))
|
||||
print(f'[DEBUG] Generated and cached new image for scene {i+1}')
|
||||
else:
|
||||
print(f'[ERROR] Image generation failed for scene {i+1}')
|
||||
st.warning(f"Image generation failed for scene {i+1}. Skipping.")
|
||||
except Exception as img_err:
|
||||
print(f'[ERROR] Exception during image generation for scene {i+1}: {img_err}')
|
||||
st.warning(f"Error generating image for scene {i+1}: {img_err}")
|
||||
|
||||
scene_count += 1
|
||||
progress_bar.progress(scene_count / num_scenes_total)
|
||||
|
||||
# Update preview after each image
|
||||
with preview_container:
|
||||
preview_container.empty() # Clear previous preview
|
||||
if preview_thumbnails:
|
||||
# Create a grid layout with 5 columns
|
||||
cols = st.columns(5)
|
||||
|
||||
# Display thumbnails in a grid
|
||||
for idx, (img_path, sc_num) in enumerate(preview_thumbnails):
|
||||
with cols[idx % 5]:
|
||||
# Create a smaller thumbnail
|
||||
img = Image.open(img_path)
|
||||
# Calculate aspect ratio to maintain 9:16
|
||||
target_width = 100 # Smaller width
|
||||
target_height = int(target_width * (16/9))
|
||||
img = img.resize((target_width, target_height), Image.LANCZOS)
|
||||
|
||||
# Display with a compact caption
|
||||
st.image(
|
||||
img,
|
||||
caption=f"Scene {sc_num}",
|
||||
use_column_width=True,
|
||||
key=f"preview_{sc_num}" # Add unique key for each image
|
||||
)
|
||||
|
||||
# Add a small progress indicator
|
||||
if idx == len(preview_thumbnails) - 1:
|
||||
st.caption(f"Generating scene {scene_count + 1}...")
|
||||
|
||||
# Add a clear divider between preview and next steps
|
||||
st.markdown("---")
|
||||
status_text.text("Image generation complete!")
|
||||
print(f'[DEBUG] Image generation complete. Total images: {len(image_paths)}')
|
||||
if not image_paths:
|
||||
print('[ERROR] No images generated')
|
||||
st.error("Failed to generate images. Please try again.")
|
||||
return
|
||||
st.info("Step 2/3: Generating narration...")
|
||||
narration_path = temp_dir / "narration.mp3"
|
||||
narration_text = st.session_state.editable_narration
|
||||
try:
|
||||
tts = gTTS(text=narration_text, lang=NARRATION_LANGUAGES[narration_language])
|
||||
tts.save(str(narration_path))
|
||||
print('[DEBUG] Narration audio generated and saved')
|
||||
|
||||
# Verify the audio file was created and is valid
|
||||
if not os.path.exists(str(narration_path)):
|
||||
raise Exception("Narration audio file was not created")
|
||||
|
||||
# Test the audio file by loading it
|
||||
test_audio = AudioFileClip(str(narration_path))
|
||||
if test_audio.duration <= 0:
|
||||
raise Exception("Generated audio file is invalid or empty")
|
||||
test_audio.close()
|
||||
|
||||
except Exception as tts_err:
|
||||
print(f'[ERROR] Failed to generate narration: {tts_err}')
|
||||
st.error(f"Failed to generate narration: {tts_err}")
|
||||
return
|
||||
|
||||
st.info("Step 3/3: Creating video...")
|
||||
video_generator = StoryVideoGenerator()
|
||||
try:
|
||||
# Verify audio file exists before video creation
|
||||
if not os.path.exists(str(narration_path)):
|
||||
raise Exception("Narration audio file not found")
|
||||
|
||||
video_path = video_generator.create_video(
|
||||
image_paths=image_paths,
|
||||
audio_path=str(narration_path),
|
||||
fps=DEFAULT_FPS,
|
||||
duration_per_image=getattr(st.session_state, 'svgen_scene_duration', DEFAULT_DURATION)
|
||||
)
|
||||
if video_path and os.path.exists(video_path):
|
||||
print(f'[DEBUG] Video generated at {video_path}')
|
||||
st.success("✨ Video generated successfully! Preview below and download your video.")
|
||||
st.video(video_path)
|
||||
safe_topic = re.sub(r'[^\w\-]+', '_', st.session_state.svgen_video_topic)
|
||||
download_filename = f"{safe_topic}_shorts_video.mp4"
|
||||
with open(video_path, "rb") as f:
|
||||
video_bytes = f.read()
|
||||
st.download_button(
|
||||
label="⬇️ Download Video",
|
||||
data=video_bytes,
|
||||
file_name=download_filename,
|
||||
mime="video/mp4"
|
||||
)
|
||||
else:
|
||||
print('[ERROR] Video file not found after generation')
|
||||
st.error("Failed to create video. Please try again.")
|
||||
except Exception as vid_err:
|
||||
print(f'[ERROR] Exception during video creation: {vid_err}')
|
||||
st.error(f"An error occurred while creating the video: {vid_err}")
|
||||
logger.error(f"Error in video generation: {vid_err}")
|
||||
logger.error(traceback.format_exc())
|
||||
except Exception as e:
|
||||
print(f'[ERROR] Exception during full video generation: {e}')
|
||||
st.error(f"An error occurred while generating the video: {str(e)}")
|
||||
logger.error(f"Error in video generation: {str(e)}")
|
||||
logger.error(traceback.format_exc())
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
st.markdown('---')
|
||||
st.info("All done! You can download your video above or go back to make changes.")
|
||||
@@ -1,406 +0,0 @@
|
||||
"""
|
||||
YouTube Tags Generator Module
|
||||
|
||||
This module provides functionality for generating and optimizing YouTube video tags.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import time
|
||||
import logging
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
from pytrends.request import TrendReq
|
||||
import pandas as pd
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
logger = logging.getLogger('youtube_tags_generator')
|
||||
|
||||
def get_pytrends_data(keyword):
|
||||
"""Get trending data using PyTrends with simplified, reliable approach."""
|
||||
logger.info(f"Getting PyTrends data for: '{keyword}'")
|
||||
|
||||
# Initialize empty results
|
||||
results = {
|
||||
'topics': [],
|
||||
'queries': [],
|
||||
'trending': []
|
||||
}
|
||||
|
||||
try:
|
||||
# Initialize PyTrends with minimal configuration
|
||||
pytrends = TrendReq(hl='en-US', tz=360)
|
||||
time.sleep(1) # Basic rate limiting
|
||||
|
||||
# 1. Get suggestions (most reliable method)
|
||||
try:
|
||||
suggestions = pytrends.suggestions(keyword)
|
||||
if suggestions:
|
||||
results['trending'] = [sugg['title'] for sugg in suggestions if sugg['title']][:3]
|
||||
except Exception as e:
|
||||
logger.warning(f"Error getting suggestions: {str(e)}")
|
||||
|
||||
# 2. Get trending searches as backup
|
||||
if not results['trending']:
|
||||
try:
|
||||
trending = pytrends.trending_searches(pn='united_states')
|
||||
if not trending.empty:
|
||||
results['trending'] = trending.head(3).values.tolist()
|
||||
except Exception as e:
|
||||
logger.warning(f"Error getting trending searches: {str(e)}")
|
||||
|
||||
# 3. Use keyword variations as fallback
|
||||
if not any(results.values()):
|
||||
results['trending'] = [keyword]
|
||||
results['queries'] = [keyword.lower(), keyword.title()]
|
||||
results['topics'] = [keyword.capitalize()]
|
||||
|
||||
return results
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in PyTrends: {str(e)}")
|
||||
# Return basic keyword variations as fallback
|
||||
return {
|
||||
'topics': [keyword.capitalize()],
|
||||
'queries': [keyword.lower()],
|
||||
'trending': [keyword]
|
||||
}
|
||||
|
||||
def get_comprehensive_trends(title, description):
|
||||
"""Get trending data from title and description keywords."""
|
||||
logger.info(f"Getting comprehensive trends for title: '{title}'")
|
||||
|
||||
# Extract main keywords (only words longer than 3 chars)
|
||||
words = [w for w in title.split() if len(w) > 3]
|
||||
if description:
|
||||
desc_words = [w for w in description.split() if len(w) > 3]
|
||||
words.extend(desc_words)
|
||||
|
||||
# Remove duplicates and limit to 2 keywords to prevent rate limiting
|
||||
keywords = list(dict.fromkeys(words))[:2]
|
||||
|
||||
# Get trending data for main keywords
|
||||
all_trends = {
|
||||
'topics': [],
|
||||
'queries': [],
|
||||
'trending': []
|
||||
}
|
||||
|
||||
for keyword in keywords:
|
||||
try:
|
||||
trends = get_pytrends_data(keyword)
|
||||
for key in all_trends:
|
||||
if trends[key]:
|
||||
all_trends[key].extend(trends[key])
|
||||
time.sleep(1) # Rate limiting between keywords
|
||||
except Exception as e:
|
||||
logger.warning(f"Error getting trends for keyword '{keyword}': {str(e)}")
|
||||
continue
|
||||
|
||||
# Remove duplicates while preserving order
|
||||
for key in all_trends:
|
||||
seen = set()
|
||||
all_trends[key] = [x for x in all_trends[key] if x and not (x.lower() in seen or seen.add(x.lower()))][:5]
|
||||
|
||||
return all_trends
|
||||
|
||||
def generate_tags_from_title_description(title, description, num_tags=10):
|
||||
"""Generate relevant tags from video title, description, and trending data."""
|
||||
logger.info(f"Generating tags for title: '{title}'")
|
||||
|
||||
# Get comprehensive trending data
|
||||
trends = get_comprehensive_trends(title, description)
|
||||
|
||||
# Create a comprehensive context for GPT
|
||||
trend_context = f"""
|
||||
Related Topics: {', '.join(trends['topics'][:10])}
|
||||
Related Queries: {', '.join(trends['queries'][:10])}
|
||||
Trending Suggestions: {', '.join(trends['trending'][:10])}
|
||||
"""
|
||||
|
||||
system_prompt = """You are a YouTube SEO expert specializing in tag optimization.
|
||||
Generate relevant, searchable tags based on the video title, description, and trending data provided.
|
||||
Focus on a mix of specific and broad tags that will help with video discovery.
|
||||
Consider the trending topics and queries provided to maximize searchability.
|
||||
Return only the tags, separated by commas."""
|
||||
|
||||
user_prompt = f"""Generate {num_tags} relevant YouTube tags for a video with:
|
||||
Title: {title}
|
||||
Description: {description}
|
||||
|
||||
Consider this trending data:
|
||||
{trend_context}
|
||||
|
||||
Include a mix of:
|
||||
- Exact match phrases from title and description
|
||||
- Related trending topics and queries
|
||||
- Broader category tags
|
||||
- Specific niche tags
|
||||
- Popular search variations
|
||||
|
||||
Format: Return only the tags, separated by commas."""
|
||||
|
||||
try:
|
||||
tags = llm_text_gen(user_prompt, system_prompt=system_prompt)
|
||||
generated_tags = [tag.strip() for tag in tags.split(',')]
|
||||
|
||||
# Add some trending tags directly
|
||||
trending_tags = (
|
||||
trends['topics'][:3] + # Top 3 related topics
|
||||
trends['queries'][:3] + # Top 3 related queries
|
||||
trends['trending'][:3] # Top 3 trending suggestions
|
||||
)
|
||||
|
||||
# Combine and remove duplicates
|
||||
all_tags = generated_tags + trending_tags
|
||||
seen = set()
|
||||
final_tags = [tag for tag in all_tags if not (tag.lower() in seen or seen.add(tag.lower()))]
|
||||
|
||||
return final_tags
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating tags: {str(e)}")
|
||||
return []
|
||||
|
||||
def analyze_tags(tags):
|
||||
"""Analyze tags for optimization opportunities."""
|
||||
analysis = {
|
||||
'total_tags': len(tags),
|
||||
'total_characters': sum(len(tag) for tag in tags),
|
||||
'avg_tag_length': sum(len(tag) for tag in tags) / len(tags) if tags else 0,
|
||||
'duplicate_tags': len(tags) - len(set(tags)),
|
||||
'tags_too_long': [tag for tag in tags if len(tag) > 30],
|
||||
'single_word_tags': [tag for tag in tags if len(tag.split()) == 1],
|
||||
'optimization_score': 0
|
||||
}
|
||||
|
||||
# Calculate optimization score (0-100)
|
||||
score = 100
|
||||
if analysis['total_tags'] < 5:
|
||||
score -= 30
|
||||
if analysis['total_characters'] > 500:
|
||||
score -= 20
|
||||
if analysis['duplicate_tags'] > 0:
|
||||
score -= 10 * analysis['duplicate_tags']
|
||||
if len(analysis['tags_too_long']) > 0:
|
||||
score -= 5 * len(analysis['tags_too_long'])
|
||||
if len(analysis['single_word_tags']) > len(tags) * 0.5:
|
||||
score -= 15
|
||||
|
||||
analysis['optimization_score'] = max(0, score)
|
||||
return analysis
|
||||
|
||||
def display_tags(tags):
|
||||
"""Display tags in a visually appealing format."""
|
||||
if not tags:
|
||||
return
|
||||
|
||||
# Create a container for all tags
|
||||
st.markdown("""
|
||||
<style>
|
||||
.tag-container {
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
gap: 8px;
|
||||
margin-bottom: 16px;
|
||||
padding: 12px;
|
||||
background-color: #f8f9fa;
|
||||
border-radius: 8px;
|
||||
}
|
||||
.tag {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
background-color: #f0f2f6;
|
||||
border-radius: 16px;
|
||||
padding: 6px 12px;
|
||||
font-size: 13px;
|
||||
color: #2c3e50;
|
||||
border: 1px solid #e6e9ef;
|
||||
white-space: nowrap;
|
||||
transition: all 0.2s ease;
|
||||
}
|
||||
.tag:hover {
|
||||
background-color: #e6e9ef;
|
||||
border-color: #d1d5db;
|
||||
transform: translateY(-1px);
|
||||
}
|
||||
</style>
|
||||
<div class="tag-container">
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Display tags
|
||||
for tag in tags:
|
||||
st.markdown(f'<div class="tag">{tag}</div>', unsafe_allow_html=True)
|
||||
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
|
||||
# Display tag count and character count
|
||||
tags_text = ", ".join(tags)
|
||||
char_count = len(tags_text)
|
||||
col1, col2 = st.columns(2)
|
||||
with col1:
|
||||
st.caption(f"Total tags: {len(tags)}")
|
||||
with col2:
|
||||
st.caption(f"Characters: {char_count}/500")
|
||||
|
||||
def write_yt_tags():
|
||||
"""Create a user interface for YouTube Tags Generator."""
|
||||
logger.info("Initializing YouTube Tags Generator UI")
|
||||
st.write("Generate optimized tags for your videos with trending tag suggestions to improve discoverability.")
|
||||
|
||||
# Initialize session state
|
||||
if "generated_tags" not in st.session_state:
|
||||
st.session_state.generated_tags = None
|
||||
if "tag_analysis" not in st.session_state:
|
||||
st.session_state.tag_analysis = None
|
||||
|
||||
# Create tabs for different sections
|
||||
tab1, tab2, tab3 = st.tabs(["Quick Generate", "Advanced Options", "Analysis"])
|
||||
|
||||
with tab1:
|
||||
# Basic information inputs
|
||||
title = st.text_input("Video Title",
|
||||
placeholder="Enter your video title")
|
||||
description = st.text_area("Video Description",
|
||||
placeholder="Enter your video description")
|
||||
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
num_tags = st.number_input("Number of Tags",
|
||||
min_value=5,
|
||||
max_value=30,
|
||||
value=15)
|
||||
|
||||
with col2:
|
||||
include_trending = st.checkbox("Include Trending Suggestions", value=True)
|
||||
|
||||
if st.button("Generate Tags"):
|
||||
if not title:
|
||||
st.error("Please enter a video title.")
|
||||
return
|
||||
|
||||
with st.spinner("Generating tags..."):
|
||||
# Generate tags using the comprehensive method
|
||||
tags = generate_tags_from_title_description(title, description, num_tags)
|
||||
|
||||
if tags:
|
||||
# Analyze tags
|
||||
st.session_state.tag_analysis = analyze_tags(tags)
|
||||
st.session_state.generated_tags = tags
|
||||
|
||||
# Display tags in the new format
|
||||
st.subheader("Generated Tags")
|
||||
display_tags(tags)
|
||||
|
||||
# Add copy button for all tags
|
||||
tags_text = ", ".join(tags)
|
||||
st.text_area("Tags (copy to use)", value=tags_text, height=100)
|
||||
|
||||
# Display character count
|
||||
char_count = len(tags_text)
|
||||
st.info(f"Total characters: {char_count}/500 ({500 - char_count} remaining)")
|
||||
|
||||
# Quick analysis summary
|
||||
col1, col2, col3 = st.columns(3)
|
||||
with col1:
|
||||
st.metric("Number of Tags", len(tags))
|
||||
with col2:
|
||||
st.metric("Optimization Score", f"{st.session_state.tag_analysis['optimization_score']}%")
|
||||
with col3:
|
||||
st.metric("Avg Tag Length", f"{st.session_state.tag_analysis['avg_tag_length']:.1f}")
|
||||
|
||||
# Display trending data summary if enabled
|
||||
if include_trending:
|
||||
st.subheader("Trending Data Used")
|
||||
trends = get_comprehensive_trends(title, description)
|
||||
|
||||
# Create columns for different trend types
|
||||
tcol1, tcol2, tcol3 = st.columns(3)
|
||||
|
||||
with tcol1:
|
||||
st.markdown("##### Related Topics")
|
||||
if trends['topics']:
|
||||
for topic in trends['topics'][:5]:
|
||||
st.markdown(f"• {topic}")
|
||||
else:
|
||||
st.markdown("*No related topics found*")
|
||||
|
||||
with tcol2:
|
||||
st.markdown("##### Related Queries")
|
||||
if trends['queries']:
|
||||
for query in trends['queries'][:5]:
|
||||
st.markdown(f"• {query}")
|
||||
else:
|
||||
st.markdown("*No related queries found*")
|
||||
|
||||
with tcol3:
|
||||
st.markdown("##### Trending Suggestions")
|
||||
if trends['trending']:
|
||||
for trend in trends['trending'][:5]:
|
||||
st.markdown(f"• {trend}")
|
||||
else:
|
||||
st.markdown("*No trending suggestions found*")
|
||||
else:
|
||||
st.error("Failed to generate tags. Please try again.")
|
||||
|
||||
with tab2:
|
||||
st.info("Advanced tag generation options coming soon!")
|
||||
st.markdown("""
|
||||
Future features will include:
|
||||
- Competitor tag analysis
|
||||
- Tag performance tracking
|
||||
- Category-specific tag suggestions
|
||||
- Multi-language tag generation
|
||||
- Tag sets management
|
||||
""")
|
||||
|
||||
with tab3:
|
||||
if st.session_state.tag_analysis:
|
||||
st.subheader("Tag Analysis")
|
||||
|
||||
# Create metrics
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
st.metric("Total Tags", st.session_state.tag_analysis['total_tags'])
|
||||
st.metric("Total Characters", st.session_state.tag_analysis['total_characters'])
|
||||
st.metric("Average Tag Length", f"{st.session_state.tag_analysis['avg_tag_length']:.1f}")
|
||||
|
||||
with col2:
|
||||
st.metric("Duplicate Tags", st.session_state.tag_analysis['duplicate_tags'])
|
||||
st.metric("Single Word Tags", len(st.session_state.tag_analysis['single_word_tags']))
|
||||
st.metric("Tags Too Long", len(st.session_state.tag_analysis['tags_too_long']))
|
||||
|
||||
# Optimization score with color
|
||||
score = st.session_state.tag_analysis['optimization_score']
|
||||
score_color = 'red' if score < 50 else 'orange' if score < 80 else 'green'
|
||||
st.markdown(f"""
|
||||
<div style='background-color: {score_color}; padding: 10px; border-radius: 5px; margin: 10px 0;'>
|
||||
<h3 style='color: white; margin: 0;'>Optimization Score: {score}%</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Optimization suggestions
|
||||
st.subheader("Optimization Suggestions")
|
||||
suggestions = []
|
||||
|
||||
if st.session_state.tag_analysis['total_tags'] < 5:
|
||||
suggestions.append("❌ Add more tags (aim for at least 15)")
|
||||
if st.session_state.tag_analysis['total_characters'] > 500:
|
||||
suggestions.append("❌ Total character count exceeds limit (max 500)")
|
||||
if st.session_state.tag_analysis['duplicate_tags'] > 0:
|
||||
suggestions.append("❌ Remove duplicate tags")
|
||||
if len(st.session_state.tag_analysis['tags_too_long']) > 0:
|
||||
suggestions.append("❌ Some tags are too long (max 30 characters)")
|
||||
if len(st.session_state.tag_analysis['single_word_tags']) > st.session_state.tag_analysis['total_tags'] * 0.5:
|
||||
suggestions.append("❌ Too many single-word tags (use more specific phrases)")
|
||||
|
||||
if not suggestions:
|
||||
st.success("✅ Your tags are well-optimized!")
|
||||
else:
|
||||
for suggestion in suggestions:
|
||||
st.warning(suggestion)
|
||||
else:
|
||||
st.info("Generate tags first to see analysis")
|
||||
@@ -1,622 +0,0 @@
|
||||
"""
|
||||
YouTube Thumbnail Generator Module
|
||||
|
||||
This module provides functionality for generating YouTube video thumbnails.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import time
|
||||
import logging
|
||||
import os
|
||||
import traceback
|
||||
from PIL import Image
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
from lib.gpt_providers.text_to_image_generation.gen_gemini_images import generate_gemini_image, edit_image
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
logger = logging.getLogger('youtube_thumbnail_generator')
|
||||
|
||||
|
||||
def generate_thumbnail_concepts(video_title, video_description, target_audience, content_type, style_preference, num_concepts=3):
|
||||
"""Generate thumbnail concept ideas based on video content."""
|
||||
logger.info(f"Generating thumbnail concepts for: '{video_title}'")
|
||||
logger.info(f"Parameters: target_audience={target_audience}, content_type={content_type}, style_preference={style_preference}, num_concepts={num_concepts}")
|
||||
|
||||
# Create a system prompt for thumbnail concept generation
|
||||
system_prompt = """You are a YouTube thumbnail expert specializing in creating engaging, click-worthy thumbnail concepts.
|
||||
Your task is to generate thumbnail concept ideas based on the provided video information.
|
||||
Focus ONLY on creating concepts that are optimized for YouTube, with proper visual hierarchy, text placement, and emotional triggers.
|
||||
Return ONLY the concept descriptions, without any additional commentary or explanations.
|
||||
Each concept should include:
|
||||
1. A main visual element or scene
|
||||
2. Text placement and content
|
||||
3. Color scheme suggestions
|
||||
4. Emotional trigger or hook
|
||||
5. Brief explanation of why this concept would be effective"""
|
||||
|
||||
# Build the prompt
|
||||
prompt = f"""
|
||||
**Instructions:**
|
||||
|
||||
Please generate {num_concepts} thumbnail concept ideas for a YouTube video with the following information:
|
||||
|
||||
**Video Title:** {video_title}
|
||||
**Video Description:** {video_description}
|
||||
**Target Audience:** {target_audience}
|
||||
**Content Type:** {content_type}
|
||||
**Style Preference:** {style_preference}
|
||||
|
||||
**Specific Instructions:**
|
||||
* Each concept should be clearly separated and numbered.
|
||||
* Focus on creating thumbnails that stand out in search results and recommendations.
|
||||
* Consider the target audience's interests and preferences.
|
||||
* Include specific details about visual elements, text placement, and color schemes.
|
||||
* Explain why each concept would be effective for this specific video.
|
||||
"""
|
||||
|
||||
try:
|
||||
logger.info("Sending request to LLM for thumbnail concepts")
|
||||
response = llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
logger.info(f"Received response from LLM: {len(response)} characters")
|
||||
return response
|
||||
except Exception as err:
|
||||
logger.error(f"Error generating thumbnail concepts: {err}")
|
||||
logger.error(traceback.format_exc())
|
||||
st.error(f"Error: Failed to generate thumbnail concepts: {err}")
|
||||
return None
|
||||
|
||||
|
||||
def generate_thumbnail_design(concept_description, style_preference, aspect_ratio="16:9", keywords=None, style=None, focus=None):
|
||||
"""Generate a thumbnail image based on the concept description."""
|
||||
logger.info(f"Generating thumbnail design for concept: '{concept_description[:50]}...'")
|
||||
logger.info(f"Parameters: style_preference={style_preference}, aspect_ratio={aspect_ratio}, keywords={keywords}, style={style}, focus={focus}")
|
||||
|
||||
# Create a prompt for the image generation
|
||||
image_prompt = f"""
|
||||
Create a YouTube thumbnail image with the following specifications:
|
||||
|
||||
Concept: {concept_description}
|
||||
Style: {style_preference}
|
||||
Aspect Ratio: {aspect_ratio}
|
||||
|
||||
The image should be:
|
||||
- High contrast and visually striking
|
||||
- Suitable for a YouTube thumbnail
|
||||
- Include the specified visual elements and text
|
||||
- Follow the color scheme described
|
||||
- Optimized for small display sizes
|
||||
|
||||
Make sure the text is large and readable, and the main subject is centered and prominent.
|
||||
"""
|
||||
|
||||
try:
|
||||
logger.info("Sending request to Gemini for thumbnail image")
|
||||
# Generate the image using Gemini with enhanced prompt
|
||||
img_path = generate_gemini_image(
|
||||
image_prompt,
|
||||
keywords=keywords,
|
||||
style=style,
|
||||
focus=focus,
|
||||
enhance_prompt=True
|
||||
)
|
||||
logger.info(f"Received image from Gemini: {img_path}")
|
||||
return img_path
|
||||
except Exception as err:
|
||||
logger.error(f"Error generating thumbnail image: {err}")
|
||||
logger.error(traceback.format_exc())
|
||||
st.error(f"Error: Failed to generate thumbnail image: {err}")
|
||||
return None
|
||||
|
||||
|
||||
def edit_thumbnail_image(img_path, edit_instructions):
|
||||
"""Edit a thumbnail image based on user instructions."""
|
||||
logger.info(f"Editing thumbnail image: '{img_path}'")
|
||||
logger.info(f"Edit instructions: '{edit_instructions}'")
|
||||
|
||||
try:
|
||||
logger.info("Sending request to Gemini for image editing")
|
||||
# Edit the image using Gemini
|
||||
edited_img_path = edit_image(img_path, edit_instructions)
|
||||
logger.info(f"Image editing completed. Edited image path: {edited_img_path}")
|
||||
|
||||
# Return the path to the edited image
|
||||
return edited_img_path
|
||||
except Exception as err:
|
||||
logger.error(f"Error editing thumbnail image: {err}")
|
||||
logger.error(traceback.format_exc())
|
||||
st.error(f"Error: Failed to edit thumbnail image: {err}")
|
||||
return None
|
||||
|
||||
|
||||
def analyze_thumbnail(thumbnail_path):
|
||||
"""Analyze a thumbnail for effectiveness."""
|
||||
logger.info(f"Analyzing thumbnail: '{thumbnail_path}'")
|
||||
|
||||
# This would typically involve image analysis, but for now we'll use AI to provide feedback
|
||||
system_prompt = """You are a YouTube thumbnail expert specializing in analyzing and providing feedback on thumbnail designs.
|
||||
Your task is to analyze the thumbnail and provide constructive feedback on its effectiveness.
|
||||
Focus on aspects like visual hierarchy, text readability, emotional impact, and click-worthiness."""
|
||||
|
||||
# For now, we'll just return a placeholder analysis
|
||||
# In a real implementation, we would analyze the actual image
|
||||
logger.info("Generating thumbnail analysis")
|
||||
return """
|
||||
**Thumbnail Analysis:**
|
||||
|
||||
- **Visual Hierarchy:** The main subject is well-positioned and stands out against the background.
|
||||
- **Text Readability:** The text is clear and readable, with good contrast against the background.
|
||||
- **Emotional Impact:** The thumbnail creates curiosity and emotional connection with the target audience.
|
||||
- **Click-worthiness:** The design is likely to attract clicks due to its visual appeal and clear value proposition.
|
||||
|
||||
**Suggestions for Improvement:**
|
||||
- Consider adding a subtle border to make the thumbnail stand out more in search results.
|
||||
- The text could be slightly larger for better readability on mobile devices.
|
||||
- Adding a small icon or logo could help with brand recognition.
|
||||
"""
|
||||
|
||||
|
||||
def parse_concepts(concepts_text):
|
||||
"""Parse the concepts text into a list of individual concepts."""
|
||||
logger.info("Parsing concepts text into individual concepts")
|
||||
|
||||
concept_list = []
|
||||
current_concept = ""
|
||||
|
||||
for line in concepts_text.split('\n'):
|
||||
if line.strip().startswith(('1.', '2.', '3.', '4.', '5.')):
|
||||
if current_concept:
|
||||
concept_list.append(current_concept.strip())
|
||||
current_concept = line
|
||||
else:
|
||||
current_concept += "\n" + line
|
||||
|
||||
if current_concept:
|
||||
concept_list.append(current_concept.strip())
|
||||
|
||||
logger.info(f"Parsed {len(concept_list)} concepts from the response")
|
||||
return concept_list
|
||||
|
||||
|
||||
def write_yt_thumbnail():
|
||||
"""Create a user interface for YouTube Thumbnail Generator."""
|
||||
logger.info("Initializing YouTube Thumbnail Generator UI")
|
||||
st.title("YouTube Thumbnail Generator")
|
||||
st.write("Create engaging, click-worthy thumbnails for your YouTube videos.")
|
||||
|
||||
# Initialize session state for generated thumbnails if it doesn't exist
|
||||
if "generated_thumbnails" not in st.session_state:
|
||||
st.session_state.generated_thumbnails = []
|
||||
if "thumbnail_concepts" not in st.session_state:
|
||||
st.session_state.thumbnail_concepts = None
|
||||
if "current_thumbnail_path" not in st.session_state:
|
||||
st.session_state.current_thumbnail_path = None
|
||||
if "concept_list" not in st.session_state:
|
||||
st.session_state.concept_list = []
|
||||
if "editing_thumbnail" not in st.session_state:
|
||||
st.session_state.editing_thumbnail = False
|
||||
if "edit_instructions" not in st.session_state:
|
||||
st.session_state.edit_instructions = ""
|
||||
if "edited_thumbnail_path" not in st.session_state:
|
||||
st.session_state.edited_thumbnail_path = None
|
||||
if "show_edit_form" not in st.session_state:
|
||||
st.session_state.show_edit_form = False
|
||||
|
||||
# Create tabs for different sections
|
||||
tab1, tab2 = st.tabs(["Basic Info", "Style & Generation"])
|
||||
|
||||
with tab1:
|
||||
# Basic information inputs
|
||||
video_title = st.text_input("Video Title",
|
||||
placeholder="e.g., 10 Tips for Better Photography")
|
||||
video_description = st.text_area("Video Description",
|
||||
placeholder="Brief description of your video content")
|
||||
target_audience = st.text_input("Target Audience",
|
||||
placeholder="e.g., photography enthusiasts, beginners")
|
||||
|
||||
# Content type selection
|
||||
content_type = st.selectbox("Content Type", [
|
||||
"Tutorial/How-to",
|
||||
"Vlog",
|
||||
"Review",
|
||||
"Educational",
|
||||
"Entertainment",
|
||||
"News/Update",
|
||||
"Product Showcase",
|
||||
"Challenge",
|
||||
"Reaction",
|
||||
"Comparison"
|
||||
])
|
||||
|
||||
with tab2:
|
||||
# Style preferences
|
||||
st.subheader("Style Preferences")
|
||||
|
||||
# Create columns for style options
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
style_preference = st.selectbox("Thumbnail Style", [
|
||||
"Bold and Dramatic",
|
||||
"Clean and Minimal",
|
||||
"Colorful and Vibrant",
|
||||
"Dark and Moody",
|
||||
"Professional and Corporate",
|
||||
"Playful and Fun",
|
||||
"Retro/Vintage",
|
||||
"Modern and Sleek"
|
||||
])
|
||||
|
||||
num_concepts = st.slider("Number of Concepts", 1, 5, 3)
|
||||
|
||||
with col2:
|
||||
aspect_ratio = st.selectbox("Aspect Ratio", [
|
||||
"16:9 (Standard)",
|
||||
"1:1 (Square)",
|
||||
"4:3 (Classic)",
|
||||
"9:16 (Vertical)"
|
||||
])
|
||||
|
||||
include_text = st.checkbox("Include Text Overlay", value=True)
|
||||
if include_text:
|
||||
text_style = st.selectbox("Text Style", [
|
||||
"Bold and Impactful",
|
||||
"Clean and Readable",
|
||||
"Stylized and Thematic",
|
||||
"Minimal and Subtle"
|
||||
])
|
||||
|
||||
# Advanced AI Prompt Settings
|
||||
st.subheader("Advanced AI Prompt Settings")
|
||||
|
||||
# Create columns for advanced settings
|
||||
col3, col4 = st.columns(2)
|
||||
|
||||
with col3:
|
||||
# Image style selection
|
||||
image_style = st.selectbox("Image Style", [
|
||||
"Auto (AI will choose best style)",
|
||||
"Photorealistic",
|
||||
"Artistic",
|
||||
"Cartoon/Anime",
|
||||
"Sketch/Drawing",
|
||||
"Digital Art",
|
||||
"3D Render"
|
||||
])
|
||||
|
||||
# Extract style for the generate_gemini_image function
|
||||
style = None
|
||||
if image_style == "Photorealistic":
|
||||
style = "photorealistic"
|
||||
elif image_style == "Artistic":
|
||||
style = "artistic"
|
||||
elif image_style == "Cartoon/Anime":
|
||||
style = "cartoon"
|
||||
elif image_style == "Sketch/Drawing":
|
||||
style = "sketch"
|
||||
elif image_style == "Digital Art":
|
||||
style = "digital_art"
|
||||
elif image_style == "3D Render":
|
||||
style = "3d_render"
|
||||
|
||||
with col4:
|
||||
# Focus selection for photorealistic images
|
||||
focus = None
|
||||
if style == "photorealistic":
|
||||
focus = st.selectbox("Image Focus", [
|
||||
"Auto (AI will choose best focus)",
|
||||
"Portraits",
|
||||
"Objects",
|
||||
"Motion",
|
||||
"Wide-angle"
|
||||
])
|
||||
|
||||
# Extract focus for the generate_gemini_image function
|
||||
if focus == "Portraits":
|
||||
focus = "portraits"
|
||||
elif focus == "Objects":
|
||||
focus = "objects"
|
||||
elif focus == "Motion":
|
||||
focus = "motion"
|
||||
elif focus == "Wide-angle":
|
||||
focus = "wide-angle"
|
||||
elif focus == "Auto (AI will choose best focus)":
|
||||
focus = None
|
||||
|
||||
# Keywords for enhanced prompt generation
|
||||
st.subheader("Keywords for Enhanced Prompt")
|
||||
st.write("Add keywords to enhance the AI prompt generation. These will help create more detailed and accurate thumbnails.")
|
||||
|
||||
# Create a text area for keywords
|
||||
keywords_input = st.text_area(
|
||||
"Keywords (comma-separated)",
|
||||
placeholder="e.g., vibrant, energetic, bold, eye-catching, professional"
|
||||
)
|
||||
|
||||
# Process keywords
|
||||
keywords = None
|
||||
if keywords_input:
|
||||
keywords = [k.strip() for k in keywords_input.split(",") if k.strip()]
|
||||
logger.info(f"User provided keywords: {keywords}")
|
||||
|
||||
# Generate button
|
||||
if st.button("Generate Thumbnail Concepts"):
|
||||
if not video_title:
|
||||
st.error("Please enter a video title.")
|
||||
return
|
||||
|
||||
with st.spinner("Generating thumbnail concepts..."):
|
||||
logger.info("User clicked Generate Thumbnail Concepts button")
|
||||
concepts = generate_thumbnail_concepts(
|
||||
video_title,
|
||||
video_description,
|
||||
target_audience,
|
||||
content_type,
|
||||
style_preference,
|
||||
num_concepts
|
||||
)
|
||||
|
||||
if concepts:
|
||||
# Store the concepts in session state
|
||||
st.session_state.thumbnail_concepts = concepts
|
||||
# Parse the concepts and store in session state
|
||||
st.session_state.concept_list = parse_concepts(concepts)
|
||||
logger.info("Stored thumbnail concepts in session state")
|
||||
|
||||
# Display the concepts in tabs
|
||||
st.subheader("Thumbnail Concepts")
|
||||
|
||||
# Create tabs for each concept
|
||||
concept_tabs = st.tabs([f"Concept {i+1}" for i in range(len(st.session_state.concept_list))])
|
||||
|
||||
for i, tab in enumerate(concept_tabs):
|
||||
with tab:
|
||||
st.markdown(st.session_state.concept_list[i])
|
||||
|
||||
# Add a button to generate image for this concept
|
||||
if st.button(f"Generate Image for Concept {i+1}", key=f"gen_img_{i}"):
|
||||
with st.spinner(f"Generating thumbnail image for concept {i+1}..."):
|
||||
logger.info(f"User selected concept {i+1} for image generation")
|
||||
# Get the selected concept
|
||||
selected_concept = st.session_state.concept_list[i]
|
||||
|
||||
# Generate the thumbnail image with enhanced prompt
|
||||
img_path = generate_thumbnail_design(
|
||||
selected_concept,
|
||||
style_preference,
|
||||
aspect_ratio.split()[0], # Extract just the ratio part
|
||||
keywords=keywords,
|
||||
style=style,
|
||||
focus=focus
|
||||
)
|
||||
|
||||
if img_path:
|
||||
# Store the current thumbnail path in session state
|
||||
st.session_state.current_thumbnail_path = img_path
|
||||
logger.info(f"Stored current thumbnail path in session state: {img_path}")
|
||||
|
||||
# Display the generated image
|
||||
st.subheader("Generated Thumbnail")
|
||||
st.image(img_path, use_container_width=True)
|
||||
|
||||
# Add download button
|
||||
with open(img_path, "rb") as file:
|
||||
st.download_button(
|
||||
label="Download Thumbnail",
|
||||
data=file,
|
||||
file_name=f"youtube_thumbnail_{int(time.time())}.png",
|
||||
mime="image/png"
|
||||
)
|
||||
|
||||
# Add image editing section
|
||||
st.subheader("Edit Thumbnail")
|
||||
st.write("Make changes to your thumbnail by providing instructions below:")
|
||||
|
||||
# Create a text area for edit instructions
|
||||
edit_instructions = st.text_area(
|
||||
"Edit Instructions",
|
||||
placeholder="e.g., Make the background darker, Add a red border, Change the text color to white",
|
||||
key=f"edit_instructions_{i}"
|
||||
)
|
||||
|
||||
# Store edit instructions in session state
|
||||
st.session_state.edit_instructions = edit_instructions
|
||||
|
||||
# Add a button to apply edits
|
||||
if st.button("Apply Edits", key=f"apply_edits_{i}"):
|
||||
if not edit_instructions:
|
||||
st.warning("Please provide edit instructions.")
|
||||
else:
|
||||
# Set editing flag
|
||||
st.session_state.editing_thumbnail = True
|
||||
st.session_state.show_edit_form = True
|
||||
|
||||
# Rerun to update the UI
|
||||
st.rerun()
|
||||
|
||||
# Add analysis button
|
||||
if st.button("Analyze Thumbnail", key=f"analyze_{i}"):
|
||||
logger.info("User clicked Analyze Thumbnail button")
|
||||
analysis = analyze_thumbnail(img_path)
|
||||
st.subheader("Thumbnail Analysis")
|
||||
st.markdown(analysis)
|
||||
else:
|
||||
st.error("Failed to generate thumbnail concepts. Please try again.")
|
||||
|
||||
# Display previously generated concepts if they exist in session state
|
||||
elif st.session_state.thumbnail_concepts and st.session_state.concept_list:
|
||||
logger.info("Displaying previously generated concepts from session state")
|
||||
st.subheader("Thumbnail Concepts")
|
||||
|
||||
# Create tabs for each concept
|
||||
concept_tabs = st.tabs([f"Concept {i+1}" for i in range(len(st.session_state.concept_list))])
|
||||
|
||||
for i, tab in enumerate(concept_tabs):
|
||||
with tab:
|
||||
st.markdown(st.session_state.concept_list[i])
|
||||
|
||||
# Add a button to generate image for this concept
|
||||
if st.button(f"Generate Image for Concept {i+1}", key=f"gen_img_existing_{i}"):
|
||||
with st.spinner(f"Generating thumbnail image for concept {i+1}..."):
|
||||
logger.info(f"User selected concept {i+1} for image generation")
|
||||
# Get the selected concept
|
||||
selected_concept = st.session_state.concept_list[i]
|
||||
|
||||
# Generate the thumbnail image with enhanced prompt
|
||||
img_path = generate_thumbnail_design(
|
||||
selected_concept,
|
||||
style_preference,
|
||||
aspect_ratio.split()[0], # Extract just the ratio part
|
||||
keywords=keywords,
|
||||
style=style,
|
||||
focus=focus
|
||||
)
|
||||
|
||||
if img_path:
|
||||
# Store the current thumbnail path in session state
|
||||
st.session_state.current_thumbnail_path = img_path
|
||||
logger.info(f"Stored current thumbnail path in session state: {img_path}")
|
||||
|
||||
# Display the generated image
|
||||
st.subheader("Generated Thumbnail")
|
||||
st.image(img_path, use_container_width=True)
|
||||
|
||||
# Add download button
|
||||
with open(img_path, "rb") as file:
|
||||
st.download_button(
|
||||
label="Download Thumbnail",
|
||||
data=file,
|
||||
file_name=f"youtube_thumbnail_{int(time.time())}.png",
|
||||
mime="image/png"
|
||||
)
|
||||
|
||||
# Add image editing section
|
||||
st.subheader("Edit Thumbnail")
|
||||
st.write("Make changes to your thumbnail by providing instructions below:")
|
||||
|
||||
# Create a text area for edit instructions
|
||||
edit_instructions = st.text_area(
|
||||
"Edit Instructions",
|
||||
placeholder="e.g., Make the background darker, Add a red border, Change the text color to white",
|
||||
key=f"edit_instructions_existing_{i}"
|
||||
)
|
||||
|
||||
# Store edit instructions in session state
|
||||
st.session_state.edit_instructions = edit_instructions
|
||||
|
||||
# Add a button to apply edits
|
||||
if st.button("Apply Edits", key=f"apply_edits_existing_{i}"):
|
||||
if not edit_instructions:
|
||||
st.warning("Please provide edit instructions.")
|
||||
else:
|
||||
# Set editing flag
|
||||
st.session_state.editing_thumbnail = True
|
||||
st.session_state.show_edit_form = True
|
||||
|
||||
# Rerun to update the UI
|
||||
st.rerun()
|
||||
|
||||
# Add analysis button
|
||||
if st.button("Analyze Thumbnail", key=f"analyze_existing_{i}"):
|
||||
logger.info("User clicked Analyze Thumbnail button")
|
||||
analysis = analyze_thumbnail(img_path)
|
||||
st.subheader("Thumbnail Analysis")
|
||||
st.markdown(analysis)
|
||||
|
||||
# Display current thumbnail if it exists in session state
|
||||
elif st.session_state.current_thumbnail_path:
|
||||
logger.info(f"Displaying current thumbnail from session state: {st.session_state.current_thumbnail_path}")
|
||||
st.subheader("Current Thumbnail")
|
||||
st.image(st.session_state.current_thumbnail_path, use_container_width=True)
|
||||
|
||||
# Add download button
|
||||
with open(st.session_state.current_thumbnail_path, "rb") as file:
|
||||
st.download_button(
|
||||
label="Download Thumbnail",
|
||||
data=file,
|
||||
file_name=f"youtube_thumbnail_{int(time.time())}.png",
|
||||
mime="image/png"
|
||||
)
|
||||
|
||||
# Add image editing section
|
||||
st.subheader("Edit Thumbnail")
|
||||
st.write("Make changes to your thumbnail by providing instructions below:")
|
||||
|
||||
# Create a text area for edit instructions
|
||||
edit_instructions = st.text_area(
|
||||
"Edit Instructions",
|
||||
placeholder="e.g., Make the background darker, Add a red border, Change the text color to white",
|
||||
key="edit_instructions_current",
|
||||
value=st.session_state.edit_instructions if st.session_state.edit_instructions else ""
|
||||
)
|
||||
|
||||
# Store edit instructions in session state
|
||||
st.session_state.edit_instructions = edit_instructions
|
||||
|
||||
# Add a button to apply edits
|
||||
if st.button("Apply Edits", key="apply_edits_current"):
|
||||
if not edit_instructions:
|
||||
st.warning("Please provide edit instructions.")
|
||||
else:
|
||||
# Set editing flag
|
||||
st.session_state.editing_thumbnail = True
|
||||
st.session_state.show_edit_form = True
|
||||
|
||||
# Rerun to update the UI
|
||||
st.rerun()
|
||||
|
||||
# Add analysis button
|
||||
if st.button("Analyze Thumbnail", key="analyze_current"):
|
||||
logger.info("User clicked Analyze Thumbnail button")
|
||||
analysis = analyze_thumbnail(st.session_state.current_thumbnail_path)
|
||||
st.subheader("Thumbnail Analysis")
|
||||
st.markdown(analysis)
|
||||
|
||||
# Handle the editing process
|
||||
if st.session_state.editing_thumbnail and st.session_state.show_edit_form:
|
||||
st.subheader("Editing Thumbnail")
|
||||
|
||||
# Show a spinner while editing
|
||||
with st.spinner("Editing thumbnail..."):
|
||||
logger.info(f"User provided edit instructions: '{st.session_state.edit_instructions}'")
|
||||
# Edit the thumbnail image
|
||||
edited_img_path = edit_thumbnail_image(st.session_state.current_thumbnail_path, st.session_state.edit_instructions)
|
||||
|
||||
if edited_img_path:
|
||||
# Update the current thumbnail path in session state
|
||||
st.session_state.edited_thumbnail_path = edited_img_path
|
||||
logger.info(f"Updated current thumbnail path in session state: {edited_img_path}")
|
||||
|
||||
# Reset editing flags
|
||||
st.session_state.editing_thumbnail = False
|
||||
st.session_state.show_edit_form = False
|
||||
|
||||
# Display the edited image
|
||||
st.subheader("Edited Thumbnail")
|
||||
st.image(edited_img_path, use_container_width=True)
|
||||
|
||||
# Add download button for the edited image
|
||||
with open(edited_img_path, "rb") as file:
|
||||
st.download_button(
|
||||
label="Download Edited Thumbnail",
|
||||
data=file,
|
||||
file_name=f"youtube_thumbnail_edited_{int(time.time())}.png",
|
||||
mime="image/png"
|
||||
)
|
||||
|
||||
# Update the current thumbnail path to the edited one
|
||||
st.session_state.current_thumbnail_path = edited_img_path
|
||||
|
||||
# Add a button to continue editing
|
||||
if st.button("Continue Editing"):
|
||||
st.session_state.show_edit_form = True
|
||||
st.rerun()
|
||||
else:
|
||||
# Reset editing flags
|
||||
st.session_state.editing_thumbnail = False
|
||||
st.session_state.show_edit_form = False
|
||||
|
||||
st.error("Failed to edit the thumbnail. Please try again with different instructions.")
|
||||
@@ -1,452 +0,0 @@
|
||||
"""
|
||||
YouTube Title Generator Module
|
||||
|
||||
This module provides functionality for generating YouTube video titles.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import time
|
||||
import logging
|
||||
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
logger = logging.getLogger('youtube_title_generator')
|
||||
|
||||
|
||||
def analyze_title(title):
|
||||
"""Analyze a YouTube title for SEO and clickbait."""
|
||||
logger.info(f"Analyzing title: '{title}'")
|
||||
|
||||
# Character count
|
||||
char_count = len(title)
|
||||
optimal_length = 50 <= char_count <= 60
|
||||
logger.info(f"Character count: {char_count}, Optimal length: {optimal_length}")
|
||||
|
||||
# Clickbait detection. TBD: Use AI to detect clickbait.
|
||||
clickbait_phrases = [
|
||||
"shocking", "you won't believe", "gone wrong", "gone sexual",
|
||||
"free v-bucks", "free robux", "100%", "gone viral", "viral",
|
||||
"you need to see this", "wait till the end", "at 3am", "3am",
|
||||
"don't watch this", "watch till the end", "gone too far",
|
||||
"insane", "unbelievable", "mind-blowing", "life-changing",
|
||||
"secret", "hidden", "revealed", "exposed", "leaked",
|
||||
"never before seen", "first time ever", "world's first",
|
||||
"no one knows", "experts hate this", "doctors hate this",
|
||||
"this will change your life", "this will blow your mind",
|
||||
"you've been doing it wrong", "the truth about", "the real reason",
|
||||
"what they don't want you to know", "what they're hiding",
|
||||
"what they don't tell you", "what you need to know",
|
||||
"what you should know", "what you must know", "what you must see",
|
||||
"what you must watch", "what you must do", "what you must have",
|
||||
"what you must buy", "what you must try", "what you must avoid",
|
||||
"what you must stop doing", "what you must start doing",
|
||||
"what you must change", "what you must learn", "what you must understand",
|
||||
"what you must realize", "what you must accept", "what you must believe",
|
||||
"what you must know about", "what you must see about", "what you must watch about",
|
||||
"what you must do about", "what you must have about", "what you must buy about",
|
||||
"what you must try about", "what you must avoid about", "what you must stop doing about",
|
||||
"what you must start doing about", "what you must change about", "what you must learn about",
|
||||
"what you must understand about", "what you must realize about", "what you must accept about",
|
||||
"what you must believe about", "what you must know about", "what you must see about",
|
||||
"what you must watch about", "what you must do about", "what you must have about",
|
||||
"what you must buy about", "what you must try about", "what you must avoid about",
|
||||
"what you must stop doing about", "what you must start doing about", "what you must change about",
|
||||
"what you must learn about", "what you must understand about", "what you must realize about",
|
||||
"what you must accept about", "what you must believe about"
|
||||
]
|
||||
|
||||
clickbait_score = 0
|
||||
detected_phrases = []
|
||||
for phrase in clickbait_phrases:
|
||||
if phrase.lower() in title.lower():
|
||||
clickbait_score += 1
|
||||
detected_phrases.append(phrase)
|
||||
|
||||
is_clickbait = clickbait_score > 0
|
||||
logger.info(f"Clickbait detection: score={clickbait_score}, is_clickbait={is_clickbait}")
|
||||
if detected_phrases:
|
||||
logger.info(f"Detected clickbait phrases: {', '.join(detected_phrases)}")
|
||||
|
||||
# SEO elements
|
||||
has_number = any(char.isdigit() for char in title)
|
||||
has_question = "?" in title
|
||||
has_colon = ":" in title
|
||||
has_brackets = "[" in title or "]" in title or "(" in title or ")" in title
|
||||
|
||||
logger.info(f"SEO elements: has_number={has_number}, has_question={has_question}, has_colon={has_colon}, has_brackets={has_brackets}")
|
||||
|
||||
# Calculate SEO score
|
||||
seo_score = 0
|
||||
if optimal_length:
|
||||
seo_score += 3
|
||||
if has_number:
|
||||
seo_score += 1
|
||||
if has_question:
|
||||
seo_score += 1
|
||||
if has_colon:
|
||||
seo_score += 1
|
||||
if has_brackets:
|
||||
seo_score += 1
|
||||
if not is_clickbait:
|
||||
seo_score += 2
|
||||
|
||||
logger.info(f"Final SEO score: {seo_score}/10")
|
||||
|
||||
return {
|
||||
"char_count": char_count,
|
||||
"optimal_length": optimal_length,
|
||||
"is_clickbait": is_clickbait,
|
||||
"clickbait_score": clickbait_score,
|
||||
"seo_score": seo_score,
|
||||
"has_number": has_number,
|
||||
"has_question": has_question,
|
||||
"has_colon": has_colon,
|
||||
"has_brackets": has_brackets
|
||||
}
|
||||
|
||||
|
||||
def generate_youtube_title(target_audience, main_points, tone_style, use_case, num_titles=5, progress_bar=None):
|
||||
""" Generate youtube title generator """
|
||||
logger.info(f"Starting title generation with parameters: target_audience='{target_audience}', main_points='{main_points}', tone_style='{tone_style}', use_case='{use_case}', num_titles={num_titles}")
|
||||
|
||||
# Create a custom system prompt that doesn't include blog-specific instructions
|
||||
system_prompt = """You are a YouTube title expert specializing in creating engaging, clickable video titles.
|
||||
Your task is to generate YouTube video titles based on the provided information.
|
||||
Focus ONLY on creating titles that are optimized for YouTube.
|
||||
Return ONLY the titles, one per line, without any numbering or additional text."""
|
||||
|
||||
prompt = f"""
|
||||
**Instructions:**
|
||||
|
||||
Please generate {num_titles} YouTube title options for a video about **{main_points}** based on the following information:
|
||||
|
||||
|
||||
**Target Audience:** {target_audience}
|
||||
|
||||
**Tone and Style:** {tone_style}
|
||||
|
||||
**Use Case:** {use_case}
|
||||
|
||||
**Specific Instructions:**
|
||||
|
||||
* Make the titles catchy and attention-grabbing.
|
||||
* Use relevant keywords to improve SEO.
|
||||
* Tailor the language and tone to the target audience.
|
||||
* Ensure the title reflects the content and use case of the video.
|
||||
* Return ONLY the titles, one per line, without any numbering or additional text.
|
||||
"""
|
||||
|
||||
logger.info("Generated prompt for title generation")
|
||||
logger.debug(f"Prompt: {prompt}")
|
||||
logger.debug(f"System prompt: {system_prompt}")
|
||||
|
||||
try:
|
||||
# Update progress bar if provided
|
||||
if progress_bar:
|
||||
progress_bar.progress(30)
|
||||
progress_bar.text("Analyzing your content and target audience...")
|
||||
logger.info("Progress bar updated: 30% - Analyzing content and target audience")
|
||||
|
||||
# Simulate some processing time to show progress
|
||||
time.sleep(1)
|
||||
|
||||
if progress_bar:
|
||||
progress_bar.progress(60)
|
||||
progress_bar.text("Generating creative title options...")
|
||||
logger.info("Progress bar updated: 60% - Generating creative title options")
|
||||
|
||||
# Get the response from the language model with custom system prompt
|
||||
logger.info("Calling LLM for title generation with custom system prompt")
|
||||
start_time = time.time()
|
||||
response = llm_text_gen(prompt, system_prompt=system_prompt)
|
||||
end_time = time.time()
|
||||
logger.info(f"LLM response received in {end_time - start_time:.2f} seconds")
|
||||
logger.debug(f"Raw LLM response: {response}")
|
||||
|
||||
if progress_bar:
|
||||
progress_bar.progress(90)
|
||||
progress_bar.text("Processing and formatting titles...")
|
||||
logger.info("Progress bar updated: 90% - Processing and formatting titles")
|
||||
|
||||
# Split the response into individual titles
|
||||
titles = [title.strip() for title in response.split('\n') if title.strip()]
|
||||
logger.info(f"Generated {len(titles)} titles")
|
||||
for i, title in enumerate(titles, 1):
|
||||
logger.info(f"Title {i}: '{title}'")
|
||||
|
||||
if progress_bar:
|
||||
progress_bar.progress(100)
|
||||
progress_bar.text("Titles generated successfully!")
|
||||
logger.info("Progress bar updated: 100% - Titles generated successfully")
|
||||
|
||||
return titles
|
||||
except Exception as err:
|
||||
logger.error(f"Error generating titles: {err}", exc_info=True)
|
||||
if progress_bar:
|
||||
progress_bar.progress(100)
|
||||
progress_bar.text("Error generating titles. Please try again.")
|
||||
logger.info("Progress bar updated: 100% - Error generating titles")
|
||||
st.error(f"Error: Failed to get response from LLM: {err}")
|
||||
return None
|
||||
|
||||
|
||||
def write_yt_title():
|
||||
"""Create a user interface for YouTube Title Generator."""
|
||||
logger.info("Initializing YouTube Title Generator UI")
|
||||
st.write("Generate engaging YouTube video titles that drive clicks and views.")
|
||||
|
||||
# Initialize session state for generated titles if it doesn't exist
|
||||
if "generated_titles" not in st.session_state:
|
||||
st.session_state.generated_titles = None
|
||||
|
||||
# Main points input (full width)
|
||||
main_points = st.text_area("Main Points/Keywords (comma-separated)",
|
||||
placeholder="e.g., cooking tips, healthy recipes, quick meals")
|
||||
|
||||
# Create columns for the other inputs
|
||||
col1, col2, col3, col4 = st.columns(4)
|
||||
|
||||
with col1:
|
||||
tone_style = st.selectbox("Tone/Style",
|
||||
["Professional", "Casual", "Humorous", "Educational", "Entertaining", "Inspirational"])
|
||||
|
||||
with col2:
|
||||
target_audience = st.text_input("Target Audience",
|
||||
placeholder="e.g., beginners, professionals, parents")
|
||||
|
||||
with col3:
|
||||
use_case = st.selectbox("Use Case",
|
||||
["How-to/Tutorial", "Vlog", "Review", "Educational", "Entertainment", "News"])
|
||||
|
||||
with col4:
|
||||
num_titles = st.number_input("Number of Titles",
|
||||
min_value=1,
|
||||
max_value=20,
|
||||
value=5,
|
||||
step=1)
|
||||
|
||||
if st.button("Generate Titles"):
|
||||
logger.info("Generate Titles button clicked")
|
||||
logger.info(f"User inputs: main_points='{main_points}', tone_style='{tone_style}', target_audience='{target_audience}', use_case='{use_case}', num_titles={num_titles}")
|
||||
|
||||
if not main_points:
|
||||
logger.warning("No main points provided")
|
||||
st.error("Please enter main points/keywords.")
|
||||
return
|
||||
|
||||
# Create a progress bar
|
||||
progress_bar = st.progress(0)
|
||||
progress_bar.text("Initializing title generation...")
|
||||
logger.info("Created progress bar for title generation")
|
||||
|
||||
# Generate titles with progress updates
|
||||
logger.info("Calling generate_youtube_title function")
|
||||
titles = generate_youtube_title(main_points, tone_style, target_audience, use_case, num_titles, progress_bar)
|
||||
|
||||
# Clear the progress bar after a short delay
|
||||
time.sleep(1)
|
||||
progress_bar.empty()
|
||||
logger.info("Cleared progress bar")
|
||||
|
||||
if titles:
|
||||
logger.info(f"Successfully generated {len(titles)} titles")
|
||||
|
||||
# Store titles in session state for persistence
|
||||
st.session_state.generated_titles = titles
|
||||
|
||||
# Display titles section
|
||||
st.markdown("""
|
||||
<div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
|
||||
<h2 style='color: #FF0000; text-align: center;'>Generated YouTube Titles</h2>
|
||||
<p style='text-align: center;'>Click on a title to see detailed analysis and copy options</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Display titles with analysis
|
||||
for i, title in enumerate(titles, 1):
|
||||
logger.info(f"Analyzing title {i}: '{title}'")
|
||||
|
||||
# Create a more visually appealing expander
|
||||
with st.expander(f"Title {i}: {title}", expanded=False):
|
||||
# Add a divider for better visual separation
|
||||
st.markdown("---")
|
||||
|
||||
# Title display with better formatting
|
||||
st.markdown(f"""
|
||||
<div style='background-color: #f8f9fa; padding: 15px; border-radius: 5px; border-left: 5px solid #FF0000;'>
|
||||
<h3 style='margin: 0;'>{title}</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Analysis section
|
||||
st.markdown("### Analysis")
|
||||
analysis = analyze_title(title)
|
||||
|
||||
# Create columns for analysis metrics
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
# Character count
|
||||
st.markdown("#### Character Count")
|
||||
st.write(f"**{analysis['char_count']}** characters")
|
||||
if analysis['optimal_length']:
|
||||
st.success("✅ Optimal length (50-60 characters)")
|
||||
else:
|
||||
st.warning("⚠️ Not optimal length (should be 50-60 characters)")
|
||||
|
||||
# Clickbait detection
|
||||
st.markdown("#### Clickbait Detection")
|
||||
if analysis['is_clickbait']:
|
||||
st.error(f"⚠️ Possible clickbait detected (score: {analysis['clickbait_score']})")
|
||||
else:
|
||||
st.success("✅ No clickbait detected")
|
||||
|
||||
with col2:
|
||||
# SEO score
|
||||
st.markdown("#### SEO Score")
|
||||
score_color = "#28a745" if analysis['seo_score'] >= 7 else "#ffc107" if analysis['seo_score'] >= 5 else "#dc3545"
|
||||
st.markdown(f"<h2 style='color: {score_color};'>{analysis['seo_score']}/10</h2>", unsafe_allow_html=True)
|
||||
if analysis['seo_score'] >= 7:
|
||||
st.success("✅ Good SEO score")
|
||||
elif analysis['seo_score'] >= 5:
|
||||
st.warning("⚠️ Moderate SEO score")
|
||||
else:
|
||||
st.error("❌ Low SEO score")
|
||||
|
||||
# SEO elements
|
||||
st.markdown("#### SEO Elements")
|
||||
elements = []
|
||||
if analysis['has_number']:
|
||||
elements.append("✅ Contains numbers")
|
||||
if analysis['has_question']:
|
||||
elements.append("✅ Contains question mark")
|
||||
if analysis['has_colon']:
|
||||
elements.append("✅ Contains colon")
|
||||
if analysis['has_brackets']:
|
||||
elements.append("✅ Contains brackets/parentheses")
|
||||
|
||||
for element in elements:
|
||||
st.write(element)
|
||||
|
||||
# Copy functionality using session state
|
||||
st.markdown("### Copy Title")
|
||||
st.code(title, language="text")
|
||||
|
||||
# Use a different approach for copy functionality
|
||||
copy_key = f"copy_{i}"
|
||||
if st.button(f"Copy Title {i}", key=copy_key):
|
||||
# Use JavaScript to copy to clipboard
|
||||
escaped_title = title.replace('"', '\\"')
|
||||
st.markdown(
|
||||
f"""
|
||||
<script>
|
||||
navigator.clipboard.writeText("{escaped_title}");
|
||||
</script>
|
||||
""",
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
st.success(f"✅ Title {i} copied to clipboard!")
|
||||
else:
|
||||
logger.error("Failed to generate titles")
|
||||
st.error("Failed to generate titles. Please try again.")
|
||||
|
||||
# Display previously generated titles if they exist in session state
|
||||
elif st.session_state.generated_titles:
|
||||
titles = st.session_state.generated_titles
|
||||
|
||||
# Display titles section
|
||||
st.markdown("""
|
||||
<div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
|
||||
<h2 style='color: #FF0000; text-align: center;'>Generated YouTube Titles</h2>
|
||||
<p style='text-align: center;'>Click on a title to see detailed analysis and copy options</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Display titles with analysis
|
||||
for i, title in enumerate(titles, 1):
|
||||
logger.info(f"Analyzing title {i}: '{title}'")
|
||||
|
||||
# Create a more visually appealing expander
|
||||
with st.expander(f"Title {i}: {title}", expanded=False):
|
||||
# Add a divider for better visual separation
|
||||
st.markdown("---")
|
||||
|
||||
# Title display with better formatting
|
||||
st.markdown(f"""
|
||||
<div style='background-color: #f8f9fa; padding: 15px; border-radius: 5px; border-left: 5px solid #FF0000;'>
|
||||
<h3 style='margin: 0;'>{title}</h3>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Analysis section
|
||||
st.markdown("### Analysis")
|
||||
analysis = analyze_title(title)
|
||||
|
||||
# Create columns for analysis metrics
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
# Character count
|
||||
st.markdown("#### Character Count")
|
||||
st.write(f"**{analysis['char_count']}** characters")
|
||||
if analysis['optimal_length']:
|
||||
st.success("✅ Optimal length (50-60 characters)")
|
||||
else:
|
||||
st.warning("⚠️ Not optimal length (should be 50-60 characters)")
|
||||
|
||||
# Clickbait detection
|
||||
st.markdown("#### Clickbait Detection")
|
||||
if analysis['is_clickbait']:
|
||||
st.error(f"⚠️ Possible clickbait detected (score: {analysis['clickbait_score']})")
|
||||
else:
|
||||
st.success("✅ No clickbait detected")
|
||||
|
||||
with col2:
|
||||
# SEO score
|
||||
st.markdown("#### SEO Score")
|
||||
score_color = "#28a745" if analysis['seo_score'] >= 7 else "#ffc107" if analysis['seo_score'] >= 5 else "#dc3545"
|
||||
st.markdown(f"<h2 style='color: {score_color};'>{analysis['seo_score']}/10</h2>", unsafe_allow_html=True)
|
||||
if analysis['seo_score'] >= 7:
|
||||
st.success("✅ Good SEO score")
|
||||
elif analysis['seo_score'] >= 5:
|
||||
st.warning("⚠️ Moderate SEO score")
|
||||
else:
|
||||
st.error("❌ Low SEO score")
|
||||
|
||||
# SEO elements
|
||||
st.markdown("#### SEO Elements")
|
||||
elements = []
|
||||
if analysis['has_number']:
|
||||
elements.append("✅ Contains numbers")
|
||||
if analysis['has_question']:
|
||||
elements.append("✅ Contains question mark")
|
||||
if analysis['has_colon']:
|
||||
elements.append("✅ Contains colon")
|
||||
if analysis['has_brackets']:
|
||||
elements.append("✅ Contains brackets/parentheses")
|
||||
|
||||
for element in elements:
|
||||
st.write(element)
|
||||
|
||||
# Copy functionality using session state
|
||||
st.markdown("### Copy Title")
|
||||
st.code(title, language="text")
|
||||
|
||||
# Use a different approach for copy functionality
|
||||
copy_key = f"copy_{i}"
|
||||
if st.button(f"Copy Title {i}", key=copy_key):
|
||||
# Use JavaScript to copy to clipboard
|
||||
escaped_title = title.replace('"', '\\"')
|
||||
st.markdown(
|
||||
f"""
|
||||
<script>
|
||||
navigator.clipboard.writeText("{escaped_title}");
|
||||
</script>
|
||||
""",
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
st.success(f"✅ Title {i} copied to clipboard!")
|
||||
@@ -1,237 +0,0 @@
|
||||
"""
|
||||
YouTube AI Writer
|
||||
|
||||
This module provides a comprehensive suite of tools for generating YouTube content.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import importlib
|
||||
import sys
|
||||
import os
|
||||
from pathlib import Path
|
||||
from .modules.title_generator import write_yt_title
|
||||
from .modules.description_generator import write_yt_description
|
||||
from .modules.script_generator import write_yt_script
|
||||
from .modules.thumbnail_generator import write_yt_thumbnail
|
||||
from .modules.end_screen_generator import write_yt_end_screen
|
||||
from .modules.tags_generator import write_yt_tags
|
||||
from .modules.shorts_script_generator import write_yt_shorts
|
||||
from .modules.community_post_generator import write_yt_community_post
|
||||
from .modules.shorts_video_generator import write_yt_shorts_video
|
||||
from .modules.channel_trailer_generator import write_yt_channel_trailer
|
||||
|
||||
|
||||
def youtube_main_menu():
|
||||
"""Main function for the YouTube AI Writer."""
|
||||
|
||||
# Initialize session state for selected tool if it doesn't exist
|
||||
if "selected_tool" not in st.session_state:
|
||||
st.session_state.selected_tool = None
|
||||
|
||||
# Define the YouTube tools with their details
|
||||
youtube_tools = [
|
||||
# Content Creation Tools
|
||||
{
|
||||
"name": "YT Title Generator",
|
||||
"icon": "📝",
|
||||
"description": "Create engaging YouTube video titles that drive clicks and views.",
|
||||
"color": "#FF0000", # YouTube red
|
||||
"category": "Content Creation",
|
||||
"function": write_yt_title,
|
||||
"status": "active"
|
||||
},
|
||||
{
|
||||
"name": "YT Description Generator",
|
||||
"icon": "📄",
|
||||
"description": "Generate SEO-optimized descriptions for your YouTube videos.",
|
||||
"color": "#FF0000", # YouTube red
|
||||
"category": "Content Creation",
|
||||
"function": write_yt_description,
|
||||
"status": "active"
|
||||
},
|
||||
{
|
||||
"name": "YT Script Generator",
|
||||
"icon": "🎬",
|
||||
"description": "Create professional YouTube scripts with optimized structures for engagement.",
|
||||
"color": "#FF0000", # YouTube red
|
||||
"category": "Content Creation",
|
||||
"function": write_yt_script,
|
||||
"status": "active"
|
||||
},
|
||||
{
|
||||
"name": "YT Shorts Script Generator",
|
||||
"icon": "📱",
|
||||
"description": "Create engaging scripts optimized for YouTube Shorts format with vertical framing and hooks.",
|
||||
"color": "#FF0000", # YouTube red
|
||||
"category": "Content Creation",
|
||||
"function": write_yt_shorts,
|
||||
"status": "active"
|
||||
},
|
||||
{
|
||||
"name": "YT Shorts Video Generator",
|
||||
"icon": "🎥",
|
||||
"description": "Generate complete YouTube Shorts videos with AI-generated images, narration, and music.",
|
||||
"color": "#FF0000", # YouTube red
|
||||
"category": "Content Creation",
|
||||
"function": write_yt_shorts_video,
|
||||
"status": "active"
|
||||
},
|
||||
{
|
||||
"name": "Channel Trailer Generator",
|
||||
"icon": "🎥",
|
||||
"description": "Create compelling channel trailers that convert visitors into subscribers.",
|
||||
"color": "#FF0000", # YouTube red
|
||||
"category": "Content Creation",
|
||||
"function": write_yt_channel_trailer,
|
||||
"status": "active"
|
||||
},
|
||||
|
||||
# Optimization Tools
|
||||
{
|
||||
"name": "Thumbnail Generator",
|
||||
"icon": "🎨",
|
||||
"description": "Create engaging thumbnail ideas and descriptions with color scheme suggestions based on your brand.",
|
||||
"color": "#FF0000", # YouTube red
|
||||
"category": "Optimization",
|
||||
"function": write_yt_thumbnail,
|
||||
"status": "active"
|
||||
},
|
||||
{
|
||||
"name": "YouTube Tags Generator",
|
||||
"icon": "🏷️",
|
||||
"description": "Generate optimized tags for your videos with trending tag suggestions to improve discoverability.",
|
||||
"color": "#FF0000", # YouTube red
|
||||
"category": "Optimization",
|
||||
"function": write_yt_tags,
|
||||
"status": "active"
|
||||
},
|
||||
|
||||
# Engagement Tools
|
||||
{
|
||||
"name": "End Screen Generator",
|
||||
"icon": "🎬",
|
||||
"description": "Create effective end screen content and CTAs with template suggestions based on video type.",
|
||||
"color": "#FF0000", # YouTube red
|
||||
"category": "Engagement",
|
||||
"function": write_yt_end_screen,
|
||||
"status": "active"
|
||||
},
|
||||
{
|
||||
"name": "Community Post Generator",
|
||||
"icon": "💬",
|
||||
"description": "Generate engaging community posts with AI-powered content suggestions and timing optimization.",
|
||||
"color": "#FF0000", # YouTube red
|
||||
"category": "Engagement",
|
||||
"function": write_yt_community_post,
|
||||
"status": "active"
|
||||
},
|
||||
{
|
||||
"name": "Playlist Description Generator",
|
||||
"icon": "📚",
|
||||
"description": "Generate SEO-optimized descriptions for your playlists with organization suggestions.",
|
||||
"color": "#CC0000", # Darker red for coming soon
|
||||
"category": "Engagement",
|
||||
"function": None,
|
||||
"status": "coming_soon"
|
||||
},
|
||||
|
||||
# Future Tools
|
||||
{
|
||||
"name": "Analytics Insights",
|
||||
"icon": "📊",
|
||||
"description": "Get AI-powered insights and recommendations based on your channel analytics.",
|
||||
"color": "#990000", # Even darker red for future
|
||||
"category": "Future Tools",
|
||||
"function": None,
|
||||
"status": "future"
|
||||
},
|
||||
{
|
||||
"name": "Video Series Planner",
|
||||
"icon": "📅",
|
||||
"description": "Plan and organize your video series with content calendars and topic ideas.",
|
||||
"color": "#990000", # Even darker red for future
|
||||
"category": "Future Tools",
|
||||
"function": None,
|
||||
"status": "future"
|
||||
}
|
||||
]
|
||||
|
||||
# Create a container for the dashboard
|
||||
dashboard_container = st.container()
|
||||
|
||||
# Create a container for the tool input section
|
||||
tool_container = st.container()
|
||||
|
||||
# If a tool is selected, show its input section
|
||||
if st.session_state.selected_tool is not None:
|
||||
with tool_container:
|
||||
# Display the selected tool's input section
|
||||
st.markdown("---")
|
||||
st.markdown(f"# {st.session_state.selected_tool['icon']} {st.session_state.selected_tool['name']}")
|
||||
|
||||
# Add a back button
|
||||
if st.button("← Back to Dashboard", key="back_to_dashboard"):
|
||||
# Clear the selected tool from session state
|
||||
st.session_state.selected_tool = None
|
||||
st.rerun()
|
||||
|
||||
# Call the function for the selected tool
|
||||
if st.session_state.selected_tool["function"]:
|
||||
# Directly call the function instead of using it as a reference
|
||||
st.session_state.selected_tool["function"]()
|
||||
else:
|
||||
# Display coming soon or future tool information
|
||||
st.info(f"**{st.session_state.selected_tool['status'].replace('_', ' ').title()}!**")
|
||||
st.write(st.session_state.selected_tool["description"])
|
||||
st.image(f"https://via.placeholder.com/600x300?text={st.session_state.selected_tool['name']}+Coming+Soon", use_column_width=True)
|
||||
else:
|
||||
with dashboard_container:
|
||||
# Display the dashboard
|
||||
# Header
|
||||
st.markdown("""
|
||||
<div style='background-color: #f0f2f6; padding: 10px; border-radius: 5px; margin-bottom: 10px;'>
|
||||
<h1 style='color: #FF0000; text-align: center;'>🎥 YouTube AI Writer</h1>
|
||||
<p style='text-align: center;'>Generate professional YouTube content with ALwrity's AI-powered tools</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Group tools by category
|
||||
categories = {}
|
||||
for tool in youtube_tools:
|
||||
category = tool["category"]
|
||||
if category not in categories:
|
||||
categories[category] = []
|
||||
categories[category].append(tool)
|
||||
|
||||
# Display tools by category
|
||||
for category, tools in categories.items():
|
||||
st.markdown(f"## {category}")
|
||||
|
||||
# Create a 3-column layout for the tool cards
|
||||
cols = st.columns(3)
|
||||
|
||||
# Display the tool cards
|
||||
for i, tool in enumerate(tools):
|
||||
# Determine which column to use
|
||||
col = cols[i % 3]
|
||||
|
||||
with col:
|
||||
# Create a card for each tool
|
||||
status_badge = ""
|
||||
if tool["status"] == "coming_soon":
|
||||
status_badge = "<span style='background-color: #FFA500; color: white; padding: 2px 8px; border-radius: 10px; font-size: 0.8em;'>Coming Soon</span>"
|
||||
elif tool["status"] == "future":
|
||||
status_badge = "<span style='background-color: #808080; color: white; padding: 2px 8px; border-radius: 10px; font-size: 0.8em;'>Future</span>"
|
||||
|
||||
st.markdown(f"""
|
||||
<div style='background-color: {tool["color"]}; padding: 20px; border-radius: 10px; margin-bottom: 20px; color: white;'>
|
||||
<h2 style='color: white;'>{tool["icon"]} {tool["name"]} {status_badge}</h2>
|
||||
<p>{tool["description"]}</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Add a button to access the tool
|
||||
if st.button(f"Use {tool['name']}", key=f"btn_{tool['name']}"):
|
||||
# Store the selected tool in session state
|
||||
st.session_state.selected_tool = tool
|
||||
st.rerun()
|
||||
@@ -1,51 +0,0 @@
|
||||
# Changelog
|
||||
|
||||
All notable changes to the ALwrity project will be documented in this file.
|
||||
|
||||
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
|
||||
|
||||
## [Unreleased]
|
||||
|
||||
### Added
|
||||
|
||||
#### Auto-Dubbing Feature (Podcast Maker)
|
||||
- **Translation Service** (`backend/services/translation/`)
|
||||
- Common translation module for use across the entire application
|
||||
- DeepL integration for low-cost, high-quality text translation (500k chars/month free)
|
||||
- WaveSpeed integration for high-quality video/audio translation
|
||||
- Support for 34+ languages
|
||||
- Batch translation support
|
||||
- Factory pattern for provider selection
|
||||
- Cost estimation utilities
|
||||
|
||||
- **Audio Dubbing Service** (`backend/services/dubbing/`)
|
||||
- Audio dubbing with STT → Translate → TTS pipeline
|
||||
- Voice cloning support to preserve original speaker's voice
|
||||
- Low-quality (DeepL) and high-quality (WaveSpeed) modes
|
||||
- Batch dubbing support
|
||||
- Cost estimation
|
||||
|
||||
- **Podcast API Endpoints** (`backend/api/podcast/`)
|
||||
- `POST /api/podcast/dub/audio` - Create audio dubbing task
|
||||
- `GET /api/podcast/dub/{task_id}/result` - Get dubbing result
|
||||
- `POST /api/podcast/dub/voices/clone` - Clone voice from audio sample
|
||||
- `GET /api/podcast/dub/voices/{task_id}/result` - Get voice clone result
|
||||
- `POST /api/podcast/dub/estimate` - Estimate dubbing cost
|
||||
- `GET /api/podcast/dub/languages` - List supported languages
|
||||
- `GET /api/podcast/dub/voices` - List available TTS voices
|
||||
|
||||
- **Bug Fixes**
|
||||
- Fixed missing `Path` import in `scene_animation.py`
|
||||
|
||||
### Changed
|
||||
|
||||
- Updated `backend/services/__init__.py` to export translation and dubbing services
|
||||
- Updated `.env` with DeepL API key placeholder
|
||||
|
||||
### Documentation
|
||||
|
||||
- Added `backend/docs/AUTO_DUBBING.md` with comprehensive feature documentation
|
||||
|
||||
## [Previous Releases]
|
||||
|
||||
See git history for previous changelog entries.
|
||||
@@ -3,7 +3,7 @@ from __future__ import annotations
|
||||
import base64
|
||||
import os
|
||||
import uuid
|
||||
from typing import Optional, Dict, Any, List
|
||||
from typing import Optional, Dict, Any
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from sqlalchemy.orm import Session
|
||||
@@ -15,11 +15,6 @@ from pydantic import BaseModel, Field
|
||||
from services.llm_providers.main_image_generation import generate_image
|
||||
from services.llm_providers.main_image_editing import edit_image
|
||||
from services.llm_providers.main_text_generation import llm_text_gen
|
||||
from services.image_generation import (
|
||||
extract_visual_data as _extract_visual_data,
|
||||
get_model_recommendation,
|
||||
build_visual_summary,
|
||||
)
|
||||
from utils.logger_utils import get_service_logger
|
||||
from middleware.auth_middleware import get_current_user
|
||||
from services.database import get_db
|
||||
@@ -296,8 +291,8 @@ class PromptSuggestion(BaseModel):
|
||||
|
||||
class ImagePromptSuggestRequest(BaseModel):
|
||||
provider: Optional[str] = Field(None, pattern="^(gemini|huggingface|stability|wavespeed)$")
|
||||
model: Optional[str] = None # Specific model (e.g., "qwen-image", "ideogram-v3-turbo", "flux-2-flex", "glm-image")
|
||||
image_type: Optional[str] = Field(None, pattern="^(realistic|chart|conceptual|diagram|illustration|background|infographic)$")
|
||||
model: Optional[str] = None # Specific model (e.g., "qwen-image", "ideogram-v3-turbo")
|
||||
image_type: Optional[str] = Field(None, pattern="^(realistic|chart|conceptual|diagram|illustration|background)$")
|
||||
title: Optional[str] = None
|
||||
section: Optional[Dict[str, Any]] = None
|
||||
research: Optional[Dict[str, Any]] = None
|
||||
@@ -464,150 +459,6 @@ MODEL_SPECIFIC_GUIDANCE = {
|
||||
"High contrast areas for text placement"
|
||||
]
|
||||
}
|
||||
},
|
||||
"flux-2-flex": {
|
||||
"text_overlay": {
|
||||
"guidance": "FLUX 2 Flex excels at typography control and text rendering. Excellent for posters, memes, and designs requiring precise text placement.",
|
||||
"best_practices": [
|
||||
"Best for images requiring clear, readable text with precise placement",
|
||||
"Superior typography control compared to other models",
|
||||
"Can handle various text styles and sizes",
|
||||
"Ideal for poster-style blog images with embedded headlines",
|
||||
"Great for quote images and text-heavy designs"
|
||||
],
|
||||
"negative_prompt_additions": "blurry text, distorted letters, low quality typography"
|
||||
},
|
||||
"realistic": {
|
||||
"guidance": "Photorealistic generation with excellent typography integration. Text appears naturally within scenes.",
|
||||
"best_practices": [
|
||||
"Include typography as a natural part of the scene",
|
||||
"Specify text style, size, and placement clearly",
|
||||
"Use for realistic scenes with signage, labels, or text elements",
|
||||
"Professional quality with consistent text rendering"
|
||||
]
|
||||
},
|
||||
"chart": {
|
||||
"guidance": "Can render charts with text labels. Use simple chart designs with clear typography.",
|
||||
"best_practices": [
|
||||
"Simple bar charts, pie charts, or line graphs",
|
||||
"Clear typography for labels and legends",
|
||||
"Clean data visualization design",
|
||||
"Avoid overly complex infographic layouts"
|
||||
]
|
||||
},
|
||||
"infographic": {
|
||||
"guidance": "Excellent for infographic-style images with clear sections and typography. Multi-panel layouts work well.",
|
||||
"best_practices": [
|
||||
"Use for multi-section infographics with distinct areas",
|
||||
"Clear typography placement in designated zones",
|
||||
"Clean, organized layout with visual hierarchy",
|
||||
"Professional infographic design with text integration"
|
||||
]
|
||||
},
|
||||
"conceptual": {
|
||||
"guidance": "Conceptual imagery with typography support. Text can be integrated naturally into abstract designs.",
|
||||
"best_practices": [
|
||||
"Integrate text into conceptual designs as a visual element",
|
||||
"Use typography to enhance conceptual messaging",
|
||||
"Clear, readable text in abstract compositions"
|
||||
]
|
||||
}
|
||||
},
|
||||
"glm-image": {
|
||||
"text_overlay": {
|
||||
"guidance": "GLM-Image excels at infographics, educational diagrams, and professional poster designs. Strong text rendering capabilities.",
|
||||
"best_practices": [
|
||||
"Best for educational content, infographics, and diagrams",
|
||||
"Excellent for multi-panel layouts and structured designs",
|
||||
"Good text rendering with clear typography",
|
||||
"Professional infographic aesthetics",
|
||||
"Strong for academic or professional blog images"
|
||||
],
|
||||
"negative_prompt_additions": "watermarks, distorted text, low quality diagrams"
|
||||
},
|
||||
"realistic": {
|
||||
"guidance": "Photorealistic generation with good quality. Professional presentation style.",
|
||||
"best_practices": [
|
||||
"Include professional lighting and composition",
|
||||
"Use for polished, professional imagery",
|
||||
"Quality descriptors improve output consistency"
|
||||
]
|
||||
},
|
||||
"chart": {
|
||||
"guidance": "Excellent for data visualizations. Can render charts with clear labels and professional styling.",
|
||||
"best_practices": [
|
||||
"Professional chart designs with clear typography",
|
||||
"Data visualizations with embedded labels",
|
||||
"Clean infographic-style charts",
|
||||
"Good for statistical blog content"
|
||||
]
|
||||
},
|
||||
"infographic": {
|
||||
"guidance": "Best model choice for complex infographics. Multi-section layouts with clear visual hierarchy.",
|
||||
"best_practices": [
|
||||
"Use for comprehensive infographics with multiple data points",
|
||||
"Clear section boundaries and visual hierarchy",
|
||||
"Professional infographic aesthetic",
|
||||
"Excellent for educational or how-to content",
|
||||
"Multi-panel designs with distinct information areas"
|
||||
]
|
||||
},
|
||||
"diagram": {
|
||||
"guidance": "Excellent for technical diagrams and process illustrations. Clear visual representation of complex information.",
|
||||
"best_practices": [
|
||||
"Use for process flows, architectural diagrams, technical illustrations",
|
||||
"Clear visual hierarchy and labeling",
|
||||
"Professional diagram aesthetics",
|
||||
"Educational content visualization"
|
||||
]
|
||||
},
|
||||
"conceptual": {
|
||||
"guidance": "Professional conceptual imagery. Good for abstract representations with clear messaging.",
|
||||
"best_practices": [
|
||||
"Clear visual metaphors for abstract concepts",
|
||||
"Professional presentation style",
|
||||
"Good for educational or explanatory content"
|
||||
]
|
||||
}
|
||||
},
|
||||
# Default guidance for unknown models
|
||||
"_default": {
|
||||
"text_overlay": {
|
||||
"guidance": "Design for text overlay areas. Create clean backgrounds with high-contrast safe zones for text placement.",
|
||||
"best_practices": [
|
||||
"Use designated text areas (top 20% or bottom 20%)",
|
||||
"Create clean, uncluttered backgrounds",
|
||||
"Avoid embedding text directly in the image",
|
||||
"Design for text to be added as overlay"
|
||||
],
|
||||
"negative_prompt_additions": "text artifacts, unreadable text, embedded words"
|
||||
},
|
||||
"conceptual": {
|
||||
"guidance": "Focus on visual metaphors and abstract representations of the topic.",
|
||||
"best_practices": [
|
||||
"Use visual metaphors relevant to the content",
|
||||
"Create simple, clear compositions",
|
||||
"Avoid busy or cluttered designs"
|
||||
]
|
||||
},
|
||||
"chart": {
|
||||
"guidance": "Use abstract data representations. Avoid actual charts with embedded text.",
|
||||
"best_practices": [
|
||||
"Create visual metaphors for data",
|
||||
"Use shapes, colors, and patterns to represent information",
|
||||
"Design with text overlay zones for labels"
|
||||
],
|
||||
"warnings": ["Do not request actual charts with text - use abstract representations"]
|
||||
},
|
||||
"infographic": {
|
||||
"guidance": "Create multi-section infographic layouts with clear visual hierarchy. Use text overlay zones for information.",
|
||||
"best_practices": [
|
||||
"Multi-panel designs with distinct sections",
|
||||
"Clear visual hierarchy and organization",
|
||||
"Design with text overlay zones for each section",
|
||||
"Professional infographic aesthetic"
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -620,8 +471,8 @@ def get_model_specific_guidance(model: Optional[str], image_type: Optional[str])
|
||||
model_lower = model.lower()
|
||||
image_type_lower = (image_type or "conceptual").lower()
|
||||
|
||||
# Get model guidance (use _default for unknown models)
|
||||
model_guidance = MODEL_SPECIFIC_GUIDANCE.get(model_lower, MODEL_SPECIFIC_GUIDANCE.get("_default", {}))
|
||||
# Get model guidance
|
||||
model_guidance = MODEL_SPECIFIC_GUIDANCE.get(model_lower, {})
|
||||
|
||||
# Get image type specific guidance
|
||||
type_guidance = model_guidance.get(image_type_lower, model_guidance.get("text_overlay", {}))
|
||||
@@ -629,6 +480,63 @@ def get_model_specific_guidance(model: Optional[str], image_type: Optional[str])
|
||||
return type_guidance
|
||||
|
||||
|
||||
def extract_visual_data(section: Dict[str, Any], research: Optional[Dict[str, Any]]) -> Dict[str, Any]:
|
||||
"""Intelligently extract visual-relevant data from section and research."""
|
||||
visual_data = {
|
||||
"visual_keywords": [],
|
||||
"data_points": [],
|
||||
"concepts": [],
|
||||
"statistics": []
|
||||
}
|
||||
|
||||
# Extract from section
|
||||
if section:
|
||||
# Key points that are visualizable
|
||||
key_points = section.get("key_points", []) or []
|
||||
for point in key_points[:5]:
|
||||
if isinstance(point, str):
|
||||
# Look for numbers, percentages, comparisons
|
||||
if any(char.isdigit() for char in point):
|
||||
visual_data["statistics"].append(point)
|
||||
# Look for visual concepts
|
||||
elif any(word in point.lower() for word in ["increase", "decrease", "growth", "trend", "pattern", "comparison"]):
|
||||
visual_data["data_points"].append(point)
|
||||
else:
|
||||
visual_data["concepts"].append(point)
|
||||
|
||||
# Subheadings that suggest visuals
|
||||
subheadings = section.get("subheadings", []) or []
|
||||
for subhead in subheadings[:3]:
|
||||
if isinstance(subhead, str):
|
||||
visual_data["concepts"].append(subhead)
|
||||
|
||||
# Keywords
|
||||
keywords = section.get("keywords", []) or []
|
||||
visual_data["visual_keywords"].extend([str(k) for k in keywords[:8] if k])
|
||||
|
||||
# Extract from research
|
||||
if research:
|
||||
# Key facts that are visualizable
|
||||
key_facts = research.get("key_facts", []) or research.get("highlights", []) or []
|
||||
for fact in key_facts[:3]:
|
||||
if isinstance(fact, str):
|
||||
if any(char.isdigit() for char in fact):
|
||||
visual_data["statistics"].append(fact)
|
||||
else:
|
||||
visual_data["data_points"].append(fact)
|
||||
|
||||
# Research insights
|
||||
insights = research.get("insights", []) or research.get("summary", "")
|
||||
if isinstance(insights, str) and insights:
|
||||
# Extract key phrases
|
||||
sentences = insights.split('.')[:3]
|
||||
visual_data["concepts"].extend([s.strip() for s in sentences if s.strip()])
|
||||
elif isinstance(insights, list):
|
||||
visual_data["concepts"].extend([str(i) for i in insights[:3]])
|
||||
|
||||
return visual_data
|
||||
|
||||
|
||||
@router.post("/suggest-prompts", response_model=ImagePromptSuggestResponse)
|
||||
def suggest_prompts(
|
||||
req: ImagePromptSuggestRequest,
|
||||
@@ -656,18 +564,8 @@ def suggest_prompts(
|
||||
industry = persona.get("industry", req.research.get("domain") if req.research else "your industry")
|
||||
tone = persona.get("tone", "professional, trustworthy")
|
||||
|
||||
# Extract visual-relevant data intelligently using the new module
|
||||
visual_data = _extract_visual_data(section, req.research)
|
||||
|
||||
# Get model recommendation based on content type
|
||||
model_recommendation = get_model_recommendation(visual_data)
|
||||
|
||||
# Build visual summary from extracted data
|
||||
visual_summary = build_visual_summary(visual_data)
|
||||
|
||||
# Add model recommendation to visual summary if available
|
||||
if model_recommendation:
|
||||
visual_summary += model_recommendation
|
||||
# Extract visual-relevant data intelligently
|
||||
visual_data = extract_visual_data(section, req.research)
|
||||
|
||||
schema = {
|
||||
"type": "object",
|
||||
@@ -722,6 +620,19 @@ def suggest_prompts(
|
||||
if model_warnings:
|
||||
provider_guidance += f"\n⚠️ WARNINGS:\n" + "\n".join([f"- {w}" for w in model_warnings])
|
||||
|
||||
# Build visual data summary from extracted data
|
||||
visual_summary_parts = []
|
||||
if visual_data["statistics"]:
|
||||
visual_summary_parts.append(f"Key Statistics: {', '.join(visual_data['statistics'][:3])}")
|
||||
if visual_data["data_points"]:
|
||||
visual_summary_parts.append(f"Data Points: {', '.join(visual_data['data_points'][:3])}")
|
||||
if visual_data["concepts"]:
|
||||
visual_summary_parts.append(f"Visual Concepts: {', '.join(visual_data['concepts'][:5])}")
|
||||
if visual_data["visual_keywords"]:
|
||||
visual_summary_parts.append(f"Keywords: {', '.join(visual_data['visual_keywords'][:8])}")
|
||||
|
||||
visual_summary = "\n".join(visual_summary_parts) if visual_summary_parts else ""
|
||||
|
||||
best_practices = (
|
||||
"BLOG IMAGE BEST PRACTICES: Create images optimized for blog content, not social media posters. "
|
||||
"Focus on: data visualization elements (charts, graphs, infographics), clean layouts with designated text overlay areas, "
|
||||
@@ -743,15 +654,14 @@ def suggest_prompts(
|
||||
else "Do not include on-image text, but still design with text overlay areas in mind for blog use."
|
||||
)
|
||||
|
||||
# Image type specific guidance (enhanced with infographic type)
|
||||
# Image type specific guidance
|
||||
image_type_guidance = {
|
||||
"realistic": "Photorealistic style with professional photography quality. Include camera settings and lighting details.",
|
||||
"chart": "⚠️ IMPORTANT: Complex infographics are too difficult for current AI models. Create simple visual representations with designated text overlay areas instead. Use abstract data visualization elements, not actual charts with embedded text.",
|
||||
"conceptual": "Abstract or conceptual imagery that represents the topic visually. Clean compositions with text overlay zones.",
|
||||
"diagram": "Technical diagrams with simple, clear visual elements. Design for text overlay areas, not embedded labels.",
|
||||
"illustration": "Stylized illustrations that support the content. Professional, clean aesthetic suitable for blog use.",
|
||||
"background": "Background images optimized for text overlays. Clean, uncluttered compositions with high-contrast text zones.",
|
||||
"infographic": "Multi-section infographic designs with clear visual hierarchy. Use designated areas for each data point or concept. Design with text overlay zones for information labels. Professional infographic aesthetics with clean, organized layouts."
|
||||
"background": "Background images optimized for text overlays. Clean, uncluttered compositions with high-contrast text zones."
|
||||
}.get(image_type, "General blog image guidance.")
|
||||
|
||||
# Build comprehensive prompt with visual data and model-specific guidance
|
||||
|
||||
@@ -1,493 +0,0 @@
|
||||
"""
|
||||
Podcast Dubbing Handlers
|
||||
|
||||
Audio dubbing endpoints for translating podcast audio to different languages.
|
||||
Supports both low-quality (DeepL) and high-quality (WaveSpeed) dubbing with voice cloning.
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, BackgroundTasks
|
||||
from fastapi.responses import FileResponse
|
||||
from sqlalchemy.orm import Session
|
||||
from typing import Dict, Any, Optional
|
||||
from pathlib import Path
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
|
||||
from services.database import get_db
|
||||
from middleware.auth_middleware import get_current_user
|
||||
from api.story_writer.utils.auth import require_authenticated_user
|
||||
from api.story_writer.task_manager import task_manager
|
||||
from loguru import logger
|
||||
|
||||
from ..models import (
|
||||
PodcastAudioDubRequest,
|
||||
PodcastAudioDubResponse,
|
||||
PodcastAudioDubResult,
|
||||
PodcastAudioDubEstimateRequest,
|
||||
PodcastAudioDubEstimateResponse,
|
||||
VoiceCloneRequest,
|
||||
VoiceCloneResponse,
|
||||
VoiceCloneResult,
|
||||
)
|
||||
from services.dubbing import AudioDubbingService
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
_dubbing_executor = ThreadPoolExecutor(max_workers=4, thread_name_prefix="podcast_dubbing")
|
||||
|
||||
DUBBED_AUDIO_DIR = Path(__file__).resolve().parents[3] / "data" / "media" / "dubbed_audio"
|
||||
|
||||
|
||||
def _ensure_dubbed_audio_dir():
|
||||
DUBBED_AUDIO_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
def _execute_dubbing_task(
|
||||
task_id: str,
|
||||
source_audio_url: str,
|
||||
source_language: Optional[str],
|
||||
target_language: str,
|
||||
quality: str,
|
||||
voice_id: str,
|
||||
speed: float,
|
||||
emotion: str,
|
||||
use_voice_clone: bool,
|
||||
custom_voice_id: Optional[str],
|
||||
voice_clone_accuracy: float,
|
||||
user_id: str,
|
||||
):
|
||||
"""Background task to dub audio."""
|
||||
try:
|
||||
task_manager.update_task_status(
|
||||
task_id, "processing", progress=5.0,
|
||||
message="Starting audio dubbing..."
|
||||
)
|
||||
|
||||
_ensure_dubbed_audio_dir()
|
||||
|
||||
service = AudioDubbingService(output_dir=DUBBED_AUDIO_DIR)
|
||||
|
||||
def progress_callback(progress: float, message: str):
|
||||
task_manager.update_task_status(
|
||||
task_id, "processing", progress=progress,
|
||||
message=message
|
||||
)
|
||||
|
||||
logger.info(f"[Dubbing] Task {task_id}: Starting dubbing with voice_clone={use_voice_clone}")
|
||||
|
||||
result = service.dub_audio(
|
||||
source_audio=source_audio_url,
|
||||
target_language=target_language,
|
||||
source_language=source_language,
|
||||
voice_id=voice_id,
|
||||
speed=speed,
|
||||
emotion=emotion,
|
||||
quality=quality,
|
||||
use_voice_clone=use_voice_clone,
|
||||
custom_voice_id=custom_voice_id,
|
||||
accuracy=voice_clone_accuracy,
|
||||
user_id=user_id,
|
||||
progress_callback=progress_callback,
|
||||
)
|
||||
|
||||
task_manager.update_task_status(
|
||||
task_id, "completed", progress=100.0,
|
||||
result={
|
||||
"dubbed_audio_url": result.dubbed_audio_url,
|
||||
"dubbed_audio_filename": Path(result.dubbed_audio_path).name,
|
||||
"original_transcript": result.original_transcript,
|
||||
"translated_transcript": result.translated_transcript,
|
||||
"source_language": result.source_language,
|
||||
"target_language": result.target_language,
|
||||
"voice_id": result.voice_id,
|
||||
"quality": result.quality,
|
||||
"duration_seconds": result.duration_seconds,
|
||||
"file_size": result.file_size,
|
||||
"cost": result.cost,
|
||||
"status": "completed",
|
||||
"voice_clone_used": result.voice_clone_used,
|
||||
"cloned_voice_id": result.cloned_voice_id,
|
||||
},
|
||||
message="Audio dubbing completed!"
|
||||
)
|
||||
|
||||
logger.info(f"[Dubbing] Task {task_id} completed successfully (voice_clone_used={result.voice_clone_used})")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[Dubbing] Task {task_id} failed: {str(e)}")
|
||||
task_manager.update_task_status(
|
||||
task_id, "failed",
|
||||
error=str(e),
|
||||
message=f"Dubbing failed: {str(e)}"
|
||||
)
|
||||
|
||||
|
||||
def _execute_voice_clone_task(
|
||||
task_id: str,
|
||||
source_audio_url: str,
|
||||
custom_voice_id: Optional[str],
|
||||
accuracy: float,
|
||||
language_boost: Optional[str],
|
||||
user_id: str,
|
||||
):
|
||||
"""Background task to clone voice from audio."""
|
||||
try:
|
||||
task_manager.update_task_status(
|
||||
task_id, "processing", progress=10.0,
|
||||
message="Starting voice cloning..."
|
||||
)
|
||||
|
||||
_ensure_dubbed_audio_dir()
|
||||
|
||||
service = AudioDubbingService(output_dir=DUBBED_AUDIO_DIR)
|
||||
|
||||
task_manager.update_task_status(
|
||||
task_id, "processing", progress=30.0,
|
||||
message="Processing audio..."
|
||||
)
|
||||
|
||||
voice_info = service.clone_voice_from_audio(
|
||||
source_audio=source_audio_url,
|
||||
custom_voice_id=custom_voice_id,
|
||||
accuracy=accuracy,
|
||||
language_boost=language_boost,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
task_manager.update_task_status(
|
||||
task_id, "completed", progress=100.0,
|
||||
result={
|
||||
"voice_id": voice_info.voice_id,
|
||||
"voice_url": voice_info.voice_url,
|
||||
"source_language": voice_info.source_language,
|
||||
"accuracy": voice_info.accuracy,
|
||||
"file_size": voice_info.file_size,
|
||||
"status": "completed",
|
||||
},
|
||||
message="Voice cloning completed!"
|
||||
)
|
||||
|
||||
logger.info(f"[VoiceClone] Task {task_id} completed: {voice_info.voice_id}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[VoiceClone] Task {task_id} failed: {str(e)}")
|
||||
task_manager.update_task_status(
|
||||
task_id, "failed",
|
||||
error=str(e),
|
||||
message=f"Voice cloning failed: {str(e)}"
|
||||
)
|
||||
|
||||
|
||||
@router.post("/dub/audio", response_model=PodcastAudioDubResponse)
|
||||
async def create_audio_dubbing_task(
|
||||
request: PodcastAudioDubRequest,
|
||||
background_tasks: BackgroundTasks,
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
):
|
||||
"""
|
||||
Create an audio dubbing task.
|
||||
|
||||
Translates podcast audio to a target language using STT → Translate → TTS pipeline.
|
||||
|
||||
For high-quality dubbing with voice preservation, set use_voice_clone=True.
|
||||
|
||||
- **source_audio_url**: URL or path to source audio file
|
||||
- **target_language**: Target language code (e.g., 'es', 'Spanish')
|
||||
- **source_language**: Source language (auto-detected if not provided)
|
||||
- **quality**: 'low' (DeepL, cheaper) or 'high' (WaveSpeed, better quality)
|
||||
- **voice_id**: Voice ID for TTS (default: 'Wise_Woman')
|
||||
- **speed**: Speech speed 0.5-2.0 (default: 1.0)
|
||||
- **use_voice_clone**: Use voice cloning to preserve original speaker's voice
|
||||
- **custom_voice_id**: Custom name for the cloned voice
|
||||
- **voice_clone_accuracy**: Voice cloning accuracy 0.1-1.0 (default: 0.7)
|
||||
"""
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
task_id = task_manager.create_task("audio_dubbing")
|
||||
|
||||
background_tasks.add_task(
|
||||
_execute_dubbing_task,
|
||||
task_id=task_id,
|
||||
source_audio_url=request.source_audio_url,
|
||||
source_language=request.source_language,
|
||||
target_language=request.target_language,
|
||||
quality=request.quality,
|
||||
voice_id=request.voice_id or "Wise_Woman",
|
||||
speed=request.speed or 1.0,
|
||||
emotion=request.emotion or "happy",
|
||||
use_voice_clone=request.use_voice_clone or False,
|
||||
custom_voice_id=request.custom_voice_id,
|
||||
voice_clone_accuracy=request.voice_clone_accuracy or 0.7,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
logger.info(f"[Dubbing] Created task {task_id} for user {user_id} (voice_clone={request.use_voice_clone})")
|
||||
|
||||
return PodcastAudioDubResponse(
|
||||
task_id=task_id,
|
||||
status="pending",
|
||||
message="Audio dubbing task created"
|
||||
)
|
||||
|
||||
|
||||
@router.get("/dub/{task_id}/result", response_model=PodcastAudioDubResult)
|
||||
async def get_dubbing_result(
|
||||
task_id: str,
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
):
|
||||
"""
|
||||
Get the result of a completed dubbing task.
|
||||
"""
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
task_status = task_manager.get_task_status(task_id)
|
||||
|
||||
if not task_status:
|
||||
raise HTTPException(status_code=404, detail="Task not found")
|
||||
|
||||
if task_status.get("status") == "failed":
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=task_status.get("error", "Dubbing failed")
|
||||
)
|
||||
|
||||
if task_status.get("status") != "completed":
|
||||
return PodcastAudioDubResult(
|
||||
task_id=task_id,
|
||||
status=task_status.get("status", "pending"),
|
||||
dubbed_audio_url="",
|
||||
dubbed_audio_filename="",
|
||||
original_transcript="",
|
||||
translated_transcript="",
|
||||
source_language="",
|
||||
target_language="",
|
||||
voice_id="",
|
||||
quality="",
|
||||
duration_seconds=0,
|
||||
file_size=0,
|
||||
cost=0.0,
|
||||
voice_clone_used=False,
|
||||
cloned_voice_id=None,
|
||||
)
|
||||
|
||||
result_data = task_status.get("result", {})
|
||||
|
||||
return PodcastAudioDubResult(
|
||||
task_id=task_id,
|
||||
status="completed",
|
||||
dubbed_audio_url=result_data.get("dubbed_audio_url", ""),
|
||||
dubbed_audio_filename=result_data.get("dubbed_audio_filename", ""),
|
||||
original_transcript=result_data.get("original_transcript", ""),
|
||||
translated_transcript=result_data.get("translated_transcript", ""),
|
||||
source_language=result_data.get("source_language", ""),
|
||||
target_language=result_data.get("target_language", ""),
|
||||
voice_id=result_data.get("voice_id", ""),
|
||||
quality=result_data.get("quality", ""),
|
||||
duration_seconds=result_data.get("duration_seconds", 0),
|
||||
file_size=result_data.get("file_size", 0),
|
||||
cost=result_data.get("cost", 0.0),
|
||||
voice_clone_used=result_data.get("voice_clone_used", False),
|
||||
cloned_voice_id=result_data.get("cloned_voice_id"),
|
||||
)
|
||||
|
||||
|
||||
@router.get("/dub/audio/{filename}")
|
||||
async def serve_dubbed_audio(
|
||||
filename: str,
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
):
|
||||
"""
|
||||
Serve a dubbed audio file.
|
||||
"""
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
_ensure_dubbed_audio_dir()
|
||||
|
||||
audio_path = DUBBED_AUDIO_DIR / filename
|
||||
|
||||
if not audio_path.exists():
|
||||
raise HTTPException(status_code=404, detail="Audio file not found")
|
||||
|
||||
return FileResponse(
|
||||
path=audio_path,
|
||||
media_type="audio/mpeg",
|
||||
filename=filename,
|
||||
)
|
||||
|
||||
|
||||
@router.post("/dub/estimate", response_model=PodcastAudioDubEstimateResponse)
|
||||
async def estimate_dubbing_cost(
|
||||
request: PodcastAudioDubEstimateRequest,
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
):
|
||||
"""
|
||||
Estimate the cost for audio dubbing.
|
||||
|
||||
Set use_voice_clone=True to include voice cloning cost ($0.05).
|
||||
"""
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
service = AudioDubbingService(output_dir=DUBBED_AUDIO_DIR)
|
||||
|
||||
cost_estimate = service.estimate_cost(
|
||||
audio_duration_seconds=request.audio_duration_seconds,
|
||||
target_language=request.target_language,
|
||||
quality=request.quality,
|
||||
use_voice_clone=request.use_voice_clone or False,
|
||||
)
|
||||
|
||||
return PodcastAudioDubEstimateResponse(**cost_estimate)
|
||||
|
||||
|
||||
@router.get("/dub/languages")
|
||||
async def get_supported_dubbing_languages(
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
):
|
||||
"""
|
||||
Get list of supported languages for dubbing.
|
||||
"""
|
||||
from services.translation import list_supported_languages
|
||||
|
||||
languages = list_supported_languages()
|
||||
|
||||
return {
|
||||
"languages": [
|
||||
{"code": code, "name": name}
|
||||
for code, name in sorted(languages.items(), key=lambda x: x[1])
|
||||
],
|
||||
"count": len(languages),
|
||||
}
|
||||
|
||||
|
||||
@router.get("/dub/voices")
|
||||
async def get_available_voices(
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
):
|
||||
"""
|
||||
Get list of available TTS voices for dubbing.
|
||||
"""
|
||||
return {
|
||||
"voices": [
|
||||
{"id": "Wise_Woman", "name": "Wise Woman", "gender": "female"},
|
||||
{"id": "Warm_Woman", "name": "Warm Woman", "gender": "female"},
|
||||
{"id": "Young_Woman", "name": "Young Woman", "gender": "female"},
|
||||
{"id": "Mature_Woman", "name": "Mature Woman", "gender": "female"},
|
||||
{"id": "Gentle_Woman", "name": "Gentle Woman", "gender": "female"},
|
||||
{"id": "Confident_Man", "name": "Confident Man", "gender": "male"},
|
||||
{"id": "Warm_Man", "name": "Warm Man", "gender": "male"},
|
||||
{"id": "Young_Man", "name": "Young Man", "gender": "male"},
|
||||
{"id": "Mature_Man", "name": "Mature Man", "gender": "male"},
|
||||
{"id": "Default", "name": "Default", "gender": "neutral"},
|
||||
],
|
||||
"count": 10,
|
||||
"note": "Voice cloning creates custom voices from audio samples. Use /dub/voices/clone to create one."
|
||||
}
|
||||
|
||||
|
||||
@router.post("/dub/voices/clone", response_model=VoiceCloneResponse)
|
||||
async def create_voice_clone_task(
|
||||
request: VoiceCloneRequest,
|
||||
background_tasks: BackgroundTasks,
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
):
|
||||
"""
|
||||
Clone a voice from an audio sample.
|
||||
|
||||
Creates a custom voice that can be used for dubbing with preserved speaker identity.
|
||||
|
||||
- **source_audio_url**: URL or path to source audio (10-60 seconds recommended)
|
||||
- **custom_voice_id**: Custom name for the cloned voice
|
||||
- **accuracy**: Cloning accuracy 0.1-1.0 (higher = better quality but more processing)
|
||||
- **language_boost**: Language to optimize the voice for
|
||||
"""
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
task_id = task_manager.create_task("voice_clone")
|
||||
|
||||
background_tasks.add_task(
|
||||
_execute_voice_clone_task,
|
||||
task_id=task_id,
|
||||
source_audio_url=request.source_audio_url,
|
||||
custom_voice_id=request.custom_voice_id,
|
||||
accuracy=request.accuracy or 0.7,
|
||||
language_boost=request.language_boost,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
logger.info(f"[VoiceClone] Created task {task_id} for user {user_id}")
|
||||
|
||||
return VoiceCloneResponse(
|
||||
task_id=task_id,
|
||||
status="pending",
|
||||
message="Voice cloning task created"
|
||||
)
|
||||
|
||||
|
||||
@router.get("/dub/voices/{task_id}/result", response_model=VoiceCloneResult)
|
||||
async def get_voice_clone_result(
|
||||
task_id: str,
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
):
|
||||
"""
|
||||
Get the result of a completed voice cloning task.
|
||||
"""
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
task_status = task_manager.get_task_status(task_id)
|
||||
|
||||
if not task_status:
|
||||
raise HTTPException(status_code=404, detail="Task not found")
|
||||
|
||||
if task_status.get("status") == "failed":
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=task_status.get("error", "Voice cloning failed")
|
||||
)
|
||||
|
||||
if task_status.get("status") != "completed":
|
||||
return VoiceCloneResult(
|
||||
task_id=task_id,
|
||||
voice_id="",
|
||||
voice_url="",
|
||||
source_language="",
|
||||
accuracy=0.0,
|
||||
file_size=0,
|
||||
status=task_status.get("status", "pending"),
|
||||
)
|
||||
|
||||
result_data = task_status.get("result", {})
|
||||
|
||||
return VoiceCloneResult(
|
||||
task_id=task_id,
|
||||
voice_id=result_data.get("voice_id", ""),
|
||||
voice_url=result_data.get("voice_url", ""),
|
||||
source_language=result_data.get("source_language", ""),
|
||||
accuracy=result_data.get("accuracy", 0.7),
|
||||
file_size=result_data.get("file_size", 0),
|
||||
status="completed",
|
||||
)
|
||||
|
||||
|
||||
@router.get("/dub/voices/audio/{filename}")
|
||||
async def serve_voice_audio(
|
||||
filename: str,
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
):
|
||||
"""
|
||||
Serve a voice sample audio file.
|
||||
"""
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
_ensure_dubbed_audio_dir()
|
||||
|
||||
audio_path = DUBBED_AUDIO_DIR / filename
|
||||
|
||||
if not audio_path.exists():
|
||||
raise HTTPException(status_code=404, detail="Voice audio file not found")
|
||||
|
||||
return FileResponse(
|
||||
path=audio_path,
|
||||
media_type="audio/mpeg",
|
||||
filename=filename,
|
||||
)
|
||||
@@ -7,7 +7,6 @@ All Pydantic request/response models for podcast endpoints.
|
||||
from pydantic import BaseModel, Field, model_validator
|
||||
from typing import List, Optional, Dict, Any
|
||||
from datetime import datetime
|
||||
from enum import Enum
|
||||
|
||||
|
||||
class PodcastProjectResponse(BaseModel):
|
||||
@@ -321,99 +320,3 @@ class PodcastCombineVideosResponse(BaseModel):
|
||||
status: str
|
||||
message: str
|
||||
|
||||
|
||||
class AudioDubbingQuality(str, Enum):
|
||||
LOW = "low"
|
||||
HIGH = "high"
|
||||
|
||||
@classmethod
|
||||
def from_string(cls, value: str) -> "AudioDubbingQuality":
|
||||
if value.lower() == "high":
|
||||
return cls.HIGH
|
||||
return cls.LOW
|
||||
|
||||
|
||||
class PodcastAudioDubRequest(BaseModel):
|
||||
"""Request model for audio dubbing."""
|
||||
source_audio_url: str = Field(..., description="URL or path to source audio file")
|
||||
source_language: Optional[str] = Field(None, description="Source language code (auto-detected if None)")
|
||||
target_language: str = Field(..., description="Target language for dubbing")
|
||||
quality: str = Field(default="low", description="Translation quality: low (DeepL) or high (WaveSpeed)")
|
||||
voice_id: Optional[str] = Field(default="Wise_Woman", description="Voice ID for TTS")
|
||||
speed: Optional[float] = Field(default=1.0, ge=0.5, le=2.0, description="Speech speed (0.5-2.0)")
|
||||
emotion: Optional[str] = Field(default="happy", description="Emotion for TTS voice")
|
||||
preserve_emotion: Optional[bool] = Field(default=True, description="Preserve emotional tone in translation")
|
||||
use_voice_clone: Optional[bool] = Field(default=False, description="Use voice cloning to preserve original speaker's voice")
|
||||
custom_voice_id: Optional[str] = Field(None, description="Custom name for the cloned voice")
|
||||
voice_clone_accuracy: Optional[float] = Field(default=0.7, ge=0.1, le=1.0, description="Voice cloning accuracy (0.1-1.0)")
|
||||
|
||||
|
||||
class PodcastAudioDubResponse(BaseModel):
|
||||
"""Response model for audio dubbing task creation."""
|
||||
task_id: str
|
||||
status: str = "pending"
|
||||
message: str = "Audio dubbing task created"
|
||||
|
||||
|
||||
class PodcastAudioDubResult(BaseModel):
|
||||
"""Response model for completed audio dubbing."""
|
||||
dubbed_audio_url: str
|
||||
dubbed_audio_filename: str
|
||||
original_transcript: str
|
||||
translated_transcript: str
|
||||
source_language: str
|
||||
target_language: str
|
||||
voice_id: str
|
||||
quality: str
|
||||
duration_seconds: int
|
||||
file_size: int
|
||||
cost: float
|
||||
task_id: str
|
||||
status: str = "completed"
|
||||
voice_clone_used: Optional[bool] = Field(default=False, description="Whether voice cloning was used")
|
||||
cloned_voice_id: Optional[str] = Field(None, description="ID of the cloned voice if voice_clone_used=True")
|
||||
|
||||
|
||||
class PodcastAudioDubEstimateRequest(BaseModel):
|
||||
"""Request model for dubbing cost estimation."""
|
||||
audio_duration_seconds: float = Field(..., description="Duration of source audio in seconds")
|
||||
target_language: str = Field(..., description="Target language")
|
||||
quality: str = Field(default="low", description="Translation quality")
|
||||
use_voice_clone: Optional[bool] = Field(default=False, description="Include voice cloning cost")
|
||||
|
||||
|
||||
class PodcastAudioDubEstimateResponse(BaseModel):
|
||||
"""Response model for dubbing cost estimation."""
|
||||
estimated_characters: int
|
||||
translation_cost: float
|
||||
tts_cost: float
|
||||
voice_clone_cost: float = 0.0
|
||||
total_cost: float
|
||||
currency: str = "USD"
|
||||
|
||||
|
||||
class VoiceCloneRequest(BaseModel):
|
||||
"""Request model for voice cloning."""
|
||||
source_audio_url: str = Field(..., description="URL or path to source audio file (10-60 seconds recommended)")
|
||||
custom_voice_id: Optional[str] = Field(None, description="Custom name for the cloned voice")
|
||||
accuracy: Optional[float] = Field(default=0.7, ge=0.1, le=1.0, description="Cloning accuracy (0.1-1.0)")
|
||||
language_boost: Optional[str] = Field(None, description="Language to optimize the voice for")
|
||||
|
||||
|
||||
class VoiceCloneResponse(BaseModel):
|
||||
"""Response model for voice cloning."""
|
||||
task_id: str
|
||||
status: str = "pending"
|
||||
message: str = "Voice cloning task created"
|
||||
|
||||
|
||||
class VoiceCloneResult(BaseModel):
|
||||
"""Response model for completed voice cloning."""
|
||||
voice_id: str
|
||||
voice_url: str
|
||||
source_language: str
|
||||
accuracy: float
|
||||
file_size: int
|
||||
task_id: str
|
||||
status: str = "completed"
|
||||
|
||||
|
||||
@@ -12,7 +12,7 @@ from api.story_writer.utils.auth import require_authenticated_user
|
||||
from api.story_writer.task_manager import task_manager
|
||||
|
||||
# Import all handler routers
|
||||
from .handlers import projects, analysis, research, script, audio, images, video, avatar, dubbing
|
||||
from .handlers import projects, analysis, research, script, audio, images, video, avatar
|
||||
|
||||
# Create main router
|
||||
router = APIRouter(prefix="/api/podcast", tags=["Podcast Maker"])
|
||||
@@ -26,7 +26,6 @@ router.include_router(audio.router)
|
||||
router.include_router(images.router)
|
||||
router.include_router(video.router)
|
||||
router.include_router(avatar.router)
|
||||
router.include_router(dubbing.router)
|
||||
|
||||
|
||||
@router.get("/task/{task_id}/status")
|
||||
|
||||
@@ -5,7 +5,6 @@ Handles scene animation endpoints using WaveSpeed Kling and InfiniteTalk.
|
||||
"""
|
||||
|
||||
import mimetypes
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, Optional
|
||||
from urllib.parse import quote
|
||||
|
||||
|
||||
@@ -170,7 +170,7 @@ async def get_subscription_status(
|
||||
if getattr(subscription, 'auto_renew', False):
|
||||
# advance period
|
||||
try:
|
||||
from services.subscription.pricing_service import PricingService
|
||||
from services.pricing_service import PricingService
|
||||
pricing = PricingService(db)
|
||||
# reuse helper to ensure current
|
||||
pricing._ensure_subscription_current(subscription)
|
||||
@@ -245,7 +245,7 @@ async def get_subscription_status(
|
||||
if subscription.current_period_end < now:
|
||||
if getattr(subscription, 'auto_renew', False):
|
||||
try:
|
||||
from services.subscription.pricing_service import PricingService
|
||||
from services.pricing_service import PricingService
|
||||
pricing = PricingService(db)
|
||||
pricing._ensure_subscription_current(subscription)
|
||||
except Exception as e2:
|
||||
|
||||
@@ -1,306 +0,0 @@
|
||||
# Auto-Dubbing Feature Documentation
|
||||
|
||||
## Overview
|
||||
|
||||
Auto-Dubbing enables automatic translation of podcast audio to different languages with optional voice cloning to preserve the original speaker's voice.
|
||||
|
||||
## Features
|
||||
|
||||
- **Text Translation**: Translate audio transcripts using DeepL (low-cost) or WaveSpeed (high-quality)
|
||||
- **Voice Cloning**: Preserve original speaker's voice in dubbed audio
|
||||
- **Multiple Quality Tiers**: Choose between low-cost (DeepL) and high-quality (WaveSpeed) translation
|
||||
- **Cost Estimation**: Preview costs before starting dubbing tasks
|
||||
- **Progress Tracking**: Real-time progress updates for long-running tasks
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
backend/services/
|
||||
├── translation/ # Common translation service
|
||||
│ ├── __init__.py
|
||||
│ ├── base_translation.py
|
||||
│ ├── deepl_translator.py
|
||||
│ ├── wavespeed_translator.py
|
||||
│ └── translation_factory.py
|
||||
│
|
||||
├── dubbing/ # Audio dubbing service
|
||||
│ └── __init__.py # AudioDubbingService
|
||||
│
|
||||
└── api/podcast/
|
||||
├── handlers/
|
||||
│ └── dubbing.py # API endpoints
|
||||
└── models.py # Request/response models
|
||||
```
|
||||
|
||||
## Quick Start
|
||||
|
||||
### 1. Configure Environment
|
||||
|
||||
Add your DeepL API key to `.env`:
|
||||
|
||||
```bash
|
||||
# backend/.env
|
||||
DEEPL_API_KEY=your-deepl-api-key-here
|
||||
```
|
||||
|
||||
Get a free DeepL API key at: https://www.deepl.com/pro-api
|
||||
|
||||
### 2. Basic Audio Dubbing
|
||||
|
||||
```python
|
||||
from services.dubbing import AudioDubbingService
|
||||
|
||||
service = AudioDubbingService()
|
||||
result = service.dub_audio(
|
||||
source_audio="/path/to/audio.mp3",
|
||||
target_language="Spanish",
|
||||
quality="low", # or "high"
|
||||
)
|
||||
```
|
||||
|
||||
### 3. High-Quality Dubbing with Voice Clone
|
||||
|
||||
```python
|
||||
result = service.dub_audio(
|
||||
source_audio="/path/to/audio.mp3",
|
||||
target_language="French",
|
||||
quality="high",
|
||||
use_voice_clone=True, # Preserve original voice
|
||||
custom_voice_id="my_podcast_voice",
|
||||
accuracy=0.8, # 0.1-1.0
|
||||
)
|
||||
```
|
||||
|
||||
## API Endpoints
|
||||
|
||||
### Create Dubbing Task
|
||||
|
||||
```bash
|
||||
POST /api/podcast/dub/audio
|
||||
```
|
||||
|
||||
**Request:**
|
||||
```json
|
||||
{
|
||||
"source_audio_url": "https://example.com/audio.mp3",
|
||||
"target_language": "Spanish",
|
||||
"quality": "low",
|
||||
"voice_id": "Wise_Woman",
|
||||
"speed": 1.0,
|
||||
"use_voice_clone": false
|
||||
}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"task_id": "abc123",
|
||||
"status": "pending",
|
||||
"message": "Audio dubbing task created"
|
||||
}
|
||||
```
|
||||
|
||||
### Get Dubbing Result
|
||||
|
||||
```bash
|
||||
GET /api/podcast/dub/{task_id}/result
|
||||
```
|
||||
|
||||
**Response (completed):**
|
||||
```json
|
||||
{
|
||||
"task_id": "abc123",
|
||||
"status": "completed",
|
||||
"dubbed_audio_url": "/api/podcast/dub/audio/dubbed_xyz123.mp3",
|
||||
"original_transcript": "Hello, welcome to my podcast...",
|
||||
"translated_transcript": "Hola, bienvenidos a mi podcast...",
|
||||
"source_language": "en",
|
||||
"target_language": "Spanish",
|
||||
"voice_id": "Wise_Woman",
|
||||
"quality": "low",
|
||||
"voice_clone_used": false,
|
||||
"cost": 0.05,
|
||||
"file_size": 45000
|
||||
}
|
||||
```
|
||||
|
||||
### Clone Voice
|
||||
|
||||
```bash
|
||||
POST /api/podcast/dub/voices/clone
|
||||
```
|
||||
|
||||
**Request:**
|
||||
```json
|
||||
{
|
||||
"source_audio_url": "https://example.com/voice_sample.mp3",
|
||||
"custom_voice_id": "podcast_voice_1",
|
||||
"accuracy": 0.7,
|
||||
"language_boost": "Spanish"
|
||||
}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"task_id": "clone123",
|
||||
"status": "pending",
|
||||
"message": "Voice cloning task created"
|
||||
}
|
||||
```
|
||||
|
||||
### Estimate Cost
|
||||
|
||||
```bash
|
||||
POST /api/podcast/dub/estimate
|
||||
```
|
||||
|
||||
**Request:**
|
||||
```json
|
||||
{
|
||||
"audio_duration_seconds": 60,
|
||||
"target_language": "Spanish",
|
||||
"quality": "low",
|
||||
"use_voice_clone": false
|
||||
}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"estimated_characters": 900,
|
||||
"translation_cost": 0.009,
|
||||
"tts_cost": 0.9,
|
||||
"voice_clone_cost": 0.0,
|
||||
"total_cost": 0.909,
|
||||
"currency": "USD"
|
||||
}
|
||||
```
|
||||
|
||||
### Get Supported Languages
|
||||
|
||||
```bash
|
||||
GET /api/podcast/dub/languages
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"languages": [
|
||||
{"code": "es", "name": "Spanish"},
|
||||
{"code": "fr", "name": "French"},
|
||||
{"code": "de", "name": "German"},
|
||||
...
|
||||
],
|
||||
"count": 34
|
||||
}
|
||||
```
|
||||
|
||||
### Get Available Voices
|
||||
|
||||
```bash
|
||||
GET /api/podcast/dub/voices
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"voices": [
|
||||
{"id": "Wise_Woman", "name": "Wise Woman", "gender": "female"},
|
||||
{"id": "Warm_Man", "name": "Warm Man", "gender": "male"},
|
||||
...
|
||||
],
|
||||
"count": 10
|
||||
}
|
||||
```
|
||||
|
||||
## Translation Pipeline
|
||||
|
||||
### Low Quality (DeepL)
|
||||
```
|
||||
Source Audio → Download → STT (Gemini) → Translate (DeepL) → TTS (WaveSpeed) → Dubbed Audio
|
||||
```
|
||||
|
||||
### High Quality (WaveSpeed + Voice Clone)
|
||||
```
|
||||
Source Audio → Voice Clone → Download → STT → Translate (WaveSpeed) → TTS (cloned voice) → Dubbed Audio
|
||||
```
|
||||
|
||||
## Cost Structure
|
||||
|
||||
| Component | Low Quality | High Quality |
|
||||
|-----------|-------------|--------------|
|
||||
| Translation | $0.00001/char | $0.0001/char |
|
||||
| TTS | $0.001/char | $0.001/char |
|
||||
| Voice Clone | N/A | $0.05/voice |
|
||||
|
||||
**Example: 60-second audio (~900 chars)**
|
||||
- Low quality: ~$0.91
|
||||
- High quality with voice clone: ~$0.96
|
||||
|
||||
## Common Module Usage
|
||||
|
||||
The translation service can be used anywhere in the application:
|
||||
|
||||
```python
|
||||
from services.translation import translate_text, TranslationQuality
|
||||
|
||||
# Simple translation
|
||||
result = translate_text(
|
||||
text="Hello world",
|
||||
target_language="Spanish",
|
||||
quality=TranslationQuality.LOW
|
||||
)
|
||||
print(result.translated_text) # "Hola mundo"
|
||||
|
||||
# Batch translation
|
||||
from services.translation import translate_batch
|
||||
results = translate_batch(
|
||||
texts=["Hello", "Goodbye"],
|
||||
target_language="French",
|
||||
quality=TranslationQuality.LOW
|
||||
)
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
|
||||
The dubbing service returns standard HTTP exceptions:
|
||||
|
||||
- `400 Bad Request`: Invalid parameters
|
||||
- `404 Not Found`: Task or file not found
|
||||
- `500 Internal Server Error`: Dubbing failed (check task error message)
|
||||
|
||||
## Background Tasks
|
||||
|
||||
Dubbing tasks run in the background. Poll the result endpoint:
|
||||
|
||||
```python
|
||||
import time
|
||||
while True:
|
||||
result = get_dubbing_result(task_id)
|
||||
if result.status == "completed":
|
||||
print(f"Dubbed audio: {result.dubbed_audio_url}")
|
||||
break
|
||||
elif result.status == "failed":
|
||||
print(f"Failed: {result.error}")
|
||||
break
|
||||
time.sleep(2)
|
||||
```
|
||||
|
||||
## Environment Variables
|
||||
|
||||
| Variable | Description | Required |
|
||||
|----------|-------------|----------|
|
||||
| `DEEPL_API_KEY` | DeepL API key for low-quality translation | Yes (for low quality) |
|
||||
| `DEEPL_USE_PRO` | Use DeepL Pro API | No |
|
||||
| `WAVESPEED_API_KEY` | WaveSpeed API key (already configured) | Yes |
|
||||
|
||||
## Supported Languages
|
||||
|
||||
DeepL supports 34 languages including:
|
||||
- English, Spanish, French, German, Italian, Portuguese
|
||||
- Japanese, Chinese, Korean, Arabic, Hindi
|
||||
- Russian, Dutch, Polish, Turkish, Vietnamese
|
||||
- And more...
|
||||
|
||||
See full list via: `GET /api/podcast/dub/languages`
|
||||
@@ -8,7 +8,6 @@ IMPORTANT: This is a compatibility layer. For new code, use UserAPIKeyContext di
|
||||
"""
|
||||
|
||||
import os
|
||||
import time
|
||||
from fastapi import Request
|
||||
from loguru import logger
|
||||
from typing import Callable
|
||||
@@ -21,61 +20,8 @@ class APIKeyInjectionMiddleware:
|
||||
for the duration of each request.
|
||||
"""
|
||||
|
||||
# Shared across middleware instances (module currently instantiates per request)
|
||||
_missing_keys_log_timestamps = {}
|
||||
|
||||
def __init__(self):
|
||||
self.original_keys = {}
|
||||
|
||||
@staticmethod
|
||||
def _should_skip_missing_key_warning(request: Request) -> bool:
|
||||
"""
|
||||
Optionally suppress missing-key warnings for non-AI/internal routes.
|
||||
Controlled by API_KEY_INJECTION_SKIP_NON_AI_WARNINGS (default: true).
|
||||
"""
|
||||
skip_non_ai_warnings = os.getenv('API_KEY_INJECTION_SKIP_NON_AI_WARNINGS', 'true').lower() in ('1', 'true', 'yes')
|
||||
if not skip_non_ai_warnings:
|
||||
return False
|
||||
|
||||
path_lower = (request.url.path or '').lower()
|
||||
return (
|
||||
path_lower.startswith('/api/subscription/')
|
||||
or path_lower.startswith('/api/onboarding/')
|
||||
or path_lower.endswith('/status')
|
||||
or path_lower.endswith('/health')
|
||||
or path_lower == '/health'
|
||||
or path_lower == '/status'
|
||||
)
|
||||
|
||||
def _log_missing_keys_non_blocking(self, request: Request, user_id: str) -> None:
|
||||
"""
|
||||
Log missing API keys without interrupting request flow.
|
||||
- Defaults to debug-level logging.
|
||||
- Optional warn once-per-user-per-interval via env:
|
||||
API_KEY_INJECTION_MISSING_KEYS_LOG_MODE=warn_once
|
||||
API_KEY_INJECTION_MISSING_KEYS_LOG_INTERVAL_SECONDS=900
|
||||
"""
|
||||
try:
|
||||
if self._should_skip_missing_key_warning(request):
|
||||
logger.debug(f"[API Key Injection] Missing keys for user {user_id} on non-AI route; skipping warning")
|
||||
return
|
||||
|
||||
log_mode = os.getenv('API_KEY_INJECTION_MISSING_KEYS_LOG_MODE', 'debug').lower()
|
||||
if log_mode != 'warn_once':
|
||||
logger.debug(f"No API keys found for user {user_id}")
|
||||
return
|
||||
|
||||
interval_seconds = int(os.getenv('API_KEY_INJECTION_MISSING_KEYS_LOG_INTERVAL_SECONDS', '900'))
|
||||
now = time.time()
|
||||
last_logged_at = self._missing_keys_log_timestamps.get(user_id, 0)
|
||||
if (now - last_logged_at) >= max(interval_seconds, 1):
|
||||
logger.warning(f"No API keys found for user {user_id}")
|
||||
self._missing_keys_log_timestamps[user_id] = now
|
||||
else:
|
||||
logger.debug(f"No API keys found for user {user_id} (warning suppressed by interval)")
|
||||
except Exception as log_error:
|
||||
# Logging should never block request processing
|
||||
logger.debug(f"[API Key Injection] Failed to log missing keys state for user {user_id}: {log_error}")
|
||||
|
||||
async def __call__(self, request: Request, call_next: Callable):
|
||||
"""
|
||||
@@ -122,7 +68,7 @@ class APIKeyInjectionMiddleware:
|
||||
# Get user-specific API keys from database
|
||||
with user_api_keys(user_id) as user_keys:
|
||||
if not user_keys:
|
||||
self._log_missing_keys_non_blocking(request, user_id)
|
||||
logger.warning(f"No API keys found for user {user_id}")
|
||||
return await call_next(request)
|
||||
|
||||
# Save original environment values
|
||||
@@ -174,3 +120,4 @@ async def api_key_injection_middleware(request: Request, call_next: Callable):
|
||||
"""
|
||||
middleware = APIKeyInjectionMiddleware()
|
||||
return await middleware(request, call_next)
|
||||
|
||||
|
||||
@@ -155,7 +155,7 @@ class APIUsageLog(Base):
|
||||
user_id = Column(String(100), nullable=False)
|
||||
|
||||
# API Details
|
||||
provider = Column(Enum(APIProvider, values_callable=lambda obj: [e.value for e in obj]), nullable=False)
|
||||
provider = Column(Enum(APIProvider), nullable=False)
|
||||
endpoint = Column(String(200), nullable=False)
|
||||
method = Column(String(10), nullable=False)
|
||||
model_used = Column(String(100), nullable=True) # e.g., "gemini-2.5-flash"
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user