-
- """.format(
- st.session_state.event_name,
- st.session_state.event_date.strftime("%B %d, %Y"),
- st.session_state.event_time.strftime("%I:%M %p"),
- event_description
- ), unsafe_allow_html=True)
-
- if preview_type == "Desktop" or preview_type == "All":
- st.markdown("##### Desktop Preview")
- st.markdown("""
- {}
-{} at {}
-
- {}
-
-
-
- """.format(
- st.session_state.event_name,
- st.session_state.event_date.strftime("%B %d, %Y"),
- st.session_state.event_time.strftime("%I:%M %p"),
- event_description
- ), unsafe_allow_html=True)
-
- if preview_type == "Social Cards" or preview_type == "All":
- st.markdown("##### Social Cards Preview")
- st.markdown("""
- {}
-{} at {}
-
- {}
-
-
-
- """.format(
- st.session_state.event_name,
- st.session_state.event_date.strftime("%B %d, %Y"),
- st.session_state.event_time.strftime("%I:%M %p"),
- event_description[:200] + "..." if len(event_description) > 200 else event_description
- ), unsafe_allow_html=True)
-
- # Export options
- st.markdown("#### Export Options")
-
- col1, col2, col3 = st.columns(3)
-
- with col1:
- if st.button("đ Copy to Clipboard"):
- st.code(event_description)
- st.success("Event description copied to clipboard!")
-
- with col2:
- if st.button("đž Download as Text"):
- download_event_description(event_description)
-
- with col3:
- if st.button("đ
Export to Calendar"):
- st.info("Calendar export feature coming soon!")
-
-def validate_event_fields() -> bool:
- """Validate required event fields with enhanced validation."""
- validation_rules = {
- "Event Name": {
- "required": True,
- "min_length": 5,
- "max_length": 100,
- "type": str
- },
- "Event Date": {
- "required": True,
- "min_date": date.today(),
- "type": Union[date, type(None)]
- },
- "Target Audience": {
- "required": True,
- "min_length": 10,
- "type": str
- },
- "Event Goal": {
- "required": True,
- "min_length": 20,
- "type": str
- }
- }
-
- errors = []
- for field, rules in validation_rules.items():
- field_key = field.lower().replace(" ", "_")
- value = st.session_state.get(field_key)
-
- # Skip validation if field is not required and value is empty
- if not rules["required"] and (value is None or value == ""):
- continue
-
- # Type validation
- if "type" in rules and value is not None:
- expected_type = rules["type"]
- if not isinstance(value, expected_type):
- errors.append(f"{field} must be of type {expected_type}")
- continue
-
- # Required field validation
- if rules["required"] and (value is None or value == ""):
- errors.append(f"{field} is required")
- continue
-
- # Length validation for strings
- if isinstance(value, str):
- if "min_length" in rules and len(value) < rules["min_length"]:
- errors.append(f"{field} must be at least {rules['min_length']} characters")
- if "max_length" in rules and len(value) > rules["max_length"]:
- errors.append(f"{field} must be less than {rules['max_length']} characters")
-
- # Date validation
- if isinstance(value, date):
- if "min_date" in rules and value < rules["min_date"]:
- errors.append(f"{field} must be in the future")
-
- if errors:
- st.error("\n".join(errors))
- return False
-
- return True
-
-def generate_event_description() -> str:
- """Generate the event description using AI with enhanced features."""
- prompt = f"""
- Create a compelling Facebook event description for:
-
- Event Name: {st.session_state.get('event_name', '')}
- Event Type: {st.session_state.get('event_type', '')}
- Event Category: {st.session_state.get('event_category', '')}
- Date & Time: {st.session_state.get('event_date', '')} at {st.session_state.get('event_time', '')}
- Target Audience: {st.session_state.get('target_audience', '')}
- Event Goal: {st.session_state.get('event_goal', '')}
-
- Key Points to Include: {st.session_state.get('key_points', '')}
-
- Style: {st.session_state.get('content_style', '')}
-
- Additional Requirements:
- 1. Include SEO-optimized keywords
- 2. Add emoji suggestions for key points
- 3. Include trending hashtags in the event category
- 4. Add engagement hooks at strategic points
- 5. Include social proof elements
- 6. Add urgency triggers
- 7. Include mobile-optimized formatting
- 8. Add accessibility considerations
-
- Format the description with:
- - Engaging opening
- - Clear event details
- - Key benefits
- - Call to action
- - Relevant hashtags
- """
-
- try:
- response = llm_text_gen(prompt)
- return response
- except Exception as err:
- st.error(f"An error occurred while generating the event description: {err}")
- return ""
-
-def download_event_description(content: str):
- """Download the event description as a text file."""
- # Create a download link
- b64 = base64.b64encode(content.encode()).decode()
- href = f'Download Event Description'
- st.markdown(href, unsafe_allow_html=True)
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/facebook_carousel/README.md b/ToBeMigrated/ai_writers/ai_facebook_writer/modules/facebook_carousel/README.md
deleted file mode 100644
index a1fffe49..00000000
--- a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/facebook_carousel/README.md
+++ /dev/null
@@ -1,164 +0,0 @@
-# Facebook Carousel Generator đ
-
-Welcome to the Facebook Carousel Generator! This powerful tool helps you create engaging, professional-looking carousel posts for Facebook with AI-powered content and visuals.
-
-## What is a Facebook Carousel? đ¤
-
-A Facebook carousel is a post format that allows you to showcase up to 10 images or videos in a single post, each with its own caption. Users can swipe through the content, making it perfect for:
-- Showcasing multiple products
-- Telling a story
-- Sharing step-by-step tutorials
-- Highlighting features and benefits
-- Displaying before-and-after transformations
-
-## Features â¨
-
-### 1. Content Setup đ
-- **Business Profile**: Customize content for your specific industry
-- **Target Audience**: Define who you want to reach
-- **Purpose Selection**: Choose from multiple carousel purposes:
- - Product Showcase
- - How-to Guide
- - Before and After
- - Features/Benefits
- - Customer Success Stories
- - Product Collection
- - Service Overview
- - Educational Series
- - Brand Story
-
-### 2. Carousel Structure đī¸
-Choose from various storytelling formats:
-- Progressive Story
-- Feature Showcase
-- Step-by-Step Guide
-- Problem-Solution
-- Collection Display
-- Before-After Series
-- FAQ Format
-- Tips & Tricks
-
-### 3. Visual Elements đ¨
-Customize your carousel's look:
-- Visual styles (Modern, Bold, Minimalist, etc.)
-- Color schemes
-- Brand color integration
-- Image styles
-- Text overlays
-- Font selections
-- Composition options
-
-### 4. AI-Powered Generation đ¤
-The tool automatically creates:
-- Engaging captions
-- Slide content
-- Image suggestions
-- Hashtag recommendations
-- Engagement prompts
-
-## How to Use đą
-
-### Step 1: Content Setup
-1. Enter your business type and target audience
-2. Select your carousel purpose
-3. Choose your brand voice
-4. Enter your key message
-5. Select number of slides (2-10)
-6. Pick your preferred carousel structure
-
-### Step 2: Customize Content
-Select what to include:
-- Statistics/Facts
-- Testimonials
-- Pricing
-- Call-to-Action
-- Hashtags
-- Emojis
-- Questions
-- Bullet Points
-- Numbered Lists
-
-### Step 3: Visual Design
-1. Choose your visual style
-2. Select color scheme
-3. Pick image style
-4. Configure text overlays (optional)
-5. Customize composition
-
-### Step 4: Preview & Export
-View your carousel in:
-- Mobile preview
-- Desktop preview
-- Content overview
-- Download options:
- - Complete ZIP package
- - Individual files
- - With posting guidelines
-
-## Best Practices đĄ
-
-### Carousel Tips
-1. **Keep it Consistent**: Maintain visual consistency across all slides
-2. **Start Strong**: Put your best image first to grab attention
-3. **Tell a Story**: Make slides flow naturally from one to the next
-4. **Clear CTAs**: Include clear call-to-actions
-5. **Mobile-First**: Design with mobile viewing in mind
-
-### Posting Tips
-1. **Best Times**: Post between 1-4 PM on weekdays
-2. **Hashtags**: Use all provided hashtags for maximum reach
-3. **Engagement**: Respond to comments within the first hour
-4. **Cross-Promote**: Share to Stories after posting
-5. **Monitor**: Track performance for the first 24 hours
-
-## Export Options đž
-
-### 1. Complete Package (ZIP)
-Includes:
-- All carousel images
-- Main caption
-- Individual slide captions
-- Hashtags file
-- Engagement prompts
-- Posting guidelines
-- Metadata
-
-### 2. Individual Files
-Download separately:
-- Individual images
-- Caption text
-- Hashtags list
-- Engagement prompts
-
-### 3. Posting Guidelines
-Receive detailed instructions on:
-- How to upload your carousel
-- Best posting practices
-- Engagement strategies
-- Performance monitoring tips
-
-## Need Help? đ
-
-If you encounter any issues or have questions:
-1. Check the preview before generating final images
-2. Ensure all required fields are filled
-3. Try different visual styles if the first attempt isn't perfect
-4. Experiment with different carousel structures
-
-## Pro Tips đ
-
-1. **Planning**: Plan your carousel story before starting
-2. **Branding**: Use your brand colors for consistency
-3. **Testing**: Preview on both mobile and desktop
-4. **Engagement**: Use the provided engagement prompts
-5. **Analytics**: Save successful carousels as templates
-
-## Technical Requirements đ§
-
-- Stable internet connection
-- Web browser (Chrome, Firefox, Safari, or Edge)
-- Facebook Business Page (for posting)
-
-Remember: The better the input, the better the output! Take time to fill in all relevant information for the best results.
-
-Happy creating! đ¨â¨
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/facebook_carousel/carousel_generator.py b/ToBeMigrated/ai_writers/ai_facebook_writer/modules/facebook_carousel/carousel_generator.py
deleted file mode 100644
index 11d59c93..00000000
--- a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/facebook_carousel/carousel_generator.py
+++ /dev/null
@@ -1,1146 +0,0 @@
-"""
-Facebook Carousel Generator
-
-This module provides functionality to generate engaging Facebook carousel posts with
-AI-powered content and visuals.
-"""
-
-import streamlit as st
-import os
-from datetime import datetime
-from typing import Dict, Any, List, Optional
-from loguru import logger
-import sys
-import json
-
-# Import text and image generation
-from .....gpt_providers.text_generation.main_text_generation import llm_text_gen
-from .....gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
-
-# Configure logging
-logger.remove()
-logger.add(sys.stdout,
- colorize=True,
- format="{}
-{} at {}
-
- {}
-
-
-
- """.format(
- content['metadata']['business_type'],
- content['metadata']['target_audience'],
- content['metadata']['carousel_purpose'],
- content['metadata']['brand_voice'],
- content['metadata']['structure_type']
- ), unsafe_allow_html=True)
-
- # Display main caption
- st.markdown("#### Main Caption")
- st.markdown(f"""
- Carousel Details
-Business Type: {}
-Target Audience: {}
-Purpose: {}
-Brand Voice: {}
-Structure: {}
-
- {content['main_caption']}
-
- """, unsafe_allow_html=True)
-
- # Display slides
- st.markdown("#### Slides")
- for slide in content['slides']:
- with st.expander(f"Slide {slide['slide_number']}"):
- st.markdown(f"**Content:** {slide['content']}")
- st.markdown(f"**Image Prompt:** {slide['image_prompt']}")
- if slide.get('overlay_text'):
- st.markdown(f"**Overlay Text:** {slide['overlay_text']}")
-
- # Display hashtags and engagement prompts
- col1, col2 = st.columns(2)
-
- with col1:
- st.markdown("#### Hashtags")
- st.markdown(" ".join([f"#{tag}" for tag in content['hashtags']]))
-
- with col2:
- st.markdown("#### Engagement Prompts")
- for prompt in content['engagement_prompts']:
- st.markdown(f"- {prompt}")
-
- with tabs[1]: # Main Caption
- st.markdown("### Main Caption")
- st.markdown(f"""
-
-
-
- """, unsafe_allow_html=True)
-
- with tabs[2]: # Slides
- st.markdown("### Slide Content")
-
- # Create a grid of slides
- cols = st.columns(2)
- for i, slide in enumerate(content['slides']):
- with cols[i % 2]:
- st.markdown(f"""
- Caption Preview
- {content['main_caption']} --
Hashtags:
- {" ".join([f"#{tag}" for tag in content['hashtags']])} -
-
- """, unsafe_allow_html=True)
-
- with tabs[3]: # Engagement Elements
- st.markdown("### Engagement Elements")
-
- col1, col2 = st.columns(2)
-
- with col1:
- st.markdown("""
- Slide {slide['slide_number']}
-Content:
{slide['content']}
Image Prompt:
{slide['image_prompt']}
Overlay Text:
{slide['overlay_text']}
-
- """.format(" ".join([f"#{tag}" for tag in content['hashtags']])), unsafe_allow_html=True)
-
- with col2:
- st.markdown("""
- Hashtags
-{}
-
-
- """.format("".join([f"Engagement Prompts
--
- {}
-
-
- """, unsafe_allow_html=True)
-
- with tabs[1]: # Slideshow
- st.markdown("#### Slideshow View")
-
- # Create a simple slideshow
- current_slide = st.slider("Select Slide", 1, len(images), 1)
- st.image(images[current_slide-1], use_container_width=True)
-
- col1, col2, col3 = st.columns([1, 2, 1])
- with col1:
- if current_slide > 1:
- if st.button("â Previous"):
- st.session_state['current_slide'] = current_slide - 1
- with col2:
- st.markdown(f"""
- Slide {i+1}
- - Download Image - -
-
- """, unsafe_allow_html=True)
- with col3:
- if current_slide < len(images):
- if st.button("Next â"):
- st.session_state['current_slide'] = current_slide + 1
-
- with tabs[2]: # Individual Images
- st.markdown("#### Individual Images")
-
- for i, image_path in enumerate(images):
- with st.expander(f"Slide {i+1}"):
- st.image(image_path, use_container_width=True)
-
- col1, col2 = st.columns(2)
- with col1:
- st.download_button(
- label=f"Download Slide {i+1}",
- data=open(image_path, "rb").read(),
- file_name=f"carousel_slide_{i+1}.png",
- mime="image/png"
- )
- with col2:
- st.markdown(f"""
- Slide {current_slide} of {len(images)}
-
-
- """, unsafe_allow_html=True)
-
- # Add a download all button
- st.markdown("### Bulk Download")
- st.markdown("""
- Image Details:
-Format: PNG
-Optimized for Facebook
-
-
- """, unsafe_allow_html=True)
-
- # Create a ZIP file containing all images
- import io
- import zipfile
-
- zip_buffer = io.BytesIO()
- with zipfile.ZipFile(zip_buffer, "w") as zip_file:
- for i, image_path in enumerate(images):
- zip_file.write(image_path, f"carousel_slide_{i+1}.png")
-
- st.download_button(
- label="Download All Images (ZIP)",
- data=zip_buffer.getvalue(),
- file_name=f"carousel_images_{datetime.now().strftime('%Y%m%d_%H%M%S')}.zip",
- mime="application/zip"
- )
-
- except Exception as e:
- logger.error(f"[display_carousel_images] Error displaying carousel images: {str(e)}")
- st.error("Error displaying carousel images. Please try again.")
-
-def preview_carousel(content: Dict[str, Any], images: List[str]):
- """
- Show a preview of the complete carousel.
-
- Args:
- content: Dictionary containing the carousel content
- images: List of paths to generated images
- """
- try:
- st.markdown("### Carousel Preview")
-
- # Create tabs for different preview modes
- tabs = st.tabs(["Mobile Preview", "Desktop Preview", "Content Overview"])
-
- with tabs[0]: # Mobile Preview
- st.markdown("#### Mobile View")
- st.markdown("""
- Download All Images
-Get all carousel images in a single ZIP file.
-
-
", unsafe_allow_html=True)
-
- with tabs[1]: # Desktop Preview
- st.markdown("#### Desktop View")
-
- # Create a wider preview container
- st.markdown("""
-
- 
- Your Business Page
-
- """, unsafe_allow_html=True)
-
- # Display the main caption
- st.markdown(f"""
-
- Your Business Page
-
- {content['main_caption']}
-
- """, unsafe_allow_html=True)
-
- # Display the carousel images
- current_slide = st.slider("Swipe through slides", 1, len(images), 1, key="mobile_slider")
- st.image(images[current_slide-1], use_container_width=True)
-
- # Display slide indicators
- st.markdown(f"""
-
- {"".join(['â' if i+1 == current_slide else 'â' for i in range(len(images))])}
-
- """, unsafe_allow_html=True)
-
- # Display engagement elements
- st.markdown("""
-
- đ Like
- đŦ Comment
- âī¸ Share
-
- """, unsafe_allow_html=True)
-
- st.markdown("
-
", unsafe_allow_html=True)
-
- with tabs[2]: # Content Overview
- st.markdown("#### Content Overview")
-
- # Display metadata
- st.markdown("""
-
-
- Your Business Page
-
- """, unsafe_allow_html=True)
-
- # Display content and images side by side
- col1, col2 = st.columns([2, 1])
-
- with col1:
- # Display the carousel images in a row
- st.image(images[0], use_container_width=True)
-
- # Small image previews
- cols = st.columns(len(images))
- for i, image in enumerate(images):
- with cols[i]:
- st.image(image, use_container_width=True)
-
- with col2:
- # Display the main caption and engagement elements
- st.markdown(f"""
-
- Your Business Page
-
-
- """, unsafe_allow_html=True)
-
- st.markdown("{content['main_caption']}
-
-
- Hashtags:
-{" ".join([f"#{tag}" for tag in content['hashtags']])}
-
- đ Like
- đŦ Comment
- âī¸ Share
-
-
-
- """.format(
- len(images),
- content['metadata']['generated_at'],
- content['metadata']['carousel_purpose']
- ), unsafe_allow_html=True)
-
- # Display content structure
- col1, col2 = st.columns(2)
-
- with col1:
- st.markdown("##### Content Elements")
- st.markdown("""
- - Main Caption
- - {} Slides
- - {} Hashtags
- - {} Engagement Prompts
- """.format(
- len(content['slides']),
- len(content['hashtags']),
- len(content['engagement_prompts'])
- ))
-
- with col2:
- st.markdown("##### Optimization Tips")
- st.markdown("""
- - Best time to post: 1-4 PM
- - Tag relevant accounts
- - Respond to early comments
- - Share to Stories for more reach
- - Use all 10 hashtags for maximum visibility
- """)
-
- except Exception as e:
- logger.error(f"[preview_carousel] Error previewing carousel: {str(e)}")
- st.error("Error displaying carousel preview. Please try again.")
-
-def export_carousel(**kwargs):
- """
- Export the carousel in the chosen format.
-
- Args:
- carousel_content (Dict[str, Any]): The carousel content
- carousel_images (List[str]): List of image paths
- export_format (str): Chosen export format
- include_guidelines (bool): Whether to include posting guidelines
- """
- try:
- content = kwargs.get('carousel_content')
- images = kwargs.get('carousel_images')
- export_format = kwargs.get('export_format')
- include_guidelines = kwargs.get('include_guidelines')
-
- if export_format == "ZIP (All Files)":
- # Create a ZIP file containing all carousel assets
- import io
- import zipfile
- from datetime import datetime
-
- # Create guidelines if requested
- if include_guidelines:
- guidelines = f"""
- Facebook Carousel Posting Guidelines
- =================================
-
- Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
-
- Carousel Details
- ---------------
- - Business Type: {content['metadata']['business_type']}
- - Target Audience: {content['metadata']['target_audience']}
- - Purpose: {content['metadata']['carousel_purpose']}
-
- Posting Instructions
- -------------------
- 1. Upload all images in the correct order
- 2. Copy the main caption from caption.txt
- 3. Add all hashtags (found in hashtags.txt)
- 4. Best posting times: 1-4 PM on weekdays
- 5. Share to Stories after posting
- 6. Engage with comments within the first hour
-
- Engagement Strategy
- ------------------
- - Use the provided engagement prompts
- - Respond to comments quickly
- - Tag relevant accounts when appropriate
- - Share to relevant groups
- - Consider boosting the post
-
- Hashtag Strategy
- ---------------
- - Use all provided hashtags
- - Mix popular and niche hashtags
- - Place hashtags in first comment for cleaner look
-
- Additional Tips
- --------------
- - Monitor performance for the first 24 hours
- - Save top-performing carousel slides as templates
- - A/B test different carousel orders
- - Use insights to optimize future posts
- """
-
- # Create the ZIP file
- zip_buffer = io.BytesIO()
- with zipfile.ZipFile(zip_buffer, "w") as zip_file:
- # Add images
- for i, image_path in enumerate(images):
- zip_file.write(image_path, f"images/slide_{i+1}.png")
-
- # Add content files
- zip_file.writestr("caption.txt", content['main_caption'])
- zip_file.writestr("hashtags.txt", "\n".join([f"#{tag}" for tag in content['hashtags']]))
- zip_file.writestr("engagement_prompts.txt", "\n".join(content['engagement_prompts']))
-
- # Add slides content
- slides_content = "\n\n".join([
- f"Slide {slide['slide_number']}:\n{slide['content']}"
- for slide in content['slides']
- ])
- zip_file.writestr("slides_content.txt", slides_content)
-
- # Add metadata
- zip_file.writestr("metadata.json", json.dumps(content['metadata'], indent=2))
-
- # Add guidelines if requested
- if include_guidelines:
- zip_file.writestr("posting_guidelines.txt", guidelines)
-
- # Offer the ZIP file for download
- st.download_button(
- label="Download Complete Carousel Package",
- data=zip_buffer.getvalue(),
- file_name=f"facebook_carousel_{datetime.now().strftime('%Y%m%d_%H%M%S')}.zip",
- mime="application/zip"
- )
-
- st.success("Your carousel package has been prepared for download!")
-
- elif export_format == "PDF Report":
- # Create a PDF report
- st.info("PDF export functionality coming soon!")
-
- elif export_format == "Individual Files":
- # Offer individual file downloads
- st.markdown("### Download Individual Files")
-
- col1, col2 = st.columns(2)
-
- with col1:
- # Content files
- st.markdown("#### Content Files")
-
- st.download_button(
- label="Download Caption",
- data=content['main_caption'],
- file_name="carousel_caption.txt",
- mime="text/plain"
- )
-
- st.download_button(
- label="Download Hashtags",
- data="\n".join([f"#{tag}" for tag in content['hashtags']]),
- file_name="carousel_hashtags.txt",
- mime="text/plain"
- )
-
- st.download_button(
- label="Download Engagement Prompts",
- data="\n".join(content['engagement_prompts']),
- file_name="carousel_engagement_prompts.txt",
- mime="text/plain"
- )
-
- with col2:
- # Image files
- st.markdown("#### Image Files")
-
- for i, image_path in enumerate(images):
- st.download_button(
- label=f"Download Slide {i+1}",
- data=open(image_path, "rb").read(),
- file_name=f"carousel_slide_{i+1}.png",
- mime="image/png",
- key=f"download_slide_{i+1}"
- )
-
- # Display export success message
- st.success("Export completed successfully!")
-
- except Exception as e:
- logger.error(f"[export_carousel] Error exporting carousel: {str(e)}")
- st.error("Error exporting carousel. Please try again.")
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/facebook_reel/README.md b/ToBeMigrated/ai_writers/ai_facebook_writer/modules/facebook_reel/README.md
deleted file mode 100644
index ca8029ce..00000000
--- a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/facebook_reel/README.md
+++ /dev/null
@@ -1,145 +0,0 @@
-# Facebook Reel Generator
-
-## Overview
-
-The Facebook Reel Generator is a powerful AI-powered tool designed to help content creators, marketers, and businesses create engaging Facebook Reels content. This tool leverages advanced AI to generate creative scripts, visual concepts, and content strategies optimized for Facebook's short-form video platform.
-
-## Features
-
-### Current Features
-
-#### 1. Reel Content Generation
-- **Script Generation**: Create complete scripts for Facebook Reels with hooks, main content, and calls-to-action
-- **Content Structure**: Automatically formats content with proper pacing for short-form video
-- **Brand Voice Customization**: Adapt content to match your brand's tone and style
-- **Target Audience Optimization**: Tailor content specifically for your target demographic
-- **Purpose-Driven Content**: Generate content aligned with specific marketing goals (awareness, engagement, conversion, etc.)
-
-#### 2. Visual Elements
-- **Image Generation**: Create custom images for your Reels using AI image generation
-- **Visual Style Options**: Choose from various visual styles (modern, minimalist, bold, etc.)
-- **Aspect Ratio Optimization**: Generate images in the optimal 9:16 aspect ratio for Reels
-- **Image Quality Settings**: Control the quality and detail level of generated images
-
-#### 3. Advanced Options
-- **Content Length Control**: Specify preferred length for your Reel content
-- **Language Style Selection**: Choose from formal, casual, humorous, or other language styles
-- **AI Provider Selection**: Choose between different AI providers for content generation
-- **Hashtag Suggestions**: Get relevant hashtag recommendations for your Reels
-
-### Coming Soon Features
-
-#### 1. Music Integration
-- **Trending Music Suggestions**: Get recommendations for popular music tracks
-- **Music Mood Matching**: Find music that matches your content's emotional tone
-- **Copyright-Safe Options**: Access royalty-free music suggestions
-
-#### 2. Video Script Generation
-- **Shot-by-Shot Breakdown**: Get detailed visual direction for each segment
-- **Transition Suggestions**: Receive creative transition ideas between scenes
-- **Visual Effects Recommendations**: Get ideas for filters and effects to enhance your Reel
-
-#### 3. Performance Optimization
-- **Trend Analysis**: Identify current trends in your niche for Reel content
-- **Engagement Prediction**: Get insights on potential performance of your content
-- **A/B Testing Suggestions**: Receive variations of content to test different approaches
-
-## How to Use
-
-### Step 1: Access the Reel Generator
-1. Navigate to the Facebook AI Writer dashboard
-2. Select the "FB Reel Generator" card
-3. Click "Use FB Reel Generator"
-
-### Step 2: Configure Your Reel Content
-1. Fill in the basic information:
- - Business type
- - Target audience
- - Reel purpose
- - Brand voice
- - Key message
- - Call to action
-2. Select content preferences:
- - Include hashtags
- - Include hooks
- - Include trending topics
-3. Click "Generate Reel Content"
-
-### Step 3: Customize Visual Elements
-1. Once your content is generated, navigate to the "Visual Elements" tab
-2. Select your preferred visual style
-3. Choose the aspect ratio (9:16 recommended for Reels)
-4. Adjust image quality settings
-5. Click "Generate Images"
-
-### Step 4: Fine-tune with Advanced Options
-1. Navigate to the "Advanced Options" tab
-2. Adjust content length preferences
-3. Select language style
-4. Choose your preferred AI provider
-5. Click "Apply Advanced Settings"
-
-## Best Practices
-
-### Content Creation
-- Keep your key message concise and focused
-- Use action-oriented language in your call to action
-- Be specific about your target audience to get more relevant content
-- Experiment with different brand voices to find what resonates with your audience
-
-### Visual Elements
-- Use the 9:16 aspect ratio for optimal display on mobile devices
-- Select visual styles that align with your brand identity
-- Consider using higher quality settings for more professional-looking images
-
-### Performance Optimization
-- Regularly update your content based on performance data
-- Test different hooks and openings to see what captures attention
-- Use the suggested hashtags to increase discoverability
-
-## Technical Requirements
-
-- A modern web browser (Chrome, Firefox, Safari, or Edge)
-- Internet connection
-- API keys for AI providers (OpenAI, Google Gemini, etc.)
-
-## Troubleshooting
-
-### Common Issues
-- **Content not generating**: Ensure all required fields are filled out
-- **Images not appearing**: Check your internet connection and API key status
-- **Error messages**: Verify that your API keys are valid and have sufficient credits
-
-### Getting Help
-If you encounter any issues not covered in this guide, please contact support or check our documentation for additional resources.
-
-## Future Roadmap
-
-### Phase 1 (Current)
-- Basic content generation
-- Image generation
-- Advanced customization options
-
-### Phase 2 (Coming Soon)
-- Music integration
-- Video script generation
-- Performance analytics
-
-### Phase 3 (Future)
-- AI-powered video editing suggestions
-- Audience sentiment analysis
-- Competitor content analysis
-- Automated content scheduling
-- Multi-platform adaptation (Instagram Reels, TikTok)
-
-## Contributing
-
-We welcome feedback and contributions to improve the Facebook Reel Generator. If you have suggestions for new features or improvements, please reach out to our development team.
-
-## License
-
-This tool is part of the ALwrity AI Writer suite and is subject to the same licensing terms.
-
----
-
-*Last updated: April 2025*
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/facebook_reel/reel_generator.py b/ToBeMigrated/ai_writers/ai_facebook_writer/modules/facebook_reel/reel_generator.py
deleted file mode 100644
index 8e7ef543..00000000
--- a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/facebook_reel/reel_generator.py
+++ /dev/null
@@ -1,902 +0,0 @@
-"""
-Facebook Reel Generator Module
-
-This module provides functionality to generate Facebook Reel content using AI.
-It leverages text and image generation capabilities to create engaging reel content.
-"""
-
-import streamlit as st
-import os
-import sys
-from loguru import logger
-from typing import Dict, Any, List, Optional
-from datetime import datetime
-
-# Import text generation
-from .....gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-# Import image generation
-from .....gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
-
-# Configure logging
-logger.remove()
-logger.add(sys.stdout,
- colorize=True,
- format="Carousel Details
-Total Slides: {}
-Generated: {}
-Purpose: {}
-
-
- """, unsafe_allow_html=True)
-
- # Create tabs for different sections
- tab1, tab2, tab3 = st.tabs(["Reel Content", "Visual Elements", "Advanced Options"])
-
- with tab1:
- render_reel_content_tab()
-
- with tab2:
- render_visual_elements_tab()
-
- with tab3:
- render_advanced_options_tab()
-
-def render_reel_content_tab():
- """Render the Reel Content tab with input fields and generation options."""
- st.markdown("### Reel Content Settings")
-
- # Create columns for input fields
- col1, col2 = st.columns(2)
-
- with col1:
- business_type = st.text_input(
- "Business/Industry Type",
- placeholder="e.g., Fashion, Food, Fitness, Tech",
- help="The type of business or industry you're creating content for"
- )
-
- target_audience = st.text_input(
- "Target Audience",
- placeholder="e.g., Young professionals, Parents, Fitness enthusiasts",
- help="Who is your content aimed at?"
- )
-
- reel_purpose = st.selectbox(
- "Reel Purpose",
- options=[
- "Product Showcase",
- "Tutorial/How-to",
- "Behind the Scenes",
- "Customer Testimonials",
- "Brand Story",
- "Promotion/Offer",
- "Educational Content",
- "Entertainment"
- ],
- help="What is the main purpose of your reel?"
- )
-
- with col2:
- brand_voice = st.selectbox(
- "Brand Voice/Tone",
- options=[
- "Professional",
- "Casual/Friendly",
- "Humorous",
- "Inspirational",
- "Educational",
- "Luxury/Elegant",
- "Energetic",
- "Relaxed"
- ],
- help="The tone and voice that represents your brand"
- )
-
- key_message = st.text_area(
- "Key Message",
- placeholder="What's the main message you want to convey in your reel?",
- help="The primary message or takeaway for your audience"
- )
-
- call_to_action = st.selectbox(
- "Call to Action",
- options=[
- "Follow Us",
- "Shop Now",
- "Learn More",
- "Sign Up",
- "Share",
- "Comment",
- "Save for Later",
- "Custom"
- ],
- help="What action do you want viewers to take after watching?"
- )
-
- if call_to_action == "Custom":
- custom_cta = st.text_input(
- "Custom Call to Action",
- placeholder="Enter your custom call to action"
- )
- else:
- custom_cta = ""
-
- # Additional content options
- st.markdown("### Content Preferences")
-
- col1, col2 = st.columns(2)
-
- with col1:
- include_hashtags = st.checkbox("Include Hashtag Suggestions", value=True)
- include_hook = st.checkbox("Include Hook/Opening", value=True)
- include_transitions = st.checkbox("Include Transition Suggestions", value=True)
-
- with col2:
- include_text_overlays = st.checkbox("Include Text Overlay Suggestions", value=True)
- include_engagement_tips = st.checkbox("Include Engagement Tips", value=True)
- include_trending_topics = st.checkbox("Include Trending Topics", value=True)
-
- # Generate button
- if st.button("Generate Reel Content", type="primary"):
- if not business_type or not target_audience or not key_message:
- st.error("Please fill in the required fields: Business Type, Target Audience, and Key Message")
- return
-
- with st.spinner("Generating your Facebook Reel content..."):
- # Generate the reel content
- reel_content = generate_reel_content(
- business_type=business_type,
- target_audience=target_audience,
- reel_purpose=reel_purpose,
- brand_voice=brand_voice,
- key_message=key_message,
- call_to_action=call_to_action,
- custom_cta=custom_cta,
- include_hashtags=include_hashtags,
- include_hook=include_hook,
- include_transitions=include_transitions,
- include_text_overlays=include_text_overlays,
- include_engagement_tips=include_engagement_tips,
- include_trending_topics=include_trending_topics
- )
-
- # Display the generated content
- if reel_content:
- st.markdown("### Generated Reel Content")
-
- # Create a container with a nice background for the content
- st.markdown("""
-
- đĨ
-
- Facebook Reel Generator
-Create engaging Facebook Reels with AI-powered content
-
-
- """, unsafe_allow_html=True)
-
- # Parse and display the content in a structured way
- display_reel_content(reel_content)
-
- # Add a download button for the content
- st.download_button(
- label="Download Reel Content",
- data=reel_content,
- file_name=f"facebook_reel_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt",
- mime="text/plain"
- )
-
- # Store the content in session state for the visual elements tab
- st.session_state['reel_content'] = reel_content
- else:
- st.error("Failed to generate reel content. Please try again.")
-
-def render_visual_elements_tab():
- """Render the Visual Elements tab for generating images and visual content."""
- st.markdown("### Visual Elements for Your Reel")
-
- # Check if reel content has been generated
- if 'reel_content' not in st.session_state:
- st.info("Please generate reel content in the 'Reel Content' tab first.")
- return
-
- st.markdown("""
- Your Facebook Reel Script
-
-
- """, unsafe_allow_html=True)
-
- # Visual element options
- st.markdown("#### Generate Visual Elements")
-
- col1, col2 = st.columns(2)
-
- with col1:
- visual_style = st.selectbox(
- "Visual Style",
- options=[
- "Modern and Clean",
- "Bold and Vibrant",
- "Minimalist",
- "Playful and Fun",
- "Professional",
- "Lifestyle",
- "Product-Focused",
- "Custom"
- ],
- help="The overall visual style for your reel"
- )
-
- if visual_style == "Custom":
- custom_style = st.text_input(
- "Custom Visual Style",
- placeholder="Describe your desired visual style"
- )
- else:
- custom_style = ""
-
- num_images = st.slider(
- "Number of Images to Generate",
- min_value=1,
- max_value=5,
- value=3,
- help="How many visual elements would you like to generate?"
- )
-
- with col2:
- image_aspect_ratio = st.selectbox(
- "Image Aspect Ratio",
- options=[
- "9:16 (Vertical - Reel)",
- "1:1 (Square)",
- "16:9 (Horizontal)",
- "4:5 (Instagram Portrait)"
- ],
- help="The aspect ratio for your visual elements"
- )
-
- image_quality = st.select_slider(
- "Image Quality",
- options=["Basic", "Standard", "High", "Premium"],
- value="Standard",
- help="The quality level for generated images"
- )
-
- # Image generation prompt customization
- st.markdown("#### Customize Image Generation")
-
- image_prompt_style = st.radio(
- "Image Prompt Style",
- options=["Automatic", "Custom"],
- help="Choose how to generate the image prompts"
- )
-
- if image_prompt_style == "Custom":
- custom_image_prompt = st.text_area(
- "Custom Image Prompt",
- placeholder="Enter a custom prompt for image generation",
- help="Describe what you want the image to look like"
- )
- else:
- custom_image_prompt = ""
-
- # Generate images button
- if st.button("Generate Visual Elements", type="primary"):
- with st.spinner("Generating visual elements for your reel..."):
- # Generate images based on the reel content
- images = generate_reel_images(
- reel_content=st.session_state['reel_content'],
- visual_style=visual_style,
- custom_style=custom_style if visual_style == "Custom" else None,
- num_images=num_images,
- image_aspect_ratio=image_aspect_ratio,
- image_quality=image_quality,
- custom_image_prompt=custom_image_prompt if image_prompt_style == "Custom" else None
- )
-
- # Display the generated images
- if images:
- st.markdown("### Generated Visual Elements")
-
- # Display images in a grid
- cols = st.columns(min(3, len(images)))
- for i, image_path in enumerate(images):
- with cols[i % len(cols)]:
- st.image(image_path, use_container_width=True)
- st.download_button(
- label=f"Download Image {i+1}",
- data=open(image_path, "rb").read(),
- file_name=f"reel_image_{i+1}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png",
- mime="image/png"
- )
- else:
- st.error("Failed to generate visual elements. Please try again.")
-
- # Coming soon features
- st.markdown("### Coming Soon Features")
-
- col1, col2 = st.columns(2)
-
- with col1:
- st.markdown("""
- Note: This feature generates visual elements for your reel. Music and video elements are coming soon.
-
-
- """, unsafe_allow_html=True)
-
- with col2:
- st.markdown("""
- đĩ Music Suggestions
-AI-powered music recommendations that match your reel's mood and content.
- Coming Soon -
-
- """, unsafe_allow_html=True)
-
-def render_advanced_options_tab():
- """Render the Advanced Options tab for additional customization."""
- st.markdown("### Advanced Options")
-
- # Check if reel content has been generated
- if 'reel_content' not in st.session_state:
- st.info("Please generate reel content in the 'Reel Content' tab first.")
- return
-
- st.markdown("""
- đŦ Video Elements
-Generate video clips, transitions, and effects to enhance your reel.
- Coming Soon -
-
- """, unsafe_allow_html=True)
-
- # Advanced content options
- st.markdown("#### Content Customization")
-
- col1, col2 = st.columns(2)
-
- with col1:
- content_length = st.select_slider(
- "Content Length",
- options=["Short (15s)", "Medium (30s)", "Long (60s)"],
- value="Medium (30s)",
- help="The target length for your reel content"
- )
-
- language_style = st.selectbox(
- "Language Style",
- options=[
- "Simple and Clear",
- "Professional",
- "Casual",
- "Technical",
- "Persuasive",
- "Storytelling"
- ],
- help="The language style for your content"
- )
-
- with col2:
- include_stats = st.checkbox("Include Statistics/Facts", value=False)
- include_quotes = st.checkbox("Include Quote Suggestions", value=False)
- include_emoji = st.checkbox("Include Emoji Suggestions", value=True)
-
- # AI model options
- st.markdown("#### AI Model Options")
-
- col1, col2 = st.columns(2)
-
- with col1:
- creativity_level = st.select_slider(
- "Creativity Level",
- options=["Conservative", "Balanced", "Creative", "Very Creative"],
- value="Balanced",
- help="How creative should the AI be in generating content"
- )
-
- with col2:
- ai_provider = st.selectbox(
- "AI Provider",
- options=["Auto-Select", "OpenAI", "Google Gemini", "Anthropic Claude"],
- help="Which AI provider to use for content generation"
- )
-
- # Generate with advanced options button
- if st.button("Regenerate with Advanced Options", type="primary"):
- with st.spinner("Regenerating your Facebook Reel content with advanced options..."):
- # Get the original content parameters from session state
- original_params = st.session_state.get('reel_params', {})
-
- # Update with advanced options
- advanced_params = {
- **original_params,
- 'content_length': content_length,
- 'language_style': language_style,
- 'include_stats': include_stats,
- 'include_quotes': include_quotes,
- 'include_emoji': include_emoji,
- 'creativity_level': creativity_level,
- 'ai_provider': ai_provider
- }
-
- # Generate the reel content with advanced options
- reel_content = generate_reel_content_advanced(advanced_params)
-
- # Display the generated content
- if reel_content:
- st.markdown("### Regenerated Reel Content")
-
- # Create a container with a nice background for the content
- st.markdown("""
- Note: These advanced options allow for more customization of your reel content.
-
-
- """, unsafe_allow_html=True)
-
- # Parse and display the content in a structured way
- display_reel_content(reel_content)
-
- # Add a download button for the content
- st.download_button(
- label="Download Reel Content",
- data=reel_content,
- file_name=f"facebook_reel_advanced_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt",
- mime="text/plain"
- )
-
- # Update the content in session state
- st.session_state['reel_content'] = reel_content
- else:
- st.error("Failed to regenerate reel content. Please try again.")
-
-def generate_reel_content(
- business_type: str,
- target_audience: str,
- reel_purpose: str,
- brand_voice: str,
- key_message: str,
- call_to_action: str,
- custom_cta: str = "",
- include_hashtags: bool = True,
- include_hook: bool = True,
- include_transitions: bool = True,
- include_text_overlays: bool = True,
- include_engagement_tips: bool = True,
- include_trending_topics: bool = True
-) -> str:
- """
- Generate Facebook Reel content based on user inputs.
-
- Args:
- business_type: The type of business or industry
- target_audience: The target audience for the reel
- reel_purpose: The purpose of the reel
- brand_voice: The brand voice or tone
- key_message: The key message to convey
- call_to_action: The call to action
- custom_cta: Custom call to action text if "Custom" is selected
- include_hashtags: Whether to include hashtag suggestions
- include_hook: Whether to include a hook/opening
- include_transitions: Whether to include transition suggestions
- include_text_overlays: Whether to include text overlay suggestions
- include_engagement_tips: Whether to include engagement tips
- include_trending_topics: Whether to include trending topics
-
- Returns:
- str: The generated reel content
- """
- try:
- # Store parameters in session state for advanced options
- st.session_state['reel_params'] = {
- 'business_type': business_type,
- 'target_audience': target_audience,
- 'reel_purpose': reel_purpose,
- 'brand_voice': brand_voice,
- 'key_message': key_message,
- 'call_to_action': call_to_action,
- 'custom_cta': custom_cta,
- 'include_hashtags': include_hashtags,
- 'include_hook': include_hook,
- 'include_transitions': include_transitions,
- 'include_text_overlays': include_text_overlays,
- 'include_engagement_tips': include_engagement_tips,
- 'include_trending_topics': include_trending_topics
- }
-
- # Construct the prompt for the AI
- prompt = f"""
- Create a detailed Facebook Reel script for a {business_type} business targeting {target_audience}.
-
- The purpose of this reel is: {reel_purpose}
- The brand voice/tone should be: {brand_voice}
- The key message to convey is: {key_message}
- The call to action should be: {call_to_action}{f" - {custom_cta}" if custom_cta else ""}
-
- Please provide a complete Facebook Reel script with the following elements:
-
- 1. A hook (first 3 seconds) that grabs attention
- 2. Main content (15-30 seconds) that delivers the key message
- 3. A strong call-to-action at the end
-
- Additional elements to include:
- {f"- Hashtag suggestions relevant to the content and industry" if include_hashtags else ""}
- {f"- Text overlay suggestions for key points" if include_text_overlays else ""}
- {f"- Transition suggestions between scenes" if include_transitions else ""}
- {f"- Engagement tips to encourage interaction" if include_engagement_tips else ""}
- {f"- Trending topics that could be incorporated" if include_trending_topics else ""}
-
- Format the output as a structured script with clear sections and timing suggestions.
- """
-
- # Generate the content using the AI
- logger.info(f"[generate_reel_content] Generating reel content for {business_type}")
- response = llm_text_gen(prompt)
-
- if response:
- logger.info(f"[generate_reel_content] Successfully generated reel content")
- return response
- else:
- logger.error(f"[generate_reel_content] Failed to generate reel content")
- return None
-
- except Exception as e:
- logger.error(f"[generate_reel_content] Error generating reel content: {str(e)}")
- return None
-
-def generate_reel_content_advanced(params: Dict[str, Any]) -> str:
- """
- Generate Facebook Reel content with advanced options.
-
- Args:
- params: Dictionary of parameters for content generation
-
- Returns:
- str: The generated reel content
- """
- try:
- # Extract parameters
- business_type = params.get('business_type', '')
- target_audience = params.get('target_audience', '')
- reel_purpose = params.get('reel_purpose', '')
- brand_voice = params.get('brand_voice', '')
- key_message = params.get('key_message', '')
- call_to_action = params.get('call_to_action', '')
- custom_cta = params.get('custom_cta', '')
- content_length = params.get('content_length', 'Medium (30s)')
- language_style = params.get('language_style', 'Simple and Clear')
- include_stats = params.get('include_stats', False)
- include_quotes = params.get('include_quotes', False)
- include_emoji = params.get('include_emoji', True)
- creativity_level = params.get('creativity_level', 'Balanced')
- ai_provider = params.get('ai_provider', 'Auto-Select')
-
- # Construct the advanced prompt
- prompt = f"""
- Create a detailed Facebook Reel script for a {business_type} business targeting {target_audience}.
-
- The purpose of this reel is: {reel_purpose}
- The brand voice/tone should be: {brand_voice}
- The key message to convey is: {key_message}
- The call to action should be: {call_to_action}{f" - {custom_cta}" if custom_cta else ""}
-
- Advanced requirements:
- - Content length: {content_length}
- - Language style: {language_style}
- - Creativity level: {creativity_level}
- {f"- Include relevant statistics or facts to support the message" if include_stats else ""}
- {f"- Include quote suggestions that could be used in the reel" if include_quotes else ""}
- {f"- Include emoji suggestions for text overlays" if include_emoji else ""}
-
- Please provide a complete Facebook Reel script with the following elements:
-
- 1. A hook (first 3 seconds) that grabs attention
- 2. Main content that delivers the key message
- 3. A strong call-to-action at the end
-
- Format the output as a structured script with clear sections and timing suggestions.
- """
-
- # Generate the content using the AI
- logger.info(f"[generate_reel_content_advanced] Generating advanced reel content for {business_type}")
- response = llm_text_gen(prompt)
-
- if response:
- logger.info(f"[generate_reel_content_advanced] Successfully generated advanced reel content")
- return response
- else:
- logger.error(f"[generate_reel_content_advanced] Failed to generate advanced reel content")
- return None
-
- except Exception as e:
- logger.error(f"[generate_reel_content_advanced] Error generating advanced reel content: {str(e)}")
- return None
-
-def generate_reel_images(
- reel_content: str,
- visual_style: str,
- custom_style: Optional[str] = None,
- num_images: int = 3,
- image_aspect_ratio: str = "9:16 (Vertical - Reel)",
- image_quality: str = "Standard",
- custom_image_prompt: Optional[str] = None
-) -> List[str]:
- """
- Generate images for a Facebook Reel based on the content.
-
- Args:
- reel_content: The generated reel content
- visual_style: The visual style for the images
- custom_style: Custom style description if "Custom" is selected
- num_images: Number of images to generate
- image_aspect_ratio: The aspect ratio for the images
- image_quality: The quality level for the images
- custom_image_prompt: Custom prompt for image generation
-
- Returns:
- List[str]: List of paths to the generated images
- """
- try:
- logger.info(f"[generate_reel_images] Generating {num_images} images for reel")
-
- # Extract key elements from the reel content
- prompt = f"""
- Based on the following Facebook Reel content, extract {num_images} key visual elements or scenes that would make compelling images.
- For each element, provide a detailed description that would work well for AI image generation.
-
- Reel Content:
- {reel_content}
-
- For each visual element, provide:
- 1. A descriptive title
- 2. A detailed image generation prompt that includes:
- - The main subject
- - The setting/background
- - The mood/atmosphere
- - The style ({visual_style}{f" - {custom_style}" if custom_style else ""})
- - The aspect ratio ({image_aspect_ratio})
- - The quality level ({image_quality})
-
- Format each element as:
- TITLE: [title]
- PROMPT: [detailed prompt]
-
- Generate exactly {num_images} elements.
- """
-
- # Generate the image prompts using the AI
- logger.info(f"[generate_reel_images] Generating image prompts")
- response = llm_text_gen(prompt)
-
- if not response:
- logger.error(f"[generate_reel_images] Failed to generate image prompts")
- return []
-
- # Parse the response to extract the image prompts
- image_prompts = []
- current_title = ""
- current_prompt = ""
-
- for line in response.split('\n'):
- if line.startswith("TITLE:"):
- if current_title and current_prompt:
- image_prompts.append((current_title, current_prompt))
- current_title = line.replace("TITLE:", "").strip()
- current_prompt = ""
- elif line.startswith("PROMPT:"):
- current_prompt = line.replace("PROMPT:", "").strip()
- elif current_prompt and line.strip():
- current_prompt += " " + line.strip()
-
- # Add the last prompt if there is one
- if current_title and current_prompt:
- image_prompts.append((current_title, current_prompt))
-
- # If we don't have enough prompts, generate generic ones
- while len(image_prompts) < num_images:
- image_prompts.append((f"Generic Image {len(image_prompts)+1}",
- f"A visually appealing image related to {visual_style} style for a Facebook Reel"))
-
- # Generate the images
- image_paths = []
- for i, (title, prompt) in enumerate(image_prompts[:num_images]):
- # Use custom prompt if provided
- if custom_image_prompt and i == 0:
- prompt = custom_image_prompt
-
- logger.info(f"[generate_reel_images] Generating image {i+1}: {title}")
- image_path = generate_image(prompt)
-
- if image_path:
- image_paths.append(image_path)
- logger.info(f"[generate_reel_images] Successfully generated image {i+1}")
- else:
- logger.error(f"[generate_reel_images] Failed to generate image {i+1}")
-
- return image_paths
-
- except Exception as e:
- logger.error(f"[generate_reel_images] Error generating reel images: {str(e)}")
- return []
-
-def display_reel_content(content: str):
- """
- Display the reel content in a structured and visually appealing way.
-
- Args:
- content: The generated reel content
- """
- # Check if the content contains markdown code blocks
- if "```markdown" in content:
- # Extract the content from the markdown code block
- try:
- # Find the start and end of the markdown block
- start_idx = content.find("```markdown") + len("```markdown")
- end_idx = content.find("```", start_idx)
- if end_idx == -1: # If no closing markdown block found
- end_idx = len(content)
-
- # Extract the markdown content
- markdown_content = content[start_idx:end_idx].strip()
-
- # Split the content into sections
- sections = markdown_content.split('\n\n')
-
- # Create tabs for different sections
- if len(sections) > 1:
- tabs = st.tabs(["Full Script", "Hook", "Main Content", "Call to Action", "Additional Elements"])
-
- with tabs[0]: # Full Script
- st.markdown(markdown_content)
-
- # Try to identify and display other sections
- hook_section = ""
- main_content = ""
- cta_section = ""
- additional_elements = ""
-
- for section in sections:
- section_lower = section.lower()
- if "hook" in section_lower or "opening" in section_lower or "first 3 seconds" in section_lower or "0-3 seconds" in section_lower:
- hook_section = section
- elif "main content" in section_lower or "key message" in section_lower or "15-30 seconds" in section_lower:
- main_content = section
- elif "call to action" in section_lower or "cta" in section_lower or "end" in section_lower:
- cta_section = section
- elif "hashtag" in section_lower or "text overlay" in section_lower or "transition" in section_lower or "engagement" in section_lower or "trending" in section_lower or "additional" in section_lower:
- additional_elements += section + "\n\n"
-
- with tabs[1]: # Hook
- if hook_section:
- st.markdown(hook_section)
- else:
- st.info("No specific hook section identified in the content.")
-
- with tabs[2]: # Main Content
- if main_content:
- st.markdown(main_content)
- else:
- st.info("No specific main content section identified in the content.")
-
- with tabs[3]: # Call to Action
- if cta_section:
- st.markdown(cta_section)
- else:
- st.info("No specific call to action section identified in the content.")
-
- with tabs[4]: # Additional Elements
- if additional_elements:
- st.markdown(additional_elements)
- else:
- st.info("No additional elements identified in the content.")
- else:
- # If we can't split into sections, just display the content
- st.markdown(markdown_content)
- except Exception as e:
- logger.error(f"Error processing markdown content: {str(e)}")
- # Fallback to displaying the raw content
- st.markdown(content)
- else:
- # If no markdown code block, use the original approach
- # Split the content into sections
- sections = content.split('\n\n')
-
- # Create tabs for different sections
- if len(sections) > 1:
- tabs = st.tabs(["Full Script", "Hook", "Main Content", "Call to Action", "Additional Elements"])
-
- with tabs[0]: # Full Script
- # Replace newlines with HTML line breaks
- formatted_content = content.replace('\n', 'Your Facebook Reel Script
-') - st.markdown(f""" -
- {formatted_content}
-
- """, unsafe_allow_html=True)
-
- # Try to identify and display other sections
- hook_section = ""
- main_content = ""
- cta_section = ""
- additional_elements = ""
-
- for section in sections:
- section_lower = section.lower()
- if "hook" in section_lower or "opening" in section_lower or "first 3 seconds" in section_lower or "0-3 seconds" in section_lower:
- hook_section = section
- elif "main content" in section_lower or "key message" in section_lower or "15-30 seconds" in section_lower:
- main_content = section
- elif "call to action" in section_lower or "cta" in section_lower or "end" in section_lower:
- cta_section = section
- elif "hashtag" in section_lower or "text overlay" in section_lower or "transition" in section_lower or "engagement" in section_lower or "trending" in section_lower or "additional" in section_lower:
- additional_elements += section + "\n\n"
-
- with tabs[1]: # Hook
- if hook_section:
- # Replace newlines with HTML line breaks
- formatted_hook = hook_section.replace('\n', '') - st.markdown(f""" -
- {formatted_hook}
-
- """, unsafe_allow_html=True)
- else:
- st.info("No specific hook section identified in the content.")
-
- with tabs[2]: # Main Content
- if main_content:
- # Replace newlines with HTML line breaks
- formatted_main = main_content.replace('\n', '') - st.markdown(f""" -
- {formatted_main}
-
- """, unsafe_allow_html=True)
- else:
- st.info("No specific main content section identified in the content.")
-
- with tabs[3]: # Call to Action
- if cta_section:
- # Replace newlines with HTML line breaks
- formatted_cta = cta_section.replace('\n', '') - st.markdown(f""" -
- {formatted_cta}
-
- """, unsafe_allow_html=True)
- else:
- st.info("No specific call to action section identified in the content.")
-
- with tabs[4]: # Additional Elements
- if additional_elements:
- # Replace newlines with HTML line breaks
- formatted_additional = additional_elements.replace('\n', '') - st.markdown(f""" -
- {formatted_additional}
-
- """, unsafe_allow_html=True)
- else:
- st.info("No additional elements identified in the content.")
- else:
- # If we can't split into sections, just display the content
- # Replace newlines with HTML line breaks
- formatted_content = content.replace('\n', '') - st.markdown(f""" -
- {formatted_content}
-
- """, unsafe_allow_html=True)
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/group_post_generator/README.md b/ToBeMigrated/ai_writers/ai_facebook_writer/modules/group_post_generator/README.md
deleted file mode 100644
index 8f0e933e..00000000
--- a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/group_post_generator/README.md
+++ /dev/null
@@ -1,124 +0,0 @@
-# Facebook Group Post Generator
-
-## Overview
-The Facebook Group Post Generator is a powerful tool designed to help content creators and community managers create engaging posts for Facebook Groups. It provides a comprehensive interface for crafting community-focused content that drives discussion and fosters engagement within Facebook Groups.
-
-## Features
-
-### Content & Media Management
-- **Multiple Post Types**: Create discussions, questions, announcements, resources, or event posts
-- **Rich Media Support**: Add images, videos, documents, or links to your posts
-- **AI-Powered Content Generation**: Generate engaging content based on your specifications
-- **Content Customization**: Fine-tune your post content with various formatting options
-
-### Interactive Elements
-- **Poll Creation**: Create polls with up to 4 options to gather member opinions
-- **Question Features**: Add questions to encourage member responses
-- **Announcement Toggle**: Mark important posts as announcements for better visibility
-- **Media Attachments**: Support for various media types to enhance post engagement
-
-### Engagement Optimization
-- **Target Audience Selection**: Customize posts for specific member segments
-- **Tone Customization**: Choose from professional, casual, friendly, formal, or humorous tones
-- **Call-to-Action Options**: Select from various actions to encourage member participation
-- **Engagement Prompts**: Add prompts to stimulate discussion and comments
-
-### Preview & Export
-- **Live Preview**: See how your post will appear before publishing
-- **Export Options**: Copy to clipboard or download as text file
-- **Format Validation**: Ensure your post meets Facebook's requirements
-- **Timestamp Tracking**: Keep track of when posts were created
-
-## How to Use
-
-1. **Access the Generator**
- - Navigate to the Facebook Group Post Generator in the AI Writer dashboard
- - Select "Create New Post" to start
-
-2. **Configure Post Settings**
- - Choose your post type
- - Add your content or let AI generate it
- - Select media options if needed
-
-3. **Add Interactive Elements**
- - Create polls or questions
- - Set announcement status
- - Configure engagement settings
-
-4. **Generate and Export**
- - Click "Generate Group Post" to create your content
- - Preview the generated post
- - Export or copy the content
-
-## Best Practices
-
-### Content Creation
-- Keep posts concise and focused
-- Use clear, engaging language
-- Include relevant hashtags
-- Add visual elements when appropriate
-
-### Engagement
-- Ask open-ended questions
-- Create polls for member input
-- Use appropriate tone for your audience
-- Include clear calls-to-action
-
-### Media Usage
-- Optimize images for Facebook
-- Keep videos short and engaging
-- Use high-quality media
-- Include alt text for accessibility
-
-## Technical Requirements
-
-- Python 3.7+
-- Streamlit
-- Required Python packages:
- - streamlit
- - typing
- - datetime
- - json
- - logging
-
-## Troubleshooting
-
-### Common Issues
-1. **Content Generation Fails**
- - Check your internet connection
- - Verify input parameters
- - Try reducing content length
-
-2. **Media Upload Issues**
- - Ensure file size is within limits
- - Check file format compatibility
- - Verify file permissions
-
-3. **Export Problems**
- - Check available disk space
- - Verify write permissions
- - Ensure proper file naming
-
-## Future Updates
-
-- Enhanced AI content generation
-- Additional media type support
-- Advanced analytics integration
-- Template library
-- Bulk post scheduling
-- Multi-language support
-- Advanced engagement metrics
-- A/B testing capabilities
-
-## Contributing
-
-We welcome contributions to improve the Facebook Group Post Generator. Please follow these steps:
-
-1. Fork the repository
-2. Create a feature branch
-3. Make your changes
-4. Submit a pull request
-
-## License
-
-This module is licensed under the MIT License. See the LICENSE file for details.
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/group_post_generator/__init__.py b/ToBeMigrated/ai_writers/ai_facebook_writer/modules/group_post_generator/__init__.py
deleted file mode 100644
index 33768b50..00000000
--- a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/group_post_generator/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-"""
-Facebook Group Post Generator Module
-
-This module provides functionality to generate engaging posts for Facebook Groups.
-It helps content creators create community-focused content that drives engagement
-and fosters discussion within Facebook Groups.
-"""
-
-from .group_post_generator import write_fb_group_post
-
-__all__ = ['write_fb_group_post']
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/group_post_generator/group_post_generator.py b/ToBeMigrated/ai_writers/ai_facebook_writer/modules/group_post_generator/group_post_generator.py
deleted file mode 100644
index ff048e22..00000000
--- a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/group_post_generator/group_post_generator.py
+++ /dev/null
@@ -1,327 +0,0 @@
-"""
-Facebook Group Post Generator Module
-
-This module provides functionality to generate engaging posts for Facebook Groups.
-It helps content creators create community-focused content that drives engagement
-and fosters discussion within Facebook Groups.
-"""
-
-import streamlit as st
-import json
-import logging
-from datetime import datetime
-from typing import Dict, List, Optional, Union, Tuple
-from .....gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-# Set up logging
-logger = logging.getLogger(__name__)
-
-def write_fb_group_post():
- """
- Generate a Facebook Group post with various customization options.
-
- This function provides a comprehensive interface for creating engaging
- Facebook Group posts with features like:
- - Content customization
- - Polls and questions
- - Announcements
- - Media attachments
- - Engagement prompts
- """
-
- st.title("Facebook Group Post Generator")
- st.markdown("""
- Create engaging posts for your Facebook Groups that drive discussion and community engagement.
- Customize your content, add interactive elements, and optimize for maximum impact.
- """)
-
- # Initialize session state for form data
- if 'group_post_data' not in st.session_state:
- st.session_state.group_post_data = {
- 'post_type': 'discussion',
- 'content': '',
- 'media_type': 'none',
- 'poll_options': [],
- 'question': '',
- 'is_announcement': False,
- 'engagement_prompt': '',
- 'hashtags': [],
- 'target_audience': 'all',
- 'post_tone': 'professional',
- 'call_to_action': 'discuss'
- }
-
- # Create tabs for different sections
- tab1, tab2, tab3, tab4 = st.tabs([
- "Content & Media",
- "Interactive Elements",
- "Engagement",
- "Preview & Export"
- ])
-
- with tab1:
- render_content_media_tab()
-
- with tab2:
- render_interactive_elements_tab()
-
- with tab3:
- render_engagement_tab()
-
- with tab4:
- render_preview_export_tab()
-
- # Generate button
- if st.button("Generate Group Post", type="primary"):
- if validate_group_post_fields():
- with st.spinner("Generating your group post..."):
- post_content = generate_group_post()
- if post_content:
- st.session_state.group_post_data['generated_content'] = post_content
- st.success("Group post generated successfully!")
- st.session_state.show_preview = True
-
-def render_content_media_tab():
- """Render the content and media input fields."""
-
- st.header("Content & Media")
-
- # Post Type Selection
- post_type = st.selectbox(
- "Post Type",
- options=['discussion', 'question', 'announcement', 'resource', 'event'],
- index=['discussion', 'question', 'announcement', 'resource', 'event'].index(
- st.session_state.group_post_data['post_type']
- ),
- help="Choose the type of post you want to create"
- )
- st.session_state.group_post_data['post_type'] = post_type
-
- # Content Input
- content = st.text_area(
- "Post Content",
- value=st.session_state.group_post_data['content'],
- height=200,
- help="Write your post content or leave blank for AI generation"
- )
- st.session_state.group_post_data['content'] = content
-
- # Media Options
- media_type = st.selectbox(
- "Media Type",
- options=['none', 'image', 'video', 'document', 'link'],
- index=['none', 'image', 'video', 'document', 'link'].index(
- st.session_state.group_post_data['media_type']
- ),
- help="Add media to your post"
- )
- st.session_state.group_post_data['media_type'] = media_type
-
- if media_type != 'none':
- if media_type == 'image':
- st.file_uploader("Upload Image", type=['jpg', 'jpeg', 'png'])
- elif media_type == 'video':
- st.file_uploader("Upload Video", type=['mp4', 'mov'])
- elif media_type == 'document':
- st.file_uploader("Upload Document", type=['pdf', 'doc', 'docx'])
- elif media_type == 'link':
- st.text_input("Enter URL", help="Paste the URL you want to share")
-
-def render_interactive_elements_tab():
- """Render the interactive elements options."""
-
- st.header("Interactive Elements")
-
- # Poll Creation
- if st.checkbox("Add Poll", help="Create a poll for group members"):
- poll_question = st.text_input("Poll Question")
- poll_options = []
- for i in range(4):
- option = st.text_input(f"Option {i+1}")
- if option:
- poll_options.append(option)
- st.session_state.group_post_data['poll_options'] = poll_options
-
- # Question Creation
- if st.checkbox("Add Question", help="Ask a question to group members"):
- question = st.text_input("Your Question")
- st.session_state.group_post_data['question'] = question
-
- # Announcement Toggle
- is_announcement = st.checkbox(
- "Mark as Announcement",
- value=st.session_state.group_post_data['is_announcement'],
- help="Pin this post as an announcement in the group"
- )
- st.session_state.group_post_data['is_announcement'] = is_announcement
-
-def render_engagement_tab():
- """Render the engagement options."""
-
- st.header("Engagement Settings")
-
- # Engagement Prompt
- engagement_prompt = st.text_area(
- "Engagement Prompt",
- value=st.session_state.group_post_data['engagement_prompt'],
- help="Add a prompt to encourage comments and discussion"
- )
- st.session_state.group_post_data['engagement_prompt'] = engagement_prompt
-
- # Target Audience
- target_audience = st.selectbox(
- "Target Audience",
- options=['all', 'new_members', 'active_members', 'specific_role'],
- index=['all', 'new_members', 'active_members', 'specific_role'].index(
- st.session_state.group_post_data['target_audience']
- ),
- help="Select who this post is primarily for"
- )
- st.session_state.group_post_data['target_audience'] = target_audience
-
- # Post Tone
- post_tone = st.selectbox(
- "Post Tone",
- options=['professional', 'casual', 'friendly', 'formal', 'humorous'],
- index=['professional', 'casual', 'friendly', 'formal', 'humorous'].index(
- st.session_state.group_post_data['post_tone']
- ),
- help="Choose the tone for your post"
- )
- st.session_state.group_post_data['post_tone'] = post_tone
-
- # Call to Action
- call_to_action = st.selectbox(
- "Call to Action",
- options=['discuss', 'share', 'learn', 'participate', 'feedback'],
- index=['discuss', 'share', 'learn', 'participate', 'feedback'].index(
- st.session_state.group_post_data['call_to_action']
- ),
- help="What action do you want members to take?"
- )
- st.session_state.group_post_data['call_to_action'] = call_to_action
-
-def render_preview_export_tab():
- """Render the preview and export options."""
-
- st.header("Preview & Export")
-
- if 'generated_content' in st.session_state.group_post_data:
- st.subheader("Generated Post")
- st.markdown(st.session_state.group_post_data['generated_content'])
-
- # Export options
- st.subheader("Export Options")
- col1, col2 = st.columns(2)
-
- with col1:
- if st.button("Copy to Clipboard"):
- st.code(st.session_state.group_post_data['generated_content'])
-
- with col2:
- if st.button("Download as Text"):
- download_group_post(st.session_state.group_post_data['generated_content'])
-
-def validate_group_post_fields() -> bool:
- """Validate the group post fields."""
-
- data = st.session_state.group_post_data
-
- # Basic validation rules
- validation_rules = {
- 'post_type': {'required': True, 'type': str},
- 'content': {'required': False, 'type': str},
- 'media_type': {'required': True, 'type': str},
- 'poll_options': {'required': False, 'type': list},
- 'question': {'required': False, 'type': str},
- 'is_announcement': {'required': True, 'type': bool},
- 'engagement_prompt': {'required': False, 'type': str},
- 'target_audience': {'required': True, 'type': str},
- 'post_tone': {'required': True, 'type': str},
- 'call_to_action': {'required': True, 'type': str}
- }
-
- errors = []
-
- for field, rules in validation_rules.items():
- value = data.get(field)
-
- # Skip validation for optional fields if empty
- if not rules['required'] and not value:
- continue
-
- # Type validation
- if value and not isinstance(value, rules['type']):
- errors.append(f"{field.replace('_', ' ').title()} must be of type {rules['type'].__name__}")
- continue
-
- # Required field validation
- if rules['required'] and not value:
- errors.append(f"{field.replace('_', ' ').title()} is required")
-
- if errors:
- for error in errors:
- st.error(error)
- return False
-
- return True
-
-def generate_group_post() -> Optional[str]:
- """Generate the group post content using AI."""
-
- try:
- data = st.session_state.group_post_data
-
- # Prepare the prompt for the LLM
- prompt = f"""
- Create a Facebook Group post with the following specifications:
-
- Post Type: {data['post_type']}
- Content: {data['content'] if data['content'] else 'Generate engaging content'}
- Media Type: {data['media_type']}
- Target Audience: {data['target_audience']}
- Post Tone: {data['post_tone']}
- Call to Action: {data['call_to_action']}
-
- Additional Elements:
- - Poll Options: {', '.join(data['poll_options']) if data['poll_options'] else 'None'}
- - Question: {data['question'] if data['question'] else 'None'}
- - Is Announcement: {data['is_announcement']}
- - Engagement Prompt: {data['engagement_prompt'] if data['engagement_prompt'] else 'None'}
-
- The post should be engaging, community-focused, and encourage discussion.
- Include appropriate formatting, emojis, and hashtags where relevant.
- """
-
- # Get response from LLM
- response = llm_text_gen(prompt)
-
- if response:
- return response
- else:
- st.error("Failed to generate group post content. Please try again.")
- return None
-
- except Exception as e:
- logger.error(f"Error generating group post: {str(e)}")
- st.error("An error occurred while generating the group post. Please try again.")
- return None
-
-def download_group_post(content: str):
- """Download the generated group post as a text file."""
-
- try:
- # Create a timestamp for the filename
- timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
- filename = f"facebook_group_post_{timestamp}.txt"
-
- # Create the download button
- st.download_button(
- label="Download Post",
- data=content,
- file_name=filename,
- mime="text/plain"
- )
- except Exception as e:
- logger.error(f"Error downloading group post: {str(e)}")
- st.error("An error occurred while downloading the post. Please try again.")
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/hashtag_generator/README.md b/ToBeMigrated/ai_writers/ai_facebook_writer/modules/hashtag_generator/README.md
deleted file mode 100644
index 664af1b6..00000000
--- a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/hashtag_generator/README.md
+++ /dev/null
@@ -1,145 +0,0 @@
-# Facebook Hashtag Generator
-
-A powerful tool for generating optimized hashtags for your Facebook content. This module helps content creators maximize their reach and engagement through strategic hashtag selection and placement.
-
-## Features
-
-### 1. Smart Hashtag Generation
-- AI-powered hashtag suggestions based on content analysis
-- Industry-specific hashtag recommendations
-- Mix of trending and niche hashtags
-- Customizable number of hashtags
-- Content type-specific optimization
-
-### 2. Performance Predictions
-- Reach estimates for each hashtag
-- Engagement rate predictions
-- Competition level analysis
-- Visual performance metrics
-- Best performing hashtag combinations
-
-### 3. Hashtag Library
-- Save and organize your hashtags
-- Create custom collections
-- Quick access to frequently used hashtags
-- Easy management of hashtag sets
-- Import/export functionality
-
-### 4. Placement Optimization
-- Smart placement suggestions
-- Integration within content
-- End-of-post placement
-- Mixed placement strategies
-- Preview of hashtag placement
-
-## How to Use
-
-1. **Generate Hashtags**
- - Enter your content or topic
- - Select your industry
- - Choose content type
- - Set hashtag preferences
- - Click "Generate Hashtags"
-
-2. **Manage Library**
- - Save generated hashtags
- - Create collections
- - Add custom hashtags
- - Remove unused hashtags
- - Organize by category
-
-3. **View Insights**
- - Check performance metrics
- - Analyze reach predictions
- - Review engagement rates
- - Compare competition levels
- - Get optimal combinations
-
-## Best Practices
-
-### Content Analysis
-- Provide detailed content description
-- Include key topics and themes
-- Mention target audience
-- Specify content goals
-- Add relevant context
-
-### Hashtag Selection
-- Mix popular and niche hashtags
-- Use industry-specific tags
-- Include trending hashtags
-- Avoid overused combinations
-- Test different variations
-
-### Placement Strategy
-- Natural integration
-- Strategic positioning
-- Readability focus
-- Engagement optimization
-- Mobile-friendly format
-
-## Technical Requirements
-
-- Python 3.7+
-- Streamlit
-- Required Python packages:
- - streamlit
- - loguru
- - typing
- - json
- - re
- - random
-
-## Troubleshooting
-
-### Common Issues
-1. **Generation Failures**
- - Check content input
- - Verify industry selection
- - Ensure internet connection
- - Clear browser cache
- - Restart application
-
-2. **Performance Issues**
- - Reduce hashtag count
- - Clear saved hashtags
- - Update browser
- - Check system resources
- - Contact support
-
-### Error Messages
-- "Please enter content" - Add content description
-- "Invalid industry" - Select from dropdown
-- "Generation failed" - Try again or use defaults
-- "Save failed" - Check storage permissions
-- "Display error" - Refresh page
-
-## Future Updates
-
-### Planned Features
-- Advanced analytics
-- Competitor analysis
-- Hashtag tracking
-- Performance history
-- Bulk generation
-- API integration
-- Custom algorithms
-- Multi-language support
-- Export options
-- Collaboration tools
-
-## Contributing
-
-We welcome contributions to improve the Hashtag Generator:
-
-1. Fork the repository
-2. Create a feature branch
-3. Make your changes
-4. Submit a pull request
-5. Follow coding standards
-6. Add tests
-7. Update documentation
-
-## License
-
-This module is part of the AI Writer suite and is subject to the same licensing terms. See the main LICENSE file for details.
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/hashtag_generator/__init__.py b/ToBeMigrated/ai_writers/ai_facebook_writer/modules/hashtag_generator/__init__.py
deleted file mode 100644
index 77479277..00000000
--- a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/hashtag_generator/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-"""
-Facebook Hashtag Generator Module
-
-This module provides functionality to generate optimized hashtags for Facebook content.
-"""
-
-from .hashtag_generator import write_fb_hashtags
-
-__all__ = ['write_fb_hashtags']
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/hashtag_generator/hashtag_generator.py b/ToBeMigrated/ai_writers/ai_facebook_writer/modules/hashtag_generator/hashtag_generator.py
deleted file mode 100644
index b3a14d7c..00000000
--- a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/hashtag_generator/hashtag_generator.py
+++ /dev/null
@@ -1,567 +0,0 @@
-"""
-Facebook Hashtag Generator Module
-
-This module provides functionality to generate relevant and trending hashtags for Facebook content.
-It analyzes content, suggests optimal hashtag combinations, and provides performance predictions.
-"""
-
-import streamlit as st
-import json
-import random
-import re
-from typing import List, Dict, Any, Tuple, Optional
-from loguru import logger
-import sys
-import base64
-from io import BytesIO
-
-from .....gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-# Configure logging
-logger.remove()
-logger.add(sys.stdout,
- colorize=True,
- format="
-
", unsafe_allow_html=True)
- else:
- st.markdown("""
- About
-
- """, unsafe_allow_html=True)
- st.write(st.session_state.page_about_data['generated_content'])
- st.markdown("
-
", unsafe_allow_html=True)
-
- # SEO Score
- seo_score = calculate_seo_score(st.session_state.page_about_data['generated_content'])
- st.subheader(f"SEO Score: {seo_score}/10")
-
- if seo_score < 7:
- st.warning("Your About section could benefit from SEO improvements. Consider adding more keywords and optimizing your content.")
-
- # Export options
- st.subheader("Export Options")
-
- col1, col2 = st.columns(2)
-
- with col1:
- if st.button("Copy to Clipboard"):
- st.code(st.session_state.page_about_data['generated_content'], language=None)
- st.success("Content copied to clipboard!")
-
- with col2:
- if st.button("Download as Text"):
- download_page_about(st.session_state.page_about_data['generated_content'])
- else:
- st.info("Generate your About section to see a preview and export options.")
-
-def validate_page_about_fields() -> bool:
- """Validate the required fields for the page about generator."""
-
- data = st.session_state.page_about_data
-
- # Check required fields
- if not data['business_name']:
- st.error("Business/Organization Name is required.")
- return False
-
- if not data['industry']:
- st.error("Industry/Category is required.")
- return False
-
- if not data['mission_statement']:
- st.error("Mission Statement is required.")
- return False
-
- if not data['target_audience']:
- st.error("Target Audience is required.")
- return False
-
- if not data['brand_personality']:
- st.error("At least one Brand Personality Trait is required.")
- return False
-
- if not data['brand_values']:
- st.error("At least one Brand Value is required.")
- return False
-
- # Check if at least one section is included
- sections_included = (
- data['include_about_us'] or
- data['include_products'] or
- data['include_usp'] or
- data['include_team'] or
- data['include_awards'] or
- data['include_testimonials'] or
- data['include_cta']
- )
-
- if not sections_included:
- st.error("At least one section must be included.")
- return False
-
- return True
-
-def generate_page_about() -> Optional[str]:
- """Generate the page about content using AI."""
-
- try:
- data = st.session_state.page_about_data
-
- # Prepare the prompt for the LLM
- prompt = f"""
- Create a Facebook Page About section for {data['business_name']}, a {data['industry']} business.
-
- Basic Information:
- - Founded: {data['founded_date']}
- - Location: {data['location']}
- - Website: {data['website']}
- - Mission Statement: {data['mission_statement']}
-
- Brand Voice & Tone:
- - Tone: {data['tone']}
- - Brand Personality Traits: {', '.join(data['brand_personality'])}
- - Target Audience: {data['target_audience']}
- - Brand Values: {', '.join(data['brand_values'])}
-
- Content Length: {data['content_length']}
-
- Include the following sections:
- """
-
- # Add sections to include
- if data['include_about_us']:
- prompt += f"""
- - About Us/Our Story: {data['about_us_content'] if data['about_us_content'] else 'Generate engaging content about the company history and journey'}
- """
-
- if data['include_products']:
- prompt += f"""
- - Products/Services: {data['products_content'] if data['products_content'] else 'Generate engaging content about the products or services offered'}
- """
-
- if data['include_usp']:
- prompt += f"""
- - Unique Selling Proposition: {data['usp_content'] if data['usp_content'] else 'Generate engaging content about what sets the business apart'}
- """
-
- if data['include_team']:
- prompt += f"""
- - Team/Leadership: {data['team_content'] if data['team_content'] else 'Generate engaging content about the team or leadership'}
- """
-
- if data['include_awards']:
- prompt += f"""
- - Awards/Recognition: {data['awards_content'] if data['awards_content'] else 'Generate engaging content about awards and recognition'}
- """
-
- if data['include_testimonials']:
- prompt += f"""
- - Customer Testimonials: {data['testimonials_content'] if data['testimonials_content'] else 'Generate engaging content with customer testimonials'}
- """
-
- if data['include_cta']:
- prompt += f"""
- - Call-to-Action: {data['cta_content'] if data['cta_content'] else 'Generate an engaging call-to-action'}
- """
-
- prompt += """
- The About section should be well-structured, engaging, and optimized for Facebook.
- Use appropriate formatting, emojis, and line breaks for better readability.
- Make sure the tone is consistent with the selected brand voice.
- Include relevant keywords for SEO optimization.
- """
-
- # Get response from LLM
- response = llm_text_gen(prompt)
-
- if response:
- return response
- else:
- st.error("Failed to generate About section content. Please try again.")
- return None
-
- except Exception as e:
- logger.error(f"Error generating About section: {str(e)}")
- st.error("An error occurred while generating the About section. Please try again.")
- return None
-
-def calculate_seo_score(content: str) -> int:
- """Calculate a simple SEO score for the content."""
-
- score = 5 # Start with a base score
-
- # Check for keywords (simple implementation)
- keywords = ['about', 'mission', 'vision', 'values', 'services', 'products', 'team', 'contact']
- for keyword in keywords:
- if keyword.lower() in content.lower():
- score += 0.5
-
- # Check for formatting
- if '\n\n' in content: # Paragraph breaks
- score += 1
-
- if '*' in content or '_' in content: # Bold or italic formatting
- score += 1
-
- # Check for length
- if len(content) > 500:
- score += 1
-
- # Cap the score at 10
- return min(score, 10)
-
-def download_page_about(content: str):
- """Download the About section content as a text file."""
-
- try:
- # Create a timestamp for the filename
- timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
- filename = f"facebook_page_about_{timestamp}.txt"
-
- # Create a download button
- st.download_button(
- label="Download Text File",
- data=content,
- file_name=filename,
- mime="text/plain"
- )
-
- except Exception as e:
- logger.error(f"Error downloading About section: {str(e)}")
- st.error("An error occurred while downloading the About section. Please try again.")
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/post_generator.py b/ToBeMigrated/ai_writers/ai_facebook_writer/modules/post_generator.py
deleted file mode 100644
index a16e383a..00000000
--- a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/post_generator.py
+++ /dev/null
@@ -1,231 +0,0 @@
-"""
-Facebook Post Generator Module
-
-This module provides functionality to generate engaging Facebook posts with various features
-and optimization options.
-"""
-
-import streamlit as st
-from ....gpt_providers.text_generation.main_text_generation import llm_text_gen
-from ....gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
-
-
-def write_fb_post():
- """Generate an engaging Facebook post with various features and optimization options."""
-
- st.markdown("""
- ### đ Facebook Post Generator
- Create engaging Facebook posts that drive engagement and reach. Customize your post with various features
- and get AI-powered suggestions for optimal performance.
- """)
-
- # Create tabs for different sections
- tab1, tab2, tab3 = st.tabs(["Post Content", "Media & Links", "Preview & Analytics"])
-
- with tab1:
- # Basic post information
- col1, col2 = st.columns(2)
-
- with col1:
- post_goal_options = [
- "Promote a product/service",
- "Share valuable content",
- "Increase engagement",
- "Build brand awareness",
- "Drive website traffic",
- "Generate leads",
- "Announce news/updates",
- "Customize"
- ]
- post_goal = st.selectbox(
- "đ¯ **What is the goal of your post?**",
- post_goal_options,
- index=2,
- help="Select the main goal of your post."
- )
-
- if post_goal == "Customize":
- post_goal = st.text_input(
- "đ¯ **Customize your goal:**",
- placeholder="e.g., Announce an event",
- help="Provide a specific goal if you selected 'Customize'."
- )
-
- target_audience = st.text_input(
- "đĨ **Describe your target audience:**",
- placeholder="e.g., Fitness enthusiasts aged 25-35",
- help="Describe the audience you are targeting with this post."
- )
-
- post_tone_options = [
- "Informative",
- "Humorous",
- "Inspirational",
- "Upbeat",
- "Casual",
- "Professional",
- "Conversational",
- "Customize"
- ]
- post_tone = st.selectbox(
- "đ¨ **What tone do you want to use?**",
- post_tone_options,
- index=3,
- help="Choose the tone you want to use for the post."
- )
-
- if post_tone == "Customize":
- post_tone = st.text_input(
- "đ¨ **Customize your tone:**",
- placeholder="e.g., Professional",
- help="Provide a specific tone if you selected 'Customize'."
- )
-
- with col2:
- business_type = st.text_input(
- "đĸ **What is your business type?**",
- placeholder="e.g., Fitness coach",
- help="Provide the type of your business. This will help tailor the post content."
- )
-
- include = st.text_input(
- "đˇ **What elements do you want to include?**",
- placeholder="e.g., Short video with a sneak peek of the challenge",
- help="Specify any elements you want to include in the post."
- )
-
- avoid = st.text_input(
- "â **What elements do you want to avoid?**",
- placeholder="e.g., Long paragraphs",
- help="Specify any elements you want to avoid in the post."
- )
-
- # Advanced options
- with st.expander("Advanced Options"):
- st.markdown("#### Post Structure")
- use_hook = st.checkbox("Use attention-grabbing hook", value=True)
- use_story = st.checkbox("Include storytelling elements", value=True)
- use_cta = st.checkbox("Add clear call-to-action", value=True)
-
- st.markdown("#### Engagement Features")
- use_question = st.checkbox("Include engagement question", value=True)
- use_emoji = st.checkbox("Use relevant emojis", value=True)
- use_hashtags = st.checkbox("Add relevant hashtags", value=True)
-
- with tab2:
- # Media and link options
- st.markdown("#### Media Options")
- media_type = st.radio(
- "Select media type:",
- ["None", "Image", "Video", "Carousel", "Link Preview"],
- horizontal=True
- )
-
- if media_type == "Image":
- col1, col2 = st.columns(2)
- with col1:
- image_source = st.radio(
- "Image source:",
- ["Upload", "Generate with AI", "Use URL"],
- horizontal=True
- )
-
- if image_source == "Upload":
- uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
- elif image_source == "Generate with AI":
- image_prompt = st.text_area("Describe the image you want to generate")
- if st.button("Generate Image"):
- with st.spinner("Generating image..."):
- # Call image generation function
- pass
- else:
- image_url = st.text_input("Enter image URL")
-
- with col2:
- st.markdown("#### Image Settings")
- image_position = st.selectbox(
- "Image position:",
- ["Above post", "Below post"]
- )
- add_image_caption = st.checkbox("Add image caption", value=True)
-
- elif media_type == "Video":
- st.file_uploader("Upload a video", type=["mp4", "mov"])
- st.checkbox("Add video thumbnail", value=True)
- st.checkbox("Add video description", value=True)
-
- elif media_type == "Carousel":
- st.file_uploader("Upload multiple images", type=["jpg", "jpeg", "png"], accept_multiple_files=True)
- st.checkbox("Add captions for each image", value=True)
-
- elif media_type == "Link Preview":
- st.text_input("Enter URL to preview")
- st.checkbox("Customize link preview", value=False)
-
- with tab3:
- # Preview and analytics section
- st.markdown("#### Post Preview")
-
- # Generate post button
- if st.button("đ Generate Facebook Post", key="generate_post"):
- with st.spinner("Generating your post..."):
- if not business_type or not target_audience:
- st.error("đĢ Please provide the required inputs: Business Type and Target Audience.")
- else:
- # Generate the post content
- prompt = f"""
- Create a Facebook post for a {business_type} targeting {target_audience}.
-
- Goal: {post_goal}
- Tone: {post_tone}
-
- Include: {include}
- Avoid: {avoid}
-
- Additional requirements:
- - Use attention-grabbing hook: {use_hook}
- - Include storytelling elements: {use_story}
- - Add clear call-to-action: {use_cta}
- - Include engagement question: {use_question}
- - Use relevant emojis: {use_emoji}
- - Add relevant hashtags: {use_hashtags}
-
- Please write a well-structured Facebook post that:
- 1. Grabs attention in the first line
- 2. Maintains consistent tone throughout
- 3. Includes engaging content that aligns with the goal
- 4. Ends with a clear call-to-action
- 5. Uses appropriate formatting and emojis
- 6. Includes relevant hashtags if requested
- """
-
- generated_post = llm_text_gen(prompt)
-
- if generated_post:
- # Display the generated post
- st.markdown("### Generated Post")
- st.markdown(generated_post)
-
- # Display engagement predictions
- st.markdown("### đ Engagement Predictions")
- col1, col2, col3 = st.columns(3)
- with col1:
- st.metric("Expected Reach", "2.5K - 5K")
- with col2:
- st.metric("Expected Engagement", "5-8%")
- with col3:
- st.metric("Best Time to Post", "2 PM - 4 PM")
-
- # Display optimization suggestions
- st.markdown("### đĄ Optimization Suggestions")
- st.info("""
- - Consider adding a question to increase comments
- - Use more emojis to increase visibility
- - Keep paragraphs shorter for better readability
- - Add a poll to increase engagement
- """)
-
- # Copy button
- st.button("đ Copy to Clipboard", key="copy_post")
- else:
- st.error("Error: Failed to generate Facebook Post.")
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/story_generator.py b/ToBeMigrated/ai_writers/ai_facebook_writer/modules/story_generator.py
deleted file mode 100644
index 9ee8b56e..00000000
--- a/ToBeMigrated/ai_writers/ai_facebook_writer/modules/story_generator.py
+++ /dev/null
@@ -1,246 +0,0 @@
-"""
-Facebook Story Generator Module
-
-This module provides functionality to generate engaging Facebook Stories with various features
-and customization options.
-"""
-
-import streamlit as st
-from ....gpt_providers.text_generation.main_text_generation import llm_text_gen
-from ....gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
-
-
-def write_fb_story():
- """Generate an engaging Facebook Story with various features and customization options."""
-
- st.markdown("""
- ### đą Facebook Story Generator
- Create engaging Facebook Stories that capture attention and drive engagement. Customize your story
- with various features and get AI-powered suggestions for optimal performance.
- """)
-
- # Create tabs for different sections
- tab1, tab2, tab3 = st.tabs(["Story Content", "Visual Elements", "Preview & Analytics"])
-
- with tab1:
- # Basic story information
- col1, col2 = st.columns(2)
-
- with col1:
- story_type_options = [
- "Product showcase",
- "Behind the scenes",
- "User testimonial",
- "Event promotion",
- "Tutorial/How-to",
- "Question/Poll",
- "Announcement",
- "Customize"
- ]
- story_type = st.selectbox(
- "đ¯ **What type of story do you want to create?**",
- story_type_options,
- index=0,
- help="Select the type of story you want to create."
- )
-
- if story_type == "Customize":
- story_type = st.text_input(
- "đ¯ **Customize your story type:**",
- placeholder="e.g., Product launch",
- help="Provide a specific story type if you selected 'Customize'."
- )
-
- target_audience = st.text_input(
- "đĨ **Describe your target audience:**",
- placeholder="e.g., Fashion enthusiasts aged 18-24",
- help="Describe the audience you are targeting with this story."
- )
-
- story_tone_options = [
- "Casual",
- "Fun",
- "Professional",
- "Inspirational",
- "Educational",
- "Entertaining",
- "Customize"
- ]
- story_tone = st.selectbox(
- "đ¨ **What tone do you want to use?**",
- story_tone_options,
- index=0,
- help="Choose the tone you want to use for the story."
- )
-
- if story_tone == "Customize":
- story_tone = st.text_input(
- "đ¨ **Customize your tone:**",
- placeholder="e.g., Playful",
- help="Provide a specific tone if you selected 'Customize'."
- )
-
- with col2:
- business_type = st.text_input(
- "đĸ **What is your business type?**",
- placeholder="e.g., Fashion brand",
- help="Provide the type of your business. This will help tailor the story content."
- )
-
- include = st.text_input(
- "đˇ **What elements do you want to include?**",
- placeholder="e.g., Product demonstration, customer testimonial",
- help="Specify any elements you want to include in the story."
- )
-
- avoid = st.text_input(
- "â **What elements do you want to avoid?**",
- placeholder="e.g., Long text overlays",
- help="Specify any elements you want to avoid in the story."
- )
-
- # Advanced options
- with st.expander("Advanced Options"):
- st.markdown("#### Story Structure")
- use_hook = st.checkbox("Use attention-grabbing opening", value=True)
- use_story = st.checkbox("Include storytelling elements", value=True)
- use_cta = st.checkbox("Add clear call-to-action", value=True)
-
- st.markdown("#### Engagement Features")
- use_question = st.checkbox("Include engagement question", value=True)
- use_emoji = st.checkbox("Use relevant emojis", value=True)
- use_hashtags = st.checkbox("Add relevant hashtags", value=True)
- use_stickers = st.checkbox("Add interactive stickers", value=True)
-
- with tab2:
- # Visual elements options
- st.markdown("#### Visual Elements")
-
- # Background options
- st.markdown("##### Background")
- background_type = st.radio(
- "Select background type:",
- ["Solid color", "Gradient", "Image", "Video"],
- horizontal=True
- )
-
- if background_type == "Solid color":
- st.color_picker("Choose background color", "#FFFFFF")
- elif background_type == "Gradient":
- col1, col2 = st.columns(2)
- with col1:
- st.color_picker("Start color", "#FFFFFF")
- with col2:
- st.color_picker("End color", "#000000")
- elif background_type == "Image":
- image_source = st.radio(
- "Image source:",
- ["Upload", "Generate with AI", "Use URL"],
- horizontal=True
- )
-
- if image_source == "Upload":
- st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
- elif image_source == "Generate with AI":
- image_prompt = st.text_area("Describe the image you want to generate")
- if st.button("Generate Image"):
- with st.spinner("Generating image..."):
- # Call image generation function
- pass
- else:
- st.text_input("Enter image URL")
- else:
- st.file_uploader("Upload a video", type=["mp4", "mov"])
-
- # Text overlay options
- st.markdown("##### Text Overlay")
- text_style = st.selectbox(
- "Text style:",
- ["Minimal", "Bold", "Playful", "Professional", "Custom"]
- )
-
- if text_style == "Custom":
- st.text_input("Custom text style description")
-
- text_color = st.color_picker("Text color", "#000000")
- text_position = st.selectbox(
- "Text position:",
- ["Top", "Middle", "Bottom", "Custom"]
- )
-
- # Interactive elements
- st.markdown("##### Interactive Elements")
- use_poll = st.checkbox("Add poll", value=False)
- use_quiz = st.checkbox("Add quiz", value=False)
- use_slider = st.checkbox("Add slider", value=False)
- use_countdown = st.checkbox("Add countdown", value=False)
-
- with tab3:
- # Preview and analytics section
- st.markdown("#### Story Preview")
-
- # Generate story button
- if st.button("đ Generate Facebook Story", key="generate_story"):
- with st.spinner("Generating your story..."):
- if not business_type or not target_audience:
- st.error("đĢ Please provide the required inputs: Business Type and Target Audience.")
- else:
- # Generate the story content
- prompt = f"""
- Create a Facebook Story for a {business_type} targeting {target_audience}.
-
- Story Type: {story_type}
- Tone: {story_tone}
-
- Include: {include}
- Avoid: {avoid}
-
- Additional requirements:
- - Use attention-grabbing opening: {use_hook}
- - Include storytelling elements: {use_story}
- - Add clear call-to-action: {use_cta}
- - Include engagement question: {use_question}
- - Use relevant emojis: {use_emoji}
- - Add relevant hashtags: {use_hashtags}
- - Add interactive stickers: {use_stickers}
-
- Please write a well-structured Facebook Story that:
- 1. Grabs attention in the first frame
- 2. Maintains consistent tone throughout
- 3. Includes engaging content that aligns with the story type
- 4. Ends with a clear call-to-action
- 5. Uses appropriate formatting and emojis
- 6. Includes relevant hashtags if requested
- 7. Incorporates interactive elements if selected
- """
-
- generated_story = llm_text_gen(prompt)
-
- if generated_story:
- # Display the generated story
- st.markdown("### Generated Story")
- st.markdown(generated_story)
-
- # Display engagement predictions
- st.markdown("### đ Engagement Predictions")
- col1, col2, col3 = st.columns(3)
- with col1:
- st.metric("Expected Views", "1K - 2K")
- with col2:
- st.metric("Expected Engagement", "8-12%")
- with col3:
- st.metric("Best Time to Post", "6 PM - 8 PM")
-
- # Display optimization suggestions
- st.markdown("### đĄ Optimization Suggestions")
- st.info("""
- - Add more interactive elements to increase engagement
- - Keep text overlays short and readable
- - Use vibrant colors to stand out
- - Add music to increase watch time
- """)
-
- # Copy button
- st.button("đ Copy to Clipboard", key="copy_story")
- else:
- st.error("Error: Failed to generate Facebook Story.")
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/README.md b/ToBeMigrated/ai_writers/linkedin_writer/README.md
deleted file mode 100644
index 191c8247..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/README.md
+++ /dev/null
@@ -1,347 +0,0 @@
-# LinkedIn AI Writer
-
-A comprehensive suite of AI-powered tools for generating professional LinkedIn content.
-
-## Overview
-
-LinkedIn AI Writer is a powerful tool designed to help professionals, businesses, and content creators generate high-quality LinkedIn content. It leverages advanced AI models to create engaging posts, articles, carousels, video scripts, and more, all optimized for LinkedIn's platform.
-
-## Tools Status
-
-| Tool | Status | Category |
-|------|--------|----------|
-| LinkedIn Post Generator | â
Active | Content Creation |
-| LinkedIn Article Generator | â
Active | Content Creation |
-| LinkedIn Carousel Post Generator | â
Active | Content Creation |
-| LinkedIn Video Script Generator | â
Active | Content Creation |
-| LinkedIn Poll Generator | â
Active | Content Creation |
-| LinkedIn Comment Response Generator | â
Active | Engagement |
-| LinkedIn Profile Optimizer | â
Active | Profile & Personal Branding |
-| LinkedIn Company Page Content Generator | â
Active | Business & Marketing |
-| LinkedIn Newsletter Generator | đ Coming Soon | Business & Marketing |
-| LinkedIn Job Description Generator | đ Coming Soon | Business & Marketing |
-| LinkedIn Sales Navigator Content Generator | đ Coming Soon | Sales & Networking |
-| LinkedIn InMail Generator | đ Coming Soon | Sales & Networking |
-| LinkedIn Learning Course Description Generator | đ Coming Soon | Learning & Education |
-| LinkedIn Event Description Generator | đ Coming Soon | Learning & Education |
-| LinkedIn Group Post Generator | đ Coming Soon | Community & Engagement |
-
-## Features
-
-### Content Creation Tools
-
-#### LinkedIn Post Generator
-- **Status**: Active
-- **Description**: Create engaging, professional posts that drive engagement and establish thought leadership.
-- **Features**:
- - Professional tone customization
- - Industry-specific terminology
- - Hashtag optimization
- - Formatting options
- - Character count optimization
- - Call-to-action suggestions
- - Engagement prediction
- - Visual content recommendations
- - Poll creation
- - Best posting time suggestions
- - Research-backed content
- - Reference tracking
-
-#### LinkedIn Article Generator
-- **Status**: Active
-- **Description**: Generate long-form professional articles that showcase expertise and drive traffic.
-- **Features**:
- - Topic research and outline generation
- - SEO optimization for LinkedIn articles
- - Professional writing style adaptation
- - Section structuring
- - Citation and reference formatting
- - Image placement suggestions
- - Headline optimization
- - Meta description generation
- - Reading time estimation
- - Internal linking suggestions
- - Multiple research sources (Metaphor, Google, Tavily)
- - AI-generated section images
-
-#### LinkedIn Carousel Post Generator
-- **Status**: Active
-- **Description**: Create engaging carousel posts that showcase information in a visually appealing way.
-- **Features**:
- - Slide content generation
- - Visual hierarchy optimization
- - Story arc development
- - Call-to-action placement
- - Brand consistency maintenance
- - Engagement element integration
- - Professional design suggestions
- - Content distribution strategy
- - Analytics integration
- - A/B testing variations
-
-#### LinkedIn Video Script Generator
-- **Status**: Active
-- **Description**: Create scripts for LinkedIn videos that drive engagement.
-- **Features**:
- - Hook generation
- - Story structure development
- - Professional speaking points
- - Visual cue suggestions
- - Call-to-action optimization
- - Engagement prompt integration
- - Caption generation
- - Thumbnail text suggestions
- - Video description optimization
- - Hashtag strategy
-
-#### LinkedIn Poll Generator
-- **Status**: Active
-- **Description**: Create engaging polls that drive interaction and gather insights.
-- **Features**:
- - Question formulation optimization
- - Option generation based on topic
- - Industry-specific poll templates
- - Engagement prediction
- - Result analysis suggestions
- - Follow-up content recommendations
- - Trending topic integration
- - Professional tone maintenance
- - Data visualization suggestions
- - Poll scheduling optimization
-
-### Engagement Tools
-
-#### LinkedIn Comment Response Generator
-- **Status**: Active
-- **Description**: Generate professional and engaging responses to LinkedIn comments with AI-powered analysis and optimization.
-- **Features**:
- - Comment analysis and categorization
- - Multiple response types (general, disagreement, value-add)
- - Brand voice customization
- - Engagement goal targeting
- - Resource suggestion generation
- - Follow-up question generation
- - Tone optimization
- - Response strategy recommendations
- - Context-aware responses
- - Professional formatting
-
-### Profile & Personal Branding Tools
-
-#### LinkedIn Profile Optimizer
-- **Status**: Active
-- **Description**: Enhance LinkedIn profiles to improve visibility and professional appeal.
-- **Features**:
- - Headline optimization
- - About section generation
- - Experience description enhancement
- - Skills recommendation
- - Project highlight creation
- - Endorsement request generation
- - Profile strength analysis
- - Keyword optimization
- - Professional summary generation
- - Custom URL suggestions
-
-### Business & Marketing Tools
-
-#### LinkedIn Company Page Content Generator
-- **Status**: Active
-- **Description**: Create content for company pages that builds brand awareness and engagement.
-- **Features**:
- - Company profile optimization
- - Company culture post generation
- - Product/service announcement templates
- - Employee spotlight content
- - Company milestone celebrations
- - Industry insights sharing
- - Event promotion content
- - Job posting templates
- - Company news updates
- - Brand voice consistency
- - Engagement metrics optimization
-
-#### LinkedIn Newsletter Generator
-- **Status**: Coming Soon
-- **Description**: Create professional newsletters that establish thought leadership and drive engagement.
-- **Features**:
- - Newsletter structure templates
- - Topic clustering and organization
- - Professional introduction and conclusion
- - Industry trend analysis integration
- - Expert quote suggestions
- - Visual content recommendations
- - Call-to-action optimization
- - Subscriber engagement prompts
- - Consistency maintenance
- - Analytics integration suggestions
-
-#### LinkedIn Job Description Generator
-- **Status**: Coming Soon
-- **Description**: Create compelling job descriptions that attract qualified candidates.
-- **Features**:
- - Role-specific templates
- - Skills and qualifications optimization
- - Company culture integration
- - Benefits and perks highlighting
- - Inclusive language checker
- - Keyword optimization
- - Application process clarity
- - Remote/hybrid work policy integration
- - Diversity and inclusion statements
- - A/B testing variations
-
-### Sales & Networking Tools
-
-#### LinkedIn Sales Navigator Content Generator
-- **Status**: Coming Soon
-- **Description**: Create personalized outreach content for sales professionals.
-- **Features**:
- - Prospect research integration
- - Industry-specific messaging
- - Personalization tokens
- - Connection request templates
- - Follow-up message sequences
- - Value proposition highlighting
- - Objection handling responses
- - Meeting request templates
- - Industry pain point addressing
- - ROI demonstration content
-
-#### LinkedIn InMail Generator
-- **Status**: Coming Soon
-- **Description**: Create personalized and effective InMail messages.
-- **Features**:
- - Prospect research integration
- - Personalization token usage
- - Value proposition highlighting
- - Call-to-action optimization
- - Follow-up sequence generation
- - Objection handling preparation
- - Industry-specific messaging
- - A/B testing variations
- - Compliance checking
- - Engagement tracking suggestions
-
-### Learning & Education Tools
-
-#### LinkedIn Learning Course Description Generator
-- **Status**: Coming Soon
-- **Description**: Create compelling descriptions for LinkedIn Learning courses.
-- **Features**:
- - Course objective optimization
- - Learning outcome generation
- - Prerequisite suggestions
- - Target audience definition
- - Skill tag recommendations
- - Course structure outline
- - Engagement element suggestions
- - Completion certificate highlighting
- - Industry relevance emphasis
- - Career path integration
-
-#### LinkedIn Event Description Generator
-- **Status**: Coming Soon
-- **Description**: Create compelling event descriptions that drive attendance and engagement.
-- **Features**:
- - Event objective highlighting
- - Speaker bio generation
- - Agenda formatting
- - Registration incentive suggestions
- - Networking opportunity emphasis
- - Industry relevance integration
- - Visual content recommendations
- - Engagement element suggestions
- - Post-event follow-up content
- - Attendance tracking integration
-
-### Community & Engagement Tools
-
-#### LinkedIn Group Post Generator
-- **Status**: Coming Soon
-- **Description**: Create content specifically optimized for LinkedIn Groups.
-- **Features**:
- - Group-specific content adaptation
- - Discussion prompt generation
- - Community guideline compliance
- - Engagement optimization
- - Moderation suggestion
- - Topic relevance checking
- - Member value highlighting
- - Cross-promotion opportunities
- - Group culture adaptation
- - Content scheduling
-
-## Current Status
-
-The LinkedIn AI Writer currently has the following tools fully implemented and active:
-
-1. LinkedIn Post Generator
-2. LinkedIn Article Generator
-3. LinkedIn Carousel Post Generator
-4. LinkedIn Video Script Generator
-5. LinkedIn Comment Response Generator
-6. LinkedIn Profile Optimizer
-7. LinkedIn Poll Generator
-8. LinkedIn Company Page Content Generator
-
-The remaining tools are marked as "Coming Soon" and will be implemented in future iterations.
-
-## Future Development
-
-### LinkedIn API Integration
-
-To provide more personalized and effective content, future development will include integration with the LinkedIn API. This will allow:
-
-- Access to user profile data for personalized content
-- Direct posting to LinkedIn
-- Analytics integration for content performance tracking
-- Engagement metrics for content optimization
-- Audience insights for targeted content creation
-
-### Enhanced AI Models
-
-We plan to enhance the AI models used for content generation to:
-
-- Better understand LinkedIn's algorithm and best practices
-- Generate more engaging and platform-specific content
-- Provide more accurate engagement predictions
-- Create more personalized content based on user data
-
-### Additional Features
-
-Future iterations will include:
-
-- Content calendar planning and scheduling
-- Multi-platform content adaptation
-- Advanced analytics and reporting
-- Team collaboration features
-- Content approval workflows
-- A/B testing capabilities
-- Custom templates and brand voice settings
-
-## Getting Started
-
-To use the LinkedIn AI Writer:
-
-1. Navigate to the LinkedIn AI Writer dashboard
-2. Select the tool you want to use
-3. Fill in the required information
-4. Generate your content
-5. Review and edit as needed
-6. Copy the content to your LinkedIn account
-
-## Requirements
-
-- Python 3.8+
-- Streamlit
-- OpenAI API key (for GPT models)
-- Optional: LinkedIn API credentials (for future integration)
-
-## Contributing
-
-We welcome contributions to the LinkedIn AI Writer project. Please feel free to submit issues, feature requests, or pull requests.
-
-## License
-
-This project is licensed under the MIT License - see the LICENSE file for details.
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/__init__.py b/ToBeMigrated/ai_writers/linkedin_writer/__init__.py
deleted file mode 100644
index 7c045de1..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-"""
-LinkedIn AI Writer Module
-
-This module provides a comprehensive suite of tools for generating LinkedIn content.
-"""
-
-from .linkedin_ai_writer import linkedin_main_menu, LinkedInAIWriter
-from .modules.post_generator.linkedin_post_generator import linkedin_post_generator_ui, LinkedInPostGenerator
-from .modules.article_generator.linkedin_article_generator import linkedin_article_generator_ui
-from .modules.carousel_generator.linkedin_carousel_generator import linkedin_carousel_generator_ui, LinkedInCarouselGenerator
-
-__all__ = [
- 'linkedin_main_menu',
- 'LinkedInAIWriter',
- 'linkedin_post_generator_ui',
- 'LinkedInPostGenerator',
- 'linkedin_article_generator_ui',
- 'linkedin_carousel_generator_ui',
- 'LinkedInCarouselGenerator'
-]
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/linkedin_ai_writer.py b/ToBeMigrated/ai_writers/linkedin_writer/linkedin_ai_writer.py
deleted file mode 100644
index f48be704..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/linkedin_ai_writer.py
+++ /dev/null
@@ -1,580 +0,0 @@
-"""
-LinkedIn AI Writer
-
-This module provides a comprehensive suite of tools for generating LinkedIn content.
-"""
-
-import time
-import os
-import json
-import requests
-import streamlit as st
-import importlib
-import sys
-from pathlib import Path
-from typing import Dict, List, Optional, Union
-from loguru import logger
-
-# Import AI text generation
-from ...gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-# Import web research tools
-from ...ai_web_researcher.gpt_online_researcher import do_google_serp_search
-from ...ai_web_researcher.metaphor_basic_neural_web_search import metaphor_search_articles, streamlit_display_metaphor_results
-from ...ai_web_researcher.tavily_ai_search import do_tavily_ai_search, streamlit_display_results
-
-# Import LinkedIn content generators
-from .modules.post_generator.linkedin_post_generator import linkedin_post_generator_ui
-from .modules.article_generator.linkedin_article_generator import linkedin_article_generator_ui
-from .modules.carousel_generator.linkedin_carousel_generator import linkedin_carousel_generator_ui
-from .modules.video_script_generator.linkedin_video_script_generator import linkedin_video_script_generator_ui
-from .modules.comment_response_generator.linkedin_comment_response_generator_ui import linkedin_comment_response_generator_ui
-from .modules.profile_optimizer.linkedin_profile_optimizer_ui import linkedin_profile_optimizer_ui
-from .modules.poll_generator import linkedin_poll_generator_ui
-from .modules.company_page_generator import linkedin_company_page_generator_ui
-
-# Import image generation
-from ...gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
-
-# Create a wrapper for the async profile optimizer UI
-def linkedin_profile_optimizer_ui_wrapper():
- """Wrapper function to call the async LinkedIn Profile Optimizer UI."""
- import asyncio
- asyncio.run(linkedin_profile_optimizer_ui())
-
-# Create a wrapper for the async company page generator UI
-def linkedin_company_page_generator_ui_wrapper():
- """Wrapper function to call the async LinkedIn Company Page Generator UI."""
- import asyncio
- asyncio.run(linkedin_company_page_generator_ui())
-
-def linkedin_main_menu():
- """Main function for the LinkedIn 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 LinkedIn tools with their details
- linkedin_tools = [
- # Content Creation Tools
- {
- "name": "LinkedIn Post Generator",
- "icon": "đ",
- "description": "Create engaging, professional posts that drive engagement and establish thought leadership.",
- "color": "#0A66C2", # LinkedIn blue
- "category": "Content Creation",
- "function": linkedin_post_generator_ui,
- "status": "active",
- "features": [
- "Professional tone customization",
- "Industry-specific terminology",
- "Hashtag optimization",
- "Formatting options",
- "Character count optimization",
- "Call-to-action suggestions",
- "Engagement prediction",
- "Visual content recommendations",
- "Poll creation",
- "Best posting time suggestions",
- "Research-backed content",
- "Reference tracking"
- ]
- },
- {
- "name": "LinkedIn Article Generator",
- "icon": "đ",
- "description": "Generate long-form professional articles that showcase expertise and drive traffic.",
- "color": "#0A66C2",
- "category": "Content Creation",
- "function": linkedin_article_generator_ui,
- "status": "active",
- "features": [
- "Topic research and outline generation",
- "SEO optimization for LinkedIn articles",
- "Professional writing style adaptation",
- "Section structuring",
- "Citation and reference formatting",
- "Image placement suggestions",
- "Headline optimization",
- "Meta description generation",
- "Reading time estimation",
- "Internal linking suggestions",
- "Multiple research sources (Metaphor, Google, Tavily)",
- "AI-generated section images"
- ]
- },
- {
- "name": "LinkedIn Carousel Post Generator",
- "icon": "đ",
- "description": "Create engaging carousel posts that showcase information in a visually appealing way.",
- "color": "#0A66C2",
- "category": "Content Creation",
- "function": linkedin_carousel_generator_ui,
- "status": "active",
- "features": [
- "Slide content generation",
- "Visual hierarchy optimization",
- "Story arc development",
- "Call-to-action placement",
- "Brand consistency maintenance",
- "Engagement element integration",
- "Professional design suggestions",
- "Content distribution strategy",
- "Analytics integration",
- "A/B testing variations"
- ]
- },
- {
- "name": "LinkedIn Video Script Generator",
- "icon": "đĨ",
- "description": "Create scripts for LinkedIn videos that drive engagement.",
- "color": "#0A66C2",
- "category": "Content Creation",
- "function": linkedin_video_script_generator_ui,
- "status": "active",
- "features": [
- "Hook generation",
- "Story structure development",
- "Professional speaking points",
- "Visual cue suggestions",
- "Call-to-action optimization",
- "Engagement prompt integration",
- "Caption generation",
- "Thumbnail text suggestions",
- "Video description optimization",
- "Hashtag strategy"
- ]
- },
- {
- "name": "LinkedIn Comment Response Generator",
- "icon": "đŦ",
- "description": "Generate professional and engaging responses to LinkedIn comments with AI-powered analysis and optimization.",
- "color": "#0A66C2",
- "category": "Engagement",
- "function": linkedin_comment_response_generator_ui,
- "status": "active",
- "features": [
- "Comment analysis and categorization",
- "Multiple response types (general, disagreement, value-add)",
- "Brand voice customization",
- "Engagement goal targeting",
- "Resource suggestion generation",
- "Follow-up question generation",
- "Tone optimization",
- "Response strategy recommendations",
- "Context-aware responses",
- "Professional formatting"
- ]
- },
-
- # Profile & Personal Branding Tools
- {
- "name": "LinkedIn Profile Optimizer",
- "icon": "đ¤",
- "description": "Enhance LinkedIn profiles to improve visibility and professional appeal.",
- "color": "#0A66C2",
- "category": "Profile & Personal Branding",
- "function": linkedin_profile_optimizer_ui_wrapper,
- "status": "active",
- "features": [
- "Headline optimization",
- "About section generation",
- "Experience description enhancement",
- "Skills recommendation",
- "Project highlight creation",
- "Endorsement request generation",
- "Profile strength analysis",
- "Keyword optimization",
- "Professional summary generation",
- "Custom URL suggestions"
- ]
- },
- {
- "name": "LinkedIn Poll Generator",
- "icon": "đ",
- "description": "Create engaging polls that drive interaction and gather insights.",
- "color": "#0A66C2",
- "category": "Profile & Personal Branding",
- "function": linkedin_poll_generator_ui,
- "status": "active",
- "features": [
- "Question formulation optimization",
- "Option generation based on topic",
- "Industry-specific poll templates",
- "Engagement prediction",
- "Result analysis suggestions",
- "Follow-up content recommendations",
- "Trending topic integration",
- "Professional tone maintenance",
- "Data visualization suggestions",
- "Poll scheduling optimization"
- ]
- },
-
- # Business & Marketing Tools
- {
- "name": "LinkedIn Company Page Content Generator",
- "icon": "đĸ",
- "description": "Create content for company pages that builds brand awareness and engagement.",
- "color": "#0A66C2",
- "category": "Business & Marketing",
- "function": linkedin_company_page_generator_ui_wrapper,
- "status": "active",
- "features": [
- "Company culture post generation",
- "Product/service announcement templates",
- "Employee spotlight content",
- "Company milestone celebrations",
- "Industry insights sharing",
- "Event promotion content",
- "Job posting templates",
- "Company news updates",
- "Brand voice consistency",
- "Engagement metrics optimization"
- ]
- },
- {
- "name": "LinkedIn Newsletter Generator",
- "icon": "đ°",
- "description": "Create professional newsletters that establish thought leadership and drive engagement.",
- "color": "#0A66C2",
- "category": "Business & Marketing",
- "function": None,
- "status": "coming_soon",
- "features": [
- "Newsletter structure templates",
- "Topic clustering and organization",
- "Professional introduction and conclusion",
- "Industry trend analysis integration",
- "Expert quote suggestions",
- "Visual content recommendations",
- "Call-to-action optimization",
- "Subscriber engagement prompts",
- "Consistency maintenance",
- "Analytics integration suggestions"
- ]
- },
- {
- "name": "LinkedIn Job Description Generator",
- "icon": "đŧ",
- "description": "Create compelling job descriptions that attract qualified candidates.",
- "color": "#0A66C2",
- "category": "Business & Marketing",
- "function": None,
- "status": "coming_soon",
- "features": [
- "Role-specific templates",
- "Skills and qualifications optimization",
- "Company culture integration",
- "Benefits and perks highlighting",
- "Inclusive language checker",
- "Keyword optimization",
- "Application process clarity",
- "Remote/hybrid work policy integration",
- "Diversity and inclusion statements",
- "A/B testing variations"
- ]
- },
-
- # Sales & Networking Tools
- {
- "name": "LinkedIn Sales Navigator Content Generator",
- "icon": "đ°",
- "description": "Create personalized outreach content for sales professionals.",
- "color": "#0A66C2",
- "category": "Sales & Networking",
- "function": None,
- "status": "coming_soon",
- "features": [
- "Prospect research integration",
- "Industry-specific messaging",
- "Personalization tokens",
- "Connection request templates",
- "Follow-up message sequences",
- "Value proposition highlighting",
- "Objection handling responses",
- "Meeting request templates",
- "Industry pain point addressing",
- "ROI demonstration content"
- ]
- },
- {
- "name": "LinkedIn InMail Generator",
- "icon": "âī¸",
- "description": "Create personalized and effective InMail messages.",
- "color": "#0A66C2",
- "category": "Sales & Networking",
- "function": None,
- "status": "coming_soon",
- "features": [
- "Prospect research integration",
- "Personalization token usage",
- "Value proposition highlighting",
- "Call-to-action optimization",
- "Follow-up sequence generation",
- "Objection handling preparation",
- "Industry-specific messaging",
- "A/B testing variations",
- "Compliance checking",
- "Engagement tracking suggestions"
- ]
- },
-
- # Learning & Education Tools
- {
- "name": "LinkedIn Learning Course Description Generator",
- "icon": "đ",
- "description": "Create compelling descriptions for LinkedIn Learning courses.",
- "color": "#0A66C2",
- "category": "Learning & Education",
- "function": None,
- "status": "coming_soon",
- "features": [
- "Course objective optimization",
- "Learning outcome generation",
- "Prerequisite suggestions",
- "Target audience definition",
- "Skill tag recommendations",
- "Course structure outline",
- "Engagement element suggestions",
- "Completion certificate highlighting",
- "Industry relevance emphasis",
- "Career path integration"
- ]
- },
- {
- "name": "LinkedIn Event Description Generator",
- "icon": "đ
",
- "description": "Create compelling event descriptions that drive attendance and engagement.",
- "color": "#0A66C2",
- "category": "Learning & Education",
- "function": None,
- "status": "coming_soon",
- "features": [
- "Event objective highlighting",
- "Speaker bio generation",
- "Agenda formatting",
- "Registration incentive suggestions",
- "Networking opportunity emphasis",
- "Industry relevance integration",
- "Visual content recommendations",
- "Engagement element suggestions",
- "Post-event follow-up content",
- "Attendance tracking integration"
- ]
- },
-
- # Community & Engagement Tools
- {
- "name": "LinkedIn Group Post Generator",
- "icon": "đĨ",
- "description": "Create content specifically optimized for LinkedIn Groups.",
- "color": "#0A66C2",
- "category": "Community & Engagement",
- "function": None,
- "status": "coming_soon",
- "features": [
- "Group-specific content adaptation",
- "Discussion prompt generation",
- "Community guideline compliance",
- "Engagement optimization",
- "Moderation suggestion",
- "Topic relevance checking",
- "Member value highlighting",
- "Cross-promotion opportunities",
- "Group culture adaptation",
- "Content scheduling"
- ]
- },
- {
- "name": "LinkedIn Comment Response Generator",
- "icon": "đŦ",
- "description": "Create professional and engaging responses to comments on LinkedIn posts.",
- "color": "#0A66C2",
- "category": "Community & Engagement",
- "function": None,
- "status": "coming_soon",
- "features": [
- "Tone adaptation based on comment",
- "Professional disagreement handling",
- "Question answering optimization",
- "Engagement continuation prompts",
- "Value-add response generation",
- "Community building suggestions",
- "Moderation guidance",
- "Follow-up question generation",
- "Resource sharing suggestions",
- "Relationship building strategies"
- ]
- }
- ]
-
- # 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:
- # Add a back button at the top
- if st.button("â Back to Dashboard", key="back_to_dashboard"):
- st.session_state.selected_tool = None
- st.rerun()
-
- # Display the tool header with card layout
- st.markdown(f"""
- About
-
- """, unsafe_allow_html=True)
- st.write(st.session_state.page_about_data['generated_content'])
- st.markdown("
-
- """, unsafe_allow_html=True)
-
- # Call the tool's function if it exists
- if st.session_state.selected_tool["function"] is not None:
- st.session_state.selected_tool["function"]()
- else:
- # Display coming soon information
- st.info(f"**{st.session_state.selected_tool['status'].replace('_', ' ').title()}!**")
- st.write(st.session_state.selected_tool["description"])
-
- # Display features
- st.subheader("Features")
- for feature in st.session_state.selected_tool["features"]:
- st.markdown(f"- {feature}")
-
- # Display placeholder image
- st.image(f"https://via.placeholder.com/600x300?text={st.session_state.selected_tool['name']}+Coming+Soon", use_container_width=True)
- else:
- with dashboard_container:
- # Display the dashboard
- # Header
- st.markdown("""
-
-
- {st.session_state.selected_tool['icon']}
-
-
- {st.session_state.selected_tool['name']}
-{st.session_state.selected_tool['description']}
-
-
- """, unsafe_allow_html=True)
-
- # Group tools by category
- categories = {}
- for tool in linkedin_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 = "Coming Soon"
- elif tool["status"] == "future":
- status_badge = "Future"
- elif tool["status"] == "active":
- status_badge = "Active"
-
- st.markdown(f"""
- đŧ LinkedIn AI Writer
-Generate professional LinkedIn content with ALwrity's AI-powered tools
-
-
- """, unsafe_allow_html=True)
-
- # Add a button to access the tool
- if st.button(f"Use {tool['name']}", key=f"btn_{tool['category']}_{tool['name']}"):
- # Store the selected tool in session state
- st.session_state.selected_tool = tool
- st.rerun()
-
-
-class LinkedInAIWriter:
- """
- AI-powered content generator for LinkedIn marketing and communication.
-
- This class provides various tools for generating LinkedIn content including:
- - Posts and articles
- - Profile optimization
- - Company page content
- - Sales and networking content
- - Learning and education content
- - Community and engagement content
- """
-
- def __init__(self):
- """Initialize the LinkedIn AI Writer."""
- pass
-
- # Methods will be implemented in future iterations
- # Each method will correspond to a specific LinkedIn content generation tool
-
-
-# List of available tools
-AVAILABLE_TOOLS = [
- 'Post Generator',
- 'Article Generator',
- 'Carousel Post Generator',
- 'Video Script Generator',
- 'Profile Optimizer',
- 'Poll Generator',
- 'Company Page Content Generator',
- 'Newsletter Generator',
- 'Job Description Generator',
- 'Sales Navigator Content Generator',
- 'InMail Generator',
- 'Learning Course Description Generator',
- 'Event Description Generator',
- 'Group Post Generator',
- 'Comment Response Generator'
-]
-
-if __name__ == "__main__":
- linkedin_main_menu()
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/__init__.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/__init__.py
deleted file mode 100644
index 10ac24e6..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-"""
-LinkedIn AI Writer Modules
-
-This package contains various modules for generating different types of LinkedIn content.
-"""
-
-from .post_generator.linkedin_post_generator import linkedin_post_generator_ui, LinkedInPostGenerator
-from .article_generator.linkedin_article_generator import linkedin_article_generator_ui
-from .carousel_generator.linkedin_carousel_generator import linkedin_carousel_generator_ui, LinkedInCarouselGenerator
-
-__all__ = [
- 'linkedin_post_generator_ui',
- 'LinkedInPostGenerator',
- 'linkedin_article_generator_ui',
- 'linkedin_carousel_generator_ui',
- 'LinkedInCarouselGenerator'
-]
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/article_generator/README.md b/ToBeMigrated/ai_writers/linkedin_writer/modules/article_generator/README.md
deleted file mode 100644
index d130c44e..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/article_generator/README.md
+++ /dev/null
@@ -1,253 +0,0 @@
-# LinkedIn Article Generator đ
-
-A powerful AI-powered tool designed specifically for LinkedIn content creators to generate professional, research-backed articles that engage and provide value to your professional network.
-
-## đ Key Features
-
-### Content Generation
-- **AI-Powered Research**: Integrates with multiple search engines (Metaphor, Google, Tavily) for up-to-date, accurate content
-- **Professional Writing**: Generates industry-specific content with expert insights and statistics
-- **Topic-Focused Content**: Ensures all content stays relevant to your chosen topic without AI explanations or meta-commentary
-- **SEO Optimization**: Built-in SEO tools for maximum visibility
-- **Image Generation**: AI-powered visuals for article header and sections
-
-### Research Integration
-- **Multi-Source Research**: Choose from Metaphor AI, Google Search, or Tavily AI
-- **Real-Time Data**: Access current industry trends and statistics
-- **Expert Insights**: Incorporate professional perspectives and quotes
-- **Fact-Checking**: Web-researched context to prevent AI hallucinations
-
-### Visual Enhancement
-- **Topic Images**: Generate professional header images
-- **Section Visuals**: Create relevant images for each section
-- **Image Refinement**: Edit and regenerate images until perfect
-- **Custom Prompts**: Write your own image generation prompts
-
-## đ Getting Started
-
-### 1. Article Generation Process
-
-#### Step 1: Topic Input
-- Enter your article topic
-- Select your industry
-- Choose writing tone:
- - Professional
- - Analytical
- - Conversational
- - Technical
- - Thought Leadership
-
-#### Step 2: Research Selection
-Choose your preferred research source:
-- đ Metaphor AI: For comprehensive industry insights
-- đ Google Search: For broad topic coverage
-- đ¤ Tavily AI: For focused, AI-curated research
-
-#### Step 3: Content Generation
-The tool will:
-1. Research your topic thoroughly
-2. Create a detailed outline
-3. Generate comprehensive content
-4. Add relevant statistics and quotes
-5. Generate professional images
-6. Optimize for SEO
-
-### 2. Article Preview and Editing
-
-#### Content Tab
-- View the complete article
-- Edit content directly
-- Download in HTML format
-- Copy to clipboard
-
-#### Images & Visuals Tab
-- Preview and regenerate topic image
-- Edit image prompts
-- Generate section-specific images
-- Choose from alternative image suggestions
-
-#### SEO & Metadata Tab
-- Review focus keywords
-- Edit meta descriptions
-- Optimize article tags
-- Customize social sharing text
-
-## đĄ Best Practices
-
-### 1. Topic Selection
-- Choose specific, industry-relevant topics
-- Focus on your area of expertise
-- Consider current trends in your field
-- Target your professional audience's needs
-
-### 2. Content Structure
-- Strong, attention-grabbing headlines
-- Professional introduction
-- Well-organized sections
-- Clear takeaways
-- Compelling call-to-action
-
-### 3. Visual Strategy
-- Professional, topic-relevant images
-- Consistent visual style
-- High-quality graphics
-- Mobile-friendly visuals
-
-## đ¯ Content Types
-
-### Industry Insights
-- Market analysis
-- Trend reports
-- Expert perspectives
-- Case studies
-
-### How-to Articles
-- Step-by-step guides
-- Best practices
-- Implementation strategies
-- Professional tips
-
-### Thought Leadership
-- Industry predictions
-- Expert opinions
-- Strategy discussions
-- Innovation insights
-
-## đ SEO Optimization
-
-### Automatic Optimization
-- Focus keyword selection
-- Secondary keywords
-- Meta descriptions
-- Article tags
-- Social sharing text
-
-### Content Enhancement
-- Keyword placement
-- Header optimization
-- Link suggestions
-- Readability improvements
-
-## đ ī¸ Advanced Features
-
-### Research Integration
-- Multiple search engines
-- Real-time data gathering
-- Expert quote integration
-- Statistical data inclusion
-
-### Image Generation
-- Custom prompt creation
-- Style customization
-- Alternative suggestions
-- Section-specific visuals
-
-### Content Export
-- HTML format
-- Clipboard copy
-- Social media optimized
-- SEO-ready structure
-
-## đą Mobile Optimization
-
-- Responsive images
-- Readable text
-- Optimized layout
-- Cross-device compatibility
-
-## đĒ Success Tips
-
-### 1. Content Strategy
-- Regular posting schedule
-- Consistent topic focus
-- Industry relevance
-- Audience engagement
-
-### 2. Visual Impact
-- Professional imagery
-- Brand consistency
-- Visual storytelling
-- Quality graphics
-
-### 3. Engagement
-- Compelling headlines
-- Interactive elements
-- Clear call-to-action
-- Professional tone
-
-## đ Technical Details
-
-### System Requirements
-- Modern web browser
-- Internet connection
-- API access (if using custom keys)
-
-### Performance
-- Fast generation times
-- Real-time preview
-- Instant updates
-- Smooth editing
-
-## đ Best Use Cases
-
-1. **Industry Analysis**
- - Market trends
- - Technology updates
- - Business insights
- - Professional perspectives
-
-2. **Professional Guides**
- - Best practices
- - Implementation strategies
- - Career development
- - Skill enhancement
-
-3. **Thought Leadership**
- - Industry predictions
- - Expert opinions
- - Innovation insights
- - Strategic analysis
-
-## đ¨ Design Guidelines
-
-1. **Visual Consistency**
- - Brand colors
- - Professional fonts
- - Clean layouts
- - Quality images
-
-2. **Content Format**
- - Clear headers
- - Organized sections
- - Professional tone
- - Engaging structure
-
-3. **Mobile Design**
- - Responsive layout
- - Readable text
- - Optimized images
- - Cross-device testing
-
-## đ Workflow Integration
-
-1. **Content Planning**
- - Topic research
- - Outline creation
- - Content generation
- - Visual design
-
-2. **Quality Control**
- - Content review
- - Image refinement
- - SEO verification
- - Final checks
-
-3. **Publishing**
- - Format selection
- - Preview verification
- - LinkedIn optimization
- - Engagement tracking
-
----
-
-Start creating professional LinkedIn articles that engage your network and establish your expertise! đ
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/article_generator/linkedin_article_generator.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/article_generator/linkedin_article_generator.py
deleted file mode 100644
index 6c5a4f76..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/article_generator/linkedin_article_generator.py
+++ /dev/null
@@ -1,1071 +0,0 @@
-"""
-LinkedIn Article Generator
-
-This module provides functionality for generating LinkedIn articles with research-backed content,
-AI-generated images, and SEO optimization.
-"""
-
-import os
-import json
-import time
-import streamlit as st
-from typing import Dict, List, Optional, Tuple, Union
-from loguru import logger
-import random
-
-from .....gpt_providers.text_generation.main_text_generation import llm_text_gen
-from .....ai_web_researcher.gpt_online_researcher import do_google_serp_search
-from .....ai_web_researcher.metaphor_basic_neural_web_search import metaphor_search_articles, streamlit_display_metaphor_results
-from .....ai_web_researcher.tavily_ai_search import do_tavily_ai_search, streamlit_display_results
-
-
-class LinkedInArticleGenerator:
- """
- A class for generating LinkedIn articles with research-backed content.
-
- This class provides methods for:
- - Researching topics using multiple search engines (Metaphor, Google, Tavily)
- - Generating detailed article outlines
- - Creating comprehensive article content
- - Generating section-specific images
- - Adding SEO optimization
- - Suggesting engagement strategies
- """
-
- def __init__(self):
- """Initialize the LinkedIn Article Generator."""
- self.research_results = {}
- self.outline = {}
- self.article_content = ""
- self.seo_metadata = {}
- self.section_images = []
- self.engagement_strategy = {}
-
- def research_topic(self, topic: str, industry: str, search_engine: str = "metaphor", status_container=None) -> Dict:
- """
- Research a topic using the selected search engine.
- """
- # Update progress
- if status_container:
- status_container.text(f"đ Researching {topic} in {industry} using {search_engine.title()}...")
-
- # Construct focused search query
- search_query = f"""
- Find detailed professional articles about '{topic}' in the {industry} industry.
- Focus on:
- - Latest industry developments
- - Expert insights and analysis
- - Real-world case studies
- - Practical applications
- - Industry-specific statistics
- - Best practices and strategies
- """
-
- # Research with selected search engine
- research_results = None
-
- # Create tabs for displaying results
- search_tabs = st.tabs([
- "đ Research Results",
- "đ Topic Analysis",
- "đ Key Insights"
- ])
-
- with search_tabs[0]:
- st.markdown(f"### Research on: {topic}")
- st.markdown(f"*Industry Context: {industry}*")
-
- if search_engine == "metaphor":
- research_results = self._research_with_metaphor(search_query, industry, status_container)
- elif search_engine == "google":
- research_results = self._research_with_google(search_query, industry, status_container)
- elif search_engine == "tavily":
- research_results = self._research_with_tavily(search_query, industry, status_container)
-
- # Analyze research results with topic focus
- with search_tabs[1]:
- st.markdown("### Topic Analysis")
- combined_results = self._analyze_research_results(research_results, topic, industry)
-
- # Display analysis in an organized way
- if combined_results:
- # Topic relevance verification
- relevance_prompt = f"""
- Verify the relevance of these research findings to the topic: '{topic}' in {industry} industry.
-
- Key Insights: {combined_results.get('key_insights', [])}
- Statistics: {combined_results.get('statistics', [])}
- Expert Opinions: {combined_results.get('expert_opinions', [])}
-
- Return only the most relevant insights that directly relate to {topic}.
- Remove any generic or tangential information.
- """
-
- verified_results = llm_text_gen(relevance_prompt)
-
- try:
- verified_data = json.loads(verified_results)
- combined_results.update(verified_data)
- except:
- logger.warning("Failed to parse verified results")
-
- col1, col2 = st.columns(2)
- with col1:
- st.markdown(f"#### Key Insights about {topic}")
- for insight in combined_results.get("key_insights", []):
- st.markdown(f"- {insight}")
-
- st.markdown("#### Industry-Specific Statistics")
- for stat in combined_results.get("statistics", []):
- st.markdown(f"- {stat}")
-
- with col2:
- st.markdown("#### Expert Perspectives")
- for opinion in combined_results.get("expert_opinions", []):
- st.markdown(f"> {opinion}")
-
- st.markdown("#### Current Trends")
- for trend in combined_results.get("trends", []):
- st.markdown(f"- {trend}")
-
- with search_tabs[2]:
- st.markdown(f"### Key Takeaways for {topic}")
- if combined_results:
- # Topic-focused insights
- col1, col2 = st.columns(2)
- with col1:
- st.markdown("#### Best Practices")
- for practice in combined_results.get("best_practices", []):
- st.markdown(f"- {practice}")
-
- with col2:
- st.markdown("#### Practical Solutions")
- for solution in combined_results.get("solutions", []):
- st.markdown(f"- {solution}")
-
- # Industry context
- st.markdown(f"#### {industry} Industry Context")
- st.markdown(combined_results.get("industry_context", ""))
-
- # Verified sources
- st.markdown("#### Expert Sources")
- sources = combined_results.get("sources", [])
- filtered_sources = [s for s in sources if topic.lower() in s.lower()]
- for source in filtered_sources:
- st.markdown(f"- {source}")
-
- # Update progress
- if status_container:
- status_container.text("â
Topic research complete!")
-
- return combined_results
-
- def _research_with_metaphor(self, topic: str, industry: str, status_container=None) -> Dict:
- """
- Research a topic using Metaphor.
-
- Args:
- topic: The topic to research
- industry: The industry context
- status_container: Optional container for status updates
-
- Returns:
- Dict containing research results
- """
- try:
- # Construct search query
- search_query = f"{topic} in {industry} industry comprehensive article"
-
- # Update progress
- if status_container:
- status_container.text("đ Searching with Metaphor...")
-
- # Search with Metaphor
- search_options = {
- "num_results": 15, # More results for comprehensive research
- "use_autoprompt": True,
- "time_range": "past_year" # Recent but comprehensive results
- }
-
- metaphor_results = metaphor_search_articles(search_query, search_options)
-
- # Display the results
- if metaphor_results:
- streamlit_display_metaphor_results(metaphor_results, search_query)
-
- # Update progress
- if status_container:
- status_container.text("â
Metaphor search complete!")
-
- return metaphor_results
-
- except Exception as e:
- logger.error(f"Error in Metaphor search: {e}")
- if status_container:
- status_container.text(f"â ī¸ Error in Metaphor search: {str(e)}")
- return {"sources": []}
-
- def _research_with_google(self, topic: str, industry: str, status_container=None) -> Dict:
- """
- Research a topic using Google.
-
- Args:
- topic: The topic to research
- industry: The industry context
- status_container: Optional container for status updates
-
- Returns:
- Dict containing research results
- """
- try:
- # Update progress
- if status_container:
- status_container.text("đ Searching with Google...")
-
- # Search with Google
- google_results = do_google_serp_search(
- f"{topic} {industry} industry comprehensive guide article",
- num_results=15
- )
-
- # Update progress
- if status_container:
- status_container.text("â
Google search complete!")
-
- return google_results
-
- except Exception as e:
- logger.error(f"Error in Google search: {e}")
- if status_container:
- status_container.text(f"â ī¸ Error in Google search: {str(e)}")
- return {"sources": []}
-
- def _research_with_tavily(self, topic: str, industry: str, status_container=None) -> Dict:
- """
- Research a topic using Tavily AI.
-
- Args:
- topic: The topic to research
- industry: The industry context
- status_container: Optional container for status updates
-
- Returns:
- Dict containing research results
- """
- try:
- # Update progress
- if status_container:
- status_container.text("đ Searching with Tavily AI...")
-
- # Search with Tavily
- search_query = f"{topic} in {industry} industry comprehensive guide"
- tavily_results = do_tavily_ai_search(search_query)
-
- # Display results
- if tavily_results:
- streamlit_display_results(tavily_results)
-
- # Update progress
- if status_container:
- status_container.text("â
Tavily search complete!")
-
- return tavily_results
-
- except Exception as e:
- logger.error(f"Error in Tavily search: {e}")
- if status_container:
- status_container.text(f"â ī¸ Error in Tavily search: {str(e)}")
- return {"sources": []}
-
- def _analyze_research_results(self, research_results: Dict, topic: str, industry: str) -> Dict:
- """
- Analyze research results to extract key insights for article creation.
-
- Args:
- research_results: Results from research
- topic: The topic being researched
- industry: The industry context
-
- Returns:
- Dict containing analyzed insights
- """
- if not research_results:
- logger.warning(f"No research results available for {topic} in {industry}")
- return {
- "key_insights": [],
- "expert_opinions": [],
- "statistics": [],
- "case_studies": [],
- "trends": [],
- "challenges": [],
- "solutions": [],
- "best_practices": [],
- "industry_context": "",
- "sources": []
- }
-
- # Extract content from research results
- sources = []
- content = ""
-
- if isinstance(research_results, dict):
- if "data" in research_results and "results" in research_results["data"]:
- sources = research_results["data"]["results"]
- content = "\n\n".join([
- f"Title: {source.get('title', '')}\n"
- f"Content: {source.get('text', '')}\n"
- f"Summary: {source.get('summary', '')}"
- for source in sources
- ])
-
- # Generate analysis prompt
- analysis_prompt = f"""
- Analyze this research about {topic} in the {industry} industry for writing a comprehensive LinkedIn article:
-
- {content}
-
- Extract and organize the following elements:
- 1. Key insights and main arguments
- 2. Expert opinions and quotes
- 3. Statistics and data points
- 4. Case studies and examples
- 5. Current trends and future predictions
- 6. Common challenges and pain points
- 7. Solutions and strategies
- 8. Best practices and recommendations
- 9. Industry-specific context and implications
-
- Format as JSON with these keys:
- {{
- "key_insights": ["List of main insights"],
- "expert_opinions": ["List of expert quotes with sources"],
- "statistics": ["List of statistics with sources"],
- "case_studies": ["List of relevant case studies"],
- "trends": ["List of current and emerging trends"],
- "challenges": ["List of common challenges"],
- "solutions": ["List of solutions and strategies"],
- "best_practices": ["List of best practices"],
- "industry_context": "Summary of industry context",
- "sources": ["List of source URLs"]
- }}
- """
-
- # Generate analysis using LLM
- analysis = llm_text_gen(analysis_prompt)
-
- try:
- analysis_dict = json.loads(analysis)
- except json.JSONDecodeError:
- logger.error("Failed to parse analysis JSON")
- analysis_dict = {
- "key_insights": [],
- "expert_opinions": [],
- "statistics": [],
- "case_studies": [],
- "trends": [],
- "challenges": [],
- "solutions": [],
- "best_practices": [],
- "industry_context": "",
- "sources": []
- }
-
- return analysis_dict
-
- def generate_outline(self, research_results: Dict) -> Dict:
- """
- Generate a detailed article outline based on research results.
- """
- # Extract key information from research results
- key_insights = research_results.get("key_insights", [])
- expert_opinions = research_results.get("expert_opinions", [])
- statistics = research_results.get("statistics", [])
- case_studies = research_results.get("case_studies", [])
- trends = research_results.get("trends", [])
- challenges = research_results.get("challenges", [])
- solutions = research_results.get("solutions", [])
- best_practices = research_results.get("best_practices", [])
- industry_context = research_results.get("industry_context", "")
-
- # Generate outline prompt
- outline_prompt = f"""
- Create a professional LinkedIn article outline focused on the exact topic and industry provided.
-
- Research Data:
- Key Insights: {key_insights}
- Expert Opinions: {expert_opinions}
- Statistics: {statistics}
- Case Studies: {case_studies}
- Trends: {trends}
- Challenges: {challenges}
- Solutions: {solutions}
- Best Practices: {best_practices}
- Industry Context: {industry_context}
-
- Requirements:
- 1. Stay strictly focused on the topic without any AI explanations
- 2. Create a compelling headline that clearly states the topic's value
- 3. Structure 4-5 main sections that logically develop the topic
- 4. Include specific places for statistics and expert quotes
- 5. Focus on practical insights and actionable takeaways
- 6. Maintain professional tone throughout
- 7. Ensure each section directly relates to the main topic
- 8. Include specific examples and case studies where relevant
-
- Format as JSON:
- {{
- "headline": "Clear, topic-focused headline",
- "subheadline": "Supporting subheadline that elaborates on the value",
- "introduction": {{
- "hook": "Attention-grabbing opening relevant to topic",
- "context": "Topic-specific background",
- "thesis": "Clear main argument or point"
- }},
- "sections": [
- {{
- "title": "Section title",
- "key_points": ["Specific, topic-focused points"],
- "supporting_evidence": ["Relevant statistics or quotes"],
- "visual_suggestions": ["Topic-specific visual ideas"]
- }}
- ],
- "conclusion": {{
- "key_takeaways": ["Practical, actionable takeaways"],
- "call_to_action": "Relevant next steps for readers"
- }},
- "seo_keywords": ["Topic-specific keywords"]
- }}
- """
-
- # Generate outline using LLM
- outline = llm_text_gen(outline_prompt)
-
- try:
- outline_dict = json.loads(outline)
- except json.JSONDecodeError:
- logger.error("Failed to parse outline JSON")
- outline_dict = {
- "headline": "",
- "subheadline": "",
- "introduction": {
- "hook": "",
- "context": "",
- "thesis": ""
- },
- "sections": [],
- "conclusion": {
- "key_takeaways": [],
- "call_to_action": ""
- },
- "seo_keywords": []
- }
-
- return outline_dict
-
- def generate_article_content(self, outline: Dict, topic: str, industry: str, tone: str = "professional") -> str:
- """
- Generate comprehensive article content based on the outline.
- """
- # Generate focused article prompt
- article_prompt = f"""
- Write a professional LinkedIn article about '{topic}' in the {industry} industry.
- Follow this outline exactly and stay focused on the specific topic.
-
- Title: {outline['headline']}
- Subheadline: {outline['subheadline']}
-
- Introduction:
- Hook: {outline['introduction']['hook']}
- Context: {outline['introduction']['context']}
- Thesis: {outline['introduction']['thesis']}
-
- Sections:
- {json.dumps(outline['sections'], indent=2)}
-
- Conclusion:
- Key Takeaways: {outline['conclusion']['key_takeaways']}
- Call to Action: {outline['conclusion']['call_to_action']}
-
- Critical Requirements:
- 1. Write in a {tone} tone appropriate for LinkedIn
- 2. Focus EXCLUSIVELY on {topic} - no deviations or tangents
- 3. Every paragraph must directly discuss {topic}
- 4. Use concrete examples and real-world applications specific to {topic}
- 5. Include relevant statistics and expert quotes that directly relate to {topic}
- 6. Use clear transitions that maintain focus on {topic}
- 7. Provide actionable insights specific to {topic}
- 8. Write as an industry expert discussing {topic}
- 9. NO meta-commentary, AI explanations, or generic content
- 10. Use proper HTML formatting for structure
- 11. Every section must explicitly connect to {topic}
-
- Return ONLY the final article in clean HTML format, ready to publish on LinkedIn.
- The article must read as if written by a {industry} expert focusing solely on {topic}.
- """
-
- # Generate initial article content
- article_content = llm_text_gen(article_prompt)
-
- # Verify topic focus
- verification_prompt = f"""
- Verify this article maintains strict focus on '{topic}' in the {industry} industry.
-
- Requirements:
- 1. Every paragraph must explicitly discuss {topic}
- 2. Remove any content not directly related to {topic}
- 3. Remove any AI explanations or meta-commentary
- 4. Ensure all examples and insights are specific to {topic}
- 5. Maintain professional {tone} tone
- 6. Keep all relevant statistics and expert quotes about {topic}
-
- Return only the verified and cleaned article content.
- """
-
- # Verify and clean the content
- verified_content = llm_text_gen(verification_prompt + "\n\nArticle:\n" + article_content)
-
- # Final topic focus check
- final_check_prompt = f"""
- Perform a final check on this article about '{topic}'.
-
- If you find ANY content that:
- 1. Isn't directly about {topic}
- 2. Contains AI explanations
- 3. Includes meta-commentary
- 4. Uses generic examples
-
- Remove or replace it with topic-specific content about {topic}.
- Return only the final, topic-focused article.
- """
-
- final_content = llm_text_gen(final_check_prompt + "\n\nArticle:\n" + verified_content)
-
- return final_content
-
- def _generate_image_prompt(self, topic: str, article_content: str, industry: str) -> str:
- """
- Generate a detailed image prompt based on the article topic and content.
- """
- prompt_generation = f"""
- Create a specific, detailed image generation prompt for a LinkedIn article about "{topic}" in the {industry} industry.
-
- Article excerpt:
- {article_content[:500]}...
-
- Requirements:
- 1. Focus on key concepts and themes from the article
- 2. Specify exact visual elements that represent the topic
- 3. Include industry-specific imagery and symbols
- 4. Define a professional color scheme that matches the topic
- 5. Describe specific composition and layout
- 6. Include relevant metaphors or visual concepts
- 7. Ensure the image will be immediately recognizable as related to {topic}
- 8. Avoid generic business imagery
-
- Format:
- 1. Main subject/focus
- 2. Style and composition
- 3. Colors and lighting
- 4. Specific elements to include
- 5. Industry-specific details
- 6. Mood and atmosphere
-
- Return a detailed, topic-specific image generation prompt.
- """
-
- return llm_text_gen(prompt_generation)
-
- def _suggest_image_variations(self, topic: str, industry: str) -> List[str]:
- """
- Generate alternative image prompt suggestions.
-
- Args:
- topic: The article topic
- industry: The industry context
-
- Returns:
- List[str]: List of alternative image prompts
- """
- variation_prompt = f"""
- Generate 3 different image prompt variations for a LinkedIn article about "{topic}" in the {industry} industry.
- Each prompt should be unique and professional:
- 1. A metaphorical/conceptual approach
- 2. A data-driven/analytical visualization
- 3. A human-centered/storytelling perspective
-
- Return only the 3 prompts, one per line, no explanations.
- """
-
- variations = llm_text_gen(variation_prompt).strip().split('\n')
- return [v.strip() for v in variations if v.strip()]
-
- def generate_section_images(self, outline: Dict, article_content: str, topic: str, industry: str) -> List[Dict]:
- """
- Generate image prompts for each section of the article.
-
- Args:
- outline: The article outline
- article_content: The generated article content
- topic: The article topic
- industry: The industry context
-
- Returns:
- List[Dict]: List of image generation prompts for each section
- """
- section_images = []
-
- # Generate main image prompt
- main_prompt = self._generate_image_prompt(topic, article_content, industry)
- alternative_prompts = self._suggest_image_variations(topic, industry)
-
- header_image = {
- "section": "header",
- "main_prompt": main_prompt,
- "alternative_prompts": alternative_prompts
- }
- section_images.append(header_image)
-
- # Generate image prompts for each section
- for section in outline['sections']:
- section_content = section.get('title', '') + ' ' + ' '.join(section.get('key_points', []))
- section_prompt = self._generate_image_prompt(section['title'], section_content, industry)
-
- section_image = {
- "section": section['title'],
- "main_prompt": section_prompt,
- "alternative_prompts": self._suggest_image_variations(section['title'], industry)
- }
- section_images.append(section_image)
-
- return section_images
-
- def generate_seo_metadata(self, article_content: str, outline: Dict) -> Dict:
- """
- Generate SEO metadata for the LinkedIn article.
-
- Args:
- article_content: The generated article content
- outline: The article outline
-
- Returns:
- Dict: SEO metadata including keywords, description, and tags
- """
- seo_prompt = f"""
- Generate SEO metadata for this LinkedIn article:
-
- Title: {outline['headline']}
- Content: {article_content[:500]}...
-
- Return a JSON object with:
- 1. Focus keyword
- 2. Secondary keywords (5-7)
- 3. Meta description (150-160 characters)
- 4. Article tags (5-7)
- 5. Social sharing description
- """
-
- seo_response = llm_text_gen(seo_prompt)
-
- try:
- seo_metadata = json.loads(seo_response)
- except json.JSONDecodeError:
- logger.error("Failed to parse SEO metadata JSON")
- seo_metadata = {
- "focus_keyword": outline['headline'],
- "secondary_keywords": outline.get('seo_keywords', []),
- "meta_description": outline['subheadline'],
- "article_tags": [],
- "social_description": outline['subheadline']
- }
-
- return seo_metadata
-
-
-def linkedin_article_generator_ui():
- """
- Streamlit UI for LinkedIn Article Generator.
- """
- st.title("đ LinkedIn Article Generator")
- st.write("Generate comprehensive LinkedIn articles with AI assistance")
-
- # Initialize generator
- generator = LinkedInArticleGenerator()
-
- # Initialize session state
- if "article_data" not in st.session_state:
- st.session_state.article_data = {
- "research_results": None,
- "outline": None,
- "article_content": "",
- "section_images": [],
- "seo_metadata": None,
- "generated_images": {},
- "topic_image": None
- }
-
- # Input form
- with st.form("linkedin_article_form"):
- topic = st.text_input("Article Topic", placeholder="Enter the main topic of your article")
- industry = st.text_input("Industry", placeholder="e.g., Technology, Healthcare, Finance")
-
- col1, col2 = st.columns(2)
- with col1:
- tone = st.selectbox(
- "Writing Tone",
- ["Professional", "Analytical", "Conversational", "Technical", "Thought Leadership"]
- )
- with col2:
- search_engine = st.radio(
- "Research Source",
- ["metaphor", "google", "tavily"],
- format_func=lambda x: {
- "metaphor": "đ Metaphor AI",
- "google": "đ Google Search",
- "tavily": "đ¤ Tavily AI"
- }[x]
- )
-
- # Add option for topic image
- generate_topic_image = st.checkbox("Generate Topic Image", value=True, help="Generate an AI image related to your article topic")
-
- submit = st.form_submit_button("Generate Article")
-
- if submit and topic and industry:
- # Create a status container
- status_container = st.empty()
-
- with st.spinner(f"Researching and generating your article using {search_engine.title()}..."):
- # Generate topic image if requested
- if generate_topic_image:
- status_container.text("đ¨ Generating topic image...")
- try:
- # Generate image prompt
- image_prompt = f"""Create a professional, LinkedIn-appropriate image for an article about {topic} in the {industry} industry.
- The image should be:
- - Professional and business-appropriate
- - Visually engaging
- - Relevant to both the topic and industry
- - Suitable as a LinkedIn article header
- """
-
- # Import the image generation function
- from .....gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
-
- # Generate the image
- image_path = generate_image(image_prompt)
- if image_path:
- st.session_state.article_data["topic_image"] = image_path
- status_container.success("â
Topic image generated!")
- except Exception as e:
- logger.error(f"Error generating topic image: {e}")
- status_container.error("â ī¸ Could not generate topic image. Proceeding with article generation.")
-
- # Research phase
- status_container.text(f"đ Phase 1: Researching topic with {search_engine.title()}...")
- research_results = generator.research_topic(topic, industry, search_engine, status_container)
- st.session_state.article_data["research_results"] = research_results
-
- if research_results:
- # Outline phase
- status_container.text("đ Phase 2: Creating article outline...")
- outline = generator.generate_outline(research_results)
- st.session_state.article_data["outline"] = outline
-
- # Content generation phase
- status_container.text("âī¸ Phase 3: Writing article content...")
- article_content = generator.generate_article_content(outline, topic, industry, tone.lower())
- st.session_state.article_data["article_content"] = article_content
-
- # Image generation phase
- status_container.text("đ¨ Phase 4: Generating section images...")
- section_images = generator.generate_section_images(outline, article_content, topic, industry)
- st.session_state.article_data["section_images"] = section_images
-
- # SEO optimization phase
- status_container.text("đ¯ Phase 5: Optimizing SEO...")
- seo_metadata = generator.generate_seo_metadata(article_content, outline)
- st.session_state.article_data["seo_metadata"] = seo_metadata
-
- status_container.text("â
Article generation complete!")
- else:
- status_container.error(f"â No results found from {search_engine.title()}. Please try a different research source or modify your topic.")
-
- # Display results if we have article data
- if st.session_state.article_data["article_content"]:
- st.markdown("---")
-
- # Create tabs for different sections
- tab1, tab2, tab3, tab4 = st.tabs([
- "đ Article Content",
- "đ¨ Images & Visuals",
- "đ¯ SEO & Metadata",
- "đ Research Results"
- ])
-
- with tab1:
- # Article preview
- st.subheader("Article Preview")
-
- # Display topic image if available
- if st.session_state.article_data.get("topic_image"):
- st.image(st.session_state.article_data["topic_image"],
- caption="Generated Topic Image",
- use_container_width=True)
-
- # Add styling for the article container
- st.markdown("""
-
- """, unsafe_allow_html=True)
-
- # Display the article in the styled container
- outline = st.session_state.article_data["outline"]
- st.markdown(f"""
- {tool["icon"]} {tool["name"]} {status_badge}
-{tool["description"]}
-
-
- """, unsafe_allow_html=True)
-
- # Action buttons
- col1, col2, col3 = st.columns(3)
- with col1:
- if st.button("đ Copy to Clipboard", key="copy_article"):
- st.success("Article copied to clipboard!")
- with col2:
- if st.button("đž Download as HTML", key="download_article"):
- st.success("Article downloaded successfully!")
- with col3:
- if st.button("đ Generate New Article", key="new_article"):
- st.session_state.article_data = {
- "research_results": None,
- "outline": None,
- "article_content": "",
- "section_images": [],
- "seo_metadata": None,
- "generated_images": {},
- "topic_image": None
- }
- st.experimental_rerun()
-
- with tab2:
- st.subheader("Article Images")
-
- # Topic image section
- st.markdown("### đ¨ Main Article Image")
-
- # Display current topic image if available
- if st.session_state.article_data.get("topic_image"):
- st.image(st.session_state.article_data["topic_image"],
- caption="Current Article Image",
- use_container_width=True)
-
- # Image prompt selection and refinement
- if st.session_state.article_data.get("section_images"):
- header_image = st.session_state.article_data["section_images"][0]
-
- # Display main prompt
- st.markdown("#### Current Image Prompt")
- current_prompt = st.text_area(
- "Edit prompt if needed:",
- value=header_image["main_prompt"],
- height=100,
- key="main_image_prompt"
- )
-
- # Display alternative prompts
- st.markdown("#### Alternative Prompt Suggestions")
- selected_prompt = st.radio(
- "Select an alternative prompt or use the current one:",
- ["Current Prompt"] + header_image["alternative_prompts"],
- key="prompt_selection"
- )
-
- # Custom prompt input
- custom_prompt = st.text_area(
- "Or write your own prompt:",
- value="",
- height=100,
- key="custom_image_prompt",
- help="Write your own custom image prompt based on the article topic"
- )
-
- # Generate image button
- col1, col2 = st.columns(2)
- with col1:
- if st.button("đ¨ Generate New Image", key="generate_new_image"):
- try:
- # Determine which prompt to use
- final_prompt = custom_prompt if custom_prompt else (
- current_prompt if selected_prompt == "Current Prompt"
- else selected_prompt
- )
-
- # Generate new image
- from .....gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
- image_path = generate_image(final_prompt)
-
- if image_path:
- st.session_state.article_data["topic_image"] = image_path
- st.success("â
New image generated successfully!")
- st.experimental_rerun()
- except Exception as e:
- st.error(f"â ī¸ Error generating image: {str(e)}")
-
- with col2:
- if st.button("đ Generate More Prompt Suggestions", key="generate_more_prompts"):
- # Generate new variations
- new_variations = generator._suggest_image_variations(
- topic,
- industry
- )
- header_image["alternative_prompts"].extend(new_variations)
- st.success("â
New prompt suggestions added!")
- st.experimental_rerun()
-
- # Section images
- st.markdown("### đ Section Images")
- if st.session_state.article_data.get("section_images"):
- for section_image in st.session_state.article_data["section_images"][1:]: # Skip header image
- with st.expander(f"đŧī¸ {section_image['section']} Image"):
- # Display current section image if available
- if section_image["section"] in st.session_state.article_data["generated_images"]:
- st.image(
- st.session_state.article_data["generated_images"][section_image["section"]],
- caption=f"Current image for {section_image['section']}",
- use_container_width=True
- )
-
- # Section image prompt and generation
- section_prompt = st.text_area(
- "Image prompt:",
- value=section_image["main_prompt"],
- height=100,
- key=f"prompt_{section_image['section']}"
- )
-
- if st.button("Generate Image", key=f"gen_img_{section_image['section']}"):
- try:
- from .....gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
- image_path = generate_image(section_prompt)
-
- if image_path:
- st.session_state.article_data["generated_images"][section_image["section"]] = image_path
- st.success(f"â
Image generated for {section_image['section']}!")
- st.experimental_rerun()
- except Exception as e:
- st.error(f"â ī¸ Error generating image: {str(e)}")
-
- with tab3:
- st.subheader("SEO Optimization")
-
- seo_metadata = st.session_state.article_data["seo_metadata"]
-
- # Display SEO metadata
- col1, col2 = st.columns(2)
-
- with col1:
- st.markdown("### Focus Keyword")
- st.code(seo_metadata["focus_keyword"])
-
- st.markdown("### Secondary Keywords")
- for keyword in seo_metadata["secondary_keywords"]:
- st.markdown(f"- {keyword}")
-
- with col2:
- st.markdown("### Meta Description")
- st.text_area(
- label="Meta Description",
- value=seo_metadata["meta_description"],
- height=100,
- disabled=True,
- key="meta_description_area",
- label_visibility="collapsed"
- )
-
- st.markdown("### Article Tags")
- for tag in seo_metadata["article_tags"]:
- st.markdown(f"- {tag}")
-
- st.markdown("### Social Sharing Description")
- st.text_area(
- label="Social Sharing Description",
- value=seo_metadata["social_description"],
- height=100,
- disabled=True,
- key="social_description_area",
- label_visibility="collapsed"
- )
-
- with tab4:
- st.subheader("Research Results")
-
- research_results = st.session_state.article_data["research_results"]
-
- # Display key insights
- st.markdown("### Key Insights")
- for insight in research_results["key_insights"]:
- st.markdown(f"- {insight}")
-
- # Display statistics
- st.markdown("### Statistics & Data Points")
- for stat in research_results["statistics"]:
- st.markdown(f"- {stat}")
-
- # Display expert opinions
- st.markdown("### Expert Opinions")
- for opinion in research_results["expert_opinions"]:
- st.markdown(f"> {opinion}")
-
- # Display sources
- st.markdown("### Sources")
- for source in research_results["sources"]:
- st.markdown(f"- {source}")
-
-
-if __name__ == "__main__":
- linkedin_article_generator_ui()
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/carousel_generator/README.md b/ToBeMigrated/ai_writers/linkedin_writer/modules/carousel_generator/README.md
deleted file mode 100644
index 7f82d932..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/carousel_generator/README.md
+++ /dev/null
@@ -1,281 +0,0 @@
-# LinkedIn Carousel Generator đ¯
-
-An AI-powered tool designed specifically for LinkedIn content creators to generate engaging, visually stunning carousel posts that drive engagement and showcase expertise.
-
-## ⨠Key Features
-
-### Content Creation
-- **AI-Powered Research**: Multi-source research integration (Metaphor, Google, Tavily)
-- **Dynamic Slide Generation**: Auto-generation of 3-10 cohesive slides
-- **Smart Content Structure**: Optimized flow from hook to call-to-action
-- **Visual Content**: AI-generated professional visuals for each slide
-- **Content Types**: How-to Guides, Lists, Stories, Case Studies, Tips & Tricks
-
-### Visual Excellence
-- **Professional Imagery**: Custom AI-generated visuals per slide
-- **Brand Consistency**: Cohesive visual style across slides
-- **Visual Hierarchy**: Optimized layout for maximum impact
-- **Custom Styling**: Flexible design options for each slide
-
-### Research Integration
-- **Triple-Engine Research**:
- - đ Metaphor AI: Deep industry insights
- - đ Google Search: Comprehensive coverage
- - đ¤ Tavily AI: Focused research
-- **Real-Time Data**: Current trends and statistics
-- **Expert Insights**: Professional quotes and perspectives
-
-## đ Getting Started
-
-### 1. Carousel Creation Process
-
-#### Initial Setup
-- Choose your topic
-- Select your industry
-- Pick your content style:
- - Professional
- - Educational
- - Conversational
- - Inspiring
- - Technical
-
-#### Content Type Selection
-Choose from:
-- How-to Guide: Step-by-step instructions
-- List: Curated points or tips
-- Story: Narrative-driven content
-- Case Study: Real-world examples
-- Tips & Tricks: Quick, actionable advice
-
-#### Slide Configuration
-- Select number of slides (3-10)
-- Choose research source
-- Enable/disable features:
- - Custom visuals
- - Content optimization
- - Research integration
-
-### 2. Carousel Preview and Editing
-
-#### Slide Navigation
-- Previous/Next navigation
-- Slide overview
-- Individual slide editing
-- Real-time preview
-
-#### Content Editing
-- Edit headings
-- Modify subheadings
-- Update main content
-- Customize visuals
-- Optimize text
-
-#### Visual Customization
-- Edit image prompts
-- Generate new visuals
-- Adjust style settings
-- Preview changes
-
-## đĄ Best Practices
-
-### 1. Content Strategy
-- **First Slide**
- - Strong hook
- - Clear value proposition
- - Attention-grabbing visual
- - Topic introduction
-
-- **Middle Slides**
- - Logical progression
- - Supporting evidence
- - Engaging visuals
- - Clear explanations
-
-- **Last Slide**
- - Strong call-to-action
- - Next steps
- - Contact information
- - Engagement prompt
-
-### 2. Visual Strategy
-- **Consistency**
- - Cohesive style
- - Brand colors
- - Font hierarchy
- - Layout patterns
-
-- **Image Quality**
- - Professional aesthetics
- - Clear messaging
- - Relevant visuals
- - Brand alignment
-
-### 3. Content Flow
-- **Progression**
- - Logical sequence
- - Clear transitions
- - Building complexity
- - Cohesive narrative
-
-## đ Content Types Guide
-
-### How-to Guide
-- Clear steps
-- Visual instructions
-- Progress indicators
-- Action items
-
-### List Format
-- Clear numbering
-- Consistent structure
-- Visual hierarchy
-- Easy scanning
-
-### Story Format
-- Engaging narrative
-- Character elements
-- Visual journey
-- Emotional connection
-
-### Case Study
-- Problem statement
-- Solution process
-- Results/outcomes
-- Key learnings
-
-### Tips & Tricks
-- Quick insights
-- Actionable advice
-- Visual examples
-- Implementation guidance
-
-## đ¨ Design Guidelines
-
-### Visual Hierarchy
-- **Headlines**
- - Clear visibility
- - Consistent sizing
- - Impactful fonts
- - Color contrast
-
-- **Content Layout**
- - Balanced design
- - White space
- - Reading flow
- - Visual anchors
-
-### Brand Elements
-- **Style Guide**
- - Color palette
- - Typography
- - Logo placement
- - Visual elements
-
-## đą Optimization
-
-### Mobile View
-- Text readability
-- Image scaling
-- Touch-friendly
-- Quick loading
-
-### Platform Specs
-- LinkedIn dimensions
-- File size limits
-- Format requirements
-- Quality standards
-
-## đĒ Engagement Tips
-
-### 1. Content Hooks
-- **First Slide**
- - Compelling question
- - Bold statement
- - Surprising fact
- - Clear benefit
-
-### 2. Visual Impact
-- **Each Slide**
- - Professional quality
- - Clear message
- - Brand alignment
- - Emotional appeal
-
-### 3. Call-to-Action
-- **Last Slide**
- - Clear direction
- - Value offer
- - Next steps
- - Engagement prompt
-
-## đ ī¸ Technical Features
-
-### Performance
-- Fast generation
-- Real-time preview
-- Smooth editing
-- Quick exports
-
-### Export Options
-- Individual slides
-- Complete carousel
-- Various formats
-- High resolution
-
-## đ Success Tips
-
-### Content Quality
-- Research-backed
-- Professional tone
-- Clear messaging
-- Valuable insights
-
-### Visual Excellence
-- Professional design
-- Consistent branding
-- Quality visuals
-- Mobile optimization
-
-### Engagement Focus
-- Interactive elements
-- Question prompts
-- Discussion starters
-- Clear CTAs
-
-## đ Quick Tips
-
-1. **Optimal Length**
- - 3-5 slides for simple topics
- - 6-8 slides for detailed guides
- - 8-10 slides for comprehensive content
-
-2. **Visual Best Practices**
- - One main point per slide
- - Clear, readable text
- - High-quality images
- - Consistent branding
-
-3. **Content Flow**
- - Hook â Content â CTA
- - Logical progression
- - Clear transitions
- - Strong ending
-
-4. **Engagement Boosters**
- - Ask questions
- - Include statistics
- - Share insights
- - Prompt discussion
-
----
-
-Transform your LinkedIn presence with professional, engaging carousel posts! đ
-
-Remember:
-- Keep each slide focused
-- Maintain visual consistency
-- Tell a compelling story
-- End with clear action steps
-- Test and optimize regularly
-
-Start creating stunning LinkedIn carousels that showcase your expertise! đĢ
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/carousel_generator/__init__.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/carousel_generator/__init__.py
deleted file mode 100644
index 643198bb..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/carousel_generator/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-"""
-LinkedIn Carousel Generator Package
-
-This package provides functionality for generating LinkedIn carousel posts with
-AI-powered content and visuals.
-"""
-
-from .linkedin_carousel_generator import linkedin_carousel_generator_ui, LinkedInCarouselGenerator, CarouselSlide
-
-__all__ = ['linkedin_carousel_generator_ui', 'LinkedInCarouselGenerator', 'CarouselSlide']
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/carousel_generator/linkedin_carousel_generator.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/carousel_generator/linkedin_carousel_generator.py
deleted file mode 100644
index 19bc9b04..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/carousel_generator/linkedin_carousel_generator.py
+++ /dev/null
@@ -1,469 +0,0 @@
-"""
-LinkedIn Carousel Post Generator
-
-This module provides functionality for generating LinkedIn carousel posts with
-AI-powered content and visuals.
-"""
-
-import streamlit as st
-from typing import Dict, List, Optional
-from loguru import logger
-import json
-from pydantic import BaseModel
-
-from .....gpt_providers.text_generation.main_text_generation import llm_text_gen
-from .....gpt_providers.text_generation.gemini_pro_text import gemini_structured_json_response
-from .....gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
-from .....ai_web_researcher.gpt_online_researcher import do_google_serp_search
-from .....ai_web_researcher.metaphor_basic_neural_web_search import metaphor_search_articles
-from .....ai_web_researcher.tavily_ai_search import do_tavily_ai_search
-
-
-class CarouselSlide(BaseModel):
- """Represents a single slide in the carousel."""
- index: int
- heading: str
- subheading: str
- content: str
- image_prompt: str
- image_path: Optional[str] = None
-
-
-class CarouselStructure(BaseModel):
- """Represents the complete carousel structure."""
- slides: List[CarouselSlide]
-
-
-def generate_structured_content(prompt: str, model: str = 'gemini-pro') -> Optional[Dict]:
- """Generate structured content using Gemini's structured output feature."""
- try:
- # Define the schema for the carousel structure
- schema = {
- "type": "object",
- "properties": {
- "slides": {
- "type": "array",
- "items": {
- "type": "object",
- "properties": {
- "index": {"type": "integer"},
- "heading": {"type": "string"},
- "subheading": {"type": "string"},
- "content": {"type": "string"},
- "image_prompt": {"type": "string"}
- },
- "required": ["index", "heading", "subheading", "content", "image_prompt"]
- }
- }
- },
- "required": ["slides"]
- }
-
- # Use the new function to generate structured content
- result = gemini_structured_json_response(prompt, schema)
-
- # Check if there was an error
- if "error" in result:
- logger.error(f"Error generating structured content: {result['error']}")
- return None
-
- logger.debug(f"Structured response: {json.dumps(result, indent=2)}")
- return result
- except Exception as e:
- logger.error(f"Error generating structured content: {e}")
- logger.exception("Full traceback:")
- return None
-
-
-class LinkedInCarouselGenerator:
- """
- Generator for LinkedIn carousel posts with AI-powered content and visuals.
- """
-
- def __init__(self):
- """Initialize the carousel generator."""
- self.slides: List[CarouselSlide] = []
- self.topic = ""
- self.industry = ""
- self.tone = ""
- self.content_type = ""
- self.num_slides = 0
- self.research_results = {}
-
- def research_topic(self, topic: str, industry: str, search_engine: str = "metaphor") -> Dict:
- """Research the topic to gather content for carousel slides."""
- try:
- # Construct research query
- search_query = f"""
- Find detailed professional content about '{topic}' in {industry} industry
- focusing on:
- - Key concepts and main points
- - Statistics and data
- - Examples and case studies
- - Best practices
- - Expert insights
- """
-
- # Perform research using selected engine
- if search_engine == "metaphor":
- results = metaphor_search_articles(search_query)
- elif search_engine == "google":
- results = do_google_serp_search(search_query)
- elif search_engine == "tavily":
- results = do_tavily_ai_search(search_query)
-
- self.research_results = results
- return results
-
- except Exception as e:
- logger.error(f"Error researching topic: {e}")
- return {}
-
- def generate_slide_content(self, topic: str, num_slides: int = 5) -> bool:
- """Generate content for carousel slides."""
- try:
- logger.info(f"Generating carousel outline for topic: {topic}")
-
- # Step 1: Generate detailed outline
- outline_prompt = f"""
- Create a detailed outline for a LinkedIn carousel about {topic}.
- The outline should include:
- 1. Main topic and key message
- 2. {num_slides} main points to cover
- 3. Supporting details for each point
- 4. Key takeaways and call-to-action
-
- Format the outline in a clear, structured way.
- """
-
- outline = llm_text_gen(outline_prompt)
- logger.debug(f"Generated outline: {outline}")
-
- # Step 2: Generate structured carousel content
- carousel_prompt = f"""
- Based on this outline:
- {outline}
-
- Create a LinkedIn carousel with {num_slides} slides.
- Each slide should have:
- - A compelling heading
- - A brief subheading
- - Concise, engaging content
- - A detailed image prompt for visual content
-
- The content should be informative, engaging, and valuable for LinkedIn professionals.
- """
-
- logger.info("Generating structured carousel content")
- carousel_structure = generate_structured_content(carousel_prompt)
-
- if not carousel_structure:
- logger.error("Failed to generate structured carousel content")
- return False
-
- logger.debug(f"Generated carousel structure: {json.dumps(carousel_structure, indent=2)}")
-
- # Validate and process the structure
- if "slides" not in carousel_structure:
- logger.error("Invalid carousel structure: missing 'slides' key")
- return False
-
- self.slides = []
- for slide_data in carousel_structure["slides"]:
- try:
- slide = CarouselSlide(
- index=slide_data["index"],
- heading=slide_data["heading"],
- subheading=slide_data["subheading"],
- content=slide_data["content"],
- image_prompt=slide_data["image_prompt"]
- )
- self.slides.append(slide)
- logger.debug(f"Created slide {slide.index}: {slide.heading}")
- except Exception as e:
- logger.error(f"Error processing slide data: {e}")
- logger.debug(f"Problematic slide data: {slide_data}")
- continue
-
- if not self.slides:
- logger.error("No valid slides were created")
- return False
-
- logger.info(f"Successfully generated {len(self.slides)} slides")
- return True
-
- except Exception as e:
- logger.error(f"Error generating slide content: {e}")
- logger.exception("Full traceback:")
- return False
-
- def generate_slide_image(self, slide: CarouselSlide) -> Optional[str]:
- """Generate an image for a carousel slide."""
- try:
- # Generate image using the slide's prompt
- image_path = generate_image(slide.image_prompt)
- slide.image_path = image_path
- return image_path
- except Exception as e:
- logger.error(f"Error generating slide image: {e}")
- return None
-
- def optimize_content(self, slide: CarouselSlide) -> CarouselSlide:
- """Optimize slide content for engagement."""
- try:
- optimization_prompt = f"""
- Optimize this carousel slide content for maximum LinkedIn engagement:
-
- Current Content:
- Heading: {slide.heading}
- Subheading: {slide.subheading}
- Content: {slide.content}
-
- Requirements:
- 1. Make heading more attention-grabbing
- 2. Ensure content is concise and impactful
- 3. Add relevant emojis where appropriate
- 4. Optimize for readability
- 5. Keep professional tone
- 6. Maintain focus on {self.topic}
-
- Return as JSON:
- {{
- "heading": "Optimized heading",
- "subheading": "Optimized subheading",
- "content": "Optimized content"
- }}
- """
-
- optimized = llm_text_gen(optimization_prompt)
-
- try:
- optimized_data = json.loads(optimized)
- slide.heading = optimized_data["heading"]
- slide.subheading = optimized_data["subheading"]
- slide.content = optimized_data["content"]
- except json.JSONDecodeError:
- logger.error("Failed to parse optimized content")
-
- return slide
-
- except Exception as e:
- logger.error(f"Error optimizing slide content: {e}")
- return slide
-
-
-def linkedin_carousel_generator_ui():
- """Streamlit UI for the LinkedIn Carousel Generator."""
- st.title("đ LinkedIn Carousel Post Generator")
- st.write("Create engaging carousel posts that showcase your content professionally")
-
- # Initialize generator
- generator = LinkedInCarouselGenerator()
-
- # Initialize session state
- if "carousel_data" not in st.session_state:
- st.session_state.carousel_data = {
- "slides": [],
- "research_results": None,
- "generated_images": {},
- "current_slide": 0
- }
-
- # Input form
- with st.form("carousel_form"):
- topic = st.text_input("Topic", placeholder="Enter the main topic of your carousel")
- industry = st.text_input("Industry", placeholder="e.g., Technology, Healthcare, Finance")
-
- col1, col2, col3 = st.columns(3)
-
- with col1:
- tone = st.selectbox(
- "Tone",
- ["Professional", "Educational", "Conversational", "Inspiring", "Technical"]
- )
-
- with col2:
- content_type = st.selectbox(
- "Content Type",
- ["How-to Guide", "List", "Story", "Case Study", "Tips & Tricks"]
- )
-
- with col3:
- num_slides = st.slider("Number of Slides", min_value=3, max_value=10, value=5)
-
- # Research source selection
- search_engine = st.radio(
- "Research Source",
- ["metaphor", "google", "tavily"],
- format_func=lambda x: {
- "metaphor": "đ Metaphor AI",
- "google": "đ Google Search",
- "tavily": "đ¤ Tavily AI"
- }[x]
- )
-
- submit = st.form_submit_button("Generate Carousel")
-
- if submit and topic and industry:
- # Create status container
- status_container = st.empty()
-
- with st.spinner("Researching and generating your carousel..."):
- # Research phase
- status_container.text("đ Phase 1: Researching topic...")
- research_results = generator.research_topic(topic, industry, search_engine)
- st.session_state.carousel_data["research_results"] = research_results
-
- if research_results:
- # Content generation phase
- status_container.text("âī¸ Phase 2: Creating carousel content...")
- slides = generator.generate_slide_content(
- topic, num_slides
- )
-
- if not slides:
- status_container.error("â Failed to generate carousel content. Please try again with different parameters.")
- st.error("""
- Tips to resolve this issue:
- 1. Try a different topic or industry
- 2. Adjust the number of slides
- 3. Change the content type or tone
- 4. Try a different research source
- """)
- return
-
- st.session_state.carousel_data["slides"] = slides
-
- # Image generation phase
- status_container.text("đ¨ Phase 3: Generating slide visuals...")
- for slide in slides:
- image_path = generator.generate_slide_image(slide)
- if image_path:
- st.session_state.carousel_data["generated_images"][slide.index] = image_path
-
- status_container.text("â
Carousel generation complete!")
- else:
- status_container.error("â No research results found. Please try a different topic or research source.")
- st.error("""
- Tips to resolve this issue:
- 1. Try a more specific topic
- 2. Use a different research source
- 3. Check if your topic is too niche or too broad
- 4. Ensure your industry selection is accurate
- """)
-
- # Display carousel if we have slides
- if st.session_state.carousel_data["slides"]:
- st.markdown("---")
-
- # Create tabs for different views
- tab1, tab2 = st.tabs(["đ¯ Carousel Preview", "đ Research Results"])
-
- with tab1:
- st.subheader("Carousel Preview")
-
- # Slide navigation
- col1, col2, col3 = st.columns([1, 3, 1])
-
- with col1:
- if st.button("âŦ
ī¸ Previous") and st.session_state.carousel_data["current_slide"] > 0:
- st.session_state.carousel_data["current_slide"] -= 1
-
- with col3:
- if st.button("Next âĄī¸") and st.session_state.carousel_data["current_slide"] < len(st.session_state.carousel_data["slides"]) - 1:
- st.session_state.carousel_data["current_slide"] += 1
-
- # Display current slide
- current_slide = st.session_state.carousel_data["slides"][st.session_state.carousel_data["current_slide"]]
-
- st.markdown(f"""
- {outline['headline']}
-- {outline['subheadline']} -
- {st.session_state.article_data["article_content"]} -
-
- """, unsafe_allow_html=True)
-
- # Display slide image if available
- if current_slide.image_path:
- st.image(current_slide.image_path,
- caption=f"Slide {current_slide.index} Image",
- use_container_width=True)
-
- # Slide controls
- st.markdown(f"**Slide {current_slide.index} of {len(st.session_state.carousel_data['slides'])}**")
-
- # Edit options
- with st.expander("âī¸ Edit Slide"):
- # Edit form for current slide
- edited_heading = st.text_input("Heading", value=current_slide.heading)
- edited_subheading = st.text_input("Subheading", value=current_slide.subheading)
- edited_content = st.text_area("Content", value=current_slide.content)
-
- if st.button("Update Slide"):
- current_slide.heading = edited_heading
- current_slide.subheading = edited_subheading
- current_slide.content = edited_content
- st.success("â
Slide updated!")
-
- if st.button("Optimize Content"):
- optimized_slide = generator.optimize_content(current_slide)
- st.success("â
Content optimized!")
- st.experimental_rerun()
-
- # Export options
- with st.expander("đž Export Options"):
- col1, col2 = st.columns(2)
- with col1:
- if st.button("Export Current Slide"):
- st.success("Slide exported successfully!")
- with col2:
- if st.button("Export Full Carousel"):
- st.success("Carousel exported successfully!")
-
- with tab2:
- st.subheader("Research Results")
-
- research_results = st.session_state.carousel_data["research_results"]
-
- if research_results:
- # Display key insights
- st.markdown("### Key Insights")
- for insight in research_results.get("key_insights", []):
- st.markdown(f"- {insight}")
-
- # Display statistics
- st.markdown("### Statistics & Data")
- for stat in research_results.get("statistics", []):
- st.markdown(f"- {stat}")
-
- # Display sources
- st.markdown("### Sources")
- for source in research_results.get("sources", []):
- st.markdown(f"- {source}")
- else:
- st.info("No research results available.")
-
-
-if __name__ == "__main__":
- linkedin_carousel_generator_ui()
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/carousel_generator/linkedin_carousel_generator_ui.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/carousel_generator/linkedin_carousel_generator_ui.py
deleted file mode 100644
index eef93c8a..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/carousel_generator/linkedin_carousel_generator_ui.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import streamlit as st
-import json
-from typing import Optional, List
-from .linkedin_carousel_generator import LinkedInCarouselGenerator, CarouselSlide
-
-def linkedin_carousel_generator_ui():
- """Streamlit UI for LinkedIn Carousel Generator."""
- st.title("LinkedIn Carousel Generator")
- st.write("Create engaging carousel posts for LinkedIn with AI-powered content generation.")
-
- # Initialize session state
- if 'generator' not in st.session_state:
- st.session_state.generator = LinkedInCarouselGenerator()
- if 'slides' not in st.session_state:
- st.session_state.slides = []
- if 'current_slide' not in st.session_state:
- st.session_state.current_slide = 0
-
- # Sidebar for input parameters
- with st.sidebar:
- st.header("Carousel Parameters")
- topic = st.text_input("Topic", help="Enter the main topic for your carousel")
- num_slides = st.slider("Number of Slides", min_value=3, max_value=10, value=5,
- help="Choose how many slides you want in your carousel")
-
- if st.button("Generate Carousel"):
- if not topic:
- st.error("Please enter a topic")
- return
-
- with st.spinner("Generating carousel content..."):
- success = st.session_state.generator.generate_slide_content(topic, num_slides)
- if success:
- st.session_state.slides = st.session_state.generator.slides
- st.session_state.current_slide = 0
- st.success("Carousel content generated successfully!")
- else:
- st.error("Failed to generate carousel content. Please try again.")
-
- # Main content area
- if st.session_state.slides:
- # Display current slide
- current_slide = st.session_state.slides[st.session_state.current_slide]
-
- col1, col2 = st.columns([2, 1])
-
- with col1:
- st.subheader(f"Slide {current_slide.index}")
- st.write("**Heading:**")
- st.write(current_slide.heading)
- st.write("**Subheading:**")
- st.write(current_slide.subheading)
- st.write("**Content:**")
- st.write(current_slide.content)
- st.write("**Image Prompt:**")
- st.write(current_slide.image_prompt)
-
- # Navigation buttons
- col_prev, col_next = st.columns(2)
- with col_prev:
- if st.session_state.current_slide > 0:
- if st.button("â Previous"):
- st.session_state.current_slide -= 1
- st.experimental_rerun()
- with col_next:
- if st.session_state.current_slide < len(st.session_state.slides) - 1:
- if st.button("Next â"):
- st.session_state.current_slide += 1
- st.experimental_rerun()
-
- with col2:
- # Display structured output
- st.subheader("Carousel Structure")
- carousel_data = {
- "slides": [
- {
- "index": slide.index,
- "heading": slide.heading,
- "subheading": slide.subheading,
- "content": slide.content,
- "image_prompt": slide.image_prompt
- }
- for slide in st.session_state.slides
- ]
- }
- st.json(carousel_data)
-
- # Export options
- st.download_button(
- "Download as JSON",
- data=json.dumps(carousel_data, indent=2),
- file_name="carousel_content.json",
- mime="application/json"
- )
-
- if st.button("Generate Images"):
- with st.spinner("Generating images for slides..."):
- for slide in st.session_state.slides:
- if not slide.image_path:
- image_path = st.session_state.generator.generate_slide_image(slide)
- if image_path:
- slide.image_path = image_path
- st.success(f"Generated image for slide {slide.index}")
- else:
- st.error(f"Failed to generate image for slide {slide.index}")
-
- else:
- st.info("Enter a topic and click 'Generate Carousel' to create your carousel content.")
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/comment_response_generator/README.md b/ToBeMigrated/ai_writers/linkedin_writer/modules/comment_response_generator/README.md
deleted file mode 100644
index 76af2649..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/comment_response_generator/README.md
+++ /dev/null
@@ -1,261 +0,0 @@
-# LinkedIn Comment Response Generator
-
-A powerful AI-powered tool for generating professional, engaging, and contextually appropriate responses to LinkedIn comments. This module helps maintain active engagement on LinkedIn by providing intelligent, well-crafted responses that build relationships and drive meaningful discussions.
-
-## Features
-
-### 1. Intelligent Comment Analysis
-- Sentiment analysis of comments
-- Identification of key discussion points
-- Recognition of user intent and tone
-- Context-aware interpretation
-- Engagement opportunity detection
-
-### 2. Response Generation
-- Multiple response types:
- - General professional responses
- - Disagreement handling
- - Value-add responses
- - Resource suggestions
- - Follow-up questions
-- Brand voice customization
-- Engagement goal targeting
-- Context-aware responses
-- Professional tone maintenance
-
-### 3. Disagreement Handling
-- Diplomatic response generation
-- Evidence-based arguments
-- Common ground identification
-- Constructive dialogue promotion
-- Relationship preservation
-- Professional conflict resolution
-
-### 4. Value-Add Responses
-- Industry-specific insights
-- Actionable recommendations
-- Expert perspective sharing
-- Resource linking
-- Knowledge sharing
-- Engagement hooks
-
-### 5. Resource Suggestions
-- Curated learning materials
-- Progressive learning paths
-- Practical application tips
-- Follow-up support
-- Expertise-level matching
-- Topic-specific resources
-
-### 6. Follow-up Questions
-- Discussion deepening
-- Multiple perspective exploration
-- Experience sharing prompts
-- Reflection encouragement
-- Engagement maintenance
-- Value exploration
-
-### 7. Tone Optimization
-- Multiple tone options:
- - Professional
- - Friendly
- - Expert
- - Supportive
- - Diplomatic
- - Appreciative
-- Audience-specific adjustments
-- Brand voice alignment
-- Engagement optimization
-- Relationship building focus
-
-## Usage
-
-### Basic Response Generation
-```python
-from lib.ai_writers.linkedin_writer.modules.comment_response_generator import LinkedInCommentResponseGenerator
-
-# Initialize the generator
-generator = LinkedInCommentResponseGenerator()
-
-# Generate a response
-response = await generator.generate_response(
- comment="Your comment here",
- post_context="Original post context",
- brand_voice="professional",
- engagement_goal="continue_discussion"
-)
-```
-
-### Handling Disagreements
-```python
-# Generate a diplomatic response
-response = await generator.handle_disagreement(
- comment="Disagreeing comment",
- post_context="Post context",
- brand_voice="diplomatic"
-)
-```
-
-### Value-Add Responses
-```python
-# Generate a value-adding response
-response = await generator.generate_value_add_response(
- comment="Comment to respond to",
- industry="Technology",
- expertise_areas=["AI", "Machine Learning", "Data Science"]
-)
-```
-
-### Resource Suggestions
-```python
-# Suggest resources
-response = await generator.suggest_resources(
- comment="Comment requesting resources",
- topic="Artificial Intelligence",
- expertise_level="intermediate"
-)
-```
-
-### Follow-up Questions
-```python
-# Generate follow-up questions
-response = await generator.generate_follow_up_questions(
- comment="Original comment",
- discussion_context="Discussion context"
-)
-```
-
-### Tone Optimization
-```python
-# Optimize response tone
-optimized = await generator.optimize_response_tone(
- response="Your response",
- target_tone="professional",
- audience="Tech professionals"
-)
-```
-
-## UI Interface
-
-The module includes a Streamlit-based user interface with the following sections:
-
-1. **General Response Tab**
- - Comment input
- - Post context
- - Brand voice selection
- - Engagement goal selection
- - Response generation
- - Strategy display
-
-2. **Handle Disagreement Tab**
- - Disagreeing comment input
- - Context input
- - Brand voice selection
- - Diplomatic response generation
- - Strategy display
-
-3. **Value-Add Response Tab**
- - Comment input
- - Industry specification
- - Expertise areas input
- - Value-adding response generation
- - Component display
-
-4. **Resource Suggestions Tab**
- - Comment input
- - Topic specification
- - Expertise level selection
- - Resource suggestion generation
- - Resource details display
-
-5. **Follow-up Questions Tab**
- - Comment input
- - Discussion context
- - Question generation
- - Strategy display
-
-6. **Tone Optimization Section**
- - Response input
- - Target tone selection
- - Audience specification
- - Tone optimization
- - Optimization details display
-
-## Implementation Details
-
-### Core Components
-
-1. **LinkedInCommentResponseGenerator Class**
- - Main generator class
- - Response tone definitions
- - Comment type definitions
- - Core response generation methods
-
-2. **Response Generation Methods**
- - `analyze_comment`: Analyzes comment sentiment and intent
- - `generate_response`: Creates contextually appropriate responses
- - `handle_disagreement`: Generates diplomatic responses
- - `generate_value_add_response`: Creates value-adding responses
- - `suggest_resources`: Provides relevant resource suggestions
- - `generate_follow_up_questions`: Creates engaging follow-up questions
- - `optimize_response_tone`: Adjusts response tone for target audience
-
-3. **UI Implementation**
- - Streamlit-based interface
- - Tab-based organization
- - Interactive input fields
- - Real-time response generation
- - Detailed strategy displays
-
-### Dependencies
-
-- Streamlit
-- AI text generation capabilities
-- Web research tools
-- JSON processing
-- Async/await support
-
-## Best Practices
-
-1. **Response Generation**
- - Always provide context
- - Select appropriate brand voice
- - Define clear engagement goals
- - Review generated responses
- - Optimize tone for audience
-
-2. **Disagreement Handling**
- - Maintain professionalism
- - Focus on common ground
- - Provide evidence
- - Encourage dialogue
- - Preserve relationships
-
-3. **Value-Add Responses**
- - Share relevant expertise
- - Provide actionable insights
- - Include supporting evidence
- - Suggest practical applications
- - Maintain engagement
-
-4. **Resource Suggestions**
- - Match expertise level
- - Provide learning path
- - Include application tips
- - Offer follow-up support
- - Ensure resource accessibility
-
-5. **Follow-up Questions**
- - Deepen discussion
- - Explore new angles
- - Encourage participation
- - Maintain professionalism
- - Drive engagement
-
-## Contributing
-
-Contributions are welcome! Please feel free to submit a Pull Request.
-
-## License
-
-This project is licensed under the MIT License - see the LICENSE file for details.
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/comment_response_generator/__init__.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/comment_response_generator/__init__.py
deleted file mode 100644
index e34d8318..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/comment_response_generator/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-"""
-LinkedIn Comment Response Generator Module
-
-This module provides AI-powered generation of professional and engaging responses
-to comments on LinkedIn posts.
-"""
-
-from .linkedin_comment_response_generator import LinkedInCommentResponseGenerator
-from .linkedin_comment_response_generator_ui import linkedin_comment_response_generator_ui
-
-__all__ = ['LinkedInCommentResponseGenerator', 'linkedin_comment_response_generator_ui']
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/comment_response_generator/linkedin_comment_response_generator.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/comment_response_generator/linkedin_comment_response_generator.py
deleted file mode 100644
index ac70d268..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/comment_response_generator/linkedin_comment_response_generator.py
+++ /dev/null
@@ -1,346 +0,0 @@
-"""
-LinkedIn Comment Response Generator
-
-This module provides AI-powered generation of professional and engaging responses
-to comments on LinkedIn posts.
-"""
-
-import json
-from typing import Dict, List, Optional
-from loguru import logger
-
-from .....gpt_providers.text_generation.main_text_generation import llm_text_gen
-from .....ai_web_researcher.gpt_online_researcher import do_google_serp_search
-from .....ai_web_researcher.metaphor_basic_neural_web_search import metaphor_search_articles
-from .....ai_web_researcher.tavily_ai_search import do_tavily_ai_search
-
-class LinkedInCommentResponseGenerator:
- """
- AI-powered generator for professional and engaging LinkedIn comment responses.
- """
-
- def __init__(self):
- """Initialize the LinkedIn Comment Response Generator."""
- self.response_tones = [
- "professional",
- "friendly",
- "expert",
- "supportive",
- "diplomatic",
- "appreciative"
- ]
-
- self.comment_types = [
- "question",
- "agreement",
- "disagreement",
- "appreciation",
- "criticism",
- "suggestion",
- "experience_sharing"
- ]
-
- async def analyze_comment(self, comment: str) -> Dict:
- """
- Analyze the comment to determine its type, tone, and key points.
-
- Args:
- comment: The comment text to analyze
-
- Returns:
- Dict containing comment analysis
- """
- prompt = f"""
- As a LinkedIn engagement expert, analyze this comment:
-
- Comment: {comment}
-
- Provide a detailed analysis including:
- - Comment type (question/agreement/disagreement/etc.)
- - Emotional tone
- - Key points or questions raised
- - Intent and context
- - Engagement potential
-
- Return a JSON with:
- - comment_type: The type of comment
- - tone: Emotional tone
- - key_points: List of main points
- - questions: Any questions raised
- - intent: Perceived intent
- - engagement_potential: High/Medium/Low
- """
-
- response = await llm_text_gen(prompt)
- return json.loads(response)
-
- async def generate_response(self,
- comment: str,
- post_context: str,
- brand_voice: str,
- engagement_goal: str
- ) -> Dict:
- """
- Generate an appropriate response to the comment.
-
- Args:
- comment: The comment to respond to
- post_context: The context of the original post
- brand_voice: Desired brand voice/tone
- engagement_goal: Goal for the response
-
- Returns:
- Dict containing response and strategy
- """
- # First analyze the comment
- analysis = await self.analyze_comment(comment)
-
- prompt = f"""
- As a LinkedIn engagement expert, generate a response to this comment:
-
- Comment: {comment}
- Post Context: {post_context}
- Brand Voice: {brand_voice}
- Engagement Goal: {engagement_goal}
-
- Comment Analysis: {json.dumps(analysis)}
-
- Generate a response that:
- - Maintains professional tone
- - Addresses key points/questions
- - Aligns with brand voice
- - Encourages further engagement
- - Builds community
- - Adds value
-
- Return a JSON with:
- - response: The generated response
- - tone_used: Tone of the response
- - key_points_addressed: Points addressed
- - engagement_hooks: Elements to encourage interaction
- - value_adds: Additional value provided
- - follow_up_suggestions: Potential follow-up points
- """
-
- response = await llm_text_gen(prompt)
- return json.loads(response)
-
- async def handle_disagreement(self,
- comment: str,
- post_context: str,
- brand_voice: str
- ) -> Dict:
- """
- Generate a professional response to a disagreement.
-
- Args:
- comment: The disagreeing comment
- post_context: Original post context
- brand_voice: Desired brand voice
-
- Returns:
- Dict containing diplomatic response
- """
- prompt = f"""
- As a LinkedIn communication expert, craft a diplomatic response to this disagreement:
-
- Comment: {comment}
- Post Context: {post_context}
- Brand Voice: {brand_voice}
-
- Generate a response that:
- - Maintains professionalism
- - Acknowledges the perspective
- - Provides supporting evidence
- - Finds common ground
- - Keeps discussion constructive
- - Invites further dialogue
-
- Return a JSON with:
- - response: The diplomatic response
- - acknowledgment: How the perspective was acknowledged
- - evidence: Supporting points provided
- - common_ground: Areas of agreement
- - dialogue_hooks: Elements to continue discussion
- """
-
- response = await llm_text_gen(prompt)
- return json.loads(response)
-
- async def generate_value_add_response(self,
- comment: str,
- industry: str,
- expertise_areas: List[str]
- ) -> Dict:
- """
- Generate a response that adds significant value.
-
- Args:
- comment: The comment to respond to
- industry: Relevant industry
- expertise_areas: Areas of expertise
-
- Returns:
- Dict containing value-adding response
- """
- # Research relevant insights
- research = await do_tavily_ai_search(
- f"latest insights trends {industry} {' '.join(expertise_areas)}"
- )
-
- prompt = f"""
- As a LinkedIn thought leader, generate a value-adding response:
-
- Comment: {comment}
- Industry: {industry}
- Expertise Areas: {expertise_areas}
- Research Insights: {json.dumps(research)}
-
- Create a response that:
- - Shares relevant insights
- - Provides actionable advice
- - References credible sources
- - Demonstrates expertise
- - Encourages implementation
- - Invites questions
-
- Return a JSON with:
- - response: The value-adding response
- - insights_shared: Key insights provided
- - action_items: Actionable takeaways
- - sources: Referenced sources
- - expertise_demonstrated: How expertise was shown
- - engagement_hooks: Questions or prompts
- """
-
- response = await llm_text_gen(prompt)
- return json.loads(response)
-
- async def suggest_resources(self,
- comment: str,
- topic: str,
- expertise_level: str
- ) -> Dict:
- """
- Suggest relevant resources in response to a comment.
-
- Args:
- comment: The comment requesting/needing resources
- topic: The topic of discussion
- expertise_level: User's expertise level
-
- Returns:
- Dict containing resource suggestions
- """
- # Research relevant resources
- resources = await metaphor_search_articles(
- f"best resources tutorials guides {topic} {expertise_level}"
- )
-
- prompt = f"""
- As a LinkedIn learning facilitator, suggest helpful resources:
-
- Comment: {comment}
- Topic: {topic}
- Expertise Level: {expertise_level}
- Found Resources: {json.dumps(resources)}
-
- Provide suggestions that:
- - Match expertise level
- - Cover key aspects
- - Include various formats
- - Are readily accessible
- - Support learning goals
- - Encourage application
-
- Return a JSON with:
- - response: Resource suggestion response
- - recommended_resources: List of resources
- - learning_path: Suggested learning sequence
- - application_tips: How to apply resources
- - follow_up_support: Additional support offered
- """
-
- response = await llm_text_gen(prompt)
- return json.loads(response)
-
- async def generate_follow_up_questions(self,
- comment: str,
- discussion_context: str
- ) -> Dict:
- """
- Generate engaging follow-up questions to continue the discussion.
-
- Args:
- comment: The comment to generate questions for
- discussion_context: Context of the discussion
-
- Returns:
- Dict containing follow-up questions
- """
- prompt = f"""
- As a LinkedIn engagement expert, generate follow-up questions:
-
- Comment: {comment}
- Discussion Context: {discussion_context}
-
- Generate questions that:
- - Deepen the discussion
- - Explore different angles
- - Draw out experiences
- - Encourage reflection
- - Maintain professionalism
- - Drive engagement
-
- Return a JSON with:
- - primary_question: Main follow-up question
- - secondary_questions: Additional questions
- - discussion_angles: New perspectives to explore
- - engagement_prompts: Ways to encourage participation
- - value_exploration: Areas to uncover value
- """
-
- response = await llm_text_gen(prompt)
- return json.loads(response)
-
- async def optimize_response_tone(self,
- response: str,
- target_tone: str,
- audience: str
- ) -> Dict:
- """
- Optimize the tone of a response for the target audience.
-
- Args:
- response: The response to optimize
- target_tone: Desired tone
- audience: Target audience
-
- Returns:
- Dict containing tone-optimized response
- """
- prompt = f"""
- As a LinkedIn communication expert, optimize this response's tone:
-
- Response: {response}
- Target Tone: {target_tone}
- Audience: {audience}
-
- Optimize the response to:
- - Match desired tone
- - Resonate with audience
- - Maintain professionalism
- - Enhance engagement
- - Build rapport
- - Reflect brand voice
-
- Return a JSON with:
- - optimized_response: Tone-adjusted response
- - tone_adjustments: Changes made
- - audience_alignment: How it matches audience
- - engagement_potential: Expected engagement
- - relationship_building: How it builds connection
- """
-
- response = await llm_text_gen(prompt)
- return json.loads(response)
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/comment_response_generator/linkedin_comment_response_generator_ui.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/comment_response_generator/linkedin_comment_response_generator_ui.py
deleted file mode 100644
index d3abdf81..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/comment_response_generator/linkedin_comment_response_generator_ui.py
+++ /dev/null
@@ -1,283 +0,0 @@
-"""
-LinkedIn Comment Response Generator UI
-
-This module provides the Streamlit UI for the LinkedIn Comment Response Generator.
-"""
-
-import streamlit as st
-import json
-from typing import Dict, List
-from .linkedin_comment_response_generator import LinkedInCommentResponseGenerator
-
-async def linkedin_comment_response_generator_ui():
- """
- Streamlit UI for the LinkedIn Comment Response Generator.
- """
- # Initialize the generator
- generator = LinkedInCommentResponseGenerator()
-
- st.title("LinkedIn Comment Response Generator")
-
- # Create tabs for different response scenarios
- tabs = st.tabs([
- "General Response",
- "Handle Disagreement",
- "Value-Add Response",
- "Resource Suggestions",
- "Follow-up Questions"
- ])
-
- # General Response Tab
- with tabs[0]:
- st.header("Generate Professional Response")
- st.info("Generate an engaging and professional response to a LinkedIn comment")
-
- comment = st.text_area("Comment to Respond to", height=100)
- post_context = st.text_area("Original Post Context", height=100)
-
- col1, col2 = st.columns(2)
- with col1:
- brand_voice = st.selectbox(
- "Brand Voice",
- ["Professional", "Friendly", "Expert", "Supportive", "Diplomatic", "Appreciative"]
- )
-
- with col2:
- engagement_goal = st.selectbox(
- "Engagement Goal",
- ["Continue Discussion", "Share Knowledge", "Build Relationship", "Address Concern", "Encourage Action"]
- )
-
- if st.button("Generate Response", key="general_response"):
- with st.spinner("Generating response..."):
- # First analyze the comment
- analysis = await generator.analyze_comment(comment)
-
- # Display analysis
- with st.expander("Comment Analysis", expanded=True):
- st.json(analysis)
-
- # Generate response
- response = await generator.generate_response(
- comment,
- post_context,
- brand_voice.lower(),
- engagement_goal
- )
-
- # Display response
- st.subheader("Generated Response")
- st.success(response['response'])
-
- # Display strategy
- with st.expander("Response Strategy"):
- st.write("**Tone Used:**", response['tone_used'])
-
- st.write("**Key Points Addressed:**")
- for point in response['key_points_addressed']:
- st.write(f"- {point}")
-
- st.write("**Engagement Hooks:**")
- for hook in response['engagement_hooks']:
- st.write(f"- {hook}")
-
- st.write("**Value Adds:**")
- for value in response['value_adds']:
- st.write(f"- {value}")
-
- st.write("**Follow-up Suggestions:**")
- for suggestion in response['follow_up_suggestions']:
- st.write(f"- {suggestion}")
-
- # Handle Disagreement Tab
- with tabs[1]:
- st.header("Handle Disagreement")
- st.info("Generate a diplomatic response to a disagreeing comment")
-
- disagreement_comment = st.text_area("Disagreeing Comment", height=100, key="disagreement_comment")
- disagreement_context = st.text_area("Post Context", height=100, key="disagreement_context")
- disagreement_voice = st.selectbox(
- "Brand Voice",
- ["Diplomatic", "Professional", "Expert", "Supportive"],
- key="disagreement_voice"
- )
-
- if st.button("Generate Diplomatic Response"):
- with st.spinner("Generating diplomatic response..."):
- response = await generator.handle_disagreement(
- disagreement_comment,
- disagreement_context,
- disagreement_voice.lower()
- )
-
- st.subheader("Diplomatic Response")
- st.success(response['response'])
-
- with st.expander("Response Strategy"):
- st.write("**Acknowledgment:**", response['acknowledgment'])
-
- st.write("**Supporting Evidence:**")
- for point in response['evidence']:
- st.write(f"- {point}")
-
- st.write("**Common Ground:**")
- for point in response['common_ground']:
- st.write(f"- {point}")
-
- st.write("**Dialogue Continuation:**")
- for hook in response['dialogue_hooks']:
- st.write(f"- {hook}")
-
- # Value-Add Response Tab
- with tabs[2]:
- st.header("Value-Add Response")
- st.info("Generate a response that provides significant value")
-
- value_comment = st.text_area("Comment", height=100, key="value_comment")
- industry = st.text_input("Industry")
- expertise_areas = st.text_area(
- "Areas of Expertise (one per line)",
- height=100
- ).split("\n")
-
- if st.button("Generate Value-Add Response"):
- with st.spinner("Researching and generating response..."):
- response = await generator.generate_value_add_response(
- value_comment,
- industry,
- expertise_areas
- )
-
- st.subheader("Value-Adding Response")
- st.success(response['response'])
-
- with st.expander("Value Components"):
- st.write("**Key Insights:**")
- for insight in response['insights_shared']:
- st.write(f"- {insight}")
-
- st.write("**Action Items:**")
- for item in response['action_items']:
- st.write(f"- {item}")
-
- st.write("**Sources:**")
- for source in response['sources']:
- st.write(f"- {source}")
-
- st.write("**Expertise Demonstrated:**")
- st.write(response['expertise_demonstrated'])
-
- st.write("**Engagement Hooks:**")
- for hook in response['engagement_hooks']:
- st.write(f"- {hook}")
-
- # Resource Suggestions Tab
- with tabs[3]:
- st.header("Resource Suggestions")
- st.info("Suggest helpful resources in response to a comment")
-
- resource_comment = st.text_area("Comment", height=100, key="resource_comment")
- topic = st.text_input("Topic")
- expertise_level = st.select_slider(
- "Expertise Level",
- options=["Beginner", "Intermediate", "Advanced", "Expert"]
- )
-
- if st.button("Generate Resource Suggestions"):
- with st.spinner("Researching and compiling resources..."):
- response = await generator.suggest_resources(
- resource_comment,
- topic,
- expertise_level.lower()
- )
-
- st.subheader("Resource Suggestion Response")
- st.success(response['response'])
-
- with st.expander("Resource Details"):
- st.write("**Recommended Resources:**")
- for resource in response['recommended_resources']:
- st.write(f"- {resource}")
-
- st.write("**Learning Path:**")
- for step in response['learning_path']:
- st.write(f"- {step}")
-
- st.write("**Application Tips:**")
- for tip in response['application_tips']:
- st.write(f"- {tip}")
-
- st.write("**Follow-up Support:**")
- st.write(response['follow_up_support'])
-
- # Follow-up Questions Tab
- with tabs[4]:
- st.header("Follow-up Questions")
- st.info("Generate engaging follow-up questions to continue the discussion")
-
- question_comment = st.text_area("Comment", height=100, key="question_comment")
- discussion_context = st.text_area("Discussion Context", height=100, key="question_context")
-
- if st.button("Generate Follow-up Questions"):
- with st.spinner("Generating questions..."):
- response = await generator.generate_follow_up_questions(
- question_comment,
- discussion_context
- )
-
- st.subheader("Primary Follow-up Question")
- st.success(response['primary_question'])
-
- st.subheader("Secondary Questions")
- for question in response['secondary_questions']:
- st.info(question)
-
- with st.expander("Discussion Strategy"):
- st.write("**Discussion Angles:**")
- for angle in response['discussion_angles']:
- st.write(f"- {angle}")
-
- st.write("**Engagement Prompts:**")
- for prompt in response['engagement_prompts']:
- st.write(f"- {prompt}")
-
- st.write("**Value Exploration:**")
- for area in response['value_exploration']:
- st.write(f"- {area}")
-
- # Add tone optimization section at the bottom
- st.divider()
- st.subheader("Response Tone Optimization")
- st.info("Optimize the tone of any generated response")
-
- response_to_optimize = st.text_area("Response to Optimize", height=100)
- col1, col2 = st.columns(2)
-
- with col1:
- target_tone = st.selectbox(
- "Target Tone",
- generator.response_tones
- )
-
- with col2:
- audience = st.text_input("Target Audience")
-
- if st.button("Optimize Tone"):
- with st.spinner("Optimizing response tone..."):
- optimized = await generator.optimize_response_tone(
- response_to_optimize,
- target_tone,
- audience
- )
-
- st.subheader("Tone-Optimized Response")
- st.success(optimized['optimized_response'])
-
- with st.expander("Optimization Details"):
- st.write("**Tone Adjustments:**")
- for adjustment in optimized['tone_adjustments']:
- st.write(f"- {adjustment}")
-
- st.write("**Audience Alignment:**", optimized['audience_alignment'])
- st.write("**Engagement Potential:**", optimized['engagement_potential'])
- st.write("**Relationship Building:**", optimized['relationship_building'])
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/company_page_generator/__init__.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/company_page_generator/__init__.py
deleted file mode 100644
index 6f22afd7..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/company_page_generator/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-"""
-LinkedIn Company Page Content Generator Module
-
-This module provides tools for generating and optimizing content for LinkedIn company pages.
-It includes features for company profile optimization, content generation, and engagement tracking.
-"""
-
-from .linkedin_company_page_generator import LinkedInCompanyPageGenerator
-from .linkedin_company_page_generator_ui import linkedin_company_page_generator_ui
-
-__all__ = [
- 'LinkedInCompanyPageGenerator',
- 'linkedin_company_page_generator_ui'
-]
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/company_page_generator/linkedin_company_page_generator.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/company_page_generator/linkedin_company_page_generator.py
deleted file mode 100644
index 93764e34..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/company_page_generator/linkedin_company_page_generator.py
+++ /dev/null
@@ -1,322 +0,0 @@
-"""
-LinkedIn Company Page Content Generator
-
-This module provides the core functionality for generating and optimizing LinkedIn company page content.
-"""
-
-import json
-from typing import Dict, List, Optional, Union
-from loguru import logger
-
-from .....gpt_providers.text_generation.main_text_generation import llm_text_gen
-from .....ai_web_researcher.gpt_online_researcher import do_google_serp_search
-from .....ai_web_researcher.metaphor_basic_neural_web_search import metaphor_search_articles
-from .....ai_web_researcher.tavily_ai_search import do_tavily_ai_search
-from .....gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
-
-class LinkedInCompanyPageGenerator:
- """Main class for generating LinkedIn company page content."""
-
- def __init__(self):
- """Initialize the LinkedIn Company Page Generator."""
- self.company_info = {}
- self.brand_voice = {}
- self.content_history = []
-
- async def optimize_company_profile(
- self,
- company_name: str,
- industry: str,
- target_audience: List[str],
- brand_voice: str,
- key_products: List[str],
- company_size: str,
- company_description: str
- ) -> Dict[str, str]:
- """
- Optimize the company profile content for LinkedIn.
-
- Args:
- company_name: Name of the company
- industry: Industry sector
- target_audience: List of target audience segments
- brand_voice: Desired brand voice/tone
- key_products: List of key products/services
- company_size: Size of the company
- company_description: Current company description
-
- Returns:
- Dict containing optimized profile sections
- """
- try:
- # Store company info for future content generation
- self.company_info = {
- "name": company_name,
- "industry": industry,
- "target_audience": target_audience,
- "brand_voice": brand_voice,
- "key_products": key_products,
- "size": company_size,
- "description": company_description
- }
-
- # Create the prompt for profile optimization
- prompt = f"""
- Optimize the LinkedIn company profile for {company_name}, a {company_size} company in the {industry} industry.
-
- Current Description:
- {company_description}
-
- Key Products/Services:
- {', '.join(key_products)}
-
- Target Audience:
- {', '.join(target_audience)}
-
- Brand Voice:
- {brand_voice}
-
- Generate a comprehensive LinkedIn company profile with the following sections:
- 1. Company Overview
- 2. Mission Statement
- 3. Value Proposition
- 4. Industry Expertise
- 5. Company Culture
- 6. Products/Services Overview
-
- Ensure the content:
- - Maintains the specified brand voice
- - Targets the identified audience segments
- - Highlights key products/services
- - Optimizes for LinkedIn's algorithm
- - Includes relevant industry keywords
- - Maintains professional tone
- """
-
- # Define the JSON structure for the response
- json_struct = {
- "company_overview": "string",
- "mission_statement": "string",
- "value_proposition": "string",
- "industry_expertise": "string",
- "company_culture": "string",
- "products_services_overview": "string",
- "recommended_hashtags": ["string"],
- "seo_keywords": ["string"]
- }
-
- # Generate the optimized profile content
- response = await llm_text_gen(
- prompt=prompt,
- json_struct=json_struct,
- temperature=0.7
- )
-
- return response
-
- except Exception as e:
- logger.error(f"Error optimizing company profile: {str(e)}")
- raise
-
- async def generate_company_update(
- self,
- update_type: str,
- topic: str,
- target_audience: Optional[List[str]] = None,
- include_hashtags: bool = True,
- include_cta: bool = True
- ) -> Dict[str, str]:
- """
- Generate a company update post for LinkedIn.
-
- Args:
- update_type: Type of update (product_launch, milestone, news, etc.)
- topic: Main topic or focus of the update
- target_audience: Optional list of target audience segments
- include_hashtags: Whether to include hashtags
- include_cta: Whether to include a call-to-action
-
- Returns:
- Dict containing the generated update content
- """
- try:
- # Use company info if target audience not specified
- if not target_audience:
- target_audience = self.company_info.get("target_audience", [])
-
- # Create the prompt for update generation
- prompt = f"""
- Generate a LinkedIn company update post for {self.company_info['name']} about {topic}.
-
- Update Type: {update_type}
- Target Audience: {', '.join(target_audience)}
- Brand Voice: {self.company_info['brand_voice']}
-
- The post should:
- - Be engaging and professional
- - Include relevant industry context
- - Highlight the company's expertise
- - Drive meaningful engagement
- - Be optimized for LinkedIn's algorithm
- """
-
- if include_hashtags:
- prompt += "\n- Include 3-5 relevant hashtags"
-
- if include_cta:
- prompt += "\n- Include a clear call-to-action"
-
- # Define the JSON structure for the response
- json_struct = {
- "post_content": "string",
- "hashtags": ["string"],
- "call_to_action": "string",
- "suggested_image_prompt": "string",
- "engagement_tips": ["string"]
- }
-
- # Generate the update content
- response = await llm_text_gen(
- prompt=prompt,
- json_struct=json_struct,
- temperature=0.7
- )
-
- return response
-
- except Exception as e:
- logger.error(f"Error generating company update: {str(e)}")
- raise
-
- async def generate_employee_spotlight(
- self,
- employee_name: str,
- role: str,
- achievements: List[str],
- spotlight_type: str = "general"
- ) -> Dict[str, str]:
- """
- Generate an employee spotlight post for LinkedIn.
-
- Args:
- employee_name: Name of the employee
- role: Employee's role
- achievements: List of key achievements
- spotlight_type: Type of spotlight (general, leadership, innovation, etc.)
-
- Returns:
- Dict containing the generated spotlight content
- """
- try:
- # Create the prompt for spotlight generation
- prompt = f"""
- Generate a LinkedIn employee spotlight post for {employee_name}, {role} at {self.company_info['name']}.
-
- Spotlight Type: {spotlight_type}
- Key Achievements:
- {chr(10).join(f'- {achievement}' for achievement in achievements)}
-
- Company Context:
- Industry: {self.company_info['industry']}
- Brand Voice: {self.company_info['brand_voice']}
-
- The post should:
- - Highlight the employee's contributions
- - Showcase company culture
- - Be engaging and professional
- - Include relevant industry context
- - Drive meaningful engagement
- """
-
- # Define the JSON structure for the response
- json_struct = {
- "spotlight_content": "string",
- "hashtags": ["string"],
- "call_to_action": "string",
- "suggested_image_prompt": "string",
- "engagement_tips": ["string"]
- }
-
- # Generate the spotlight content
- response = await llm_text_gen(
- prompt=prompt,
- json_struct=json_struct,
- temperature=0.7
- )
-
- return response
-
- except Exception as e:
- logger.error(f"Error generating employee spotlight: {str(e)}")
- raise
-
- async def generate_industry_content(
- self,
- content_type: str,
- topic: str,
- target_audience: Optional[List[str]] = None
- ) -> Dict[str, str]:
- """
- Generate industry-focused content for LinkedIn.
-
- Args:
- content_type: Type of content (insight, trend, analysis, etc.)
- topic: Main topic or focus
- target_audience: Optional list of target audience segments
-
- Returns:
- Dict containing the generated content
- """
- try:
- # Use company info if target audience not specified
- if not target_audience:
- target_audience = self.company_info.get("target_audience", [])
-
- # Research industry trends and insights
- research_results = await do_tavily_ai_search(
- query=f"{topic} in {self.company_info['industry']} industry trends insights",
- search_depth="advanced"
- )
-
- # Create the prompt for content generation
- prompt = f"""
- Generate LinkedIn industry content for {self.company_info['name']} about {topic}.
-
- Content Type: {content_type}
- Target Audience: {', '.join(target_audience)}
- Industry: {self.company_info['industry']}
- Brand Voice: {self.company_info['brand_voice']}
-
- Research Context:
- {research_results}
-
- The content should:
- - Provide valuable industry insights
- - Position the company as a thought leader
- - Be data-driven and professional
- - Drive meaningful engagement
- - Include relevant examples and context
- """
-
- # Define the JSON structure for the response
- json_struct = {
- "content": "string",
- "hashtags": ["string"],
- "call_to_action": "string",
- "suggested_image_prompt": "string",
- "engagement_tips": ["string"],
- "key_insights": ["string"]
- }
-
- # Generate the industry content
- response = await llm_text_gen(
- prompt=prompt,
- json_struct=json_struct,
- temperature=0.7
- )
-
- return response
-
- except Exception as e:
- logger.error(f"Error generating industry content: {str(e)}")
- raise
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/company_page_generator/linkedin_company_page_generator_ui.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/company_page_generator/linkedin_company_page_generator_ui.py
deleted file mode 100644
index 5b4e571f..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/company_page_generator/linkedin_company_page_generator_ui.py
+++ /dev/null
@@ -1,321 +0,0 @@
-"""
-LinkedIn Company Page Generator UI
-
-This module provides the Streamlit UI for the LinkedIn Company Page Generator.
-"""
-
-import streamlit as st
-import json
-from typing import Dict, List, Optional
-from loguru import logger
-
-from .linkedin_company_page_generator import LinkedInCompanyPageGenerator
-
-async def linkedin_company_page_generator_ui():
- """Main UI function for the LinkedIn Company Page Generator."""
-
- st.title("đĸ LinkedIn Company Page Content Generator")
- st.markdown("""
- Create engaging and professional content for your LinkedIn company page.
- This tool helps you generate optimized company profiles, updates, employee spotlights, and industry content.
- """)
-
- # Initialize the generator
- generator = LinkedInCompanyPageGenerator()
-
- # Create tabs for different content types
- tab1, tab2, tab3, tab4 = st.tabs([
- "Company Profile",
- "Company Updates",
- "Employee Spotlights",
- "Industry Content"
- ])
-
- # Company Profile Tab
- with tab1:
- st.header("Optimize Your Company Profile")
- st.markdown("Generate an optimized LinkedIn company profile that highlights your brand and drives engagement.")
-
- # Company Information Form
- with st.form("company_profile_form"):
- company_name = st.text_input("Company Name", placeholder="Enter your company name")
- industry = st.text_input("Industry", placeholder="e.g., Technology, Healthcare, Finance")
- company_size = st.selectbox(
- "Company Size",
- ["1-10", "11-50", "51-200", "201-500", "501-1000", "1000+"]
- )
- target_audience = st.multiselect(
- "Target Audience",
- ["Job Seekers", "Customers", "Partners", "Investors", "Industry Professionals", "Media"],
- default=["Job Seekers", "Customers"]
- )
- brand_voice = st.selectbox(
- "Brand Voice",
- ["Professional", "Innovative", "Friendly", "Authoritative", "Casual", "Technical"]
- )
- key_products = st.text_area(
- "Key Products/Services",
- placeholder="Enter your key products or services, one per line"
- ).split("\n")
- company_description = st.text_area(
- "Current Company Description",
- placeholder="Enter your current company description"
- )
-
- submit_profile = st.form_submit_button("Generate Optimized Profile")
-
- if submit_profile:
- if not all([company_name, industry, target_audience, brand_voice, key_products, company_description]):
- st.error("Please fill in all required fields.")
- else:
- with st.spinner("Generating optimized company profile..."):
- try:
- profile_content = await generator.optimize_company_profile(
- company_name=company_name,
- industry=industry,
- target_audience=target_audience,
- brand_voice=brand_voice,
- key_products=key_products,
- company_size=company_size,
- company_description=company_description
- )
-
- # Display the results
- st.success("Profile generated successfully!")
-
- # Company Overview
- st.subheader("Company Overview")
- st.write(profile_content["company_overview"])
-
- # Mission Statement
- st.subheader("Mission Statement")
- st.write(profile_content["mission_statement"])
-
- # Value Proposition
- st.subheader("Value Proposition")
- st.write(profile_content["value_proposition"])
-
- # Industry Expertise
- st.subheader("Industry Expertise")
- st.write(profile_content["industry_expertise"])
-
- # Company Culture
- st.subheader("Company Culture")
- st.write(profile_content["company_culture"])
-
- # Products/Services Overview
- st.subheader("Products/Services Overview")
- st.write(profile_content["products_services_overview"])
-
- # SEO Keywords
- st.subheader("Recommended SEO Keywords")
- st.write(", ".join(profile_content["seo_keywords"]))
-
- # Hashtags
- st.subheader("Recommended Hashtags")
- st.write(" ".join([f"#{tag}" for tag in profile_content["recommended_hashtags"]]))
-
- except Exception as e:
- st.error(f"Error generating profile: {str(e)}")
-
- # Company Updates Tab
- with tab2:
- st.header("Generate Company Updates")
- st.markdown("Create engaging company updates for your LinkedIn page.")
-
- # Update Generation Form
- with st.form("company_update_form"):
- update_type = st.selectbox(
- "Update Type",
- ["Product Launch", "Company Milestone", "Industry News", "Company News", "Event Announcement"]
- )
- topic = st.text_input("Topic", placeholder="Enter the main topic of your update")
- target_audience = st.multiselect(
- "Target Audience",
- ["Job Seekers", "Customers", "Partners", "Investors", "Industry Professionals", "Media"],
- default=["Customers", "Industry Professionals"]
- )
- include_hashtags = st.checkbox("Include Hashtags", value=True)
- include_cta = st.checkbox("Include Call-to-Action", value=True)
-
- submit_update = st.form_submit_button("Generate Update")
-
- if submit_update:
- if not topic:
- st.error("Please enter a topic for your update.")
- else:
- with st.spinner("Generating company update..."):
- try:
- update_content = await generator.generate_company_update(
- update_type=update_type,
- topic=topic,
- target_audience=target_audience,
- include_hashtags=include_hashtags,
- include_cta=include_cta
- )
-
- # Display the results
- st.success("Update generated successfully!")
-
- # Post Content
- st.subheader("Generated Post")
- st.write(update_content["post_content"])
-
- # Hashtags
- if include_hashtags:
- st.subheader("Recommended Hashtags")
- st.write(" ".join([f"#{tag}" for tag in update_content["hashtags"]]))
-
- # Call-to-Action
- if include_cta:
- st.subheader("Call-to-Action")
- st.write(update_content["call_to_action"])
-
- # Engagement Tips
- st.subheader("Engagement Tips")
- for tip in update_content["engagement_tips"]:
- st.write(f"âĸ {tip}")
-
- # Image Prompt
- st.subheader("Suggested Image Prompt")
- st.write(update_content["suggested_image_prompt"])
-
- except Exception as e:
- st.error(f"Error generating update: {str(e)}")
-
- # Employee Spotlights Tab
- with tab3:
- st.header("Generate Employee Spotlights")
- st.markdown("Create engaging employee spotlight posts to showcase your team.")
-
- # Spotlight Generation Form
- with st.form("employee_spotlight_form"):
- employee_name = st.text_input("Employee Name", placeholder="Enter employee's name")
- role = st.text_input("Role", placeholder="Enter employee's role")
- achievements = st.text_area(
- "Key Achievements",
- placeholder="Enter key achievements, one per line"
- ).split("\n")
- spotlight_type = st.selectbox(
- "Spotlight Type",
- ["General", "Leadership", "Innovation", "Team Player", "Career Growth"]
- )
-
- submit_spotlight = st.form_submit_button("Generate Spotlight")
-
- if submit_spotlight:
- if not all([employee_name, role, achievements]):
- st.error("Please fill in all required fields.")
- else:
- with st.spinner("Generating employee spotlight..."):
- try:
- spotlight_content = await generator.generate_employee_spotlight(
- employee_name=employee_name,
- role=role,
- achievements=achievements,
- spotlight_type=spotlight_type
- )
-
- # Display the results
- st.success("Spotlight generated successfully!")
-
- # Spotlight Content
- st.subheader("Generated Spotlight")
- st.write(spotlight_content["spotlight_content"])
-
- # Hashtags
- st.subheader("Recommended Hashtags")
- st.write(" ".join([f"#{tag}" for tag in spotlight_content["hashtags"]]))
-
- # Call-to-Action
- st.subheader("Call-to-Action")
- st.write(spotlight_content["call_to_action"])
-
- # Engagement Tips
- st.subheader("Engagement Tips")
- for tip in spotlight_content["engagement_tips"]:
- st.write(f"âĸ {tip}")
-
- # Image Prompt
- st.subheader("Suggested Image Prompt")
- st.write(spotlight_content["suggested_image_prompt"])
-
- except Exception as e:
- st.error(f"Error generating spotlight: {str(e)}")
-
- # Industry Content Tab
- with tab4:
- st.header("Generate Industry Content")
- st.markdown("Create thought leadership content to position your company as an industry expert.")
-
- # Industry Content Generation Form
- with st.form("industry_content_form"):
- content_type = st.selectbox(
- "Content Type",
- ["Industry Insight", "Trend Analysis", "Best Practices", "Case Study", "Market Update"]
- )
- topic = st.text_input("Topic", placeholder="Enter the main topic of your content")
- target_audience = st.multiselect(
- "Target Audience",
- ["Job Seekers", "Customers", "Partners", "Investors", "Industry Professionals", "Media"],
- default=["Industry Professionals", "Customers"]
- )
-
- submit_content = st.form_submit_button("Generate Content")
-
- if submit_content:
- if not topic:
- st.error("Please enter a topic for your content.")
- else:
- with st.spinner("Generating industry content..."):
- try:
- content = await generator.generate_industry_content(
- content_type=content_type,
- topic=topic,
- target_audience=target_audience
- )
-
- # Display the results
- st.success("Content generated successfully!")
-
- # Content
- st.subheader("Generated Content")
- st.write(content["content"])
-
- # Key Insights
- st.subheader("Key Insights")
- for insight in content["key_insights"]:
- st.write(f"âĸ {insight}")
-
- # Hashtags
- st.subheader("Recommended Hashtags")
- st.write(" ".join([f"#{tag}" for tag in content["hashtags"]]))
-
- # Call-to-Action
- st.subheader("Call-to-Action")
- st.write(content["call_to_action"])
-
- # Engagement Tips
- st.subheader("Engagement Tips")
- for tip in content["engagement_tips"]:
- st.write(f"âĸ {tip}")
-
- # Image Prompt
- st.subheader("Suggested Image Prompt")
- st.write(content["suggested_image_prompt"])
-
- except Exception as e:
- st.error(f"Error generating content: {str(e)}")
-
- # Add a footer with tips
- st.markdown("---")
- st.markdown("""
- ### Tips for Effective LinkedIn Company Page Content:
-
- - **Consistency**: Maintain a consistent posting schedule and brand voice
- - **Engagement**: Encourage comments and discussions on your posts
- - **Visuals**: Use high-quality images and videos to increase engagement
- - **Hashtags**: Use relevant industry hashtags to increase visibility
- - **Analytics**: Monitor your content performance and adjust your strategy
- - **Employee Advocacy**: Encourage employees to share and engage with company content
- """)
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/poll_generator/README.md b/ToBeMigrated/ai_writers/linkedin_writer/modules/poll_generator/README.md
deleted file mode 100644
index 4871cc45..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/poll_generator/README.md
+++ /dev/null
@@ -1,216 +0,0 @@
-# LinkedIn Poll Generator
-
-## Overview
-
-The LinkedIn Poll Generator is an AI-powered tool designed to help professionals create engaging, data-driven polls for LinkedIn. This tool leverages advanced AI to generate poll questions, options, and engagement predictions, helping users gather valuable insights from their professional network.
-
-## Features
-
-### 1. Poll Creation
-- **Multiple Poll Types**: Create various types of polls including:
- - Multiple Choice (2-4 options)
- - Yes/No
- - Rating Scale (1-5)
- - Ranking (order items by preference)
- - Open-ended (with suggested responses)
-- **Customizable Tone**: Generate polls with different tones (professional, casual, authoritative, conversational, thoughtful)
-- **Industry-Specific Content**: Tailor polls to specific industries and professional contexts
-
-### 2. Research Integration
-- **Multi-Source Research**: Gather insights from multiple search engines:
- - Metaphor (neural search)
- - Google SERP
- - Tavily AI
-- **Insight Extraction**: Automatically extract key insights and trends from research
-- **Question Generation**: Generate potential poll questions based on research findings
-
-### 3. Engagement Prediction
-- **Response Prediction**: Forecast expected engagement levels (low, medium, high, viral)
-- **Comment & Share Likelihood**: Predict the likelihood of comments and shares
-- **Response Distribution**: Estimate the expected distribution of responses across options
-- **Insight Generation**: Identify potential insights that could be gained from the poll
-
-### 4. Optimization
-- **Question Improvements**: Get suggestions for improving poll question wording and clarity
-- **Option Improvements**: Receive recommendations for enhancing poll options
-- **Timing Suggestions**: Learn the optimal days and times to post your poll
-- **Audience Targeting**: Identify the most relevant audience segments for your poll
-- **Hashtag Recommendations**: Get industry-specific hashtag suggestions
-
-### 5. Follow-up Content
-- **Post Templates**: Receive templates for sharing poll results
-- **Visual Suggestions**: Get recommendations for visualizing poll results
-- **Next Poll Ideas**: Discover ideas for follow-up polls that build on previous insights
-- **Data Visualization**: Receive suggestions for effective data visualization of poll results
-
-## Usage
-
-### Basic Workflow
-
-1. **Select Topic and Industry**: Enter your poll topic and target industry
-2. **Choose Poll Type**: Select the type of poll you want to create
-3. **Set Tone**: Choose the tone for your poll (professional, casual, etc.)
-4. **Research Topic**: Gather insights about your topic (optional)
-5. **Generate Poll**: Create your poll with AI-generated questions and options
-6. **Review Predictions**: See engagement predictions and response distribution
-7. **Optimize**: Get suggestions for improving your poll
-8. **Plan Follow-up**: Receive templates and ideas for sharing results
-
-### Advanced Features
-
-#### Research Integration
-- Use the "Research Topic" button to gather insights before creating your poll
-- View key insights, emerging trends, and potential questions based on research
-- Use research findings to inform your poll creation
-
-#### Engagement Prediction
-- View predicted engagement levels before posting
-- See expected response distribution across options
-- Identify potential insights that could be gained
-
-#### Optimization
-- Get suggestions for improving your poll question and options
-- Learn the best times to post for maximum engagement
-- Identify the most relevant audience segments
-- Receive hashtag recommendations
-
-#### Follow-up Content
-- Get templates for sharing poll results
-- Receive visual content suggestions
-- Discover ideas for follow-up polls
-- Get data visualization recommendations
-
-## Best Practices
-
-### Creating Effective Polls
-
-1. **Be Specific**: Ask clear, specific questions that your audience can answer confidently
-2. **Keep it Concise**: Use concise language for both questions and options
-3. **Avoid Bias**: Ensure your poll doesn't lead respondents toward a particular answer
-4. **Use Appropriate Options**: Make sure options are mutually exclusive and collectively exhaustive
-5. **Consider Timing**: Post polls at times when your audience is most active
-6. **Follow Up**: Share results and insights after the poll closes
-
-### Maximizing Engagement
-
-1. **Target Your Audience**: Ensure your poll is relevant to your specific audience
-2. **Use Visuals**: Include relevant images or graphics with your poll
-3. **Add Context**: Provide brief context or explanation for your poll
-4. **Engage with Comments**: Respond to comments to encourage discussion
-5. **Share Results**: Follow up with a post sharing the results and insights
-6. **Use Hashtags**: Include relevant hashtags to increase visibility
-
-### Industry-Specific Tips
-
-#### Technology
-- Focus on emerging trends and technologies
-- Ask about adoption rates and preferences
-- Include technical and non-technical options
-
-#### Healthcare
-- Address current healthcare challenges
-- Ask about patient experiences and preferences
-- Include options that reflect different stakeholder perspectives
-
-#### Finance
-- Focus on investment preferences and strategies
-- Ask about financial planning and management
-- Include options that reflect different risk tolerances
-
-#### Marketing
-- Address current marketing trends and challenges
-- Ask about content preferences and consumption habits
-- Include options that reflect different marketing approaches
-
-#### Education
-- Focus on learning preferences and methods
-- Ask about educational technology and tools
-- Include options that reflect different learning styles
-
-## Technical Details
-
-### Dependencies
-- Streamlit: For the user interface
-- Plotly: For data visualization
-- Loguru: For logging
-- GPT Providers: For AI text generation
-- Web Research Tools: For gathering insights
-
-### Architecture
-The LinkedIn Poll Generator consists of:
-- `LinkedInPollGenerator` class: Core functionality for poll generation
-- `linkedin_poll_generator_ui` function: Streamlit UI implementation
-
-### Integration
-The Poll Generator is integrated into the LinkedIn AI Writer suite and can be accessed through the main LinkedIn AI Writer interface.
-
-## Examples
-
-### Example 1: Technology Industry Poll
-**Question**: "What emerging technology will have the biggest impact on business in 2023?"
-**Options**:
-1. Artificial Intelligence
-2. Blockchain
-3. Quantum Computing
-4. Extended Reality (XR)
-
-### Example 2: Healthcare Industry Poll
-**Question**: "What is the most significant barrier to telehealth adoption?"
-**Options**:
-1. Technical issues
-2. Privacy concerns
-3. Lack of insurance coverage
-4. Patient preference for in-person care
-
-### Example 3: Finance Industry Poll
-**Question**: "What investment strategy are you most likely to pursue in a volatile market?"
-**Options**:
-1. Increase cash reserves
-2. Focus on dividend stocks
-3. Invest in defensive sectors
-4. Look for opportunistic buys
-
-## Troubleshooting
-
-### Common Issues
-
-1. **Research Not Returning Results**
- - Try a different search engine
- - Use more specific search terms
- - Check your internet connection
-
-2. **Low Engagement Predictions**
- - Review question wording for clarity
- - Ensure options are relevant and distinct
- - Consider targeting a more specific audience
-
-3. **JSON Parsing Errors**
- - This is typically handled automatically by the system
- - If persistent, try regenerating the poll
-
-## Future Enhancements
-
-- **A/B Testing**: Compare different poll versions
-- **Historical Data Analysis**: Learn from past poll performance
-- **Competitor Poll Analysis**: Analyze successful polls in your industry
-- **Advanced Visualization**: More sophisticated data visualization options
-- **Integration with LinkedIn API**: Direct posting to LinkedIn
-- **Poll Templates**: Pre-built templates for common use cases
-- **Multi-language Support**: Generate polls in multiple languages
-
-## Contributing
-
-Contributions to the LinkedIn Poll Generator are welcome! Please follow these steps:
-
-1. Fork the repository
-2. Create a feature branch
-3. Make your changes
-4. Submit a pull request
-
-## License
-
-This project is licensed under the MIT License - see the LICENSE file for details.
-
-## Contact
-
-For questions or feedback about the LinkedIn Poll Generator, please contact the development team.
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/poll_generator/__init__.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/poll_generator/__init__.py
deleted file mode 100644
index 3002b6d4..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/poll_generator/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-"""
-LinkedIn Poll Generator Module
-
-This module provides functionality for generating LinkedIn polls with
-AI-powered content and engagement optimization.
-"""
-
-from .linkedin_poll_generator import LinkedInPollGenerator, linkedin_poll_generator_ui
-
-__all__ = ["LinkedInPollGenerator", "linkedin_poll_generator_ui"]
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/poll_generator/linkedin_poll_generator.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/poll_generator/linkedin_poll_generator.py
deleted file mode 100644
index 0776e0c6..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/poll_generator/linkedin_poll_generator.py
+++ /dev/null
@@ -1,1056 +0,0 @@
-"""
-LinkedIn Poll Generator
-
-This module provides functionality for generating LinkedIn polls with
-AI-powered content and engagement optimization.
-"""
-
-import streamlit as st
-import time
-import json
-import random
-from typing import Dict, List, Optional, Union, Tuple
-from loguru import logger
-from datetime import datetime, timedelta
-
-from .....gpt_providers.text_generation.main_text_generation import llm_text_gen
-from .....ai_web_researcher.gpt_online_researcher import do_google_serp_search
-from .....ai_web_researcher.metaphor_basic_neural_web_search import metaphor_search_articles
-from .....ai_web_researcher.tavily_ai_search import do_tavily_ai_search
-
-
-class LinkedInPollGenerator:
- """
- AI-powered LinkedIn poll generator that creates engaging polls with optimized options,
- engagement predictions, and follow-up content suggestions.
- """
-
- def __init__(self):
- """Initialize the LinkedIn Poll Generator."""
- self.poll_types = {
- "multiple_choice": "Multiple Choice (2-4 options)",
- "yes_no": "Yes/No",
- "rating": "Rating Scale (1-5 or 1-10)",
- "ranking": "Ranking (order items by preference)",
- "open_ended": "Open-ended (with suggested responses)"
- }
-
- self.poll_durations = {
- "1_day": "1 day",
- "3_days": "3 days",
- "5_days": "5 days",
- "7_days": "7 days",
- "14_days": "14 days"
- }
-
- self.engagement_levels = {
- "low": "Low (0-10 responses)",
- "medium": "Medium (11-50 responses)",
- "high": "High (51-200 responses)",
- "viral": "Viral (200+ responses)"
- }
-
- def research_topic(self, topic: str, industry: str, search_engine: str = "metaphor") -> Dict:
- """
- Research a topic to inform poll question generation.
-
- Args:
- topic: The topic to research
- industry: The industry context
- search_engine: The search engine to use (metaphor, google, tavily)
-
- Returns:
- Dict containing research results
- """
- logger.info(f"Researching topic: {topic} in industry: {industry}")
-
- research_results = {
- "topic": topic,
- "industry": industry,
- "search_engine": search_engine,
- "articles": [],
- "trends": [],
- "insights": [],
- "questions": []
- }
-
- # Perform research based on selected search engine
- if search_engine == "metaphor":
- try:
- articles = metaphor_search_articles(
- query=f"{topic} {industry} trends insights",
- num_results=5
- )
- research_results["articles"] = articles
- except Exception as e:
- logger.error(f"Error researching with Metaphor: {str(e)}")
- st.error(f"Error researching with Metaphor: {str(e)}")
- elif search_engine == "google":
- try:
- search_results = do_google_serp_search(
- query=f"{topic} {industry} trends insights",
- num_results=5
- )
- research_results["articles"] = search_results
- except Exception as e:
- logger.error(f"Error researching with Google: {str(e)}")
- st.error(f"Error researching with Google: {str(e)}")
- elif search_engine == "tavily":
- try:
- search_results = do_tavily_ai_search(
- query=f"{topic} {industry} trends insights",
- search_depth="advanced",
- include_answer=True,
- include_raw_content=True,
- max_results=5
- )
- research_results["articles"] = search_results
- except Exception as e:
- logger.error(f"Error researching with Tavily: {str(e)}")
- st.error(f"Error researching with Tavily: {str(e)}")
-
- # Extract insights and trends from research
- if research_results["articles"]:
- research_results["insights"], research_results["trends"] = self._extract_insights_and_trends(research_results["articles"])
-
- # Generate potential poll questions based on research
- research_results["questions"] = self._generate_potential_questions(
- topic, industry, research_results["insights"], research_results["trends"]
- )
-
- return research_results
-
- def _extract_insights_and_trends(self, articles: List[Dict]) -> Tuple[List[str], List[str]]:
- """
- Extract insights and trends from research articles.
-
- Args:
- articles: List of research articles
-
- Returns:
- Tuple of (insights, trends)
- """
- insights = []
- trends = []
-
- # Extract text content from articles
- article_texts = []
- for article in articles:
- if isinstance(article, dict):
- if "content" in article:
- article_texts.append(article["content"])
- elif "snippet" in article:
- article_texts.append(article["snippet"])
- elif "description" in article:
- article_texts.append(article["description"])
- elif isinstance(article, str):
- article_texts.append(article)
-
- if not article_texts:
- return insights, trends
-
- # Use AI to extract insights and trends
- try:
- prompt = f"""
- Based on the following research articles, extract:
- 1. 5 key insights about the topic
- 2. 5 emerging trends related to the topic
-
- Research articles:
- {' '.join(article_texts[:3])}
-
- Format your response as:
- INSIGHTS:
- - Insight 1
- - Insight 2
- ...
-
- TRENDS:
- - Trend 1
- - Trend 2
- ...
- """
-
- response = llm_text_gen(prompt=prompt, max_tokens=500)
-
- # Parse the response
- if "INSIGHTS:" in response and "TRENDS:" in response:
- insights_section = response.split("INSIGHTS:")[1].split("TRENDS:")[0].strip()
- trends_section = response.split("TRENDS:")[1].strip()
-
- insights = [insight.strip("- ").strip() for insight in insights_section.split("\n") if insight.strip()]
- trends = [trend.strip("- ").strip() for trend in trends_section.split("\n") if trend.strip()]
- except Exception as e:
- logger.error(f"Error extracting insights and trends: {str(e)}")
-
- return insights, trends
-
- def _generate_potential_questions(self, topic: str, industry: str, insights: List[str], trends: List[str]) -> List[str]:
- """
- Generate potential poll questions based on research.
-
- Args:
- topic: The topic
- industry: The industry
- insights: List of insights
- trends: List of trends
-
- Returns:
- List of potential poll questions
- """
- questions = []
-
- try:
- prompt = f"""
- Generate 5 engaging LinkedIn poll questions about {topic} in the {industry} industry.
-
- Use these insights and trends to inform your questions:
- INSIGHTS:
- {chr(10).join([f"- {insight}" for insight in insights])}
-
- TRENDS:
- {chr(10).join([f"- {trend}" for trend in trends])}
-
- The questions should:
- 1. Be thought-provoking and encourage discussion
- 2. Be relevant to professionals in the {industry} industry
- 3. Be concise and clear
- 4. Avoid leading or biased language
- 5. Be suitable for a LinkedIn poll format
-
- Format each question as a separate line starting with a number.
- """
-
- response = llm_text_gen(prompt=prompt, max_tokens=500)
-
- # Parse the response
- for line in response.split("\n"):
- line = line.strip()
- if line and any(line.startswith(str(i)) for i in range(1, 10)):
- question = line.split(".", 1)[1].strip() if "." in line else line
- questions.append(question)
- except Exception as e:
- logger.error(f"Error generating potential questions: {str(e)}")
-
- return questions
-
- def generate_poll_question(self, topic: str, industry: str, poll_type: str, tone: str = "professional") -> str:
- """
- Generate a poll question based on the topic, industry, and poll type.
-
- Args:
- topic: The topic for the poll
- industry: The industry context
- poll_type: The type of poll (multiple_choice, yes_no, rating, ranking, open_ended)
- tone: The tone to use (professional, casual, authoritative, etc.)
-
- Returns:
- The generated poll question
- """
- logger.info(f"Generating {poll_type} poll question about {topic} in {industry} industry with {tone} tone")
-
- try:
- prompt = f"""
- Generate a LinkedIn poll question about {topic} in the {industry} industry.
-
- Poll type: {self.poll_types[poll_type]}
- Tone: {tone}
-
- The question should:
- 1. Be thought-provoking and encourage discussion
- 2. Be relevant to professionals in the {industry} industry
- 3. Be concise and clear (ideally under 100 characters)
- 4. Avoid leading or biased language
- 5. Be suitable for a LinkedIn poll format
- 6. Use a {tone} tone
-
- Return only the question text without any additional formatting or explanation.
- """
-
- question = llm_text_gen(prompt=prompt, max_tokens=100).strip()
-
- # Ensure the question ends with a question mark if it doesn't already
- if not question.endswith("?"):
- question += "?"
-
- return question
- except Exception as e:
- logger.error(f"Error generating poll question: {str(e)}")
- return f"What's your opinion on {topic} in the {industry} industry?"
-
- def generate_poll_options(self, question: str, poll_type: str, industry: str, num_options: int = 4) -> List[str]:
- """
- Generate poll options based on the question and poll type.
-
- Args:
- question: The poll question
- poll_type: The type of poll (multiple_choice, yes_no, rating, ranking, open_ended)
- industry: The industry context
- num_options: The number of options to generate (for multiple choice)
-
- Returns:
- List of poll options
- """
- logger.info(f"Generating {poll_type} poll options for question: {question}")
-
- options = []
-
- if poll_type == "yes_no":
- return ["Yes", "No"]
- elif poll_type == "rating":
- return [str(i) for i in range(1, 6)] # 1-5 rating scale
- elif poll_type == "ranking":
- # For ranking, we'll generate items to rank
- try:
- prompt = f"""
- Generate 4 items to rank in a LinkedIn poll about: {question}
-
- Industry context: {industry}
-
- The items should:
- 1. Be relevant to the question
- 2. Be concise (1-5 words each)
- 3. Be distinct from each other
- 4. Be suitable for ranking in order of preference or importance
-
- Return only the items, one per line, without numbering or additional formatting.
- """
-
- response = llm_text_gen(prompt=prompt, max_tokens=200)
-
- # Parse the response
- for line in response.split("\n"):
- line = line.strip()
- if line and not line.startswith(("1.", "2.", "3.", "4.", "-", "*")):
- options.append(line)
- elif line and line.startswith(("1.", "2.", "3.", "4.", "-", "*")):
- # Remove numbering or bullets
- option = line.split(".", 1)[1].strip() if "." in line else line.lstrip("- *").strip()
- options.append(option)
-
- # Ensure we have exactly 4 options
- if len(options) > 4:
- options = options[:4]
- elif len(options) < 4:
- # Add generic options if we don't have enough
- generic_options = ["Option 1", "Option 2", "Option 3", "Option 4"]
- options.extend(generic_options[len(options):4])
- except Exception as e:
- logger.error(f"Error generating ranking options: {str(e)}")
- options = ["Option 1", "Option 2", "Option 3", "Option 4"]
- elif poll_type == "open_ended":
- # For open-ended, we'll generate suggested responses
- try:
- prompt = f"""
- Generate 3 suggested responses for an open-ended LinkedIn poll about: {question}
-
- Industry context: {industry}
-
- The responses should:
- 1. Be relevant to the question
- 2. Be concise (5-15 words each)
- 3. Represent different perspectives or approaches
- 4. Encourage others to share their own responses
-
- Return only the responses, one per line, without numbering or additional formatting.
- """
-
- response = llm_text_gen(prompt=prompt, max_tokens=200)
-
- # Parse the response
- for line in response.split("\n"):
- line = line.strip()
- if line and not line.startswith(("1.", "2.", "3.", "-", "*")):
- options.append(line)
- elif line and line.startswith(("1.", "2.", "3.", "-", "*")):
- # Remove numbering or bullets
- option = line.split(".", 1)[1].strip() if "." in line else line.lstrip("- *").strip()
- options.append(option)
-
- # Ensure we have exactly 3 options
- if len(options) > 3:
- options = options[:3]
- elif len(options) < 3:
- # Add generic options if we don't have enough
- generic_options = ["Response 1", "Response 2", "Response 3"]
- options.extend(generic_options[len(options):3])
- except Exception as e:
- logger.error(f"Error generating open-ended options: {str(e)}")
- options = ["Response 1", "Response 2", "Response 3"]
- else: # multiple_choice
- try:
- prompt = f"""
- Generate {num_options} options for a LinkedIn poll about: {question}
-
- Industry context: {industry}
-
- The options should:
- 1. Be relevant to the question
- 2. Be concise (1-5 words each)
- 3. Be distinct from each other
- 4. Cover a range of possible answers
- 5. Be suitable for a multiple-choice poll
-
- Return only the options, one per line, without numbering or additional formatting.
- """
-
- response = llm_text_gen(prompt=prompt, max_tokens=200)
-
- # Parse the response
- for line in response.split("\n"):
- line = line.strip()
- if line and not line.startswith(("1.", "2.", "3.", "4.", "-", "*")):
- options.append(line)
- elif line and line.startswith(("1.", "2.", "3.", "4.", "-", "*")):
- # Remove numbering or bullets
- option = line.split(".", 1)[1].strip() if "." in line else line.lstrip("- *").strip()
- options.append(option)
-
- # Ensure we have exactly num_options
- if len(options) > num_options:
- options = options[:num_options]
- elif len(options) < num_options:
- # Add generic options if we don't have enough
- generic_options = [f"Option {i+1}" for i in range(num_options)]
- options.extend(generic_options[len(options):num_options])
- except Exception as e:
- logger.error(f"Error generating multiple choice options: {str(e)}")
- options = [f"Option {i+1}" for i in range(num_options)]
-
- return options
-
- def predict_engagement(self, question: str, options: List[str], industry: str) -> Dict:
- """
- Predict engagement levels for a poll.
-
- Args:
- question: The poll question
- options: The poll options
- industry: The industry context
-
- Returns:
- Dict containing engagement predictions
- """
- logger.info(f"Predicting engagement for poll: {question}")
-
- engagement_prediction = {
- "expected_responses": 0,
- "engagement_level": "low",
- "response_distribution": {},
- "comment_likelihood": "low",
- "share_likelihood": "low",
- "insights": []
- }
-
- try:
- prompt = f"""
- Predict engagement for a LinkedIn poll with the following details:
-
- Question: {question}
- Options: {', '.join(options)}
- Industry: {industry}
-
- Provide predictions for:
- 1. Expected number of responses (low: 0-10, medium: 11-50, high: 51-200, viral: 200+)
- 2. Likelihood of comments (low, medium, high)
- 3. Likelihood of shares (low, medium, high)
- 4. Expected distribution of responses across options (as percentages)
- 5. 3 key insights that might be gained from this poll
-
- Format your response as JSON:
- {{
- "expected_responses": "low/medium/high/viral",
- "response_count": number,
- "comment_likelihood": "low/medium/high",
- "share_likelihood": "low/medium/high",
- "response_distribution": {{
- "option1": percentage,
- "option2": percentage,
- ...
- }},
- "insights": [
- "insight1",
- "insight2",
- "insight3"
- ]
- }}
- """
-
- response = llm_text_gen(prompt=prompt, max_tokens=500)
-
- # Parse the JSON response
- try:
- prediction_data = json.loads(response)
-
- engagement_prediction["engagement_level"] = prediction_data.get("expected_responses", "low")
- engagement_prediction["expected_responses"] = prediction_data.get("response_count", 0)
- engagement_prediction["comment_likelihood"] = prediction_data.get("comment_likelihood", "low")
- engagement_prediction["share_likelihood"] = prediction_data.get("share_likelihood", "low")
- engagement_prediction["response_distribution"] = prediction_data.get("response_distribution", {})
- engagement_prediction["insights"] = prediction_data.get("insights", [])
- except json.JSONDecodeError:
- logger.error("Error parsing engagement prediction JSON")
- # Set default values
- engagement_prediction["engagement_level"] = "low"
- engagement_prediction["expected_responses"] = 5
- engagement_prediction["comment_likelihood"] = "low"
- engagement_prediction["share_likelihood"] = "low"
- engagement_prediction["response_distribution"] = {option: 100/len(options) for option in options}
- engagement_prediction["insights"] = ["No insights available"]
- except Exception as e:
- logger.error(f"Error predicting engagement: {str(e)}")
-
- return engagement_prediction
-
- def suggest_poll_duration(self, question: str, industry: str) -> str:
- """
- Suggest an optimal poll duration based on the question and industry.
-
- Args:
- question: The poll question
- industry: The industry context
-
- Returns:
- Suggested poll duration
- """
- logger.info(f"Suggesting poll duration for: {question}")
-
- try:
- prompt = f"""
- Suggest an optimal duration for a LinkedIn poll with the following details:
-
- Question: {question}
- Industry: {industry}
-
- Consider:
- 1. The complexity of the question
- 2. The industry's typical response patterns
- 3. The target audience's likely engagement patterns
- 4. The time sensitivity of the topic
-
- Return only one of these options: 1_day, 3_days, 5_days, 7_days, 14_days
- """
-
- response = llm_text_gen(prompt=prompt, max_tokens=50).strip().lower()
-
- # Validate the response
- if response in self.poll_durations:
- return response
- else:
- # Default to 3 days if the response is invalid
- return "3_days"
- except Exception as e:
- logger.error(f"Error suggesting poll duration: {str(e)}")
- return "3_days"
-
- def generate_follow_up_content(self, question: str, options: List[str], industry: str) -> Dict:
- """
- Generate follow-up content suggestions based on poll results.
-
- Args:
- question: The poll question
- options: The poll options
- industry: The industry context
-
- Returns:
- Dict containing follow-up content suggestions
- """
- logger.info(f"Generating follow-up content for poll: {question}")
-
- follow_up_content = {
- "post_templates": [],
- "visual_suggestions": [],
- "hashtag_suggestions": [],
- "next_poll_suggestions": []
- }
-
- try:
- prompt = f"""
- Generate follow-up content suggestions for a LinkedIn poll with the following details:
-
- Question: {question}
- Options: {', '.join(options)}
- Industry: {industry}
-
- Provide:
- 1. 3 post templates for sharing poll results (with placeholders for actual results)
- 2. 3 visual content suggestions (e.g., charts, infographics)
- 3. 5 relevant hashtags for the follow-up content
- 4. 3 suggestions for follow-up polls that would build on this poll's insights
-
- Format your response as JSON:
- {{
- "post_templates": [
- "template1",
- "template2",
- "template3"
- ],
- "visual_suggestions": [
- "suggestion1",
- "suggestion2",
- "suggestion3"
- ],
- "hashtag_suggestions": [
- "hashtag1",
- "hashtag2",
- "hashtag3",
- "hashtag4",
- "hashtag5"
- ],
- "next_poll_suggestions": [
- "suggestion1",
- "suggestion2",
- "suggestion3"
- ]
- }}
- """
-
- response = llm_text_gen(prompt=prompt, max_tokens=800)
-
- # Parse the JSON response
- try:
- content_data = json.loads(response)
-
- follow_up_content["post_templates"] = content_data.get("post_templates", [])
- follow_up_content["visual_suggestions"] = content_data.get("visual_suggestions", [])
- follow_up_content["hashtag_suggestions"] = content_data.get("hashtag_suggestions", [])
- follow_up_content["next_poll_suggestions"] = content_data.get("next_poll_suggestions", [])
- except json.JSONDecodeError:
- logger.error("Error parsing follow-up content JSON")
- # Set default values
- follow_up_content["post_templates"] = ["Thank you for participating in our poll about [Topic]! Here are the results..."]
- follow_up_content["visual_suggestions"] = ["Bar chart showing distribution of responses"]
- follow_up_content["hashtag_suggestions"] = [f"#{industry.replace(' ', '')}", "#LinkedInPoll", "#IndustryInsights"]
- follow_up_content["next_poll_suggestions"] = ["What factors influenced your decision in the previous poll?"]
- except Exception as e:
- logger.error(f"Error generating follow-up content: {str(e)}")
-
- return follow_up_content
-
- def generate_data_visualization(self, question: str, options: List[str], response_distribution: Dict) -> str:
- """
- Generate a data visualization suggestion for poll results.
-
- Args:
- question: The poll question
- options: The poll options
- response_distribution: The predicted response distribution
-
- Returns:
- Visualization suggestion
- """
- logger.info(f"Generating data visualization for poll: {question}")
-
- try:
- prompt = f"""
- Suggest a data visualization for a LinkedIn poll with the following details:
-
- Question: {question}
- Options: {', '.join(options)}
- Predicted response distribution: {json.dumps(response_distribution)}
-
- Consider:
- 1. The type of data (categorical, ordinal, etc.)
- 2. The number of options
- 3. The clarity and impact of different chart types
- 4. LinkedIn's visual presentation capabilities
-
- Return a detailed description of the recommended visualization, including:
- 1. Chart type
- 2. Color scheme
- 3. Layout
- 4. Key elements to highlight
- """
-
- visualization = llm_text_gen(prompt=prompt, max_tokens=300)
- return visualization
- except Exception as e:
- logger.error(f"Error generating data visualization: {str(e)}")
- return "Bar chart showing distribution of responses across options"
-
- def optimize_poll_for_engagement(self, question: str, options: List[str], industry: str) -> Dict:
- """
- Optimize a poll for maximum engagement.
-
- Args:
- question: The poll question
- options: The poll options
- industry: The industry context
-
- Returns:
- Dict containing optimization suggestions
- """
- logger.info(f"Optimizing poll for engagement: {question}")
-
- optimization = {
- "question_improvements": [],
- "option_improvements": [],
- "timing_suggestions": [],
- "audience_targeting": [],
- "hashtag_suggestions": []
- }
-
- try:
- prompt = f"""
- Optimize a LinkedIn poll for maximum engagement with the following details:
-
- Question: {question}
- Options: {', '.join(options)}
- Industry: {industry}
-
- Provide suggestions for:
- 1. Question improvements (wording, clarity, impact)
- 2. Option improvements (wording, order, completeness)
- 3. Timing suggestions (best day/time to post)
- 4. Audience targeting (who would be most interested)
- 5. Hashtag suggestions (5-7 relevant hashtags)
-
- Format your response as JSON:
- {{
- "question_improvements": [
- "improvement1",
- "improvement2",
- "improvement3"
- ],
- "option_improvements": [
- "improvement1",
- "improvement2",
- "improvement3"
- ],
- "timing_suggestions": [
- "suggestion1",
- "suggestion2",
- "suggestion3"
- ],
- "audience_targeting": [
- "audience1",
- "audience2",
- "audience3"
- ],
- "hashtag_suggestions": [
- "hashtag1",
- "hashtag2",
- "hashtag3",
- "hashtag4",
- "hashtag5",
- "hashtag6",
- "hashtag7"
- ]
- }}
- """
-
- response = llm_text_gen(prompt=prompt, max_tokens=800)
-
- # Parse the JSON response
- try:
- optimization_data = json.loads(response)
-
- optimization["question_improvements"] = optimization_data.get("question_improvements", [])
- optimization["option_improvements"] = optimization_data.get("option_improvements", [])
- optimization["timing_suggestions"] = optimization_data.get("timing_suggestions", [])
- optimization["audience_targeting"] = optimization_data.get("audience_targeting", [])
- optimization["hashtag_suggestions"] = optimization_data.get("hashtag_suggestions", [])
- except json.JSONDecodeError:
- logger.error("Error parsing optimization JSON")
- # Set default values
- optimization["question_improvements"] = ["Make the question more specific"]
- optimization["option_improvements"] = ["Ensure options are mutually exclusive"]
- optimization["timing_suggestions"] = ["Post on Tuesday or Wednesday during business hours"]
- optimization["audience_targeting"] = [f"Professionals in the {industry} industry"]
- optimization["hashtag_suggestions"] = [f"#{industry.replace(' ', '')}", "#LinkedInPoll", "#IndustryInsights"]
- except Exception as e:
- logger.error(f"Error optimizing poll: {str(e)}")
-
- return optimization
-
-
-def linkedin_poll_generator_ui():
- """Streamlit UI for the LinkedIn Poll Generator."""
-
- st.title("LinkedIn Poll Generator")
- st.markdown("""
- Create engaging LinkedIn polls that drive interaction and gather valuable insights from your network.
- """)
-
- # Initialize the poll generator
- poll_generator = LinkedInPollGenerator()
-
- # Create tabs for different sections
- tab1, tab2, tab3 = st.tabs(["Create Poll", "Research & Insights", "Optimization & Follow-up"])
-
- with tab1:
- st.header("Create Your LinkedIn Poll")
-
- # Topic and industry inputs
- col1, col2 = st.columns(2)
- with col1:
- topic = st.text_input("Topic", placeholder="e.g., Remote Work, AI in Business, Leadership Styles")
- with col2:
- industry = st.text_input("Industry", placeholder="e.g., Technology, Healthcare, Finance")
-
- # Poll type selection
- poll_type = st.selectbox(
- "Poll Type",
- options=list(poll_generator.poll_types.keys()),
- format_func=lambda x: poll_generator.poll_types[x]
- )
-
- # Number of options for multiple choice
- if poll_type == "multiple_choice":
- num_options = st.slider("Number of Options", min_value=2, max_value=4, value=4)
- else:
- num_options = 4 # Default for other poll types
-
- # Tone selection
- tone = st.selectbox(
- "Tone",
- options=["professional", "casual", "authoritative", "conversational", "thoughtful"],
- index=0
- )
-
- # Research button
- if st.button("Research Topic", key="research_button"):
- with st.spinner("Researching topic..."):
- research_results = poll_generator.research_topic(topic, industry)
-
- # Store research results in session state
- st.session_state.research_results = research_results
-
- # Display research results
- st.subheader("Research Results")
-
- if research_results["articles"]:
- st.write(f"Found {len(research_results['articles'])} relevant articles.")
-
- # Display insights
- if research_results["insights"]:
- st.subheader("Key Insights")
- for insight in research_results["insights"]:
- st.markdown(f"- {insight}")
-
- # Display trends
- if research_results["trends"]:
- st.subheader("Emerging Trends")
- for trend in research_results["trends"]:
- st.markdown(f"- {trend}")
-
- # Display potential questions
- if research_results["questions"]:
- st.subheader("Potential Poll Questions")
- for i, question in enumerate(research_results["questions"]):
- st.markdown(f"{i+1}. {question}")
- else:
- st.warning("No research results found. Try a different topic or industry.")
-
- # Generate poll button
- if st.button("Generate Poll", key="generate_button"):
- with st.spinner("Generating poll..."):
- # Generate poll question
- question = poll_generator.generate_poll_question(topic, industry, poll_type, tone)
-
- # Generate poll options
- options = poll_generator.generate_poll_options(question, poll_type, industry, num_options)
-
- # Predict engagement
- engagement = poll_generator.predict_engagement(question, options, industry)
-
- # Suggest poll duration
- duration = poll_generator.suggest_poll_duration(question, industry)
-
- # Store poll data in session state
- st.session_state.poll_data = {
- "question": question,
- "options": options,
- "engagement": engagement,
- "duration": duration
- }
-
- # Display poll
- st.subheader("Your LinkedIn Poll")
-
- # Display question
- st.markdown(f"### {question}")
-
- # Display options
- for i, option in enumerate(options):
- st.markdown(f"**{i+1}.** {option}")
-
- # Display engagement prediction
- st.subheader("Engagement Prediction")
-
- col1, col2, col3 = st.columns(3)
- with col1:
- st.metric("Expected Responses", engagement["engagement_level"].title())
- with col2:
- st.metric("Comment Likelihood", engagement["comment_likelihood"].title())
- with col3:
- st.metric("Share Likelihood", engagement["share_likelihood"].title())
-
- # Display response distribution
- if engagement["response_distribution"]:
- st.subheader("Predicted Response Distribution")
-
- # Create a bar chart
- import plotly.express as px
-
- # Prepare data for the chart
- chart_data = []
- for option, percentage in engagement["response_distribution"].items():
- chart_data.append({"Option": option, "Percentage": percentage})
-
- # Create the chart
- fig = px.bar(chart_data, x="Option", y="Percentage",
- title="Predicted Response Distribution",
- color="Option",
- color_discrete_sequence=px.colors.qualitative.Set3)
-
- st.plotly_chart(fig, use_container_width=True)
-
- # Display poll duration
- st.subheader("Recommended Poll Duration")
- st.write(f"**{poll_generator.poll_durations[duration]}**")
-
- # Display insights
- if engagement["insights"]:
- st.subheader("Potential Insights")
- for insight in engagement["insights"]:
- st.markdown(f"- {insight}")
-
- with tab2:
- st.header("Research & Insights")
-
- # Check if research results exist
- if "research_results" in st.session_state:
- research_results = st.session_state.research_results
-
- # Display research results
- st.subheader("Research Results")
-
- if research_results["articles"]:
- st.write(f"Found {len(research_results['articles'])} relevant articles.")
-
- # Display insights
- if research_results["insights"]:
- st.subheader("Key Insights")
- for insight in research_results["insights"]:
- st.markdown(f"- {insight}")
-
- # Display trends
- if research_results["trends"]:
- st.subheader("Emerging Trends")
- for trend in research_results["trends"]:
- st.markdown(f"- {trend}")
-
- # Display potential questions
- if research_results["questions"]:
- st.subheader("Potential Poll Questions")
- for i, question in enumerate(research_results["questions"]):
- st.markdown(f"{i+1}. {question}")
- else:
- st.warning("No research results found. Try a different topic or industry.")
- else:
- st.info("Generate a poll first to see research results.")
-
- with tab3:
- st.header("Optimization & Follow-up")
-
- # Check if poll data exists
- if "poll_data" in st.session_state:
- poll_data = st.session_state.poll_data
-
- # Optimization section
- st.subheader("Poll Optimization")
-
- if st.button("Optimize Poll", key="optimize_button"):
- with st.spinner("Optimizing poll..."):
- # Optimize poll
- optimization = poll_generator.optimize_poll_for_engagement(
- poll_data["question"],
- poll_data["options"],
- industry
- )
-
- # Store optimization in session state
- st.session_state.optimization = optimization
-
- # Display question improvements
- st.subheader("Question Improvements")
- for improvement in optimization["question_improvements"]:
- st.markdown(f"- {improvement}")
-
- # Display option improvements
- st.subheader("Option Improvements")
- for improvement in optimization["option_improvements"]:
- st.markdown(f"- {improvement}")
-
- # Display timing suggestions
- st.subheader("Timing Suggestions")
- for suggestion in optimization["timing_suggestions"]:
- st.markdown(f"- {suggestion}")
-
- # Display audience targeting
- st.subheader("Audience Targeting")
- for audience in optimization["audience_targeting"]:
- st.markdown(f"- {audience}")
-
- # Display hashtag suggestions
- st.subheader("Hashtag Suggestions")
- hashtags = " ".join([f"#{tag.strip('#')}" for tag in optimization["hashtag_suggestions"]])
- st.markdown(hashtags)
-
- # Follow-up content section
- st.subheader("Follow-up Content")
-
- if st.button("Generate Follow-up Content", key="followup_button"):
- with st.spinner("Generating follow-up content..."):
- # Generate follow-up content
- follow_up = poll_generator.generate_follow_up_content(
- poll_data["question"],
- poll_data["options"],
- industry
- )
-
- # Store follow-up in session state
- st.session_state.follow_up = follow_up
-
- # Display post templates
- st.subheader("Post Templates")
- for i, template in enumerate(follow_up["post_templates"]):
- st.markdown(f"**Template {i+1}:**")
- st.markdown(template)
- st.markdown("---")
-
- # Display visual suggestions
- st.subheader("Visual Suggestions")
- for suggestion in follow_up["visual_suggestions"]:
- st.markdown(f"- {suggestion}")
-
- # Display hashtag suggestions
- st.subheader("Hashtag Suggestions")
- hashtags = " ".join([f"#{tag.strip('#')}" for tag in follow_up["hashtag_suggestions"]])
- st.markdown(hashtags)
-
- # Display next poll suggestions
- st.subheader("Next Poll Suggestions")
- for suggestion in follow_up["next_poll_suggestions"]:
- st.markdown(f"- {suggestion}")
-
- # Data visualization section
- st.subheader("Data Visualization")
-
- if st.button("Generate Visualization", key="visualization_button"):
- with st.spinner("Generating visualization suggestion..."):
- # Generate visualization
- visualization = poll_generator.generate_data_visualization(
- poll_data["question"],
- poll_data["options"],
- poll_data["engagement"]["response_distribution"]
- )
-
- # Store visualization in session state
- st.session_state.visualization = visualization
-
- # Display visualization
- st.markdown(visualization)
- else:
- st.info("Generate a poll first to see optimization and follow-up suggestions.")
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/post_generator/README.md b/ToBeMigrated/ai_writers/linkedin_writer/modules/post_generator/README.md
deleted file mode 100644
index 093b988e..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/post_generator/README.md
+++ /dev/null
@@ -1,291 +0,0 @@
-# LinkedIn Post Generator đ
-
-An AI-powered tool designed for LinkedIn professionals to create engaging, research-backed posts that drive engagement and establish thought leadership.
-
-## ⨠Key Features
-
-### Content Creation
-- **AI-Powered Research**: Multi-source research integration (Metaphor, Google, Tavily)
-- **Smart Content Generation**: Industry-specific, engagement-optimized content
-- **Hashtag Optimization**: Data-driven hashtag suggestions
-- **Visual Content**: AI-generated images with professional aesthetics
-- **Engagement Predictions**: AI-powered performance analytics
-- **Poll Creation**: Interactive audience engagement tools
-- **Posting Time Optimization**: Industry-specific timing recommendations
-
-### Research Excellence
-- **Triple-Engine Research**:
- - đ Metaphor AI: Deep industry insights
- - đ Google Search: Comprehensive coverage
- - đ¤ Tavily AI: Focused research
-- **Real-Time Data**: Current trends and statistics
-- **Expert Insights**: Professional quotes and perspectives
-- **Fact Verification**: Web-researched authenticity
-
-### Visual Enhancement
-- **Professional Imagery**: AI-generated visuals
-- **Custom Prompts**: Tailored image generation
-- **Style Customization**: Brand-aligned visuals
-- **Multiple Formats**: Various visual content options
-
-## đ¯ Getting Started
-
-### 1. Post Creation Process
-
-#### Initial Setup
-- Choose your topic
-- Select your industry
-- Pick your writing tone:
- - Professional
- - Casual
- - Informative
- - Inspirational
-
-#### Research Configuration
-Select your preferred research source:
-- Metaphor AI: Comprehensive insights
-- Google Search: Wide coverage
-- Tavily AI: Focused research
-
-#### Content Options
-Customize your post with:
-- â
Hashtags
-- đŧī¸ Visual content
-- đ Polls
-- â° Posting time recommendations
-
-### 2. Post Generation and Preview
-
-#### Content Tab
-- View generated post
-- Edit content directly
-- Copy to clipboard
-- Download as text
-
-#### Analytics Tab
-- Engagement predictions
-- Optimal posting times
-- Performance metrics
-- Audience insights
-
-#### Visual Content Tab
-- Preview generated images
-- Edit image prompts
-- Generate alternatives
-- Download visuals
-
-## đĄ Best Practices
-
-### 1. Content Strategy
-- **Topic Selection**
- - Industry relevance
- - Current trends
- - Audience interest
- - Professional expertise
-
-- **Content Structure**
- - Strong hook
- - Value proposition
- - Supporting points
- - Clear CTA
-
-### 2. Engagement Optimization
-- **Hashtag Strategy**
- - Industry-specific tags
- - Trending topics
- - Balanced visibility
- - Professional relevance
-
-- **Visual Impact**
- - Professional imagery
- - Brand consistency
- - Clear messaging
- - Mobile optimization
-
-### 3. Timing and Frequency
-- **Posting Schedule**
- - Industry peak times
- - Audience activity
- - Time zone consideration
- - Consistency
-
-## đ Content Types
-
-### 1. Thought Leadership
-- Industry insights
-- Expert opinions
-- Trend analysis
-- Future predictions
-
-### 2. Professional Tips
-- Best practices
-- How-to guides
-- Career advice
-- Industry hacks
-
-### 3. Success Stories
-- Case studies
-- Achievements
-- Lessons learned
-- Professional growth
-
-### 4. Industry Updates
-- Market trends
-- News analysis
-- Technology updates
-- Professional developments
-
-## đ¨ Visual Guidelines
-
-### Image Generation
-- **Style Options**
- - Professional photography
- - Abstract concepts
- - Data visualization
- - Brand elements
-
-- **Customization**
- - Color schemes
- - Text overlays
- - Visual hierarchy
- - Professional aesthetics
-
-### Brand Alignment
-- **Visual Identity**
- - Consistent style
- - Professional tone
- - Brand colors
- - Quality standards
-
-## đą Platform Optimization
-
-### Mobile Experience
-- Text readability
-- Image scaling
-- Content preview
-- Performance check
-
-### Cross-Platform
-- LinkedIn app compatibility
-- Web version optimization
-- Image resolution
-- Loading speed
-
-## đĒ Engagement Strategies
-
-### 1. Interactive Elements
-- **Polls**
- - Topic relevance
- - Response options
- - Duration setting
- - Follow-up strategy
-
-- **Call-to-Action**
- - Clear direction
- - Value proposition
- - Professional tone
- - Measurable goals
-
-### 2. Hashtag Optimization
-- **Selection Criteria**
- - Relevance
- - Reach
- - Professional context
- - Trending topics
-
-### 3. Timing Strategy
-- **Post Scheduling**
- - Peak engagement times
- - Industry patterns
- - Audience availability
- - Global considerations
-
-## đ ī¸ Technical Features
-
-### Performance
-- Fast generation
-- Real-time preview
-- Smooth editing
-- Quick updates
-
-### Integration
-- Browser compatibility
-- API connections
-- Data security
-- Regular updates
-
-## đ Analytics and Insights
-
-### Engagement Metrics
-- Like predictions
-- Comment estimates
-- Share potential
-- Profile visit forecasts
-
-### Performance Tracking
-- Engagement rates
-- Reach estimates
-- Audience response
-- Content effectiveness
-
-## đ Workflow Integration
-
-### 1. Content Planning
-- Topic research
-- Content creation
-- Visual design
-- Optimization
-
-### 2. Quality Assurance
-- Content review
-- Image verification
-- Hashtag check
-- Timing optimization
-
-### 3. Publication
-- Final preview
-- Platform check
-- Scheduling
-- Performance monitoring
-
-## đ¯ Success Metrics
-
-### Engagement Goals
-- Professional visibility
-- Audience growth
-- Industry authority
-- Network expansion
-
-### Content Quality
-- Professional standards
-- Industry relevance
-- Value delivery
-- Brand alignment
-
-## đ Support and Resources
-
-### Help Center
-- User guides
-- Best practices
-- FAQs
-- Technical support
-
-### Updates
-- Feature releases
-- Performance improvements
-- Platform updates
-- Content tips
-
----
-
-Transform your LinkedIn presence with professional, engaging posts that drive results! đ
-
-### Quick Tips
-- Keep posts between 800-1200 characters
-- Include 3-5 relevant hashtags
-- Add professional visuals
-- Engage with comments
-- Monitor performance
-- Stay consistent
-
-Start creating impactful LinkedIn posts that establish your professional authority! đĢ
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/post_generator/__init__.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/post_generator/__init__.py
deleted file mode 100644
index 489ea028..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/post_generator/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-"""
-LinkedIn Post Generator Package
-
-This package provides functionality for generating LinkedIn posts with research-backed content,
-optimized hashtags, and engagement predictions.
-"""
-
-from .linkedin_post_generator import linkedin_post_generator_ui, LinkedInPostGenerator
-
-__all__ = ['linkedin_post_generator_ui', 'LinkedInPostGenerator']
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/post_generator/linkedin_post_generator.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/post_generator/linkedin_post_generator.py
deleted file mode 100644
index f938eebd..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/post_generator/linkedin_post_generator.py
+++ /dev/null
@@ -1,1207 +0,0 @@
-"""
-LinkedIn Post Generator
-
-This module provides functionality for generating LinkedIn posts with research-backed content,
-optimized hashtags, and engagement predictions.
-"""
-
-import os
-import json
-import time
-import streamlit as st
-from typing import Dict, List, Optional, Tuple, Union
-from loguru import logger
-import random
-
-from .....gpt_providers.text_generation.main_text_generation import llm_text_gen
-from .....ai_web_researcher.gpt_online_researcher import do_google_serp_search
-from .....ai_web_researcher.metaphor_basic_neural_web_search import metaphor_search_articles, streamlit_display_metaphor_results
-from .....ai_web_researcher.tavily_ai_search import do_tavily_ai_search, streamlit_display_results
-
-
-class LinkedInPostGenerator:
- """
- A class for generating LinkedIn posts with research-backed content.
-
- This class provides methods for:
- - Researching topics using Metaphor and Google
- - Generating outlines based on research
- - Creating post content optimized for engagement
- - Optimizing hashtags
- - Generating visual content recommendations
- - Predicting engagement metrics
- - Suggesting optimal posting times
- - Creating polls
- """
-
- def __init__(self):
- """Initialize the LinkedIn Post Generator."""
- self.research_results = {}
- self.outline = {}
- self.post_content = ""
- self.hashtags = []
- self.visual_content = {}
- self.engagement_prediction = {}
- self.posting_time_suggestions = []
- self.poll = {}
-
- def research_topic(self, topic: str, industry: str, search_engine: str = "metaphor", status_container=None) -> Dict:
- """
- Research a topic using the selected search engine.
-
- Args:
- topic: The topic to research
- industry: The industry context
- search_engine: The search engine to use (metaphor, google, tavily)
- status_container: Optional container for status updates
-
- Returns:
- Dict containing research results
- """
- # Update progress
- if status_container:
- status_container.text("đ Researching topic...")
-
- # Research with selected search engine
- research_results = None
- if search_engine == "metaphor":
- research_results = self._research_with_metaphor(topic, industry, status_container)
- elif search_engine == "google":
- research_results = self._research_with_google(topic, industry, status_container)
- elif search_engine == "tavily":
- research_results = self._research_with_tavily(topic, industry, status_container)
- else:
- research_results = self._research_with_metaphor(topic, industry, status_container)
-
- # Analyze research results
- combined_results = self._analyze_research_results(research_results, topic, industry)
-
- # Add topic and industry to the results
- combined_results["topic"] = topic
- combined_results["industry"] = industry
-
- # Update progress
- if status_container:
- status_container.text("â
Research complete!")
-
- return combined_results
-
- def _research_with_metaphor(self, topic: str, industry: str, status_container=None) -> Dict:
- """
- Research a topic using Metaphor.
-
- Args:
- topic: The topic to research
- industry: The industry context
- status_container: Optional container for status updates
-
- Returns:
- Dict containing research results
- """
- # Update progress
- if status_container:
- status_container.text("đ Searching with Metaphor...")
-
- try:
- # Construct search query
- search_query = f"{topic} in {industry} industry"
-
- # Search with Metaphor
- metaphor_results = metaphor_search_articles(search_query)
-
- # Display the results using streamlit_display_metaphor_results
- if metaphor_results:
- streamlit_display_metaphor_results(metaphor_results, search_query)
-
- # Update progress
- if status_container:
- status_container.text("â
Metaphor search complete!")
-
- # Ensure we return a valid dictionary even if metaphor_results is None
- if metaphor_results is None:
- logger.warning(f"No results returned from Metaphor search for: {search_query}")
- return {"sources": []}
-
- return metaphor_results
- except Exception as e:
- logger.error(f"Error in Metaphor search: {e}")
- if status_container:
- status_container.text(f"â ī¸ Error in Metaphor search: {str(e)}")
- return {"sources": []}
-
- def _research_with_google(self, topic: str, industry: str, status_container=None) -> Dict:
- """
- Research a topic using Google.
-
- Args:
- topic: The topic to research
- industry: The industry context
- status_container: Optional container for status updates
-
- Returns:
- Dict containing research results
- """
- # Update progress
- if status_container:
- status_container.text("đ Searching with Google...")
-
- try:
- # Search with Google
- google_results = do_google_serp_search(topic, industry)
-
- # Display the results using streamlit_display_google_results
- if google_results:
- streamlit_display_google_results(google_results)
-
- # Update progress
- if status_container:
- status_container.text("â
Google search complete!")
-
- # Ensure we return a valid dictionary even if google_results is None
- if google_results is None:
- logger.warning(f"No results returned from Google search for: {topic} in {industry}")
- return {"sources": []}
-
- return google_results
- except Exception as e:
- logger.error(f"Error in Google search: {e}")
- if status_container:
- status_container.text(f"â ī¸ Error in Google search: {str(e)}")
- return {"sources": []}
-
- def _research_with_tavily(self, topic: str, industry: str, status_container=None) -> Dict:
- """
- Research a topic using Tavily AI.
-
- Args:
- topic: The topic to research
- industry: The industry context
- status_container: Optional container for status updates
-
- Returns:
- Dict containing research results
- """
- # Update progress
- if status_container:
- status_container.text("đ Searching with Tavily AI...")
-
- # Construct search query
- search_query = f"{topic} in {industry} industry"
-
- try:
- # Search with Tavily
- tavily_results = do_tavily_ai_search(search_query)
-
- # Display the results using streamlit_display_results
- if tavily_results:
- streamlit_display_results(tavily_results)
-
- # Update progress
- if status_container:
- status_container.text("â
Tavily search complete!")
-
- # Ensure we return a valid dictionary even if tavily_results is None
- if tavily_results is None:
- logger.warning(f"No results returned from Tavily search for: {search_query}")
- return {"sources": []}
-
- return tavily_results
- except Exception as e:
- logger.error(f"Error in Tavily search: {e}")
- if status_container:
- status_container.text(f"â ī¸ Error in Tavily search: {str(e)}")
- return {"sources": []}
-
- def _analyze_research_results(self, research_results: Dict, topic: str, industry: str) -> Dict:
- """
- Analyze research results to extract key insights.
-
- Args:
- research_results: Results from research
- topic: The topic being researched
- industry: The industry context
-
- Returns:
- Dict containing analyzed insights
- """
- # Handle case where research_results is None
- if research_results is None:
- logger.warning(f"No research results available for {topic} in {industry}")
- return {
- "key_insights": [f"Unable to find specific insights about {topic} in {industry}"],
- "data_points": [f"No specific data available for {topic} in {industry}"],
- "expert_quotes": [f"No expert quotes found about {topic} in {industry}"],
- "industry_context": f"Limited information available about {topic} in {industry} industry"
- }
-
- # Extract sources and content from research results
- sources = research_results.get("sources", [])
- content = "\n".join([source.get("content", "") for source in sources])
-
- # Generate analysis prompt
- analysis_prompt = f"""
- Analyze the following research about {topic} in the {industry} industry:
-
- {content}
-
- Extract the following:
- 1. Key insights and trends
- 2. Supporting data and statistics
- 3. Expert opinions and quotes
- 4. Industry-specific context
-
- Format the response as a JSON with these keys:
- - key_insights: List of main insights
- - data_points: List of relevant data/statistics
- - expert_quotes: List of expert opinions
- - industry_context: Relevant industry information
- """
-
- # Generate analysis using LLM
- analysis = llm_text_gen(analysis_prompt)
-
- # Parse the analysis
- try:
- analysis_dict = json.loads(analysis)
- except json.JSONDecodeError:
- # Fallback if JSON parsing fails
- analysis_dict = {
- "key_insights": ["Unable to parse analysis"],
- "data_points": [],
- "expert_quotes": [],
- "industry_context": "Unable to parse industry context"
- }
-
- return analysis_dict
-
- def generate_outline(self, research_results: Dict) -> Dict:
- """
- Generate a detailed post outline based on research results.
-
- Args:
- research_results: Dictionary containing analyzed research results
-
- Returns:
- Dict containing the post outline
- """
- # Extract key information from research results
- topic = research_results.get("topic", "")
- industry = research_results.get("industry", "")
- key_insights = research_results.get("key_insights", [])
- data_points = research_results.get("data_points", [])
- expert_quotes = research_results.get("expert_quotes", [])
- industry_context = research_results.get("industry_context", "")
-
- # Generate outline prompt
- outline_prompt = f"""
- Create a detailed LinkedIn post outline about {topic} in the {industry} industry.
-
- Use these research insights:
- Key Insights: {', '.join(key_insights)}
- Data Points: {', '.join(data_points)}
- Expert Quotes: {', '.join(expert_quotes)}
- Industry Context: {industry_context}
-
- The outline should include:
- 1. A compelling hook that grabs attention and introduces the topic
- 2. 3-4 main points that provide valuable insights, each supported by:
- - Specific data or statistics
- - Expert opinions or quotes
- - Real-world examples or case studies
- 3. A thought-provoking conclusion
- 4. A clear call to action that encourages engagement
-
- Format the response as a JSON with these keys:
- - hook: The opening statement (1-2 sentences)
- - main_points: List of 3-4 main points (each 1-2 sentences)
- - supporting_evidence: List of supporting evidence for each point (include data, quotes, examples)
- - conclusion: A strong conclusion (1-2 sentences)
- - call_to_action: The closing call to action (1-2 sentences)
- - key_statistics: List of 2-3 key statistics to highlight
- - expert_insights: List of 2-3 expert insights to include
- """
-
- # Generate outline using LLM
- outline = llm_text_gen(outline_prompt)
-
- # Parse the outline
- try:
- outline_dict = json.loads(outline)
- except json.JSONDecodeError:
- # Fallback if JSON parsing fails
- outline_dict = {
- "hook": f"Let's talk about {topic}",
- "main_points": ["Unable to generate main points"],
- "supporting_evidence": ["Unable to generate supporting evidence"],
- "conclusion": f"These insights highlight the importance of {topic}",
- "call_to_action": "What are your thoughts on this topic?",
- "key_statistics": ["No statistics available"],
- "expert_insights": ["No expert insights available"]
- }
-
- # Add topic and industry to the outline
- outline_dict["topic"] = topic
- outline_dict["industry"] = industry
-
- return outline_dict
-
- def generate_post_content(self, outline: Dict, tone: str = "professional", include_hashtags: bool = True) -> str:
- """
- Generate detailed LinkedIn post content based on the outline.
-
- Args:
- outline: Dictionary containing the post outline
- tone: The tone to use for the post
- include_hashtags: Whether to include hashtags
-
- Returns:
- str: The generated post content
- """
- # Extract outline components
- hook = outline.get("hook", "")
- main_points = outline.get("main_points", [])
- supporting_evidence = outline.get("supporting_evidence", [])
- conclusion = outline.get("conclusion", "")
- call_to_action = outline.get("call_to_action", "")
- key_statistics = outline.get("key_statistics", [])
- expert_insights = outline.get("expert_insights", [])
- topic = outline.get("topic", "")
- industry = outline.get("industry", "")
-
- # Generate post prompt
- post_prompt = f"""
- Create a detailed, insightful LinkedIn post about {topic} in the {industry} industry with the following components:
-
- Hook: {hook}
- Main Points: {', '.join(main_points)}
- Supporting Evidence: {', '.join(supporting_evidence)}
- Conclusion: {conclusion}
- Call to Action: {call_to_action}
- Key Statistics: {', '.join(key_statistics)}
- Expert Insights: {', '.join(expert_insights)}
-
- Tone: {tone}
-
- IMPORTANT INSTRUCTIONS:
- 1. Write ONLY the post content - no explanations or meta-commentary
- 2. Do not include phrases like "Here's a LinkedIn post" or "I wrote this post"
- 3. Format with appropriate emojis and line breaks for readability
- 4. Make it engaging, professional, and insightful
- 5. Include specific data points, statistics, and expert quotes where relevant
- 6. Structure the post with clear paragraphs and bullet points where appropriate
- 7. Keep the post between 800-1200 characters for optimal engagement
- 8. Focus specifically on {topic} in the {industry} industry
- 9. Include a personal perspective or experience if relevant
- 10. End with a thought-provoking question to encourage comments
- 11. Use line breaks between paragraphs for better readability
- 12. Include relevant emojis to highlight key points
- """
-
- # Generate post content
- post_content = llm_text_gen(post_prompt)
-
- # Clean up any potential explanations
- post_content = post_content.replace("Here's a LinkedIn post:", "")
- post_content = post_content.replace("LinkedIn post:", "")
- post_content = post_content.replace("Post:", "")
- post_content = post_content.strip()
-
- # Add hashtags if requested
- if include_hashtags:
- hashtags = self.optimize_hashtags(post_content)
- if hashtags:
- post_content += "\n\n" + " ".join(hashtags)
-
- return post_content
-
- def optimize_hashtags(self, post_content: str) -> List[str]:
- """
- Generate optimized hashtags for a LinkedIn post.
-
- Args:
- post_content: The content of the post
-
- Returns:
- List[str]: List of optimized hashtags
- """
- # Generate hashtag prompt
- hashtag_prompt = f"""
- Generate relevant hashtags for this LinkedIn post:
-
- {post_content}
-
- IMPORTANT INSTRUCTIONS:
- 1. Return ONLY a comma-separated list of hashtags
- 2. Do not include any explanations or commentary
- 3. Each hashtag should start with #
- 4. Do not include spaces in hashtags
- 5. Include a mix of broad and specific hashtags
- 6. Limit to 5-7 most relevant hashtags
- 7. Make hashtags searchable and professional
- 8. Do not include any text before or after the hashtags
- """
-
- # Generate hashtags
- hashtag_response = llm_text_gen(hashtag_prompt)
-
- # Clean up the response
- hashtag_response = hashtag_response.replace("Hashtags:", "")
- hashtag_response = hashtag_response.replace("Suggested hashtags:", "")
- hashtag_response = hashtag_response.strip()
-
- # Parse hashtags
- hashtags = [tag.strip() for tag in hashtag_response.split(",")]
- hashtags = [tag for tag in hashtags if tag.startswith("#")]
-
- return hashtags
-
- def generate_visual_content(self, post_content: str, topic: str) -> Dict:
- """
- Generate visual content recommendations and create images for a LinkedIn post.
-
- Args:
- post_content: The post content
- topic: The post topic
-
- Returns:
- Dict containing visual content recommendations and generated images
- """
- logger.info(f"Generating visual content for topic: {topic}")
-
- # Create a progress bar
- progress_bar = st.progress(0)
- status_text = st.empty()
-
- # Update progress
- progress_bar.progress(20)
- status_text.text("Analyzing post content...")
-
- # Extract key elements from post content for better image generation
- key_elements_prompt = f"""
- Analyze this LinkedIn post content and extract SPECIFIC visual elements that would create a UNIQUE and TARGETED image:
-
- {post_content}
-
- Extract these elements and be EXTREMELY SPECIFIC:
- 1. Main concept or message (What's the core idea we need to visualize?)
- 2. Key data or statistics mentioned (Can we visualize these?)
- 3. Industry-specific elements (What visuals would resonate with this industry?)
- 4. Abstract concepts mentioned (How can we represent these visually?)
- 5. Text to potentially overlay (What 2-3 word phrase would enhance the image?)
-
- Return ONLY a JSON object with these keys:
- {{
- "main_concept": "The specific core concept to visualize",
- "data_elements": ["List of specific data points to potentially visualize"],
- "industry_visuals": ["List of industry-specific visual elements"],
- "abstract_concepts": ["List of abstract concepts to represent"],
- "potential_text": ["2-3 short phrases that could be overlaid on the image"],
- "style_keywords": ["List of specific style keywords for this topic"]
- }}
- """
-
- # Get key elements from LLM
- key_elements_response = llm_text_gen(key_elements_prompt)
-
- # Parse the key elements
- try:
- key_elements = json.loads(key_elements_response)
- except json.JSONDecodeError:
- key_elements = {
- "main_concept": topic,
- "data_elements": [],
- "industry_visuals": [],
- "abstract_concepts": [],
- "potential_text": [],
- "style_keywords": ["professional", "modern", "clean"]
- }
-
- # Update progress
- progress_bar.progress(40)
- status_text.text("Generating visual content recommendations...")
-
- # Randomly decide whether to include text overlay (30% chance)
- include_text = random.random() < 0.3
- text_overlay = ""
- if include_text and key_elements.get("potential_text"):
- text_overlay = random.choice(key_elements.get("potential_text"))
-
- # Generate visual content recommendations using the extracted key elements
- prompt = f"""
- Generate HIGHLY SPECIFIC visual content recommendations for a LinkedIn post about {topic}.
-
- Content Focus:
- - Main concept: {key_elements.get('main_concept')}
- - Data elements: {', '.join(key_elements.get('data_elements', []))}
- - Industry visuals: {', '.join(key_elements.get('industry_visuals', []))}
- - Abstract concepts: {', '.join(key_elements.get('abstract_concepts', []))}
- - Style keywords: {', '.join(key_elements.get('style_keywords', []))}
-
- Create recommendations that:
- 1. Are UNIQUE to this specific post and topic
- 2. Incorporate actual elements from the post content
- 3. Use industry-specific imagery and symbolism
- 4. {"Include text overlay: " + text_overlay if include_text else "Focus on purely visual elements"}
-
- Format as JSON:
- {{
- "main_image": {{
- "concept": "Detailed description of the main image",
- "prompt": "Detailed generation prompt that is SPECIFIC to this post",
- "colors": ["Color 1", "Color 2", "Color 3"],
- "text_overlay": "Text to overlay (if any)"
- }},
- "alternative_formats": [
- {{
- "format": "Format name",
- "description": "Description",
- "suggestions": ["Suggestion 1", "Suggestion 2"]
- }}
- ]
- }}
- """
-
- # Get visual content recommendations from LLM
- visual_content_response = llm_text_gen(prompt)
-
- # Parse the visual content recommendations
- try:
- visual_content = json.loads(visual_content_response)
-
- # Enhance the prompt with specific style and quality requirements
- base_prompt = visual_content["main_image"]["prompt"]
- text_part = f", with elegant text overlay: '{text_overlay}'" if include_text else ""
-
- visual_content["main_image"]["prompt"] = f"""
- {base_prompt}{text_part},
- Style requirements: ultra high quality, 8k resolution, professional photography,
- dramatic lighting, perfect composition, extremely detailed,
- corporate style, professional context, LinkedIn-optimized,
- specific to {topic}, unique visualization
- """.strip()
-
- except json.JSONDecodeError:
- visual_content = {
- "main_image": {
- "concept": f"Professional visualization of {topic}",
- "prompt": f"Professional visualization of {topic}, ultra high quality, 8k resolution, dramatic lighting",
- "colors": ["#0A66C2", "#FFFFFF", "#000000"],
- "text_overlay": text_overlay if include_text else ""
- },
- "alternative_formats": []
- }
-
- # Update progress
- progress_bar.progress(100)
- status_text.text("Visual content recommendations generated!")
- time.sleep(0.5)
-
- return visual_content
-
- def generate_image(self, prompt: str, refinement: str = "") -> str:
- """
- Generate an image using Stable Diffusion.
-
- Args:
- prompt: The image generation prompt
- refinement: Optional refinement to the prompt
-
- Returns:
- str: Path to the generated image
- """
- try:
- # Import the image generation function from the correct location
- from .....gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image as generate_image_from_prompt
-
- # Enhance the prompt with additional details for better quality
- enhanced_prompt = f"""
- {prompt}
-
- Additional requirements:
- - High resolution, 4K quality
- - Professional lighting and composition
- - Sharp focus and clear details
- - Suitable for LinkedIn's professional audience
- - Clean, modern aesthetic
- - No text or watermarks
- """
-
- # Combine enhanced prompt with refinement if provided
- full_prompt = f"{enhanced_prompt} {refinement}".strip()
-
- # Generate the image
- image_path = generate_image_from_prompt(full_prompt)
-
- return image_path
- except Exception as e:
- logger.error(f"Error generating image: {e}")
- return None
-
- def predict_engagement(self, post_content: str, hashtags: List[str]) -> Dict:
- """
- Predict engagement metrics for a LinkedIn post.
-
- Args:
- post_content: The post content
- hashtags: The hashtags used in the post
-
- Returns:
- Dict containing engagement predictions
- """
- logger.info("Predicting engagement metrics")
-
- # Create a progress bar
- progress_bar = st.progress(0)
- status_text = st.empty()
-
- # Update progress
- progress_bar.progress(20)
- status_text.text("Analyzing post content and hashtags...")
-
- # Predict engagement using LLM
- prompt = f"""
- Predict engagement metrics for the following LinkedIn post:
-
- Content:
- {post_content}
-
- Hashtags:
- {', '.join(hashtags)}
-
- Predict the following metrics:
- 1. Likely number of likes
- 2. Likely number of comments
- 3. Likely number of shares
- 4. Likely number of profile visits
- 5. Engagement rate
-
- Format the predictions as a JSON object with the following structure:
- {{
- "likes": "Predicted number of likes",
- "comments": "Predicted number of comments",
- "shares": "Predicted number of shares",
- "profile_visits": "Predicted number of profile visits",
- "engagement_rate": "Predicted engagement rate"
- }}
- """
-
- # Update progress
- progress_bar.progress(40)
- status_text.text("Predicting engagement with AI...")
-
- # Get engagement predictions from LLM
- engagement_response = llm_text_gen(prompt)
-
- # Parse the engagement predictions
- try:
- engagement_prediction = json.loads(engagement_response)
- except json.JSONDecodeError:
- # Fallback engagement prediction if JSON parsing fails
- engagement_prediction = {
- "likes": "50-100",
- "comments": "10-20",
- "shares": "5-10",
- "profile_visits": "20-30",
- "engagement_rate": "3-5%"
- }
-
- # Update progress
- progress_bar.progress(80)
- status_text.text("Finalizing engagement predictions...")
-
- # Store the engagement predictions
- self.engagement_prediction = engagement_prediction
-
- # Complete progress
- progress_bar.progress(100)
- status_text.text("Engagement predictions generated!")
- time.sleep(0.5)
-
- return engagement_prediction
-
- def suggest_posting_time(self, industry: str) -> List[Dict]:
- """
- Suggest optimal posting times based on industry and audience activity.
-
- Args:
- industry: The industry context
-
- Returns:
- List of suggested posting times
- """
- logger.info(f"Suggesting posting times for industry: {industry}")
-
- # Create a progress bar
- progress_bar = st.progress(0)
- status_text = st.empty()
-
- # Update progress
- progress_bar.progress(20)
- status_text.text("Analyzing industry data...")
-
- # Suggest posting times using LLM
- prompt = f"""
- Suggest optimal posting times for LinkedIn posts in the {industry} industry.
-
- Consider:
- - Industry-specific audience activity patterns
- - Global time zones
- - Day of the week variations
- - Best practices for the {industry} industry
-
- Format the suggestions as a JSON array with the following structure:
- [
- {{
- "day": "Day of the week",
- "time": "Time of day",
- "timezone": "Timezone",
- "reason": "Reason for this suggestion"
- }}
- ]
-
- Provide 3-5 suggestions.
- """
-
- # Update progress
- progress_bar.progress(40)
- status_text.text("Generating posting time suggestions...")
-
- # Get posting time suggestions from LLM
- posting_time_response = llm_text_gen(prompt)
-
- # Parse the posting time suggestions
- try:
- posting_time_suggestions = json.loads(posting_time_response)
- except json.JSONDecodeError:
- # Fallback posting time suggestions if JSON parsing fails
- posting_time_suggestions = [
- {
- "day": "Tuesday",
- "time": "9:00 AM",
- "timezone": "EST",
- "reason": "Highest engagement for professional content"
- },
- {
- "day": "Wednesday",
- "time": "12:00 PM",
- "timezone": "EST",
- "reason": "Lunch break engagement peak"
- },
- {
- "day": "Thursday",
- "time": "3:00 PM",
- "timezone": "EST",
- "reason": "End of workday engagement"
- }
- ]
-
- # Update progress
- progress_bar.progress(80)
- status_text.text("Finalizing posting time suggestions...")
-
- # Store the posting time suggestions
- self.posting_time_suggestions = posting_time_suggestions
-
- # Complete progress
- progress_bar.progress(100)
- status_text.text("Posting time suggestions generated!")
- time.sleep(0.5)
-
- return posting_time_suggestions
-
- def create_poll(self, topic: str, industry: str) -> Dict:
- """
- Create a poll for a LinkedIn post.
-
- Args:
- topic: The post topic
- industry: The industry context
-
- Returns:
- Dict containing the poll
- """
- logger.info(f"Creating poll for topic: {topic}")
-
- # Create a progress bar
- progress_bar = st.progress(0)
- status_text = st.empty()
-
- # Update progress
- progress_bar.progress(20)
- status_text.text("Analyzing topic and industry...")
-
- # Create poll using LLM
- prompt = f"""
- Create a LinkedIn poll about {topic} in the {industry} industry.
-
- The poll should:
- - Be relevant to the topic and industry
- - Have 4 options
- - Be engaging and encourage participation
- - Follow LinkedIn poll best practices
-
- Format the poll as a JSON object with the following structure:
- {{
- "question": "The poll question",
- "options": ["Option 1", "Option 2", "Option 3", "Option 4"],
- "duration": "Poll duration in days",
- "follow_up_post": "Suggested follow-up post after poll ends"
- }}
- """
-
- # Update progress
- progress_bar.progress(40)
- status_text.text("Generating poll with AI...")
-
- # Get poll from LLM
- poll_response = llm_text_gen(prompt)
-
- # Parse the poll
- try:
- poll = json.loads(poll_response)
- except json.JSONDecodeError:
- # Fallback poll if JSON parsing fails
- poll = {
- "question": f"What's your biggest challenge with {topic}?",
- "options": [
- f"Option 1 related to {topic}",
- f"Option 2 related to {topic}",
- f"Option 3 related to {topic}",
- f"Option 4 related to {topic}"
- ],
- "duration": "7",
- "follow_up_post": f"Thanks for participating in our poll about {topic}! Here are the results and insights..."
- }
-
- # Update progress
- progress_bar.progress(80)
- status_text.text("Finalizing poll...")
-
- # Store the poll
- self.poll = poll
-
- # Complete progress
- progress_bar.progress(100)
- status_text.text("Poll created!")
- time.sleep(0.5)
-
- return poll
-
- def _extract_image_prompts_from_post(self, post_content: str) -> List[str]:
- """
- Extract potential image prompts from the post content.
-
- Args:
- post_content: The content of the post
-
- Returns:
- List[str]: List of extracted image prompts
- """
- # Generate prompt for extracting image prompts
- prompt = f"""
- Create 3 HIGHLY SPECIFIC image prompts from this LinkedIn post content.
- Each prompt should create a unique, content-specific image that directly relates to the post's message.
-
- Post content:
- {post_content}
-
- For each prompt, follow these requirements:
- 1. Focus on SPECIFIC concepts, data, or insights from the post
- 2. Include clear visual elements that represent the post's main message
- 3. Specify exact composition, style, and technical details
- 4. Add relevant industry-specific elements
- 5. Consider including a short text overlay (2-3 words max) that enhances the message
- 6. Make the image unique to this post - avoid generic business imagery
-
- Technical Requirements for Each Prompt:
- - Main subject placement and composition
- - Lighting style and atmosphere
- - Color scheme and mood
- - Camera angle and perspective
- - Background elements and context
- - Foreground details and focal points
- - Text overlay (if appropriate)
- - Industry-specific visual elements
-
- Return ONLY a JSON array of 3 complete image prompts.
- Each prompt should be extremely detailed and specific to this post's content.
- Do not include any explanations or additional text.
- """
-
- # Generate image prompts using LLM
- image_prompts_response = llm_text_gen(prompt)
-
- try:
- # Parse the image prompts
- image_prompts = json.loads(image_prompts_response)
-
- # Enhance each prompt with quality requirements
- enhanced_prompts = []
- for prompt in image_prompts:
- # Randomly decide whether to include text overlay (30% chance)
- include_text = random.random() < 0.3
-
- # Extract a potential text overlay from the prompt if it exists
- text_overlay = ""
- if include_text:
- # Look for text in quotes within the prompt
- import re
- text_matches = re.findall(r'"([^"]*)"', prompt)
- if text_matches:
- text_overlay = text_matches[0]
- if len(text_overlay.split()) > 3: # Ensure text is not too long
- text_overlay = " ".join(text_overlay.split()[:3])
-
- enhanced_prompt = f"""
- {prompt},
- Style requirements: ultra high quality, 8k resolution, professional photography,
- dramatic lighting, perfect composition, extremely detailed,
- corporate style, professional context, LinkedIn-optimized
- {f', with elegant text overlay: "{text_overlay}"' if text_overlay else ''}
- """.strip()
-
- enhanced_prompts.append(enhanced_prompt)
-
- return enhanced_prompts
-
- except json.JSONDecodeError:
- logger.error("Failed to parse image prompts JSON")
- return []
-
-
-def linkedin_post_generator_ui():
- """
- Streamlit UI for LinkedIn Post Generator.
- """
- st.title("đ LinkedIn Post Generator")
- st.write("Generate engaging LinkedIn posts with AI assistance")
-
- # Initialize generator
- generator = LinkedInPostGenerator()
-
- # Initialize session state for storing post data
- if "post_data" not in st.session_state:
- st.session_state.post_data = {
- "post_content": "",
- "hashtags": [],
- "visual_content": None,
- "topic": "",
- "industry": "",
- "tone": "Professional",
- "include_hashtags": True,
- "include_visual": True,
- "include_poll": False,
- "include_timing": True,
- "search_engine": "metaphor"
- }
-
- # Input form
- with st.form("linkedin_post_form"):
- topic = st.text_input("Post Topic", placeholder="Enter the main topic of your post")
- industry = st.text_input("Industry", placeholder="e.g., Technology, Healthcare, Finance")
- tone = st.selectbox("Tone", ["Professional", "Casual", "Informative", "Inspirational"])
-
- # Search engine selection
- search_engine = st.radio(
- "Select Search Engine",
- ["Metaphor AI Search", "Google Search", "Tavily AI Search"],
- index=0,
- format_func=lambda x: x
- )
- search_engine = search_engine.lower().replace(" search", "").replace(" ai", "")
-
- col1, col2 = st.columns(2)
- with col1:
- include_hashtags = st.checkbox("Include Hashtags", value=True)
- include_visual = st.checkbox("Include Visual Content", value=True)
- with col2:
- include_poll = st.checkbox("Include Poll", value=False)
- include_timing = st.checkbox("Include Posting Time", value=True)
-
- submit = st.form_submit_button("Generate Post")
-
- if submit and topic and industry:
- with st.spinner("Generating your LinkedIn post..."):
- # Research the topic
- research_results = generator.research_topic(topic, industry, search_engine)
-
- # Generate outline
- outline = generator.generate_outline(research_results)
-
- # Generate post content
- post_content = generator.generate_post_content(outline, tone, include_hashtags)
-
- # Generate hashtags if requested
- hashtags = []
- if include_hashtags:
- hashtags = generator.optimize_hashtags(post_content)
-
- # Generate visual content if requested
- visual_content = None
- if include_visual:
- visual_content = generator.generate_visual_content(post_content, topic)
- # Generate image automatically
- if visual_content and "main_image" in visual_content:
- image_path = generator.generate_image(visual_content["main_image"]["prompt"])
- if image_path:
- st.session_state.generated_image_path = image_path
-
- # Store data in session state
- st.session_state.post_data = {
- "post_content": post_content,
- "hashtags": hashtags,
- "visual_content": visual_content,
- "topic": topic,
- "industry": industry,
- "tone": tone,
- "include_hashtags": include_hashtags,
- "include_visual": include_visual,
- "include_poll": include_poll,
- "include_timing": include_timing,
- "search_engine": search_engine
- }
-
- # Display results if we have post data
- if st.session_state.post_data["post_content"]:
- # Display results
- st.markdown("---")
- st.subheader("đ Your LinkedIn Post")
-
- # Create tabs for different sections
- tab1, tab2, tab3 = st.tabs(["đ Post Content", "đ Analytics & Timing", "đŧī¸ Visual Content"])
-
- with tab1:
- # Post preview container with image if available
- with st.container():
- col1, col2 = st.columns([2, 1])
- with col1:
- st.markdown("### Post Content")
- st.markdown(st.session_state.post_data["post_content"])
-
- if st.session_state.post_data["hashtags"]:
- st.markdown("### Hashtags")
- st.markdown(" ".join(st.session_state.post_data["hashtags"]))
-
- with col2:
- if "generated_image_path" in st.session_state:
- st.image(st.session_state.generated_image_path, caption="Generated Image", use_container_width=True)
- with open(st.session_state.generated_image_path, "rb") as file:
- st.download_button(
- label="Download Image",
- data=file,
- file_name=f"linkedin_image_{st.session_state.post_data['topic'].replace(' ', '_').lower()}.png",
- mime="image/png"
- )
-
- # Action buttons
- col1, col2, col3 = st.columns(3)
- with col1:
- if st.button("đ Copy to Clipboard"):
- st.write("Post copied to clipboard!")
- with col2:
- if st.button("đž Download as Text"):
- st.write("Post downloaded successfully!")
- with col3:
- if st.button("đ Generate New Post"):
- # Clear session state
- st.session_state.post_data = {
- "post_content": "",
- "hashtags": [],
- "visual_content": None,
- "topic": "",
- "industry": "",
- "tone": "Professional",
- "include_hashtags": True,
- "include_visual": True,
- "include_poll": False,
- "include_timing": True,
- "search_engine": "metaphor"
- }
- if "generated_image_path" in st.session_state:
- del st.session_state.generated_image_path
- st.experimental_rerun()
-
- with tab2:
- # Engagement predictions
- st.markdown("### Engagement Predictions")
- st.info("This post is predicted to perform well based on current LinkedIn trends.")
-
- # Posting time suggestions
- if st.session_state.post_data["include_timing"]:
- st.markdown("### Suggested Posting Times")
- st.info("Best times to post: Tuesday-Thursday, 9:00 AM - 11:00 AM")
-
- with tab3:
- if st.session_state.post_data["include_visual"] and st.session_state.post_data["visual_content"]:
- visual_content = st.session_state.post_data["visual_content"]
-
- # Display image concept
- st.markdown("#### Image Concept")
- st.write(visual_content["main_image"]["concept"])
-
- # Display color scheme
- st.markdown("#### Color Scheme")
- colors = visual_content["main_image"]["colors"]
- for i, color in enumerate(colors):
- st.markdown(f"", unsafe_allow_html=True)
-
- # Image generation section
- st.markdown("#### Generate Image")
-
- # Store the image prompt in session state if not already there
- if "image_prompt" not in st.session_state:
- st.session_state.image_prompt = visual_content["main_image"]["prompt"]
-
- # Display the current prompt
- st.markdown("**Current Prompt:**")
- st.code(st.session_state.image_prompt)
-
- # Refinement input
- refinement = st.text_input("Refine the image prompt (optional)",
- placeholder="Add details like 'more vibrant colors' or 'include text overlay'")
-
- # Generate image button
- if st.button("Generate New Image"):
- with st.spinner("Generating image..."):
- # Generate the image
- image_path = generator.generate_image(st.session_state.image_prompt, refinement)
-
- if image_path:
- # Store the image path in session state
- st.session_state.generated_image_path = image_path
- st.success("Image generated successfully!")
- else:
- st.error("Failed to generate image. Please try again.")
-
- # Extract image prompts from post content
- st.markdown("#### Image Prompts from Post")
- post_content = st.session_state.post_data["post_content"]
-
- # Generate image prompts from post content
- image_prompts = generator._extract_image_prompts_from_post(post_content)
-
- if image_prompts:
- for i, prompt in enumerate(image_prompts):
- st.markdown(f"**Prompt {i+1}:**")
- st.code(prompt)
- if st.button(f"Use Prompt {i+1}", key=f"use_prompt_{i}"):
- st.session_state.image_prompt = prompt
- st.success(f"Prompt {i+1} set as current prompt!")
- st.experimental_rerun()
- else:
- st.info("No image prompts found in the post content.")
-
- # Alternative formats
- st.markdown("#### Alternative Formats")
- for alt_format in visual_content["alternative_formats"]:
- st.markdown(f"**{alt_format['format']}:**")
- st.write(alt_format["description"])
- st.markdown("**Suggestions:**")
- for suggestion in alt_format["suggestions"]:
- st.write(f"- {suggestion}")
- elif submit:
- st.error("Please provide both a topic and industry.")
-
-
-if __name__ == "__main__":
- linkedin_post_generator_ui()
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/profile_optimizer/README.md b/ToBeMigrated/ai_writers/linkedin_writer/modules/profile_optimizer/README.md
deleted file mode 100644
index dc9fdaa3..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/profile_optimizer/README.md
+++ /dev/null
@@ -1,184 +0,0 @@
-# LinkedIn Profile Optimizer
-
-## Overview
-The LinkedIn Profile Optimizer is an AI-powered tool designed to help content creators maximize their LinkedIn presence and professional appeal. This tool analyzes and enhances every aspect of your LinkedIn profile to increase visibility, engagement, and professional opportunities.
-
-## Key Features
-
-### 1. Profile Strength Analysis đ
-- Comprehensive profile audit with section-by-section scoring
-- SEO optimization recommendations
-- Missing elements identification
-- Prioritized improvement suggestions
-- Engagement potential assessment
-- Overall profile strength score
-
-### 2. Headline Optimizer đ¯
-- Industry-specific keyword optimization
-- Value proposition enhancement
-- Achievement integration
-- Character count optimization
-- Professional title recommendations
-- SEO-friendly formatting
-
-### 3. About Section Generator đ
-- Compelling narrative creation
-- Professional journey storytelling
-- Achievement showcase
-- Industry expertise highlighting
-- Call-to-action optimization
-- Target audience alignment
-- Proper formatting and structure
-
-### 4. Experience Description Enhancer đŧ
-- Action verb optimization
-- Quantifiable achievement integration
-- Key responsibility highlighting
-- Industry-relevant keyword incorporation
-- Professional formatting
-- Impact measurement metrics
-- Role-specific enhancements
-
-### 5. Skills Recommender đ
-- Industry-trending skills analysis
-- Role-specific recommendations
-- Technical and soft skills balance
-- Skill categorization
-- Obsolete skill identification
-- Endorsement strategy suggestions
-
-## Getting Started
-
-### Prerequisites
-- Python 3.8 or higher
-- Streamlit
-- Access to ALwrity's AI services
-
-### Usage
-
-1. **Profile Analysis**
- ```python
- # Initialize the optimizer
- optimizer = LinkedInProfileOptimizer()
-
- # Analyze profile
- analysis = await optimizer.analyze_profile_strength(profile_data)
- ```
-
-2. **Headline Optimization**
- ```python
- # Optimize your headline
- headline_result = await optimizer.optimize_headline(
- current_headline="Your current headline",
- industry="Your industry",
- role="Your role"
- )
- ```
-
-3. **About Section Generation**
- ```python
- # Generate optimized About section
- about_section = await optimizer.generate_about_section(
- current_about="Your current about",
- experience=experience_list,
- achievements=achievements_list,
- target_audience="Your target audience"
- )
- ```
-
-## Best Practices for Content Creators
-
-### 1. Profile Optimization Strategy
-- Start with the Profile Analysis to identify key improvement areas
-- Focus on your headline first - it's your first impression
-- Craft your About section to showcase your content creation expertise
-- Highlight your content creation achievements with metrics
-- Include multimedia samples of your work
-
-### 2. Content Creator Specific Tips
-- Emphasize your content creation specialties in your headline
-- Showcase engagement metrics in your experience descriptions
-- Include platform-specific expertise (LinkedIn, YouTube, etc.)
-- Highlight collaboration experiences with brands
-- Demonstrate thought leadership in your niche
-
-### 3. SEO Optimization
-- Use industry-standard content creation terms
-- Include platform-specific keywords
-- Incorporate trending industry hashtags
-- Balance creative and professional terminology
-- Optimize for both human readers and search algorithms
-
-### 4. Skills Strategy
-- Balance technical content creation skills with soft skills
-- Include platform-specific skills (LinkedIn content creation, etc.)
-- Add emerging content formats (Shorts, Lives, etc.)
-- Include analytics and measurement skills
-- Showcase collaboration and community management abilities
-
-## Advanced Features
-
-### Custom URL Optimization
-- Professional URL structure recommendations
-- Brand alignment suggestions
-- SEO-friendly formatting
-- Consistency with other social profiles
-
-### Project Highlights
-- Content campaign showcases
-- Viral content examples
-- Brand collaboration features
-- Impact metrics display
-- Portfolio integration
-
-### Endorsement Strategy
-- Skill endorsement prioritization
-- Network engagement tactics
-- Reciprocal endorsement approaches
-- Expertise validation methods
-
-## Performance Metrics
-
-The Profile Optimizer evaluates profiles based on:
-- Profile Completeness Score
-- Keyword Optimization Level
-- Content Quality Metrics
-- Engagement Potential
-- Network Growth Indicators
-- Professional Appeal Score
-
-## Tips for Maximum Impact
-
-1. **Regular Updates**
- - Review and update your profile monthly
- - Add new content creation achievements regularly
- - Keep skills current with industry trends
- - Update metrics and performance statistics
-
-2. **Content Strategy Integration**
- - Align profile messaging with your content
- - Cross-reference your content platforms
- - Showcase your content creation process
- - Highlight your unique value proposition
-
-3. **Network Growth**
- - Optimize for your target audience
- - Use industry-specific terminology
- - Showcase collaboration opportunities
- - Highlight your community engagement
-
-## Support and Resources
-
-For additional support:
-- Check the [ALwrity Documentation](https://docs.alwrity.com)
-- Join our [Content Creator Community](https://community.alwrity.com)
-- Follow our [LinkedIn Page](https://linkedin.com/company/alwrity)
-- Contact support at support@alwrity.com
-
-## Contributing
-
-We welcome contributions from the content creator community! Please read our [Contributing Guidelines](CONTRIBUTING.md) for details on submitting pull requests.
-
-## License
-
-This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/profile_optimizer/__init__.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/profile_optimizer/__init__.py
deleted file mode 100644
index 9ea2d0a5..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/profile_optimizer/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-"""
-LinkedIn Profile Optimizer Module
-
-This module provides AI-powered optimization for LinkedIn profiles to improve visibility
-and professional appeal.
-"""
-
-from .linkedin_profile_optimizer import LinkedInProfileOptimizer
-from .linkedin_profile_optimizer_ui import linkedin_profile_optimizer_ui
-
-__all__ = ['LinkedInProfileOptimizer', 'linkedin_profile_optimizer_ui']
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/profile_optimizer/linkedin_profile_optimizer.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/profile_optimizer/linkedin_profile_optimizer.py
deleted file mode 100644
index 49b07e80..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/profile_optimizer/linkedin_profile_optimizer.py
+++ /dev/null
@@ -1,240 +0,0 @@
-"""
-LinkedIn Profile Optimizer
-
-This module provides AI-powered optimization for LinkedIn profiles to improve visibility
-and professional appeal.
-"""
-
-import json
-from typing import Dict, List, Optional
-from loguru import logger
-
-from .....gpt_providers.text_generation.main_text_generation import llm_text_gen
-from .....ai_web_researcher.gpt_online_researcher import do_google_serp_search
-from .....ai_web_researcher.metaphor_basic_neural_web_search import metaphor_search_articles
-from .....ai_web_researcher.tavily_ai_search import do_tavily_ai_search
-
-class LinkedInProfileOptimizer:
- """
- AI-powered LinkedIn Profile Optimizer that enhances profiles for better visibility
- and professional appeal.
- """
-
- def __init__(self):
- """Initialize the LinkedIn Profile Optimizer."""
- self.industry_keywords = {}
- self.seo_patterns = {}
- self.profile_sections = [
- "headline",
- "about",
- "experience",
- "skills",
- "projects",
- "endorsements",
- "summary",
- "custom_url"
- ]
-
- async def optimize_headline(self, current_headline: str, industry: str, role: str) -> Dict:
- """
- Optimize the LinkedIn headline for better visibility and impact.
-
- Args:
- current_headline: Current LinkedIn headline
- industry: User's industry
- role: User's current or target role
-
- Returns:
- Dict containing optimized headline and explanation
- """
- prompt = f"""
- As an expert LinkedIn profile optimizer, enhance this headline for maximum impact and visibility:
- Current Headline: {current_headline}
- Industry: {industry}
- Role: {role}
-
- Consider:
- - Including relevant keywords for {industry}
- - Highlighting unique value proposition
- - Using industry-standard titles
- - Incorporating achievements or specialties
- - Keeping it under LinkedIn's character limit
-
- Return a JSON with:
- - optimized_headline: The enhanced headline
- - explanation: Why changes were made
- - keywords_used: Key terms included
- """
-
- response = await llm_text_gen(prompt)
- return json.loads(response)
-
- async def generate_about_section(self,
- current_about: str,
- experience: List[Dict],
- achievements: List[str],
- target_audience: str
- ) -> Dict:
- """
- Generate an optimized About section.
-
- Args:
- current_about: Current About section content
- experience: List of work experiences
- achievements: List of key achievements
- target_audience: Intended profile visitors
-
- Returns:
- Dict containing new About section and explanation
- """
- prompt = f"""
- As an expert LinkedIn profile writer, create an engaging About section that showcases professional value:
-
- Current About: {current_about}
- Key Experiences: {json.dumps(experience)}
- Achievements: {json.dumps(achievements)}
- Target Audience: {target_audience}
-
- Consider:
- - Strong opening hook
- - Professional journey narrative
- - Key achievements and impact
- - Industry expertise
- - Call to action
- - Proper formatting and structure
-
- Return a JSON with:
- - about_section: The optimized content
- - structure_explanation: Section breakdown
- - impact_factors: Key elements that drive engagement
- """
-
- response = await llm_text_gen(prompt)
- return json.loads(response)
-
- async def enhance_experience_descriptions(self,
- experiences: List[Dict]
- ) -> List[Dict]:
- """
- Enhance work experience descriptions for better impact.
-
- Args:
- experiences: List of work experiences with roles and descriptions
-
- Returns:
- List of enhanced experience descriptions
- """
- enhanced_experiences = []
-
- for exp in experiences:
- prompt = f"""
- As an expert LinkedIn profile writer, enhance this work experience description:
-
- Role: {exp.get('role')}
- Company: {exp.get('company')}
- Current Description: {exp.get('description')}
-
- Enhance the description to:
- - Lead with strong action verbs
- - Include quantifiable achievements
- - Highlight key responsibilities
- - Incorporate relevant keywords
- - Use proper formatting
-
- Return a JSON with:
- - enhanced_description: The improved description
- - achievements_highlighted: Key accomplishments
- - keywords_used: Industry terms included
- """
-
- response = await llm_text_gen(prompt)
- enhanced_exp = json.loads(response)
- enhanced_experiences.append({
- **exp,
- 'enhanced_description': enhanced_exp['enhanced_description'],
- 'achievements': enhanced_exp['achievements_highlighted'],
- 'keywords': enhanced_exp['keywords_used']
- })
-
- return enhanced_experiences
-
- async def recommend_skills(self,
- current_skills: List[str],
- industry: str,
- role: str
- ) -> Dict:
- """
- Recommend relevant skills based on industry and role.
-
- Args:
- current_skills: List of current skills
- industry: User's industry
- role: User's role
-
- Returns:
- Dict containing skill recommendations
- """
- # Research trending skills in the industry
- industry_research = await do_tavily_ai_search(
- f"most in-demand skills for {role} in {industry} LinkedIn 2024"
- )
-
- prompt = f"""
- As a LinkedIn profile optimization expert, recommend skills based on:
-
- Current Skills: {json.dumps(current_skills)}
- Industry: {industry}
- Role: {role}
- Industry Research: {json.dumps(industry_research)}
-
- Provide:
- - Must-have technical skills
- - Important soft skills
- - Trending skills in the industry
- - Skills to remove (if any)
-
- Return a JSON with:
- - recommended_skills: New skills to add
- - skills_to_remove: Skills to consider removing
- - skill_categories: Grouping of skills by category
- - trending_skills: Currently popular skills
- """
-
- response = await llm_text_gen(prompt)
- return json.loads(response)
-
- async def analyze_profile_strength(self,
- profile_data: Dict
- ) -> Dict:
- """
- Analyze overall profile strength and provide improvement recommendations.
-
- Args:
- profile_data: Complete profile information
-
- Returns:
- Dict containing analysis and recommendations
- """
- prompt = f"""
- As a LinkedIn profile optimization expert, analyze this profile:
-
- Profile Data: {json.dumps(profile_data)}
-
- Provide a comprehensive analysis including:
- - Overall profile strength score
- - Section-by-section analysis
- - Missing elements
- - Improvement opportunities
- - SEO optimization suggestions
- - Engagement potential
-
- Return a JSON with:
- - strength_score: 0-100 rating
- - section_scores: Individual section ratings
- - missing_elements: Key missing components
- - priority_improvements: Ordered list of suggestions
- - seo_recommendations: Keyword and optimization tips
- """
-
- response = await llm_text_gen(prompt)
- return json.loads(response)
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/profile_optimizer/linkedin_profile_optimizer_ui.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/profile_optimizer/linkedin_profile_optimizer_ui.py
deleted file mode 100644
index 439c5d86..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/profile_optimizer/linkedin_profile_optimizer_ui.py
+++ /dev/null
@@ -1,215 +0,0 @@
-"""
-LinkedIn Profile Optimizer UI
-
-This module provides the Streamlit UI for the LinkedIn Profile Optimizer.
-"""
-
-import streamlit as st
-import json
-from typing import Dict, List
-from .linkedin_profile_optimizer import LinkedInProfileOptimizer
-
-async def linkedin_profile_optimizer_ui():
- """
- Streamlit UI for the LinkedIn Profile Optimizer.
- """
- # Initialize the profile optimizer
- optimizer = LinkedInProfileOptimizer()
-
- # Create tabs for different optimization sections
- tabs = st.tabs([
- "Profile Analysis",
- "Headline Optimizer",
- "About Section",
- "Experience Enhancer",
- "Skills Recommender"
- ])
-
- # Profile Analysis Tab
- with tabs[0]:
- st.header("Profile Strength Analysis")
- st.info("Upload your profile information for a comprehensive analysis")
-
- # Profile Data Input
- st.subheader("Enter Profile Information")
- profile_data = {
- "headline": st.text_input("Current Headline", key="profile_headline"),
- "about": st.text_area("About Section", key="profile_about"),
- "industry": st.text_input("Industry", key="profile_industry"),
- "current_role": st.text_input("Current Role", key="profile_role"),
- "experience": [],
- "skills": st.text_area("Current Skills (one per line)", key="profile_skills").split("\n"),
- "education": st.text_area("Education (one per line)", key="profile_education").split("\n")
- }
-
- # Experience Input
- st.subheader("Work Experience")
- num_experiences = st.number_input("Number of experiences to add", min_value=0, max_value=10, value=1, key="profile_num_exp")
-
- for i in range(num_experiences):
- st.markdown(f"**Experience {i+1}**")
- exp = {
- "role": st.text_input(f"Role {i+1}", key=f"profile_role_{i}"),
- "company": st.text_input(f"Company {i+1}", key=f"profile_company_{i}"),
- "description": st.text_area(f"Description {i+1}", key=f"profile_desc_{i}")
- }
- profile_data["experience"].append(exp)
- st.divider()
-
- if st.button("Analyze Profile", key="profile_analyze_btn"):
- with st.spinner("Analyzing your profile..."):
- analysis = await optimizer.analyze_profile_strength(profile_data)
-
- # Display Analysis Results
- col1, col2 = st.columns(2)
-
- with col1:
- st.metric("Profile Strength Score", f"{analysis['strength_score']}/100")
-
- st.subheader("Section Scores")
- for section, score in analysis['section_scores'].items():
- st.progress(score/100, text=f"{section}: {score}%")
-
- with col2:
- st.subheader("Priority Improvements")
- for improvement in analysis['priority_improvements']:
- st.warning(improvement)
-
- st.subheader("SEO Recommendations")
- for rec in analysis['seo_recommendations']:
- st.info(rec)
-
- # Headline Optimizer Tab
- with tabs[1]:
- st.header("Headline Optimizer")
- st.info("Optimize your headline for better visibility and impact")
-
- current_headline = st.text_input("Current Headline", key="headline_current")
- industry = st.text_input("Industry", key="headline_industry")
- role = st.text_input("Current/Target Role", key="headline_role")
-
- if st.button("Optimize Headline", key="headline_optimize_btn"):
- with st.spinner("Generating optimized headline..."):
- headline_optimization = await optimizer.optimize_headline(
- current_headline,
- industry,
- role
- )
-
- st.subheader("Optimized Headline")
- st.success(headline_optimization['optimized_headline'])
-
- st.subheader("Optimization Explanation")
- st.write(headline_optimization['explanation'])
-
- st.subheader("Keywords Used")
- for keyword in headline_optimization['keywords_used']:
- st.info(keyword)
-
- # About Section Tab
- with tabs[2]:
- st.header("About Section Generator")
- st.info("Create an engaging and professional About section")
-
- current_about = st.text_area("Current About Section", key="about_current")
- achievements = st.text_area("Key Achievements (one per line)", key="about_achievements").split("\n")
- target_audience = st.text_input("Target Audience", key="about_audience")
-
- if st.button("Generate About Section", key="about_generate_btn"):
- with st.spinner("Generating optimized About section..."):
- about_optimization = await optimizer.generate_about_section(
- current_about,
- profile_data.get("experience", []),
- achievements,
- target_audience
- )
-
- st.subheader("Optimized About Section")
- st.markdown(about_optimization['about_section'])
-
- st.subheader("Section Structure")
- for section, explanation in about_optimization['structure_explanation'].items():
- st.markdown(f"**{section}**")
- st.write(explanation)
- st.divider()
-
- st.subheader("Impact Factors")
- for factor in about_optimization['impact_factors']:
- st.success(factor)
-
- # Experience Enhancer Tab
- with tabs[3]:
- st.header("Experience Description Enhancer")
- st.info("Enhance your work experience descriptions for maximum impact")
-
- experiences = []
- num_exp = st.number_input("Number of experiences to enhance", min_value=1, max_value=10, value=1, key="exp_num")
-
- for i in range(num_exp):
- st.markdown(f"**Experience {i+1}**")
- exp = {
- "role": st.text_input(f"Role {i+1}", key=f"exp_role_{i}"),
- "company": st.text_input(f"Company {i+1}", key=f"exp_company_{i}"),
- "description": st.text_area(f"Current Description {i+1}", key=f"exp_desc_{i}")
- }
- experiences.append(exp)
- st.divider()
-
- if st.button("Enhance Experiences", key="exp_enhance_btn"):
- with st.spinner("Enhancing experience descriptions..."):
- enhanced_experiences = await optimizer.enhance_experience_descriptions(experiences)
-
- for i, exp in enumerate(enhanced_experiences):
- st.markdown(f"**Enhanced Experience {i+1}**")
- st.subheader(f"{exp['role']} at {exp['company']}")
- st.markdown(exp['enhanced_description'])
-
- st.subheader("Key Achievements")
- for achievement in exp['achievements']:
- st.success(achievement)
-
- st.subheader("Keywords Used")
- for keyword in exp['keywords']:
- st.info(keyword)
-
- st.divider()
-
- # Skills Recommender Tab
- with tabs[4]:
- st.header("Skills Recommender")
- st.info("Get personalized skill recommendations for your profile")
-
- current_skills = st.text_area("Current Skills (one per line)", key="skills_current").split("\n")
- industry = st.text_input("Industry (for skills)", key="skills_industry")
- role = st.text_input("Role (for skills)", key="skills_role")
-
- if st.button("Get Skill Recommendations", key="skills_recommend_btn"):
- with st.spinner("Analyzing and recommending skills..."):
- skill_recommendations = await optimizer.recommend_skills(
- current_skills,
- industry,
- role
- )
-
- col1, col2 = st.columns(2)
-
- with col1:
- st.subheader("Recommended Skills to Add")
- for skill in skill_recommendations['recommended_skills']:
- st.success(skill)
-
- st.subheader("Consider Removing")
- for skill in skill_recommendations['skills_to_remove']:
- st.warning(skill)
-
- with col2:
- st.subheader("Trending Skills")
- for skill in skill_recommendations['trending_skills']:
- st.info(skill)
-
- st.subheader("Skill Categories")
- for category, skills in skill_recommendations['skill_categories'].items():
- st.markdown(f"**{category}**")
- for skill in skills:
- st.write(f"- {skill}")
- st.divider()
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/video_script_generator/README.md b/ToBeMigrated/ai_writers/linkedin_writer/modules/video_script_generator/README.md
deleted file mode 100644
index bf490a6e..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/video_script_generator/README.md
+++ /dev/null
@@ -1,207 +0,0 @@
-# LinkedIn Video Script Generator
-
-## Overview
-
-The LinkedIn Video Script Generator is an AI-powered tool designed to help professionals create engaging and effective video scripts for LinkedIn content. This tool combines research-driven insights with professional scriptwriting techniques to generate compelling video content that resonates with your target audience.
-
-## Features
-
-### 1. Script Generation
-- **Hook Creation**: Generate attention-grabbing openings that capture viewer interest
-- **Story Structure**: Create well-organized scripts with clear sections and transitions
-- **Professional Speaking Points**: Generate key talking points and emphasis areas
-- **Multiple Video Types**:
- - Thought Leadership
- - Tutorial/How-to
- - Product Demo
- - Company Culture
- - Industry Insights
- - Event Highlights
- - Customer Story
- - Behind the Scenes
-
-### 2. Research Integration
-- **Multi-Source Research**: Gather insights from:
- - Metaphor (neural search)
- - Google SERP
- - Tavily AI
-- **Insight Extraction**: Automatically extract key insights and trends
-- **Topic Analysis**: Research current trends and best practices
-
-### 3. Visual Elements
-- **Visual Cue Suggestions**: Get recommendations for:
- - B-roll footage
- - Graphics and animations
- - Text overlays
- - Visual transitions
-- **Timing Guidance**: Precise timing suggestions for each visual element
-- **Visual Hierarchy**: Optimize visual flow and emphasis
-
-### 4. Engagement Optimization
-- **Call-to-Action Generation**: Create compelling CTAs
-- **Engagement Prompts**: Generate discussion questions and interaction points
-- **Audience Targeting**: Tailor content to specific professional audiences
-- **Tone Customization**: Multiple tone options:
- - Professional & Authoritative
- - Conversational & Friendly
- - Educational & Informative
- - Inspirational & Motivational
- - Storytelling & Narrative
-
-### 5. Length Options
-- Short (< 1 minute)
-- Medium (1-3 minutes)
-- Long (3-5 minutes)
-- Extended (5+ minutes)
-
-## Usage
-
-### Basic Workflow
-
-1. **Enter Basic Details**
- - Choose your topic
- - Select your industry
- - Pick a video type
- - Set desired length
-
-2. **Configure Advanced Options**
- - Select target audience
- - Choose tone
- - Set additional preferences
-
-3. **Research Your Topic**
- - Choose research source
- - Review insights and trends
- - Incorporate key findings
-
-4. **Generate Script**
- - Get complete script with sections
- - Review visual suggestions
- - Check engagement elements
-
-### Best Practices
-
-#### Script Structure
-1. **Strong Hook (0-15 seconds)**
- - Capture attention immediately
- - Present clear value proposition
- - Use professional tone
-
-2. **Content Flow (Main Body)**
- - Clear section transitions
- - Logical progression
- - Engaging visuals
-
-3. **Effective Closing**
- - Strong call-to-action
- - Engagement prompts
- - Next steps
-
-#### Visual Elements
-1. **Professional Quality**
- - High-quality visuals
- - Consistent branding
- - Clear text overlays
-
-2. **Engagement Focus**
- - Strategic visual pacing
- - Attention-holding elements
- - Professional transitions
-
-3. **Brand Consistency**
- - Aligned with brand guidelines
- - Professional appearance
- - Consistent style
-
-#### Content Tips by Video Type
-
-##### Thought Leadership
-- Focus on unique insights
-- Share expert perspective
-- Include industry trends
-
-##### Tutorial/How-to
-- Clear step-by-step structure
-- Visual demonstrations
-- Practical examples
-
-##### Product Demo
-- Feature highlights
-- Use cases
-- Value proposition
-
-##### Company Culture
-- Authentic representation
-- Team involvement
-- Behind-the-scenes elements
-
-## Technical Details
-
-### Dependencies
-- Streamlit: For the user interface
-- GPT Providers: For AI text generation
-- Web Research Tools: For gathering insights
-
-### Integration
-The Video Script Generator is fully integrated into the LinkedIn AI Writer suite and can be accessed through the main interface.
-
-## Examples
-
-### Example 1: Tech Industry Thought Leadership
-**Topic**: "The Future of AI in Business"
-**Length**: 2-3 minutes
-**Target**: Decision Makers
-
-### Example 2: Healthcare Tutorial
-**Topic**: "Implementing Telehealth Solutions"
-**Length**: 3-5 minutes
-**Target**: Healthcare Professionals
-
-### Example 3: Product Demo
-**Topic**: "New CRM Features Overview"
-**Length**: 1-2 minutes
-**Target**: Business Users
-
-## Troubleshooting
-
-### Common Issues
-
-1. **Research Not Returning Results**
- - Try different search terms
- - Switch research sources
- - Check connection
-
-2. **Script Generation Issues**
- - Verify all fields are filled
- - Try different topic phrasing
- - Check length settings
-
-3. **Visual Suggestions**
- - Ensure script sections are clear
- - Check timing specifications
- - Review section content
-
-## Future Enhancements
-
-- **AI Voice Generation**: Add AI voiceover capabilities
-- **Template Library**: Pre-built script templates
-- **Analytics Integration**: Performance tracking
-- **Multi-language Support**: Generate scripts in multiple languages
-- **Direct Publishing**: Integration with LinkedIn video upload
-
-## Contributing
-
-Contributions to the LinkedIn Video Script Generator are welcome! Please follow these steps:
-
-1. Fork the repository
-2. Create a feature branch
-3. Make your changes
-4. Submit a pull request
-
-## License
-
-This project is licensed under the MIT License - see the LICENSE file for details.
-
-## Contact
-
-For questions or feedback about the LinkedIn Video Script Generator, please contact the development team.
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/video_script_generator/__init__.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/video_script_generator/__init__.py
deleted file mode 100644
index 0a393a03..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/video_script_generator/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-"""
-LinkedIn Video Script Generator Module
-
-This module provides functionality for generating professional video scripts
-for LinkedIn content with AI-powered optimization and engagement features.
-"""
-
-from .linkedin_video_script_generator import LinkedInVideoScriptGenerator, linkedin_video_script_generator_ui
-
-__all__ = ["LinkedInVideoScriptGenerator", "linkedin_video_script_generator_ui"]
\ No newline at end of file
diff --git a/ToBeMigrated/ai_writers/linkedin_writer/modules/video_script_generator/linkedin_video_script_generator.py b/ToBeMigrated/ai_writers/linkedin_writer/modules/video_script_generator/linkedin_video_script_generator.py
deleted file mode 100644
index 0073d6ae..00000000
--- a/ToBeMigrated/ai_writers/linkedin_writer/modules/video_script_generator/linkedin_video_script_generator.py
+++ /dev/null
@@ -1,571 +0,0 @@
-"""
-LinkedIn Video Script Generator
-
-This module provides functionality for generating professional video scripts
-for LinkedIn content with AI-powered optimization and engagement features.
-"""
-
-import streamlit as st
-import json
-from typing import Dict, List, Optional, Union
-from loguru import logger
-
-from .....gpt_providers.text_generation.main_text_generation import llm_text_gen
-from .....gpt_providers.text_generation.gemini_pro_text import gemini_structured_json_response
-from .....ai_web_researcher.gpt_online_researcher import do_google_serp_search
-from .....ai_web_researcher.metaphor_basic_neural_web_search import metaphor_search_articles
-from .....ai_web_researcher.tavily_ai_search import do_tavily_ai_search
-
-
-class LinkedInVideoScriptGenerator:
- """
- AI-powered LinkedIn video script generator that creates engaging scripts with
- hooks, story structure, and visual suggestions.
- """
-
- def __init__(self):
- """Initialize the LinkedIn Video Script Generator."""
- self.video_types = {
- "thought_leadership": "Thought Leadership",
- "tutorial": "Tutorial/How-to",
- "product_demo": "Product Demo",
- "company_culture": "Company Culture",
- "industry_insights": "Industry Insights",
- "event_highlights": "Event Highlights",
- "customer_story": "Customer Story",
- "behind_scenes": "Behind the Scenes"
- }
-
- self.video_lengths = {
- "short": "Short (< 1 minute)",
- "medium": "Medium (1-3 minutes)",
- "long": "Long (3-5 minutes)",
- "extended": "Extended (5+ minutes)"
- }
-
- self.tone_options = {
- "professional": "Professional & Authoritative",
- "conversational": "Conversational & Friendly",
- "educational": "Educational & Informative",
- "inspirational": "Inspirational & Motivational",
- "storytelling": "Storytelling & Narrative"
- }
-
- self.target_audiences = {
- "professionals": "Industry Professionals",
- "decision_makers": "Decision Makers",
- "job_seekers": "Job Seekers",
- "students": "Students & Early Career",
- "entrepreneurs": "Entrepreneurs & Business Owners",
- "general": "General Professional Network"
- }
-
- def research_topic(self, topic: str, industry: str, search_engine: str = "metaphor") -> Dict:
- """
- Research a topic to gather insights for video content.
-
- Args:
- topic: The main topic for the video
- industry: The target industry
- search_engine: The search engine to use (metaphor, google, tavily)
-
- Returns:
- Dict containing research results and insights
- """
- try:
- search_query = f"{topic} {industry} trends insights best practices"
-
- if search_engine == "metaphor":
- articles = metaphor_search_articles(search_query)
- elif search_engine == "google":
- articles = do_google_serp_search(search_query)
- elif search_engine == "tavily":
- articles = do_tavily_ai_search(search_query)
- else:
- raise ValueError(f"Unsupported search engine: {search_engine}")
-
- insights, trends = self._extract_insights_and_trends(articles)
-
- return {
- "articles": articles,
- "insights": insights,
- "trends": trends
- }
-
- except Exception as e:
- logger.error(f"Error researching topic: {str(e)}")
- return {
- "articles": [],
- "insights": [],
- "trends": []
- }
-
- def _extract_insights_and_trends(self, articles: List[Dict]) -> tuple[List[str], List[str]]:
- """Extract key insights and trends from research articles."""
- try:
- prompt = f"""
- Analyze these articles and extract key insights and trends:
-
- Articles:
- {json.dumps(articles, indent=2)}
-
- Identify the most valuable insights and emerging trends from these articles.
- """
-
- # Define the schema for insights and trends
- schema = {
- "type": "object",
- "properties": {
- "insights": {
- "type": "array",
- "items": {"type": "string"}
- },
- "trends": {
- "type": "array",
- "items": {"type": "string"}
- }
- },
- "required": ["insights", "trends"]
- }
-
- # Use the structured JSON response function
- result = gemini_structured_json_response(prompt, schema)
-
- # Check if there was an error
- if "error" in result:
- logger.error(f"Error extracting insights and trends: {result['error']}")
- return [], []
-
- return result.get("insights", []), result.get("trends", [])
-
- except Exception as e:
- logger.error(f"Error extracting insights and trends: {str(e)}")
- return [], []
-
- def generate_hook(self, topic: str, video_type: str, target_audience: str, tone: str) -> str:
- """
- Generate an attention-grabbing hook for the video.
-
- Args:
- topic: The main topic of the video
- video_type: Type of video content
- target_audience: Target audience for the video
- tone: Desired tone of the hook
-
- Returns:
- str: The generated hook
- """
- try:
- prompt = f"""
- Create an attention-grabbing hook for a LinkedIn video with:
- - Topic: {topic}
- - Video Type: {self.video_types[video_type]}
- - Target Audience: {self.target_audiences[target_audience]}
- - Tone: {self.tone_options[tone]}
-
- The hook should be:
- 1. Under 15 seconds when spoken
- 2. Immediately capture attention
- 3. Clear value proposition
- 4. Professional and engaging
-
- Return only the hook text.
- """
-
- return llm_text_gen(prompt)
-
- except Exception as e:
- logger.error(f"Error generating hook: {str(e)}")
- return ""
-
- def generate_story_structure(self, topic: str, video_type: str, length: str, insights: List[str]) -> Dict:
- """Generate a structured story for the video."""
- try:
- prompt = f"""
- Create a story structure for a LinkedIn video:
- - Topic: {topic}
- - Video Type: {self.video_types[video_type]}
- - Length: {self.video_lengths[length]}
- - Key Insights: {json.dumps(insights)}
-
- Create a well-structured story with clear sections, transitions, and key points.
- Include guidance on pacing and delivery.
- """
-
- # Define the schema for the story structure
- schema = {
- "type": "object",
- "properties": {
- "sections": {
- "type": "array",
- "items": {
- "type": "object",
- "properties": {
- "title": {"type": "string"},
- "content": {"type": "string"},
- "duration": {"type": "string"}
- },
- "required": ["title", "content", "duration"]
- }
- },
- "transitions": {
- "type": "array",
- "items": {"type": "string"}
- },
- "key_points": {
- "type": "array",
- "items": {"type": "string"}
- },
- "pacing_notes": {
- "type": "array",
- "items": {"type": "string"}
- }
- },
- "required": ["sections", "transitions", "key_points", "pacing_notes"]
- }
-
- # Use the structured JSON response function
- result = gemini_structured_json_response(prompt, schema)
-
- # Check if there was an error
- if "error" in result:
- logger.error(f"Error generating story structure: {result['error']}")
- return {
- "sections": [],
- "transitions": [],
- "key_points": [],
- "pacing_notes": []
- }
-
- return result
-
- except Exception as e:
- logger.error(f"Error generating story structure: {str(e)}")
- return {
- "sections": [],
- "transitions": [],
- "key_points": [],
- "pacing_notes": []
- }
-
- def generate_visual_cues(self, script_sections: List[Dict]) -> List[Dict]:
- """Generate visual cues for the video script."""
- try:
- prompt = f"""
- Create visual cues for this video script:
-
- Script Sections:
- {json.dumps(script_sections, indent=2)}
-
- For each section, suggest:
- 1. Visual type (b-roll, graphics, text overlay, etc.)
- 2. Description of visual content
- 3. Timing and duration
- 4. Visual transitions
- """
-
- # Define the schema for visual cues
- schema = {
- "type": "array",
- "items": {
- "type": "object",
- "properties": {
- "section_title": {"type": "string"},
- "visual_type": {"type": "string"},
- "description": {"type": "string"},
- "timing": {"type": "string"},
- "transitions": {"type": "string"}
- },
- "required": ["section_title", "visual_type", "description", "timing", "transitions"]
- }
- }
-
- # Use the structured JSON response function
- result = gemini_structured_json_response(prompt, schema)
-
- # Check if there was an error
- if "error" in result:
- logger.error(f"Error generating visual cues: {result['error']}")
- return []
-
- return result
-
- except Exception as e:
- logger.error(f"Error generating visual cues: {str(e)}")
- return []
-
- def generate_full_script(self, topic: str, video_type: str, length: str,
- target_audience: str, tone: str, insights: List[str]) -> Dict:
- """
- Generate a complete video script with all components.
-
- Args:
- topic: Main topic of the video
- video_type: Type of video content
- length: Target video length
- target_audience: Target audience
- tone: Desired tone
- insights: Research insights
-
- Returns:
- Dict containing the complete script
- """
- try:
- # Generate hook
- hook = self.generate_hook(topic, video_type, target_audience, tone)
-
- # Generate story structure
- structure = self.generate_story_structure(topic, video_type, length, insights)
-
- # Generate visual cues
- visuals = self.generate_visual_cues(structure["sections"])
-
- # Generate call-to-action
- cta = self.generate_cta(topic, video_type, target_audience)
-
- # Combine all components
- script = {
- "metadata": {
- "topic": topic,
- "video_type": video_type,
- "length": length,
- "target_audience": target_audience,
- "tone": tone
- },
- "hook": hook,
- "structure": structure,
- "visuals": visuals,
- "cta": cta
- }
-
- return script
-
- except Exception as e:
- logger.error(f"Error generating full script: {str(e)}")
- return {}
-
- def generate_cta(self, topic: str, video_type: str, target_audience: str) -> Dict:
- """Generate a compelling call-to-action for the video."""
- try:
- prompt = f"""
- Create a compelling call-to-action for a LinkedIn video:
- - Topic: {topic}
- - Video Type: {self.video_types[video_type]}
- - Target Audience: {target_audience}
-
- Generate a clear, engaging call-to-action that encourages viewer engagement.
- """
-
- # Define the schema for the CTA
- schema = {
- "type": "object",
- "properties": {
- "primary_cta": {
- "type": "string",
- "description": "Main call-to-action text"
- },
- "secondary_cta": {
- "type": "string",
- "description": "Optional secondary call-to-action"
- },
- "engagement_hooks": {
- "type": "array",
- "items": {"type": "string"},
- "description": "Additional engagement prompts"
- },
- "hashtag_suggestions": {
- "type": "array",
- "items": {"type": "string"},
- "description": "Relevant hashtags for the CTA"
- }
- },
- "required": ["primary_cta", "secondary_cta", "engagement_hooks", "hashtag_suggestions"]
- }
-
- # Use the structured JSON response function
- result = gemini_structured_json_response(prompt, schema)
-
- # Check if there was an error
- if "error" in result:
- logger.error(f"Error generating CTA: {result['error']}")
- return {
- "primary_cta": "Thanks for watching!",
- "secondary_cta": "Let me know your thoughts in the comments.",
- "engagement_hooks": ["What did you think about this topic?"],
- "hashtag_suggestions": ["#LinkedInVideo"]
- }
-
- return result
-
- except Exception as e:
- logger.error(f"Error generating CTA: {str(e)}")
- return {
- "primary_cta": "Thanks for watching!",
- "secondary_cta": "Let me know your thoughts in the comments.",
- "engagement_hooks": ["What did you think about this topic?"],
- "hashtag_suggestions": ["#LinkedInVideo"]
- }
-
-def linkedin_video_script_generator_ui():
- """Streamlit UI for the LinkedIn Video Script Generator."""
-
- st.title("LinkedIn Video Script Generator")
- st.markdown("""
- Create professional video scripts for LinkedIn that drive engagement and showcase your expertise.
- """)
-
- # Initialize the video script generator
- generator = LinkedInVideoScriptGenerator()
-
- # Create tabs for different sections
- tab1, tab2, tab3 = st.tabs(["Script Details", "Research & Insights", "Generated Script"])
-
- with tab1:
- st.header("Video Script Details")
-
- # Basic information
- col1, col2 = st.columns(2)
- with col1:
- topic = st.text_input("Topic", placeholder="e.g., AI in Healthcare, Remote Work Best Practices")
- industry = st.text_input("Industry", placeholder="e.g., Technology, Healthcare, Finance")
-
- with col2:
- video_type = st.selectbox(
- "Video Type",
- options=list(generator.video_types.keys()),
- format_func=lambda x: generator.video_types[x]
- )
- length = st.selectbox(
- "Video Length",
- options=list(generator.video_lengths.keys()),
- format_func=lambda x: generator.video_lengths[x]
- )
-
- # Advanced options
- with st.expander("Advanced Options"):
- col3, col4 = st.columns(2)
- with col3:
- target_audience = st.selectbox(
- "Target Audience",
- options=list(generator.target_audiences.keys()),
- format_func=lambda x: generator.target_audiences[x]
- )
-
- with col4:
- tone = st.selectbox(
- "Tone",
- options=list(generator.tone_options.keys()),
- format_func=lambda x: generator.tone_options[x]
- )
-
- with tab2:
- st.header("Research & Insights")
-
- if topic and industry:
- # Research options
- search_engine = st.selectbox(
- "Research Source",
- options=["metaphor", "google", "tavily"],
- format_func=lambda x: x.title()
- )
-
- if st.button("Research Topic"):
- with st.spinner("Researching topic..."):
- research_results = generator.research_topic(topic, industry, search_engine)
-
- if research_results["insights"] or research_results["trends"]:
- # Store results in session state
- st.session_state.research_results = research_results
-
- # Display insights
- st.subheader("Key Insights")
- for insight in research_results["insights"]:
- st.markdown(f"- {insight}")
-
- # Display trends
- st.subheader("Current Trends")
- for trend in research_results["trends"]:
- st.markdown(f"- {trend}")
- else:
- st.warning("No insights found. Try adjusting your topic or using a different research source.")
- else:
- st.info("Please enter a topic and industry in the Script Details tab to research insights.")
-
- with tab3:
- st.header("Generated Script")
-
- if all([topic, industry, video_type, length, target_audience, tone]):
- if st.button("Generate Script"):
- with st.spinner("Generating video script..."):
- # Get insights from research if available
- insights = []
- if hasattr(st.session_state, 'research_results'):
- insights = st.session_state.research_results.get("insights", [])
-
- # Generate full script
- script = generator.generate_full_script(
- topic=topic,
- video_type=video_type,
- length=length,
- target_audience=target_audience,
- tone=tone,
- insights=insights
- )
-
- if script:
- # Display hook
- st.subheader("Hook")
- st.write(script["hook"])
-
- # Display structure
- st.subheader("Script Structure")
- for i, section in enumerate(script["structure"]["sections"], 1):
- with st.expander(f"Section {i}"):
- st.write(f"**Timing:** {section.get('timing', 'N/A')}")
- st.write(f"**Content:** {section.get('content', 'N/A')}")
-
- # Display visual cues for this section
- if script["visuals"]:
- visual = next((v for v in script["visuals"] if v.get("section") == i), None)
- if visual:
- st.write("**Visual Elements:**")
- st.write(f"- Type: {visual.get('type', 'N/A')}")
- st.write(f"- Description: {visual.get('description', 'N/A')}")
- st.write(f"- Duration: {visual.get('duration', 'N/A')}")
-
- # Display transitions
- st.subheader("Transitions")
- for transition in script["structure"]["transitions"]:
- st.markdown(f"- {transition}")
-
- # Display key points
- st.subheader("Key Points to Emphasize")
- for point in script["structure"]["key_points"]:
- st.markdown(f"- {point}")
-
- # Display CTA
- st.subheader("Call-to-Action")
- st.write(f"**Primary CTA:** {script['cta'].get('primary_cta', 'N/A')}")
- if script['cta'].get('secondary_cta'):
- st.write(f"**Secondary CTA:** {script['cta']['secondary_cta']}")
-
- # Display engagement prompts
- if script['cta'].get('engagement_hooks'):
- st.subheader("Engagement Prompts")
- for prompt in script['cta']['engagement_hooks']:
- st.markdown(f"- {prompt}")
-
- # Display hashtag suggestions
- if script['cta'].get('hashtag_suggestions'):
- st.subheader("Hashtag Suggestions")
- for hashtag in script['cta']['hashtag_suggestions']:
- st.markdown(f"- {hashtag}")
-
- # Display pacing notes
- st.subheader("Pacing & Delivery Notes")
- for note in script["structure"]["pacing_notes"]:
- st.markdown(f"- {note}")
- else:
- st.error("Failed to generate script. Please try again.")
- else:
- st.info("Please fill in all required fields in the Script Details tab to generate a script.")
\ No newline at end of file
diff --git a/backend/api/linkedin_image_generation.py b/backend/api/linkedin_image_generation.py
new file mode 100644
index 00000000..5c18af38
--- /dev/null
+++ b/backend/api/linkedin_image_generation.py
@@ -0,0 +1,220 @@
+from fastapi import APIRouter, HTTPException, UploadFile, File
+from pydantic import BaseModel
+from typing import List, Optional, Dict, Any
+import json
+import logging
+
+# Import our LinkedIn image generation services
+from services.linkedin.image_generation import LinkedInImageGenerator, LinkedInImageStorage
+from services.linkedin.image_prompts import LinkedInPromptGenerator
+from services.api_key_manager import APIKeyManager
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Initialize router
+router = APIRouter(prefix="/api/linkedin", tags=["linkedin-image-generation"])
+
+# Initialize services
+api_key_manager = APIKeyManager()
+image_generator = LinkedInImageGenerator(api_key_manager)
+prompt_generator = LinkedInPromptGenerator(api_key_manager)
+image_storage = LinkedInImageStorage(api_key_manager=api_key_manager)
+
+# Request/Response models
+class ImagePromptRequest(BaseModel):
+ content_type: str
+ topic: str
+ industry: str
+ content: str
+
+class ImageGenerationRequest(BaseModel):
+ prompt: str
+ content_context: Dict[str, Any]
+ aspect_ratio: Optional[str] = "1:1"
+
+class ImagePromptResponse(BaseModel):
+ style: str
+ prompt: str
+ description: str
+ prompt_index: int
+ enhanced_at: Optional[str] = None
+ linkedin_optimized: Optional[bool] = None
+ fallback: Optional[bool] = None
+ content_context: Optional[Dict[str, Any]] = None
+
+class ImageGenerationResponse(BaseModel):
+ success: bool
+ image_url: Optional[str] = None
+ image_id: Optional[str] = None
+ style: Optional[str] = None
+ aspect_ratio: Optional[str] = None
+ error: Optional[str] = None
+
+@router.post("/generate-image-prompts", response_model=List[ImagePromptResponse])
+async def generate_image_prompts(request: ImagePromptRequest):
+ """
+ Generate three AI-optimized image prompts for LinkedIn content
+ """
+ try:
+ logger.info(f"Generating image prompts for {request.content_type} about {request.topic}")
+
+ # Use our LinkedIn prompt generator service
+ prompts = await prompt_generator.generate_three_prompts({
+ 'content_type': request.content_type,
+ 'topic': request.topic,
+ 'industry': request.industry,
+ 'content': request.content
+ })
+
+ logger.info(f"Generated {len(prompts)} image prompts successfully")
+ return prompts
+
+ except Exception as e:
+ logger.error(f"Error generating image prompts: {str(e)}")
+ raise HTTPException(status_code=500, detail=f"Failed to generate image prompts: {str(e)}")
+
+@router.post("/generate-image", response_model=ImageGenerationResponse)
+async def generate_linkedin_image(request: ImageGenerationRequest):
+ """
+ Generate LinkedIn-optimized image from selected prompt
+ """
+ try:
+ logger.info(f"Generating LinkedIn image with prompt: {request.prompt[:100]}...")
+
+ # Use our LinkedIn image generator service
+ image_result = await image_generator.generate_image(
+ prompt=request.prompt,
+ content_context=request.content_context
+ )
+
+ if image_result and image_result.get('success'):
+ # Store the generated image
+ image_id = await image_storage.store_image(
+ image_data=image_result['image_data'],
+ metadata={
+ 'prompt': request.prompt,
+ 'style': request.content_context.get('style', 'Generated'),
+ 'aspect_ratio': request.aspect_ratio,
+ 'content_type': request.content_context.get('content_type'),
+ 'topic': request.content_context.get('topic'),
+ 'industry': request.content_context.get('industry')
+ }
+ )
+
+ logger.info(f"Image generated and stored successfully with ID: {image_id}")
+
+ return ImageGenerationResponse(
+ success=True,
+ image_url=image_result.get('image_url'),
+ image_id=image_id,
+ style=request.content_context.get('style', 'Generated'),
+ aspect_ratio=request.aspect_ratio
+ )
+ else:
+ error_msg = image_result.get('error', 'Unknown error during image generation')
+ logger.error(f"Image generation failed: {error_msg}")
+ return ImageGenerationResponse(
+ success=False,
+ error=error_msg
+ )
+
+ except Exception as e:
+ logger.error(f"Error generating LinkedIn image: {str(e)}")
+ return ImageGenerationResponse(
+ success=False,
+ error=f"Failed to generate image: {str(e)}"
+ )
+
+@router.get("/image-status/{image_id}")
+async def get_image_status(image_id: str):
+ """
+ Check the status of an image generation request
+ """
+ try:
+ # Get image metadata from storage
+ metadata = await image_storage.get_image_metadata(image_id)
+ if metadata:
+ return {
+ "success": True,
+ "status": "completed",
+ "metadata": metadata
+ }
+ else:
+ return {
+ "success": False,
+ "status": "not_found",
+ "error": "Image not found"
+ }
+ except Exception as e:
+ logger.error(f"Error checking image status: {str(e)}")
+ return {
+ "success": False,
+ "status": "error",
+ "error": str(e)
+ }
+
+@router.get("/images/{image_id}")
+async def get_generated_image(image_id: str):
+ """
+ Retrieve a generated image by ID
+ """
+ try:
+ image_data = await image_storage.retrieve_image(image_id)
+ if image_data:
+ return {
+ "success": True,
+ "image_data": image_data
+ }
+ else:
+ raise HTTPException(status_code=404, detail="Image not found")
+ except Exception as e:
+ logger.error(f"Error retrieving image: {str(e)}")
+ raise HTTPException(status_code=500, detail=f"Failed to retrieve image: {str(e)}")
+
+@router.delete("/images/{image_id}")
+async def delete_generated_image(image_id: str):
+ """
+ Delete a generated image by ID
+ """
+ try:
+ success = await image_storage.delete_image(image_id)
+ if success:
+ return {"success": True, "message": "Image deleted successfully"}
+ else:
+ return {"success": False, "message": "Failed to delete image"}
+ except Exception as e:
+ logger.error(f"Error deleting image: {str(e)}")
+ return {"success": False, "error": str(e)}
+
+# Health check endpoint
+@router.get("/image-generation-health")
+async def health_check():
+ """
+ Health check for image generation services
+ """
+ try:
+ # Test basic service functionality
+ test_prompts = await prompt_generator.generate_three_prompts({
+ 'content_type': 'post',
+ 'topic': 'Test',
+ 'industry': 'Technology',
+ 'content': 'Test content for health check'
+ })
+
+ return {
+ "status": "healthy",
+ "services": {
+ "prompt_generator": "operational",
+ "image_generator": "operational",
+ "image_storage": "operational"
+ },
+ "test_prompts_generated": len(test_prompts)
+ }
+ except Exception as e:
+ logger.error(f"Health check failed: {str(e)}")
+ return {
+ "status": "unhealthy",
+ "error": str(e)
+ }
diff --git a/backend/app.py b/backend/app.py
index fdcb98ff..b32cff82 100644
--- a/backend/app.py
+++ b/backend/app.py
@@ -54,6 +54,8 @@ from routers.seo_tools import router as seo_tools_router
from api.facebook_writer.routers import facebook_router
# Import LinkedIn content generation router
from routers.linkedin import router as linkedin_router
+# Import LinkedIn image generation router
+from api.linkedin_image_generation import router as linkedin_image_router
# Import user data endpoints
# Import content planning endpoints
@@ -375,6 +377,8 @@ app.include_router(seo_tools_router)
app.include_router(facebook_router)
# Include LinkedIn content generation router
app.include_router(linkedin_router)
+# Include LinkedIn image generation router
+app.include_router(linkedin_image_router)
# Include user data router
# Include content planning router
diff --git a/backend/models/linkedin_models.py b/backend/models/linkedin_models.py
index 3b453639..cc4d3693 100644
--- a/backend/models/linkedin_models.py
+++ b/backend/models/linkedin_models.py
@@ -254,6 +254,7 @@ class ContentQualityMetrics(BaseModel):
content_length: int = Field(..., description="Content length in characters")
word_count: int = Field(..., description="Word count")
analysis_timestamp: str = Field(..., description="Timestamp of quality analysis")
+ recommendations: Optional[List[str]] = Field(default_factory=list, description="List of improvement recommendations")
# New Citation Model
diff --git a/backend/services/linkedin/__init__.py b/backend/services/linkedin/__init__.py
index 1e4beffa..17ef382f 100644
--- a/backend/services/linkedin/__init__.py
+++ b/backend/services/linkedin/__init__.py
@@ -1,11 +1,53 @@
"""
LinkedIn Services Package
-Contains specialized services for LinkedIn content generation.
+This package provides comprehensive LinkedIn content generation and management services
+including content generation, image generation, and various LinkedIn-specific utilities.
"""
-from .quality_handler import QualityHandler
+# Import existing services
from .content_generator import ContentGenerator
-from .research_handler import ResearchHandler
+from .content_generator_prompts import (
+ PostPromptBuilder,
+ ArticlePromptBuilder,
+ CarouselPromptBuilder,
+ VideoScriptPromptBuilder,
+ CommentResponsePromptBuilder,
+ CarouselGenerator,
+ VideoScriptGenerator
+)
-__all__ = ["QualityHandler", "ContentGenerator", "ResearchHandler"]
+# Import new image generation services
+from .image_generation import (
+ LinkedInImageGenerator,
+ LinkedInImageEditor,
+ LinkedInImageStorage
+)
+from .image_prompts import LinkedInPromptGenerator
+
+__all__ = [
+ # Content Generation
+ 'ContentGenerator',
+
+ # Prompt Builders
+ 'PostPromptBuilder',
+ 'ArticlePromptBuilder',
+ 'CarouselPromptBuilder',
+ 'VideoScriptPromptBuilder',
+ 'CommentResponsePromptBuilder',
+
+ # Specialized Generators
+ 'CarouselGenerator',
+ 'VideoScriptGenerator',
+
+ # Image Generation Services
+ 'LinkedInImageGenerator',
+ 'LinkedInImageEditor',
+ 'LinkedInImageStorage',
+ 'LinkedInPromptGenerator'
+]
+
+# Version information
+__version__ = "2.0.0"
+__author__ = "Alwrity Team"
+__description__ = "LinkedIn Content and Image Generation Services"
diff --git a/backend/services/linkedin/content_generator.py b/backend/services/linkedin/content_generator.py
index 6c646ada..e9e5d8cb 100644
--- a/backend/services/linkedin/content_generator.py
+++ b/backend/services/linkedin/content_generator.py
@@ -12,6 +12,15 @@ from models.linkedin_models import (
PostContent, ArticleContent, GroundingLevel, ResearchSource
)
from services.linkedin.quality_handler import QualityHandler
+from services.linkedin.content_generator_prompts import (
+ PostPromptBuilder,
+ ArticlePromptBuilder,
+ CarouselPromptBuilder,
+ VideoScriptPromptBuilder,
+ CommentResponsePromptBuilder,
+ CarouselGenerator,
+ VideoScriptGenerator
+)
class ContentGenerator:
@@ -22,6 +31,10 @@ class ContentGenerator:
self.quality_analyzer = quality_analyzer
self.gemini_grounded = gemini_grounded
self.fallback_provider = fallback_provider
+
+ # Initialize specialized generators
+ self.carousel_generator = CarouselGenerator(citation_manager, quality_analyzer)
+ self.video_script_generator = VideoScriptGenerator(citation_manager, quality_analyzer)
def _transform_gemini_sources(self, gemini_sources):
"""Transform Gemini sources to ResearchSource format."""
@@ -258,91 +271,10 @@ class ContentGenerator:
content_result: Dict[str, Any],
grounding_enabled: bool
):
- """Generate LinkedIn carousel with all processing steps."""
- try:
- start_time = datetime.now()
-
- # Step 3: Add citations if requested
- citations = []
- source_list = None
- if request.include_citations and research_sources:
- # Extract citations from all slides
- all_content = " ".join([slide['content'] for slide in content_result['slides']])
- citations = self.citation_manager.extract_citations(all_content) if self.citation_manager else []
- source_list = self.citation_manager.generate_source_list(research_sources) if self.citation_manager else None
-
- # Step 4: Analyze content quality
- quality_metrics = None
- if grounding_enabled and self.quality_analyzer:
- try:
- all_content = " ".join([slide['content'] for slide in content_result['slides']])
- quality_handler = QualityHandler(self.quality_analyzer)
- quality_metrics = quality_handler.create_quality_metrics(
- content=all_content,
- sources=research_sources,
- industry=request.industry,
- grounding_enabled=grounding_enabled
- )
- except Exception as e:
- logger.warning(f"Quality analysis failed: {e}")
-
- # Step 5: Build response
- slides = []
- for i, slide_data in enumerate(content_result['slides']):
- slide_citations = []
- if request.include_citations and research_sources and self.citation_manager:
- slide_citations = self.citation_manager.extract_citations(slide_data['content'])
-
- slides.append({
- 'slide_number': i + 1,
- 'title': slide_data['title'],
- 'content': slide_data['content'],
- 'visual_elements': slide_data.get('visual_elements', []),
- 'design_notes': slide_data.get('design_notes'),
- 'citations': slide_citations
- })
-
- carousel_content = {
- 'title': content_result['title'],
- 'slides': slides,
- 'cover_slide': content_result.get('cover_slide'),
- 'cta_slide': content_result.get('cta_slide'),
- 'design_guidelines': content_result.get('design_guidelines', {}),
- 'citations': citations,
- 'source_list': source_list,
- 'quality_metrics': quality_metrics,
- 'grounding_enabled': grounding_enabled
- }
-
- generation_time = (datetime.now() - start_time).total_seconds()
-
- # Build grounding status
- grounding_status = {
- 'status': 'success' if grounding_enabled else 'disabled',
- 'sources_used': len(research_sources),
- 'citation_coverage': len(citations) / max(len(research_sources), 1) if research_sources else 0,
- 'quality_score': quality_metrics.overall_score if quality_metrics else 0.0
- } if grounding_enabled else None
-
- return {
- 'success': True,
- 'data': carousel_content,
- 'research_sources': research_sources,
- 'generation_metadata': {
- 'model_used': 'gemini-2.0-flash-001',
- 'generation_time': generation_time,
- 'research_time': research_time,
- 'grounding_enabled': grounding_enabled
- },
- 'grounding_status': grounding_status
- }
-
- except Exception as e:
- logger.error(f"Error generating LinkedIn carousel: {str(e)}")
- return {
- 'success': False,
- 'error': f"Failed to generate LinkedIn carousel: {str(e)}"
- }
+ """Generate LinkedIn carousel using the specialized CarouselGenerator."""
+ return await self.carousel_generator.generate_carousel(
+ request, research_sources, research_time, content_result, grounding_enabled
+ )
async def generate_video_script(
self,
@@ -352,76 +284,10 @@ class ContentGenerator:
content_result: Dict[str, Any],
grounding_enabled: bool
):
- """Generate LinkedIn video script with all processing steps."""
- try:
- start_time = datetime.now()
-
- # Step 3: Add citations if requested
- citations = []
- source_list = None
- if request.include_citations and research_sources and self.citation_manager:
- all_content = f"{content_result['hook']} {' '.join([scene['content'] for scene in content_result['main_content']])} {content_result['conclusion']}"
- citations = self.citation_manager.extract_citations(all_content)
- source_list = self.citation_manager.generate_source_list(research_sources)
-
- # Step 4: Analyze content quality
- quality_metrics = None
- if grounding_enabled and self.quality_analyzer:
- try:
- all_content = f"{content_result['hook']} {' '.join([scene['content'] for scene in content_result['main_content']])} {content_result['conclusion']}"
- quality_handler = QualityHandler(self.quality_analyzer)
- quality_metrics = quality_handler.create_quality_metrics(
- content=all_content,
- sources=research_sources,
- industry=request.industry,
- grounding_enabled=grounding_enabled
- )
- except Exception as e:
- logger.warning(f"Quality analysis failed: {e}")
-
- # Step 5: Build response
- video_script = {
- 'hook': content_result['hook'],
- 'main_content': content_result['main_content'],
- 'conclusion': content_result['conclusion'],
- 'captions': content_result.get('captions'),
- 'thumbnail_suggestions': content_result.get('thumbnail_suggestions', []),
- 'video_description': content_result.get('video_description', ''),
- 'citations': citations,
- 'source_list': source_list,
- 'quality_metrics': quality_metrics,
- 'grounding_enabled': grounding_enabled
- }
-
- generation_time = (datetime.now() - start_time).total_seconds()
-
- # Build grounding status
- grounding_status = {
- 'status': 'success' if grounding_enabled else 'disabled',
- 'sources_used': len(research_sources),
- 'citation_coverage': len(citations) / max(len(research_sources), 1) if research_sources else 0,
- 'quality_score': quality_metrics.overall_score if quality_metrics else 0.0
- } if grounding_enabled else None
-
- return {
- 'success': True,
- 'data': video_script,
- 'research_sources': research_sources,
- 'generation_metadata': {
- 'model_used': 'gemini-2.0-flash-001',
- 'generation_time': generation_time,
- 'research_time': research_time,
- 'grounding_enabled': grounding_enabled
- },
- 'grounding_status': grounding_status
- }
-
- except Exception as e:
- logger.error(f"Error generating LinkedIn video script: {str(e)}")
- return {
- 'success': False,
- 'error': f"Failed to generate LinkedIn video script: {str(e)}"
- }
+ """Generate LinkedIn video script using the specialized VideoScriptGenerator."""
+ return await self.video_script_generator.generate_video_script(
+ request, research_sources, research_time, content_result, grounding_enabled
+ )
async def generate_comment_response(
self,
@@ -471,11 +337,11 @@ class ContentGenerator:
"""Generate grounded post content using the enhanced Gemini provider with native grounding."""
try:
if not self.gemini_grounded:
- logger.warning("Gemini Grounded Provider not available, using fallback")
- return await self.generate_fallback_post_content(request)
+ logger.error("Gemini Grounded Provider not available - cannot generate content without AI provider")
+ raise Exception("Gemini Grounded Provider not available - cannot generate content without AI provider")
- # Build the prompt for grounded generation
- prompt = self._build_post_prompt(request)
+ # Build the prompt for grounded generation using the new prompt builder
+ prompt = PostPromptBuilder.build_post_prompt(request)
# Generate grounded content using native Google Search grounding
result = await self.gemini_grounded.generate_grounded_content(
@@ -489,18 +355,17 @@ class ContentGenerator:
except Exception as e:
logger.error(f"Error generating grounded post content: {str(e)}")
- # Fallback to basic generation
- return await self.generate_fallback_post_content(request)
+ raise Exception(f"Failed to generate grounded post content: {str(e)}")
async def generate_grounded_article_content(self, request, research_sources: List) -> Dict[str, Any]:
"""Generate grounded article content using the enhanced Gemini provider with native grounding."""
try:
if not self.gemini_grounded:
- logger.warning("Gemini Grounded Provider not available, using fallback")
- return await self.generate_fallback_article_content(request)
+ logger.error("Gemini Grounded Provider not available - cannot generate content without AI provider")
+ raise Exception("Gemini Grounded Provider not available - cannot generate content without AI provider")
- # Build the prompt for grounded generation
- prompt = self._build_article_prompt(request)
+ # Build the prompt for grounded generation using the new prompt builder
+ prompt = ArticlePromptBuilder.build_article_prompt(request)
# Generate grounded content using native Google Search grounding
result = await self.gemini_grounded.generate_grounded_content(
@@ -514,18 +379,17 @@ class ContentGenerator:
except Exception as e:
logger.error(f"Error generating grounded article content: {str(e)}")
- # Fallback to basic generation
- return await self.generate_fallback_article_content(request)
+ raise Exception(f"Failed to generate grounded article content: {str(e)}")
async def generate_grounded_carousel_content(self, request, research_sources: List) -> Dict[str, Any]:
"""Generate grounded carousel content using the enhanced Gemini provider with native grounding."""
try:
if not self.gemini_grounded:
- logger.warning("Gemini Grounded Provider not available, using fallback")
- return await self.generate_fallback_carousel_content(request)
+ logger.error("Gemini Grounded Provider not available - cannot generate content without AI provider")
+ raise Exception("Gemini Grounded Provider not available - cannot generate content without AI provider")
- # Build the prompt for grounded generation
- prompt = self._build_carousel_prompt(request)
+ # Build the prompt for grounded generation using the new prompt builder
+ prompt = CarouselPromptBuilder.build_carousel_prompt(request)
# Generate grounded content using native Google Search grounding
result = await self.gemini_grounded.generate_grounded_content(
@@ -539,18 +403,17 @@ class ContentGenerator:
except Exception as e:
logger.error(f"Error generating grounded carousel content: {str(e)}")
- # Fallback to basic generation
- return await self.generate_fallback_carousel_content(request)
+ raise Exception(f"Failed to generate grounded carousel content: {str(e)}")
async def generate_grounded_video_script_content(self, request, research_sources: List) -> Dict[str, Any]:
"""Generate grounded video script content using the enhanced Gemini provider with native grounding."""
try:
if not self.gemini_grounded:
- logger.warning("Gemini Grounded Provider not available, using fallback")
- return await self.generate_fallback_video_script_content(request)
+ logger.error("Gemini Grounded Provider not available - cannot generate content without AI provider")
+ raise Exception("Gemini Grounded Provider not available - cannot generate content without AI provider")
- # Build the prompt for grounded generation
- prompt = self._build_video_script_prompt(request)
+ # Build the prompt for grounded generation using the new prompt builder
+ prompt = VideoScriptPromptBuilder.build_video_script_prompt(request)
# Generate grounded content using native Google Search grounding
result = await self.gemini_grounded.generate_grounded_content(
@@ -564,185 +427,28 @@ class ContentGenerator:
except Exception as e:
logger.error(f"Error generating grounded video script content: {str(e)}")
- # Fallback to basic generation
- return await self.generate_fallback_video_script_content(request)
+ raise Exception(f"Failed to generate grounded video script content: {str(e)}")
async def generate_grounded_comment_response(self, request, research_sources: List) -> Dict[str, Any]:
"""Generate grounded comment response using the enhanced Gemini provider with native grounding."""
try:
if not self.gemini_grounded:
- logger.warning("Gemini Grounded Provider not available, using fallback")
- return await self.generate_fallback_comment_response(request)
+ logger.error("Gemini Grounded Provider not available - cannot generate content without AI provider")
+ raise Exception("Gemini Grounded Provider not available - cannot generate content without AI provider")
- # Build the prompt for grounded generation
- prompt = self._build_comment_response_prompt(request)
+ # Build the prompt for grounded generation using the new prompt builder
+ prompt = CommentResponsePromptBuilder.build_comment_response_prompt(request)
# Generate grounded content using native Google Search grounding
result = await self.gemini_grounded.generate_grounded_content(
prompt=prompt,
content_type="linkedin_comment_response",
temperature=0.7,
- max_tokens=500
+ max_tokens=2000
)
return result
except Exception as e:
logger.error(f"Error generating grounded comment response: {str(e)}")
- # Fallback to basic generation
- return await self.generate_fallback_comment_response(request)
-
- # Fallback content generation methods
- async def generate_fallback_post_content(self, request) -> Dict[str, Any]:
- """Generate post content using fallback provider."""
- if not self.fallback_provider:
- raise Exception("No fallback provider available")
-
- return {
- 'content': f"Professional LinkedIn post about {request.topic} in the {request.industry} industry.",
- 'hashtags': [{'hashtag': f'#{request.industry.lower().replace(" ", "")}', 'category': 'industry', 'popularity_score': 0.8}],
- 'call_to_action': "What are your thoughts on this? Share in the comments!",
- 'engagement_prediction': {'estimated_likes': 50, 'estimated_comments': 5}
- }
-
- async def generate_fallback_article_content(self, request) -> Dict[str, Any]:
- """Generate article content using fallback provider."""
- if not self.fallback_provider:
- raise Exception("No fallback provider available")
-
- return {
- 'title': f"Comprehensive Guide to {request.topic} in {request.industry}",
- 'content': f"Detailed article about {request.topic} in the {request.industry} industry.",
- 'sections': [{'title': 'Introduction', 'content': 'Industry overview and context'}],
- 'seo_metadata': {'keywords': [request.topic, request.industry]},
- 'image_suggestions': ['Industry-related visual content'],
- 'reading_time': '5 minutes'
- }
-
- async def generate_fallback_carousel_content(self, request) -> Dict[str, Any]:
- """Generate carousel content using fallback provider."""
- if not self.fallback_provider:
- raise Exception("No fallback provider available")
-
- return {
- 'title': f"Key Insights: {request.topic} in {request.industry}",
- 'slides': [
- {'title': 'Overview', 'content': f'Introduction to {request.topic}', 'visual_elements': [], 'design_notes': 'Clean, professional design'},
- {'title': 'Key Points', 'content': f'Main insights about {request.topic}', 'visual_elements': [], 'design_notes': 'Bullet points with icons'}
- ],
- 'cover_slide': {'title': 'Cover', 'content': 'Professional cover slide', 'visual_elements': [], 'design_notes': 'Eye-catching design'},
- 'cta_slide': {'title': 'Call to Action', 'content': 'Engage with this content', 'visual_elements': [], 'design_notes': 'Clear CTA design'},
- 'design_guidelines': {'style': 'professional', 'colors': 'brand colors'}
- }
-
- async def generate_fallback_video_script_content(self, request) -> Dict[str, Any]:
- """Generate video script content using fallback provider."""
- if not self.fallback_provider:
- raise Exception("No fallback provider available")
-
- return {
- 'hook': f"Discover how {request.topic} is transforming the {request.industry} industry!",
- 'main_content': [
- {'content': f'Introduction to {request.topic}', 'duration': '30s'},
- {'content': f'Key insights about {request.topic}', 'duration': '45s'}
- ],
- 'conclusion': f"Ready to explore {request.topic}? Let's dive in!",
- 'captions': [f'Key point about {request.topic}'],
- 'thumbnail_suggestions': ['Professional thumbnail with industry imagery'],
- 'video_description': f"Video description about {request.topic}"
- }
-
- async def generate_fallback_comment_response(self, request) -> Dict[str, Any]:
- """Generate comment response using fallback provider."""
- if not self.fallback_provider:
- raise Exception("No fallback provider available")
-
- return {
- 'response': f"Thank you for your comment about {request.original_comment}",
- 'alternative_responses': [],
- 'tone_analysis': None
- }
-
- # Prompt building methods
- def _build_post_prompt(self, request) -> str:
- """Build prompt for post generation."""
- prompt = f"""
- Generate a professional LinkedIn post about {request.topic} in the {request.industry} industry.
-
- Requirements:
- - Tone: {request.tone}
- - Target audience: {request.target_audience or 'Industry professionals'}
- - Maximum length: {request.max_length} characters
- - Include engaging hashtags
- - Include a call to action
- - Make it informative and shareable
-
- Key points to include: {', '.join(request.key_points) if request.key_points else 'Industry insights and trends'}
- """
- return prompt.strip()
-
- def _build_article_prompt(self, request) -> str:
- """Build prompt for article generation."""
- prompt = f"""
- Generate a comprehensive LinkedIn article about {request.topic} in the {request.industry} industry.
-
- Requirements:
- - Tone: {request.tone}
- - Target audience: {request.target_audience or 'Industry professionals'}
- - Word count: {request.word_count} words
- - Include SEO optimization
- - Include image suggestions
- - Make it informative and engaging
-
- Key sections to include: {', '.join(request.key_sections) if request.key_sections else 'Introduction, main content, conclusion'}
- """
- return prompt.strip()
-
- def _build_carousel_prompt(self, request) -> str:
- """Build prompt for carousel generation."""
- prompt = f"""
- Generate a LinkedIn carousel about {request.topic} in the {request.industry} industry.
-
- Requirements:
- - Tone: {request.tone}
- - Target audience: {request.target_audience or 'Industry professionals'}
- - Number of slides: {request.number_of_slides}
- - Include cover slide: {request.include_cover_slide}
- - Include CTA slide: {request.include_cta_slide}
- - Make each slide informative and visually appealing
-
- Each slide should contain valuable insights and be designed for social media engagement.
- """
- return prompt.strip()
-
- def _build_video_script_prompt(self, request) -> str:
- """Build prompt for video script generation."""
- prompt = f"""
- Generate a LinkedIn video script about {request.topic} in the {request.industry} industry.
-
- Requirements:
- - Tone: {request.tone}
- - Target audience: {request.target_audience or 'Industry professionals'}
- - Duration: {request.video_duration} seconds
- - Include captions: {request.include_captions}
- - Include thumbnail suggestions: {request.include_thumbnail_suggestions}
- - Make it engaging and informative
-
- Structure: Hook, main content (divided into scenes), conclusion
- """
- return prompt.strip()
-
- def _build_comment_response_prompt(self, request) -> str:
- """Build prompt for comment response generation."""
- prompt = f"""
- Generate a LinkedIn comment response to: "{request.original_comment}"
-
- Context: {request.post_context}
- Industry: {request.industry}
- Tone: {request.tone}
- Response length: {request.response_length}
- Include questions: {request.include_questions}
-
- Make the response engaging, professional, and add value to the conversation.
- """
- return prompt.strip()
+ raise Exception(f"Failed to generate grounded comment response: {str(e)}")
diff --git a/backend/services/linkedin/content_generator_prompts/__init__.py b/backend/services/linkedin/content_generator_prompts/__init__.py
new file mode 100644
index 00000000..9c83e214
--- /dev/null
+++ b/backend/services/linkedin/content_generator_prompts/__init__.py
@@ -0,0 +1,24 @@
+"""
+Content Generator Prompts Package
+
+This package contains all the prompt templates and generation logic used by the ContentGenerator class
+for generating various types of LinkedIn content.
+"""
+
+from .post_prompts import PostPromptBuilder
+from .article_prompts import ArticlePromptBuilder
+from .carousel_prompts import CarouselPromptBuilder
+from .video_script_prompts import VideoScriptPromptBuilder
+from .comment_response_prompts import CommentResponsePromptBuilder
+from .carousel_generator import CarouselGenerator
+from .video_script_generator import VideoScriptGenerator
+
+__all__ = [
+ 'PostPromptBuilder',
+ 'ArticlePromptBuilder',
+ 'CarouselPromptBuilder',
+ 'VideoScriptPromptBuilder',
+ 'CommentResponsePromptBuilder',
+ 'CarouselGenerator',
+ 'VideoScriptGenerator'
+]
diff --git a/backend/services/linkedin/content_generator_prompts/article_prompts.py b/backend/services/linkedin/content_generator_prompts/article_prompts.py
new file mode 100644
index 00000000..3938523c
--- /dev/null
+++ b/backend/services/linkedin/content_generator_prompts/article_prompts.py
@@ -0,0 +1,65 @@
+"""
+LinkedIn Article Generation Prompts
+
+This module contains prompt templates and builders for generating LinkedIn articles.
+"""
+
+from typing import Any
+
+
+class ArticlePromptBuilder:
+ """Builder class for LinkedIn article generation prompts."""
+
+ @staticmethod
+ def build_article_prompt(request: Any) -> str:
+ """
+ Build prompt for article generation.
+
+ Args:
+ request: LinkedInArticleRequest object containing generation parameters
+
+ Returns:
+ Formatted prompt string for article generation
+ """
+ prompt = f"""
+ You are a senior content strategist and industry expert specializing in {request.industry}. Create a comprehensive, thought-provoking LinkedIn article that establishes authority, drives engagement, and provides genuine value to professionals in this field.
+
+ TOPIC: {request.topic}
+ INDUSTRY: {request.industry}
+ TONE: {request.tone}
+ TARGET AUDIENCE: {request.target_audience or 'Industry professionals, executives, and thought leaders'}
+ WORD COUNT: {request.word_count} words
+
+ CONTENT STRUCTURE:
+ - Compelling headline that promises specific value
+ - Engaging introduction with a hook and clear value proposition
+ - 3-5 main sections with actionable insights and examples
+ - Data-driven insights with proper citations
+ - Practical takeaways and next steps
+ - Strong conclusion with a call-to-action
+
+ CONTENT QUALITY REQUIREMENTS:
+ - Include current industry statistics and trends (2024-2025)
+ - Provide real-world examples and case studies
+ - Address common challenges and pain points
+ - Offer actionable strategies and frameworks
+ - Use industry-specific terminology appropriately
+ - Include expert quotes or insights when relevant
+
+ SEO & ENGAGEMENT OPTIMIZATION:
+ - Use relevant keywords naturally throughout the content
+ - Include engaging subheadings for scannability
+ - Add bullet points and numbered lists for key insights
+ - Include relevant hashtags for discoverability
+ - End with thought-provoking questions to encourage comments
+
+ VISUAL ELEMENTS:
+ - Suggest 2-3 relevant images or graphics
+ - Recommend data visualization opportunities
+ - Include pull quotes for key insights
+
+ KEY SECTIONS TO COVER: {', '.join(request.key_sections) if request.key_sections else 'Industry overview, current challenges, emerging trends, practical solutions, future outlook'}
+
+ REMEMBER: This article should position the author as a thought leader while providing actionable insights that readers can immediately apply in their professional lives.
+ """
+ return prompt.strip()
diff --git a/backend/services/linkedin/content_generator_prompts/carousel_generator.py b/backend/services/linkedin/content_generator_prompts/carousel_generator.py
new file mode 100644
index 00000000..493b1f5d
--- /dev/null
+++ b/backend/services/linkedin/content_generator_prompts/carousel_generator.py
@@ -0,0 +1,112 @@
+"""
+LinkedIn Carousel Generation Module
+
+This module handles the generation of LinkedIn carousels with all processing steps.
+"""
+
+from typing import Dict, Any, List
+from datetime import datetime
+from loguru import logger
+from services.linkedin.quality_handler import QualityHandler
+
+
+class CarouselGenerator:
+ """Handles LinkedIn carousel generation with all processing steps."""
+
+ def __init__(self, citation_manager=None, quality_analyzer=None):
+ self.citation_manager = citation_manager
+ self.quality_analyzer = quality_analyzer
+
+ async def generate_carousel(
+ self,
+ request,
+ research_sources: List,
+ research_time: float,
+ content_result: Dict[str, Any],
+ grounding_enabled: bool
+ ):
+ """Generate LinkedIn carousel with all processing steps."""
+ try:
+ start_time = datetime.now()
+
+ # Step 3: Add citations if requested
+ citations = []
+ source_list = None
+ if request.include_citations and research_sources:
+ # Extract citations from all slides
+ all_content = " ".join([slide['content'] for slide in content_result['slides']])
+ citations = self.citation_manager.extract_citations(all_content) if self.citation_manager else []
+ source_list = self.citation_manager.generate_source_list(research_sources) if self.citation_manager else None
+
+ # Step 4: Analyze content quality
+ quality_metrics = None
+ if grounding_enabled and self.quality_analyzer:
+ try:
+ all_content = " ".join([slide['content'] for slide in content_result['slides']])
+ quality_handler = QualityHandler(self.quality_analyzer)
+ quality_metrics = quality_handler.create_quality_metrics(
+ content=all_content,
+ sources=research_sources,
+ industry=request.industry,
+ grounding_enabled=grounding_enabled
+ )
+ except Exception as e:
+ logger.warning(f"Quality analysis failed: {e}")
+
+ # Step 5: Build response
+ slides = []
+ for i, slide_data in enumerate(content_result['slides']):
+ slide_citations = []
+ if request.include_citations and research_sources and self.citation_manager:
+ slide_citations = self.citation_manager.extract_citations(slide_data['content'])
+
+ slides.append({
+ 'slide_number': i + 1,
+ 'title': slide_data['title'],
+ 'content': slide_data['content'],
+ 'visual_elements': slide_data.get('visual_elements', []),
+ 'design_notes': slide_data.get('design_notes'),
+ 'citations': slide_citations
+ })
+
+ carousel_content = {
+ 'title': content_result['title'],
+ 'slides': slides,
+ 'cover_slide': content_result.get('cover_slide'),
+ 'cta_slide': content_result.get('cta_slide'),
+ 'design_guidelines': content_result.get('design_guidelines', {}),
+ 'citations': citations,
+ 'source_list': source_list,
+ 'quality_metrics': quality_metrics,
+ 'grounding_enabled': grounding_enabled
+ }
+
+ generation_time = (datetime.now() - start_time).total_seconds()
+
+ # Build grounding status
+ grounding_status = {
+ 'status': 'success' if grounding_enabled else 'disabled',
+ 'sources_used': len(research_sources),
+ 'citation_coverage': len(citations) / max(len(research_sources), 1) if research_sources else 0,
+ 'quality_score': quality_metrics.overall_score if quality_metrics else 0.0
+ } if grounding_enabled else None
+
+ return {
+ 'success': True,
+ 'data': carousel_content,
+ 'research_sources': research_sources,
+ 'generation_metadata': {
+ 'model_used': 'gemini-2.0-flash-001',
+ 'generation_time': generation_time,
+ 'research_time': research_time,
+ 'grounding_enabled': grounding_enabled
+ },
+ 'grounding_status': grounding_status
+ }
+
+ except Exception as e:
+ logger.error(f"Error generating LinkedIn carousel: {str(e)}")
+ return {
+ 'success': False,
+ 'error': f"Failed to generate LinkedIn carousel: {str(e)}"
+ }
diff --git a/backend/services/linkedin/content_generator_prompts/carousel_prompts.py b/backend/services/linkedin/content_generator_prompts/carousel_prompts.py
new file mode 100644
index 00000000..7341f45f
--- /dev/null
+++ b/backend/services/linkedin/content_generator_prompts/carousel_prompts.py
@@ -0,0 +1,63 @@
+"""
+LinkedIn Carousel Generation Prompts
+
+This module contains prompt templates and builders for generating LinkedIn carousels.
+"""
+
+from typing import Any
+
+
+class CarouselPromptBuilder:
+ """Builder class for LinkedIn carousel generation prompts."""
+
+ @staticmethod
+ def build_carousel_prompt(request: Any) -> str:
+ """
+ Build prompt for carousel generation.
+
+ Args:
+ request: LinkedInCarouselRequest object containing generation parameters
+
+ Returns:
+ Formatted prompt string for carousel generation
+ """
+ prompt = f"""
+ You are a visual content strategist and {request.industry} industry expert. Create a compelling LinkedIn carousel that tells a cohesive story and drives engagement through visual storytelling and valuable insights.
+
+ TOPIC: {request.topic}
+ INDUSTRY: {request.industry}
+ TONE: {request.tone}
+ TARGET AUDIENCE: {request.target_audience or 'Industry professionals and decision-makers'}
+ NUMBER OF SLIDES: {request.number_of_slides}
+ INCLUDE COVER SLIDE: {request.include_cover_slide}
+ INCLUDE CTA SLIDE: {request.include_cta_slide}
+
+ CAROUSEL STRUCTURE & DESIGN:
+ - Cover Slide: Compelling headline with visual hook and clear value proposition
+ - Content Slides: Each slide should focus on ONE key insight with supporting data
+ - Visual Flow: Create a logical progression that builds understanding
+ - CTA Slide: Clear next steps and engagement prompts
+
+ CONTENT REQUIREMENTS PER SLIDE:
+ - Maximum 3-4 bullet points per slide for readability
+ - Include relevant statistics, percentages, or data points
+ - Use action-oriented language and specific examples
+ - Each slide should be self-contained but contribute to the overall narrative
+
+ VISUAL DESIGN GUIDELINES:
+ - Suggest color schemes that match the industry (professional yet engaging)
+ - Recommend icon styles and visual elements for each slide
+ - Include layout suggestions (text placement, image positioning)
+ - Suggest data visualization opportunities (charts, graphs, infographics)
+
+ ENGAGEMENT STRATEGY:
+ - Include thought-provoking questions on key slides
+ - Suggest interactive elements (polls, surveys, comment prompts)
+ - Use storytelling elements to create emotional connection
+ - End with clear call-to-action and hashtag suggestions
+
+ KEY INSIGHTS TO COVER: {', '.join(request.key_points) if request.key_points else 'Industry trends, challenges, solutions, and opportunities'}
+
+ REMEMBER: Each slide should be visually appealing, informative, and encourage the viewer to continue reading. The carousel should provide immediate value while building anticipation for the next slide.
+ """
+ return prompt.strip()
diff --git a/backend/services/linkedin/content_generator_prompts/comment_response_prompts.py b/backend/services/linkedin/content_generator_prompts/comment_response_prompts.py
new file mode 100644
index 00000000..03c6e31d
--- /dev/null
+++ b/backend/services/linkedin/content_generator_prompts/comment_response_prompts.py
@@ -0,0 +1,64 @@
+"""
+LinkedIn Comment Response Generation Prompts
+
+This module contains prompt templates and builders for generating LinkedIn comment responses.
+"""
+
+from typing import Any
+
+
+class CommentResponsePromptBuilder:
+ """Builder class for LinkedIn comment response generation prompts."""
+
+ @staticmethod
+ def build_comment_response_prompt(request: Any) -> str:
+ """
+ Build prompt for comment response generation.
+
+ Args:
+ request: LinkedInCommentResponseRequest object containing generation parameters
+
+ Returns:
+ Formatted prompt string for comment response generation
+ """
+ prompt = f"""
+ You are a {request.industry} industry expert and LinkedIn engagement specialist. Create a thoughtful, professional comment response that adds genuine value to the conversation and encourages further engagement.
+
+ ORIGINAL COMMENT: "{request.original_comment}"
+ POST CONTEXT: {request.post_context}
+ INDUSTRY: {request.industry}
+ TONE: {request.tone}
+ RESPONSE LENGTH: {request.response_length}
+ INCLUDE QUESTIONS: {request.include_questions}
+
+ RESPONSE STRATEGY:
+ - Acknowledge the commenter's perspective or question
+ - Provide specific, actionable insights or examples
+ - Share relevant industry knowledge or experience
+ - Encourage further discussion and engagement
+ - Maintain professional yet conversational tone
+
+ CONTENT REQUIREMENTS:
+ - Start with appreciation or acknowledgment of the comment
+ - Include 1-2 specific insights that add value
+ - Use industry-specific examples when relevant
+ - End with a thought-provoking question or invitation to continue
+ - Keep the tone consistent with the original post
+
+ ENGAGEMENT TECHNIQUES:
+ - Ask follow-up questions that encourage response
+ - Share relevant statistics or data points
+ - Include personal experiences or case studies
+ - Suggest additional resources or next steps
+ - Use inclusive language that welcomes others to join
+
+ PROFESSIONAL GUIDELINES:
+ - Always be respectful and constructive
+ - Avoid controversial or polarizing statements
+ - Focus on building relationships, not just responding
+ - Demonstrate expertise without being condescending
+ - Use appropriate emojis and formatting for warmth
+
+ REMEMBER: This response should feel like a natural continuation of the conversation, not just a reply. It should encourage the original commenter and others to engage further.
+ """
+ return prompt.strip()
diff --git a/backend/services/linkedin/content_generator_prompts/post_prompts.py b/backend/services/linkedin/content_generator_prompts/post_prompts.py
new file mode 100644
index 00000000..133db45b
--- /dev/null
+++ b/backend/services/linkedin/content_generator_prompts/post_prompts.py
@@ -0,0 +1,57 @@
+"""
+LinkedIn Post Generation Prompts
+
+This module contains prompt templates and builders for generating LinkedIn posts.
+"""
+
+from typing import Any
+
+
+class PostPromptBuilder:
+ """Builder class for LinkedIn post generation prompts."""
+
+ @staticmethod
+ def build_post_prompt(request: Any) -> str:
+ """
+ Build prompt for post generation.
+
+ Args:
+ request: LinkedInPostRequest object containing generation parameters
+
+ Returns:
+ Formatted prompt string for post generation
+ """
+ prompt = f"""
+ You are an expert LinkedIn content strategist with 10+ years of experience in the {request.industry} industry. Create a highly engaging, professional LinkedIn post that drives meaningful engagement and establishes thought leadership.
+
+ TOPIC: {request.topic}
+ INDUSTRY: {request.industry}
+ TONE: {request.tone}
+ TARGET AUDIENCE: {request.target_audience or 'Industry professionals, decision-makers, and thought leaders'}
+ MAX LENGTH: {request.max_length} characters
+
+ CONTENT REQUIREMENTS:
+ - Start with a compelling hook that addresses a pain point or opportunity
+ - Include 2-3 specific, actionable insights or data points
+ - Use storytelling elements to make it relatable and memorable
+ - Include industry-specific examples or case studies when relevant
+ - End with a thought-provoking question or clear call-to-action
+ - Use professional yet conversational language that encourages discussion
+
+ ENGAGEMENT STRATEGY:
+ - Include 3-5 highly relevant, trending hashtags (mix of broad and niche)
+ - Use line breaks and emojis strategically for readability
+ - Encourage comments by asking for opinions or experiences
+ - Make it shareable by providing genuine value
+
+ KEY POINTS TO COVER: {', '.join(request.key_points) if request.key_points else 'Current industry trends, challenges, and opportunities'}
+
+ FORMATTING:
+ - Use bullet points or numbered lists for key insights
+ - Include relevant emojis to enhance visual appeal
+ - Break text into digestible paragraphs (2-3 lines max)
+ - Leave space for engagement (don't fill the entire character limit)
+
+ REMEMBER: This post should position the author as a knowledgeable industry expert while being genuinely helpful to the audience.
+ """
+ return prompt.strip()
diff --git a/backend/services/linkedin/content_generator_prompts/video_script_generator.py b/backend/services/linkedin/content_generator_prompts/video_script_generator.py
new file mode 100644
index 00000000..c34ccbf6
--- /dev/null
+++ b/backend/services/linkedin/content_generator_prompts/video_script_generator.py
@@ -0,0 +1,97 @@
+"""
+LinkedIn Video Script Generation Module
+
+This module handles the generation of LinkedIn video scripts with all processing steps.
+"""
+
+from typing import Dict, Any, List
+from datetime import datetime
+from loguru import logger
+from services.linkedin.quality_handler import QualityHandler
+
+
+class VideoScriptGenerator:
+ """Handles LinkedIn video script generation with all processing steps."""
+
+ def __init__(self, citation_manager=None, quality_analyzer=None):
+ self.citation_manager = citation_manager
+ self.quality_analyzer = quality_analyzer
+
+ async def generate_video_script(
+ self,
+ request,
+ research_sources: List,
+ research_time: float,
+ content_result: Dict[str, Any],
+ grounding_enabled: bool
+ ):
+ """Generate LinkedIn video script with all processing steps."""
+ try:
+ start_time = datetime.now()
+
+ # Step 3: Add citations if requested
+ citations = []
+ source_list = None
+ if request.include_citations and research_sources and self.citation_manager:
+ all_content = f"{content_result['hook']} {' '.join([scene['content'] for scene in content_result['main_content']])} {content_result['conclusion']}"
+ citations = self.citation_manager.extract_citations(all_content)
+ source_list = self.citation_manager.generate_source_list(research_sources)
+
+ # Step 4: Analyze content quality
+ quality_metrics = None
+ if grounding_enabled and self.quality_analyzer:
+ try:
+ all_content = f"{content_result['hook']} {' '.join([scene['content'] for scene in content_result['main_content']])} {content_result['conclusion']}"
+ quality_handler = QualityHandler(self.quality_analyzer)
+ quality_metrics = quality_handler.create_quality_metrics(
+ content=all_content,
+ sources=research_sources,
+ industry=request.industry,
+ grounding_enabled=grounding_enabled
+ )
+ except Exception as e:
+ logger.warning(f"Quality analysis failed: {e}")
+
+ # Step 5: Build response
+ video_script = {
+ 'hook': content_result['hook'],
+ 'main_content': content_result['main_content'],
+ 'conclusion': content_result['conclusion'],
+ 'captions': content_result.get('captions'),
+ 'thumbnail_suggestions': content_result.get('thumbnail_suggestions', []),
+ 'video_description': content_result.get('video_description', ''),
+ 'citations': citations,
+ 'source_list': source_list,
+ 'quality_metrics': quality_metrics,
+ 'grounding_enabled': grounding_enabled
+ }
+
+ generation_time = (datetime.now() - start_time).total_seconds()
+
+ # Build grounding status
+ grounding_status = {
+ 'status': 'success' if grounding_enabled else 'disabled',
+ 'sources_used': len(research_sources),
+ 'citation_coverage': len(citations) / max(len(research_sources), 1) if research_sources else 0,
+ 'quality_score': quality_metrics.overall_score if quality_metrics else 0.0
+ } if grounding_enabled else None
+
+ return {
+ 'success': True,
+ 'data': video_script,
+ 'research_sources': research_sources,
+ 'generation_metadata': {
+ 'model_used': 'gemini-2.0-flash-001',
+ 'generation_time': generation_time,
+ 'research_time': research_time,
+ 'grounding_enabled': grounding_enabled
+ },
+ 'grounding_status': grounding_status
+ }
+
+ except Exception as e:
+ logger.error(f"Error generating LinkedIn video script: {str(e)}")
+ return {
+ 'success': False,
+ 'error': f"Failed to generate LinkedIn video script: {str(e)}"
+ }
diff --git a/backend/services/linkedin/content_generator_prompts/video_script_prompts.py b/backend/services/linkedin/content_generator_prompts/video_script_prompts.py
new file mode 100644
index 00000000..257002e1
--- /dev/null
+++ b/backend/services/linkedin/content_generator_prompts/video_script_prompts.py
@@ -0,0 +1,75 @@
+"""
+LinkedIn Video Script Generation Prompts
+
+This module contains prompt templates and builders for generating LinkedIn video scripts.
+"""
+
+from typing import Any
+
+
+class VideoScriptPromptBuilder:
+ """Builder class for LinkedIn video script generation prompts."""
+
+ @staticmethod
+ def build_video_script_prompt(request: Any) -> str:
+ """
+ Build prompt for video script generation.
+
+ Args:
+ request: LinkedInVideoScriptRequest object containing generation parameters
+
+ Returns:
+ Formatted prompt string for video script generation
+ """
+ prompt = f"""
+ You are a video content strategist and {request.industry} industry expert. Create a compelling LinkedIn video script that captures attention in the first 3 seconds and maintains engagement throughout the entire duration.
+
+ TOPIC: {request.topic}
+ INDUSTRY: {request.industry}
+ TONE: {request.tone}
+ TARGET AUDIENCE: {request.target_audience or 'Industry professionals and decision-makers'}
+ DURATION: {request.video_duration} seconds
+ INCLUDE CAPTIONS: {request.include_captions}
+ INCLUDE THUMBNAIL SUGGESTIONS: {request.include_thumbnail_suggestions}
+
+ VIDEO STRUCTURE & TIMING:
+ - Hook (0-3 seconds): Compelling opening that stops the scroll
+ - Introduction (3-8 seconds): Establish credibility and preview value
+ - Main Content (8-{request.video_duration-5} seconds): 2-3 key insights with examples
+ - Conclusion (Last 5 seconds): Clear call-to-action and engagement prompt
+
+ CONTENT REQUIREMENTS:
+ - Start with a surprising statistic, question, or bold statement
+ - Include specific examples and case studies from the industry
+ - Use conversational, engaging language that feels natural when spoken
+ - Include 2-3 actionable takeaways viewers can implement immediately
+ - End with a question that encourages comments and discussion
+
+ VISUAL & AUDIO GUIDELINES:
+ - Suggest background music style and mood
+ - Recommend visual elements (text overlays, graphics, charts)
+ - Include specific camera angle and movement suggestions
+ - Suggest props or visual aids that enhance the message
+
+ CAPTION OPTIMIZATION:
+ - Write captions that are engaging even without audio
+ - Include emojis and formatting for visual appeal
+ - Ensure captions complement the spoken content
+ - Make captions scannable and easy to read
+
+ THUMBNAIL DESIGN:
+ - Suggest compelling thumbnail text and imagery
+ - Recommend color schemes that match the industry
+ - Include specific design elements that increase click-through rates
+
+ ENGAGEMENT STRATEGY:
+ - Include moments that encourage viewers to pause and think
+ - Suggest interactive elements (polls, questions, challenges)
+ - Create emotional connection through storytelling
+ - End with clear next steps and hashtag suggestions
+
+ KEY INSIGHTS TO COVER: {', '.join(request.key_points) if request.key_points else 'Industry trends, challenges, solutions, and opportunities'}
+
+ REMEMBER: This video should provide immediate value while building the creator's authority. Every second should count toward engagement and viewer retention.
+ """
+ return prompt.strip()
diff --git a/backend/services/linkedin/image_generation/__init__.py b/backend/services/linkedin/image_generation/__init__.py
new file mode 100644
index 00000000..9cb52cf1
--- /dev/null
+++ b/backend/services/linkedin/image_generation/__init__.py
@@ -0,0 +1,22 @@
+"""
+LinkedIn Image Generation Package
+
+This package provides AI-powered image generation capabilities for LinkedIn content
+using Google's Gemini API. It includes image generation, editing, storage, and
+management services optimized for professional business use.
+"""
+
+from .linkedin_image_generator import LinkedInImageGenerator
+from .linkedin_image_editor import LinkedInImageEditor
+from .linkedin_image_storage import LinkedInImageStorage
+
+__all__ = [
+ 'LinkedInImageGenerator',
+ 'LinkedInImageEditor',
+ 'LinkedInImageStorage'
+]
+
+# Version information
+__version__ = "1.0.0"
+__author__ = "Alwrity Team"
+__description__ = "LinkedIn AI Image Generation Services"
diff --git a/backend/services/linkedin/image_generation/linkedin_image_editor.py b/backend/services/linkedin/image_generation/linkedin_image_editor.py
new file mode 100644
index 00000000..3a75cf82
--- /dev/null
+++ b/backend/services/linkedin/image_generation/linkedin_image_editor.py
@@ -0,0 +1,530 @@
+"""
+LinkedIn Image Editor Service
+
+This service handles image editing capabilities for LinkedIn content using Gemini's
+conversational editing features. It provides professional image refinement and
+optimization specifically for LinkedIn use cases.
+"""
+
+import os
+import base64
+from typing import Dict, Any, Optional, List
+from datetime import datetime
+from PIL import Image, ImageEnhance, ImageFilter
+from io import BytesIO
+from loguru import logger
+
+# Import existing infrastructure
+from ...api_key_manager import APIKeyManager
+
+
+class LinkedInImageEditor:
+ """
+ Handles LinkedIn image editing and refinement using Gemini's capabilities.
+
+ This service provides both AI-powered editing through Gemini and traditional
+ image processing for LinkedIn-specific optimizations.
+ """
+
+ def __init__(self, api_key_manager: Optional[APIKeyManager] = None):
+ """
+ Initialize the LinkedIn Image Editor.
+
+ Args:
+ api_key_manager: API key manager for Gemini authentication
+ """
+ self.api_key_manager = api_key_manager or APIKeyManager()
+ self.model = "gemini-2.5-flash-image-preview"
+
+ # LinkedIn-specific editing parameters
+ self.enhancement_factors = {
+ 'brightness': 1.1, # Slightly brighter for mobile viewing
+ 'contrast': 1.05, # Subtle contrast enhancement
+ 'sharpness': 1.2, # Enhanced sharpness for clarity
+ 'saturation': 1.05 # Slight saturation boost
+ }
+
+ logger.info("LinkedIn Image Editor initialized")
+
+ async def edit_image_conversationally(
+ self,
+ base_image: bytes,
+ edit_prompt: str,
+ content_context: Dict[str, Any]
+ ) -> Dict[str, Any]:
+ """
+ Edit image using Gemini's conversational editing capabilities.
+
+ Args:
+ base_image: Base image data in bytes
+ edit_prompt: Natural language description of desired edits
+ content_context: LinkedIn content context for optimization
+
+ Returns:
+ Dict containing edited image result and metadata
+ """
+ try:
+ start_time = datetime.now()
+ logger.info(f"Starting conversational image editing: {edit_prompt[:100]}...")
+
+ # Enhance edit prompt for LinkedIn optimization
+ enhanced_prompt = self._enhance_edit_prompt_for_linkedin(
+ edit_prompt, content_context
+ )
+
+ # TODO: Implement Gemini conversational editing when available
+ # For now, we'll use traditional image processing based on prompt analysis
+ edited_image = await self._apply_traditional_editing(
+ base_image, edit_prompt, content_context
+ )
+
+ if not edited_image.get('success'):
+ return edited_image
+
+ generation_time = (datetime.now() - start_time).total_seconds()
+
+ return {
+ 'success': True,
+ 'image_data': edited_image['image_data'],
+ 'metadata': {
+ 'edit_prompt': edit_prompt,
+ 'enhanced_prompt': enhanced_prompt,
+ 'editing_method': 'traditional_processing',
+ 'editing_time': generation_time,
+ 'content_context': content_context,
+ 'model_used': self.model
+ },
+ 'linkedin_optimization': {
+ 'mobile_optimized': True,
+ 'professional_aesthetic': True,
+ 'brand_compliant': True,
+ 'engagement_optimized': True
+ }
+ }
+
+ except Exception as e:
+ logger.error(f"Error in conversational image editing: {str(e)}")
+ return {
+ 'success': False,
+ 'error': f"Conversational editing failed: {str(e)}",
+ 'generation_time': (datetime.now() - start_time).total_seconds() if 'start_time' in locals() else 0
+ }
+
+ async def apply_style_transfer(
+ self,
+ base_image: bytes,
+ style_reference: bytes,
+ content_context: Dict[str, Any]
+ ) -> Dict[str, Any]:
+ """
+ Apply style transfer from reference image to base image.
+
+ Args:
+ base_image: Base image data in bytes
+ style_reference: Reference image for style transfer
+ content_context: LinkedIn content context
+
+ Returns:
+ Dict containing style-transferred image result
+ """
+ try:
+ start_time = datetime.now()
+ logger.info("Starting style transfer for LinkedIn image")
+
+ # TODO: Implement Gemini style transfer when available
+ # For now, return placeholder implementation
+
+ return {
+ 'success': False,
+ 'error': 'Style transfer not yet implemented - coming in next Gemini API update',
+ 'generation_time': (datetime.now() - start_time).total_seconds()
+ }
+
+ except Exception as e:
+ logger.error(f"Error in style transfer: {str(e)}")
+ return {
+ 'success': False,
+ 'error': f"Style transfer failed: {str(e)}",
+ 'generation_time': (datetime.now() - start_time).total_seconds() if 'start_time' in locals() else 0
+ }
+
+ async def enhance_image_quality(
+ self,
+ image_data: bytes,
+ enhancement_type: str = "linkedin_optimized",
+ content_context: Optional[Dict[str, Any]] = None
+ ) -> Dict[str, Any]:
+ """
+ Enhance image quality using traditional image processing.
+
+ Args:
+ image_data: Image data in bytes
+ enhancement_type: Type of enhancement to apply
+ content_context: LinkedIn content context for optimization
+
+ Returns:
+ Dict containing enhanced image result
+ """
+ try:
+ start_time = datetime.now()
+ logger.info(f"Starting image quality enhancement: {enhancement_type}")
+
+ # Open image for processing
+ image = Image.open(BytesIO(image_data))
+ original_size = image.size
+
+ # Apply LinkedIn-specific enhancements
+ if enhancement_type == "linkedin_optimized":
+ enhanced_image = self._apply_linkedin_enhancements(image, content_context)
+ elif enhancement_type == "professional":
+ enhanced_image = self._apply_professional_enhancements(image)
+ elif enhancement_type == "creative":
+ enhanced_image = self._apply_creative_enhancements(image)
+ else:
+ enhanced_image = self._apply_linkedin_enhancements(image, content_context)
+
+ # Convert back to bytes
+ output_buffer = BytesIO()
+ enhanced_image.save(output_buffer, format=image.format or "PNG", optimize=True)
+ enhanced_data = output_buffer.getvalue()
+
+ enhancement_time = (datetime.now() - start_time).total_seconds()
+
+ return {
+ 'success': True,
+ 'image_data': enhanced_data,
+ 'metadata': {
+ 'enhancement_type': enhancement_type,
+ 'original_size': original_size,
+ 'enhanced_size': enhanced_image.size,
+ 'enhancement_time': enhancement_time,
+ 'content_context': content_context
+ }
+ }
+
+ except Exception as e:
+ logger.error(f"Error in image quality enhancement: {str(e)}")
+ return {
+ 'success': False,
+ 'error': f"Quality enhancement failed: {str(e)}",
+ 'generation_time': (datetime.now() - start_time).total_seconds() if 'start_time' in locals() else 0
+ }
+
+ def _enhance_edit_prompt_for_linkedin(
+ self,
+ edit_prompt: str,
+ content_context: Dict[str, Any]
+ ) -> str:
+ """
+ Enhance edit prompt for LinkedIn optimization.
+
+ Args:
+ edit_prompt: Original edit prompt
+ content_context: LinkedIn content context
+
+ Returns:
+ Enhanced edit prompt
+ """
+ industry = content_context.get('industry', 'business')
+ content_type = content_context.get('content_type', 'post')
+
+ linkedin_edit_enhancements = [
+ f"Maintain professional business aesthetic for {industry} industry",
+ f"Ensure mobile-optimized composition for LinkedIn {content_type}",
+ "Keep professional color scheme and typography",
+ "Maintain brand consistency and visual hierarchy",
+ "Optimize for LinkedIn feed viewing and engagement"
+ ]
+
+ enhanced_prompt = f"{edit_prompt}\n\n"
+ enhanced_prompt += "\n".join(linkedin_edit_enhancements)
+
+ return enhanced_prompt
+
+ async def _apply_traditional_editing(
+ self,
+ base_image: bytes,
+ edit_prompt: str,
+ content_context: Dict[str, Any]
+ ) -> Dict[str, Any]:
+ """
+ Apply traditional image processing based on edit prompt analysis.
+
+ Args:
+ base_image: Base image data in bytes
+ edit_prompt: Description of desired edits
+ content_context: LinkedIn content context
+
+ Returns:
+ Dict containing edited image result
+ """
+ try:
+ # Open image for processing
+ image = Image.open(BytesIO(base_image))
+
+ # Analyze edit prompt and apply appropriate processing
+ edit_prompt_lower = edit_prompt.lower()
+
+ if any(word in edit_prompt_lower for word in ['brighter', 'light', 'lighting']):
+ image = self._adjust_brightness(image, 1.2)
+ logger.info("Applied brightness adjustment")
+
+ if any(word in edit_prompt_lower for word in ['sharper', 'sharp', 'clear']):
+ image = self._apply_sharpening(image)
+ logger.info("Applied sharpening")
+
+ if any(word in edit_prompt_lower for word in ['warmer', 'warm', 'color']):
+ image = self._adjust_color_temperature(image, 'warm')
+ logger.info("Applied warm color adjustment")
+
+ if any(word in edit_prompt_lower for word in ['professional', 'business']):
+ image = self._apply_professional_enhancements(image)
+ logger.info("Applied professional enhancements")
+
+ # Convert back to bytes
+ output_buffer = BytesIO()
+ image.save(output_buffer, format=image.format or "PNG", optimize=True)
+ edited_data = output_buffer.getvalue()
+
+ return {
+ 'success': True,
+ 'image_data': edited_data
+ }
+
+ except Exception as e:
+ logger.error(f"Error in traditional editing: {str(e)}")
+ return {
+ 'success': False,
+ 'error': f"Traditional editing failed: {str(e)}"
+ }
+
+ def _apply_linkedin_enhancements(
+ self,
+ image: Image.Image,
+ content_context: Optional[Dict[str, Any]] = None
+ ) -> Image.Image:
+ """
+ Apply LinkedIn-specific image enhancements.
+
+ Args:
+ image: PIL Image object
+ content_context: LinkedIn content context
+
+ Returns:
+ Enhanced image
+ """
+ try:
+ # Apply standard LinkedIn optimizations
+ image = self._adjust_brightness(image, self.enhancement_factors['brightness'])
+ image = self._adjust_contrast(image, self.enhancement_factors['contrast'])
+ image = self._apply_sharpening(image)
+ image = self._adjust_saturation(image, self.enhancement_factors['saturation'])
+
+ # Ensure professional appearance
+ image = self._ensure_professional_appearance(image, content_context)
+
+ return image
+
+ except Exception as e:
+ logger.error(f"Error applying LinkedIn enhancements: {str(e)}")
+ return image
+
+ def _apply_professional_enhancements(self, image: Image.Image) -> Image.Image:
+ """
+ Apply professional business aesthetic enhancements.
+
+ Args:
+ image: PIL Image object
+
+ Returns:
+ Enhanced image
+ """
+ try:
+ # Subtle enhancements for professional appearance
+ image = self._adjust_brightness(image, 1.05)
+ image = self._adjust_contrast(image, 1.03)
+ image = self._apply_sharpening(image)
+
+ return image
+
+ except Exception as e:
+ logger.error(f"Error applying professional enhancements: {str(e)}")
+ return image
+
+ def _apply_creative_enhancements(self, image: Image.Image) -> Image.Image:
+ """
+ Apply creative and engaging enhancements.
+
+ Args:
+ image: PIL Image object
+
+ Returns:
+ Enhanced image
+ """
+ try:
+ # More pronounced enhancements for creative appeal
+ image = self._adjust_brightness(image, 1.1)
+ image = self._adjust_contrast(image, 1.08)
+ image = self._adjust_saturation(image, 1.1)
+ image = self._apply_sharpening(image)
+
+ return image
+
+ except Exception as e:
+ logger.error(f"Error applying creative enhancements: {str(e)}")
+ return image
+
+ def _adjust_brightness(self, image: Image.Image, factor: float) -> Image.Image:
+ """Adjust image brightness."""
+ try:
+ enhancer = ImageEnhance.Brightness(image)
+ return enhancer.enhance(factor)
+ except Exception as e:
+ logger.error(f"Error adjusting brightness: {str(e)}")
+ return image
+
+ def _adjust_contrast(self, image: Image.Image, factor: float) -> Image.Image:
+ """Adjust image contrast."""
+ try:
+ enhancer = ImageEnhance.Contrast(image)
+ return enhancer.enhance(factor)
+ except Exception as e:
+ logger.error(f"Error adjusting contrast: {str(e)}")
+ return image
+
+ def _adjust_saturation(self, image: Image.Image, factor: float) -> Image.Image:
+ """Adjust image saturation."""
+ try:
+ enhancer = ImageEnhance.Color(image)
+ return enhancer.enhance(factor)
+ except Exception as e:
+ logger.error(f"Error adjusting saturation: {str(e)}")
+ return image
+
+ def _apply_sharpening(self, image: Image.Image) -> Image.Image:
+ """Apply image sharpening."""
+ try:
+ # Apply unsharp mask for professional sharpening
+ return image.filter(ImageFilter.UnsharpMask(radius=1, percent=150, threshold=3))
+ except Exception as e:
+ logger.error(f"Error applying sharpening: {str(e)}")
+ return image
+
+ def _adjust_color_temperature(self, image: Image.Image, temperature: str) -> Image.Image:
+ """Adjust image color temperature."""
+ try:
+ if temperature == 'warm':
+ # Apply warm color adjustment
+ enhancer = ImageEnhance.Color(image)
+ image = enhancer.enhance(1.1)
+
+ # Slight red tint for warmth
+ # This is a simplified approach - more sophisticated color grading could be implemented
+ return image
+ else:
+ return image
+ except Exception as e:
+ logger.error(f"Error adjusting color temperature: {str(e)}")
+ return image
+
+ def _ensure_professional_appearance(
+ self,
+ image: Image.Image,
+ content_context: Optional[Dict[str, Any]] = None
+ ) -> Image.Image:
+ """
+ Ensure image meets professional LinkedIn standards.
+
+ Args:
+ image: PIL Image object
+ content_context: LinkedIn content context
+
+ Returns:
+ Professionally optimized image
+ """
+ try:
+ # Ensure minimum quality standards
+ if image.mode in ('RGBA', 'LA', 'P'):
+ # Convert to RGB for better compatibility
+ background = Image.new('RGB', image.size, (255, 255, 255))
+ if image.mode == 'P':
+ image = image.convert('RGBA')
+ background.paste(image, mask=image.split()[-1] if image.mode == 'RGBA' else None)
+ image = background
+
+ # Ensure minimum resolution for LinkedIn
+ min_resolution = (1024, 1024)
+ if image.size[0] < min_resolution[0] or image.size[1] < min_resolution[1]:
+ # Resize to minimum resolution while maintaining aspect ratio
+ ratio = max(min_resolution[0] / image.size[0], min_resolution[1] / image.size[1])
+ new_size = (int(image.size[0] * ratio), int(image.size[1] * ratio))
+ image = image.resize(new_size, Image.Resampling.LANCZOS)
+ logger.info(f"Resized image to {new_size} for LinkedIn professional standards")
+
+ return image
+
+ except Exception as e:
+ logger.error(f"Error ensuring professional appearance: {str(e)}")
+ return image
+
+ async def get_editing_suggestions(
+ self,
+ image_data: bytes,
+ content_context: Dict[str, Any]
+ ) -> List[Dict[str, Any]]:
+ """
+ Get AI-powered editing suggestions for LinkedIn image.
+
+ Args:
+ image_data: Image data in bytes
+ content_context: LinkedIn content context
+
+ Returns:
+ List of editing suggestions
+ """
+ try:
+ # Analyze image and provide contextual suggestions
+ suggestions = []
+
+ # Professional enhancement suggestions
+ suggestions.append({
+ 'id': 'professional_enhancement',
+ 'title': 'Professional Enhancement',
+ 'description': 'Apply subtle professional enhancements for business appeal',
+ 'prompt': 'Enhance this image with professional business aesthetics',
+ 'priority': 'high'
+ })
+
+ # Mobile optimization suggestions
+ suggestions.append({
+ 'id': 'mobile_optimization',
+ 'title': 'Mobile Optimization',
+ 'description': 'Optimize for LinkedIn mobile feed viewing',
+ 'prompt': 'Optimize this image for mobile LinkedIn viewing',
+ 'priority': 'medium'
+ })
+
+ # Industry-specific suggestions
+ industry = content_context.get('industry', 'business')
+ suggestions.append({
+ 'id': 'industry_optimization',
+ 'title': f'{industry.title()} Industry Optimization',
+ 'description': f'Apply {industry} industry-specific visual enhancements',
+ 'prompt': f'Enhance this image with {industry} industry aesthetics',
+ 'priority': 'medium'
+ })
+
+ # Engagement optimization suggestions
+ suggestions.append({
+ 'id': 'engagement_optimization',
+ 'title': 'Engagement Optimization',
+ 'description': 'Make this image more engaging for LinkedIn audience',
+ 'prompt': 'Make this image more engaging and shareable for LinkedIn',
+ 'priority': 'low'
+ })
+
+ return suggestions
+
+ except Exception as e:
+ logger.error(f"Error getting editing suggestions: {str(e)}")
+ return []
diff --git a/backend/services/linkedin/image_generation/linkedin_image_generator.py b/backend/services/linkedin/image_generation/linkedin_image_generator.py
new file mode 100644
index 00000000..f4ac5e2b
--- /dev/null
+++ b/backend/services/linkedin/image_generation/linkedin_image_generator.py
@@ -0,0 +1,480 @@
+"""
+LinkedIn Image Generator Service
+
+This service generates LinkedIn-optimized images using Google's Gemini API.
+It provides professional, business-appropriate imagery for LinkedIn content.
+"""
+
+import os
+import asyncio
+import logging
+from datetime import datetime
+from typing import Dict, Any, Optional, Tuple
+from pathlib import Path
+from PIL import Image
+from io import BytesIO
+
+# Import existing infrastructure
+from ...api_key_manager import APIKeyManager
+from ...llm_providers.text_to_image_generation.gen_gemini_images import generate_gemini_image
+
+# Set up logging
+logger = logging.getLogger(__name__)
+
+
+class LinkedInImageGenerator:
+ """
+ Handles LinkedIn-optimized image generation using Gemini API.
+
+ This service integrates with the existing Gemini provider infrastructure
+ and provides LinkedIn-specific image optimization, quality assurance,
+ and professional business aesthetics.
+ """
+
+ def __init__(self, api_key_manager: Optional[APIKeyManager] = None):
+ """
+ Initialize the LinkedIn Image Generator.
+
+ Args:
+ api_key_manager: API key manager for Gemini authentication
+ """
+ self.api_key_manager = api_key_manager or APIKeyManager()
+ self.model = "gemini-2.5-flash-image-preview"
+ self.default_aspect_ratio = "1:1" # LinkedIn post optimal ratio
+ self.max_retries = 3
+
+ # LinkedIn-specific image requirements
+ self.min_resolution = (1024, 1024)
+ self.max_file_size_mb = 5
+ self.supported_formats = ["PNG", "JPEG"]
+
+ logger.info("LinkedIn Image Generator initialized")
+
+ async def generate_image(
+ self,
+ prompt: str,
+ content_context: Dict[str, Any],
+ aspect_ratio: str = "1:1",
+ style_preference: str = "professional"
+ ) -> Dict[str, Any]:
+ """
+ Generate LinkedIn-optimized image using Gemini API.
+
+ Args:
+ prompt: User's image generation prompt
+ content_context: LinkedIn content context (topic, industry, content_type)
+ aspect_ratio: Image aspect ratio (1:1, 16:9, 4:3)
+ style_preference: Style preference (professional, creative, industry-specific)
+
+ Returns:
+ Dict containing generation result, image data, and metadata
+ """
+ try:
+ start_time = datetime.now()
+ logger.info(f"Starting LinkedIn image generation for topic: {content_context.get('topic', 'Unknown')}")
+
+ # Enhance prompt with LinkedIn-specific context
+ enhanced_prompt = self._enhance_prompt_for_linkedin(
+ prompt, content_context, style_preference, aspect_ratio
+ )
+
+ # Generate image using existing Gemini infrastructure
+ generation_result = await self._generate_with_gemini(enhanced_prompt, aspect_ratio)
+
+ if not generation_result.get('success'):
+ return {
+ 'success': False,
+ 'error': generation_result.get('error', 'Image generation failed'),
+ 'generation_time': (datetime.now() - start_time).total_seconds()
+ }
+
+ # Process and validate generated image
+ processed_image = await self._process_generated_image(
+ generation_result['image_data'],
+ content_context,
+ aspect_ratio
+ )
+
+ generation_time = (datetime.now() - start_time).total_seconds()
+
+ return {
+ 'success': True,
+ 'image_data': processed_image['image_data'],
+ 'image_url': processed_image.get('image_url'),
+ 'metadata': {
+ 'prompt_used': enhanced_prompt,
+ 'original_prompt': prompt,
+ 'style_preference': style_preference,
+ 'aspect_ratio': aspect_ratio,
+ 'content_context': content_context,
+ 'generation_time': generation_time,
+ 'model_used': self.model,
+ 'image_format': processed_image['format'],
+ 'image_size': processed_image['size'],
+ 'resolution': processed_image['resolution']
+ },
+ 'linkedin_optimization': {
+ 'mobile_optimized': True,
+ 'professional_aesthetic': True,
+ 'brand_compliant': True,
+ 'engagement_optimized': True
+ }
+ }
+
+ except Exception as e:
+ logger.error(f"Error in LinkedIn image generation: {str(e)}")
+ return {
+ 'success': False,
+ 'error': f"Image generation failed: {str(e)}",
+ 'generation_time': (datetime.now() - start_time).total_seconds() if 'start_time' in locals() else 0
+ }
+
+ async def edit_image(
+ self,
+ base_image: bytes,
+ edit_prompt: str,
+ content_context: Dict[str, Any]
+ ) -> Dict[str, Any]:
+ """
+ Edit existing image using Gemini's conversational editing capabilities.
+
+ Args:
+ base_image: Base image data in bytes
+ edit_prompt: Description of desired edits
+ content_context: LinkedIn content context for optimization
+
+ Returns:
+ Dict containing edited image result and metadata
+ """
+ try:
+ start_time = datetime.now()
+ logger.info(f"Starting LinkedIn image editing with prompt: {edit_prompt[:100]}...")
+
+ # Enhance edit prompt for LinkedIn optimization
+ enhanced_edit_prompt = self._enhance_edit_prompt_for_linkedin(
+ edit_prompt, content_context
+ )
+
+ # Use Gemini's image editing capabilities
+ # Note: This will be implemented when Gemini's image editing is fully available
+ # For now, we'll return a placeholder implementation
+
+ return {
+ 'success': False,
+ 'error': 'Image editing not yet implemented - coming in next Gemini API update',
+ 'generation_time': (datetime.now() - start_time).total_seconds()
+ }
+
+ except Exception as e:
+ logger.error(f"Error in LinkedIn image editing: {str(e)}")
+ return {
+ 'success': False,
+ 'error': f"Image editing failed: {str(e)}",
+ 'generation_time': (datetime.now() - start_time).total_seconds() if 'start_time' in locals() else 0
+ }
+
+ def _enhance_prompt_for_linkedin(
+ self,
+ prompt: str,
+ content_context: Dict[str, Any],
+ style_preference: str,
+ aspect_ratio: str
+ ) -> str:
+ """
+ Enhance user prompt with LinkedIn-specific context and best practices.
+
+ Args:
+ prompt: Original user prompt
+ content_context: LinkedIn content context
+ style_preference: Preferred visual style
+ aspect_ratio: Image aspect ratio
+
+ Returns:
+ Enhanced prompt optimized for LinkedIn
+ """
+ topic = content_context.get('topic', 'business')
+ industry = content_context.get('industry', 'business')
+ content_type = content_context.get('content_type', 'post')
+
+ # Base LinkedIn optimization
+ linkedin_optimizations = [
+ f"Create a professional LinkedIn {content_type} image for {topic}",
+ f"Industry: {industry}",
+ f"Professional business aesthetic suitable for LinkedIn audience",
+ f"Mobile-optimized design for LinkedIn feed viewing",
+ f"Aspect ratio: {aspect_ratio}",
+ "High-quality, modern design with clear visual hierarchy",
+ "Professional color scheme and typography",
+ "Suitable for business and professional networking"
+ ]
+
+ # Style-specific enhancements
+ if style_preference == "professional":
+ style_enhancements = [
+ "Corporate aesthetics with clean lines and geometric shapes",
+ "Professional color palette (blues, grays, whites)",
+ "Modern business environment or abstract business concepts",
+ "Clean, minimalist design approach"
+ ]
+ elif style_preference == "creative":
+ style_enhancements = [
+ "Eye-catching and engaging visual style",
+ "Vibrant colors while maintaining professional appeal",
+ "Creative composition that encourages social media engagement",
+ "Modern design elements with business context"
+ ]
+ else: # industry-specific
+ style_enhancements = [
+ f"Industry-specific visual elements for {industry}",
+ "Professional yet creative approach",
+ "Balanced design suitable for business audience",
+ "Industry-relevant imagery and color schemes"
+ ]
+
+ # Combine all enhancements
+ enhanced_prompt = f"{prompt}\n\n"
+ enhanced_prompt += "\n".join(linkedin_optimizations)
+ enhanced_prompt += "\n" + "\n".join(style_enhancements)
+
+ logger.info(f"Enhanced prompt for LinkedIn: {enhanced_prompt[:200]}...")
+ return enhanced_prompt
+
+ def _enhance_edit_prompt_for_linkedin(
+ self,
+ edit_prompt: str,
+ content_context: Dict[str, Any]
+ ) -> str:
+ """
+ Enhance edit prompt for LinkedIn optimization.
+
+ Args:
+ edit_prompt: Original edit prompt
+ content_context: LinkedIn content context
+
+ Returns:
+ Enhanced edit prompt
+ """
+ industry = content_context.get('industry', 'business')
+
+ linkedin_edit_enhancements = [
+ f"Maintain professional business aesthetic for {industry} industry",
+ "Ensure mobile-optimized composition for LinkedIn feed",
+ "Keep professional color scheme and typography",
+ "Maintain brand consistency and visual hierarchy"
+ ]
+
+ enhanced_edit_prompt = f"{edit_prompt}\n\n"
+ enhanced_edit_prompt += "\n".join(linkedin_edit_enhancements)
+
+ return enhanced_edit_prompt
+
+ async def _generate_with_gemini(self, prompt: str, aspect_ratio: str) -> Dict[str, Any]:
+ """
+ Generate image using existing Gemini infrastructure.
+
+ Args:
+ prompt: Enhanced prompt for image generation
+ aspect_ratio: Desired aspect ratio
+
+ Returns:
+ Generation result from Gemini
+ """
+ try:
+ # Use existing Gemini image generation function
+ # This integrates with the current infrastructure
+ result = generate_gemini_image(prompt, aspect_ratio=aspect_ratio)
+
+ if result and os.path.exists(result):
+ # Read the generated image
+ with open(result, 'rb') as f:
+ image_data = f.read()
+
+ return {
+ 'success': True,
+ 'image_data': image_data,
+ 'image_path': result
+ }
+ else:
+ return {
+ 'success': False,
+ 'error': 'Gemini image generation returned no result'
+ }
+
+ except Exception as e:
+ logger.error(f"Error in Gemini image generation: {str(e)}")
+ return {
+ 'success': False,
+ 'error': f"Gemini generation failed: {str(e)}"
+ }
+
+ async def _process_generated_image(
+ self,
+ image_data: bytes,
+ content_context: Dict[str, Any],
+ aspect_ratio: str
+ ) -> Dict[str, Any]:
+ """
+ Process and validate generated image for LinkedIn use.
+
+ Args:
+ image_data: Raw image data
+ content_context: LinkedIn content context
+ aspect_ratio: Image aspect ratio
+
+ Returns:
+ Processed image information
+ """
+ try:
+ # Open image for processing
+ image = Image.open(BytesIO(image_data))
+
+ # Get image information
+ width, height = image.size
+ format_name = image.format or "PNG"
+
+ # Validate resolution
+ if width < self.min_resolution[0] or height < self.min_resolution[1]:
+ logger.warning(f"Generated image resolution {width}x{height} below minimum {self.min_resolution}")
+
+ # Validate file size
+ image_size_mb = len(image_data) / (1024 * 1024)
+ if image_size_mb > self.max_file_size_mb:
+ logger.warning(f"Generated image size {image_size_mb:.2f}MB exceeds maximum {self.max_file_size_mb}MB")
+
+ # LinkedIn-specific optimizations
+ optimized_image = self._optimize_for_linkedin(image, content_context)
+
+ # Convert back to bytes
+ output_buffer = BytesIO()
+ optimized_image.save(output_buffer, format=format_name, optimize=True)
+ optimized_data = output_buffer.getvalue()
+
+ return {
+ 'image_data': optimized_data,
+ 'format': format_name,
+ 'size': len(optimized_data),
+ 'resolution': (width, height),
+ 'aspect_ratio': f"{width}:{height}"
+ }
+
+ except Exception as e:
+ logger.error(f"Error processing generated image: {str(e)}")
+ # Return original image data if processing fails
+ return {
+ 'image_data': image_data,
+ 'format': 'PNG',
+ 'size': len(image_data),
+ 'resolution': (1024, 1024),
+ 'aspect_ratio': aspect_ratio
+ }
+
+ def _optimize_for_linkedin(self, image: Image.Image, content_context: Dict[str, Any]) -> Image.Image:
+ """
+ Optimize image specifically for LinkedIn display.
+
+ Args:
+ image: PIL Image object
+ content_context: LinkedIn content context
+
+ Returns:
+ Optimized image
+ """
+ try:
+ # Ensure minimum resolution
+ width, height = image.size
+ if width < self.min_resolution[0] or height < self.min_resolution[1]:
+ # Resize to minimum resolution while maintaining aspect ratio
+ ratio = max(self.min_resolution[0] / width, self.min_resolution[1] / height)
+ new_width = int(width * ratio)
+ new_height = int(height * ratio)
+ image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
+ logger.info(f"Resized image to {new_width}x{new_height} for LinkedIn optimization")
+
+ # Convert to RGB if necessary (for JPEG compatibility)
+ if image.mode in ('RGBA', 'LA', 'P'):
+ # Create white background for transparent images
+ background = Image.new('RGB', image.size, (255, 255, 255))
+ if image.mode == 'P':
+ image = image.convert('RGBA')
+ background.paste(image, mask=image.split()[-1] if image.mode == 'RGBA' else None)
+ image = background
+
+ return image
+
+ except Exception as e:
+ logger.error(f"Error optimizing image for LinkedIn: {str(e)}")
+ return image # Return original if optimization fails
+
+ async def validate_image_for_linkedin(self, image_data: bytes) -> Dict[str, Any]:
+ """
+ Validate image for LinkedIn compliance and quality standards.
+
+ Args:
+ image_data: Image data to validate
+
+ Returns:
+ Validation results
+ """
+ try:
+ image = Image.open(BytesIO(image_data))
+ width, height = image.size
+
+ validation_results = {
+ 'resolution_ok': width >= self.min_resolution[0] and height >= self.min_resolution[1],
+ 'aspect_ratio_suitable': self._is_aspect_ratio_suitable(width, height),
+ 'file_size_ok': len(image_data) <= self.max_file_size_mb * 1024 * 1024,
+ 'format_supported': image.format in self.supported_formats,
+ 'professional_aesthetic': True, # Placeholder for future AI-based validation
+ 'overall_score': 0
+ }
+
+ # Calculate overall score
+ score = 0
+ if validation_results['resolution_ok']: score += 25
+ if validation_results['aspect_ratio_suitable']: score += 25
+ if validation_results['file_size_ok']: score += 20
+ if validation_results['format_supported']: score += 20
+ if validation_results['professional_aesthetic']: score += 10
+
+ validation_results['overall_score'] = score
+
+ return validation_results
+
+ except Exception as e:
+ logger.error(f"Error validating image: {str(e)}")
+ return {
+ 'resolution_ok': False,
+ 'aspect_ratio_suitable': False,
+ 'file_size_ok': False,
+ 'format_supported': False,
+ 'professional_aesthetic': False,
+ 'overall_score': 0,
+ 'error': str(e)
+ }
+
+ def _is_aspect_ratio_suitable(self, width: int, height: int) -> bool:
+ """
+ Check if image aspect ratio is suitable for LinkedIn.
+
+ Args:
+ width: Image width
+ height: Image height
+
+ Returns:
+ True if aspect ratio is suitable for LinkedIn
+ """
+ ratio = width / height
+
+ # LinkedIn-optimized aspect ratios
+ suitable_ratios = [
+ (0.9, 1.1), # 1:1 (square)
+ (1.6, 1.8), # 16:9 (landscape)
+ (0.7, 0.8), # 4:3 (portrait)
+ (1.2, 1.4), # 5:4 (landscape)
+ ]
+
+ for min_ratio, max_ratio in suitable_ratios:
+ if min_ratio <= ratio <= max_ratio:
+ return True
+
+ return False
diff --git a/backend/services/linkedin/image_generation/linkedin_image_storage.py b/backend/services/linkedin/image_generation/linkedin_image_storage.py
new file mode 100644
index 00000000..2d82c321
--- /dev/null
+++ b/backend/services/linkedin/image_generation/linkedin_image_storage.py
@@ -0,0 +1,536 @@
+"""
+LinkedIn Image Storage Service
+
+This service handles image storage, retrieval, and management for LinkedIn image generation.
+It provides secure storage, efficient retrieval, and metadata management for generated images.
+"""
+
+import os
+import hashlib
+import json
+from typing import Dict, Any, Optional, List, Tuple
+from datetime import datetime, timedelta
+from pathlib import Path
+from PIL import Image
+from io import BytesIO
+from loguru import logger
+
+# Import existing infrastructure
+from ...api_key_manager import APIKeyManager
+
+
+class LinkedInImageStorage:
+ """
+ Handles storage and management of LinkedIn generated images.
+
+ This service provides secure storage, efficient retrieval, metadata management,
+ and cleanup functionality for LinkedIn image generation.
+ """
+
+ def __init__(self, storage_path: Optional[str] = None, api_key_manager: Optional[APIKeyManager] = None):
+ """
+ Initialize the LinkedIn Image Storage service.
+
+ Args:
+ storage_path: Base path for image storage
+ api_key_manager: API key manager for authentication
+ """
+ self.api_key_manager = api_key_manager or APIKeyManager()
+
+ # Set up storage paths
+ if storage_path:
+ self.base_storage_path = Path(storage_path)
+ else:
+ # Default to project-relative path
+ self.base_storage_path = Path(__file__).parent.parent.parent.parent / "linkedin_images"
+
+ # Create storage directories
+ self.images_path = self.base_storage_path / "images"
+ self.metadata_path = self.base_storage_path / "metadata"
+ self.temp_path = self.base_storage_path / "temp"
+
+ # Ensure directories exist
+ self._create_storage_directories()
+
+ # Storage configuration
+ self.max_storage_size_gb = 10 # Maximum storage size in GB
+ self.image_retention_days = 30 # Days to keep images
+ self.max_image_size_mb = 10 # Maximum individual image size in MB
+
+ logger.info(f"LinkedIn Image Storage initialized at {self.base_storage_path}")
+
+ def _create_storage_directories(self):
+ """Create necessary storage directories."""
+ try:
+ self.images_path.mkdir(parents=True, exist_ok=True)
+ self.metadata_path.mkdir(parents=True, exist_ok=True)
+ self.temp_path.mkdir(parents=True, exist_ok=True)
+
+ # Create subdirectories for organization
+ (self.images_path / "posts").mkdir(exist_ok=True)
+ (self.images_path / "articles").mkdir(exist_ok=True)
+ (self.images_path / "carousels").mkdir(exist_ok=True)
+ (self.images_path / "video_scripts").mkdir(exist_ok=True)
+
+ logger.info("Storage directories created successfully")
+
+ except Exception as e:
+ logger.error(f"Error creating storage directories: {str(e)}")
+ raise
+
+ async def store_image(
+ self,
+ image_data: bytes,
+ metadata: Dict[str, Any],
+ content_type: str = "post"
+ ) -> Dict[str, Any]:
+ """
+ Store generated image with metadata.
+
+ Args:
+ image_data: Image data in bytes
+ image_metadata: Image metadata and context
+ content_type: Type of LinkedIn content (post, article, carousel, video_script)
+
+ Returns:
+ Dict containing storage result and image ID
+ """
+ try:
+ start_time = datetime.now()
+
+ # Generate unique image ID
+ image_id = self._generate_image_id(image_data, metadata)
+
+ # Validate image data
+ validation_result = await self._validate_image_for_storage(image_data)
+ if not validation_result['valid']:
+ return {
+ 'success': False,
+ 'error': f"Image validation failed: {validation_result['error']}"
+ }
+
+ # Determine storage path based on content type
+ storage_path = self._get_storage_path(content_type, image_id)
+
+ # Store image file
+ image_stored = await self._store_image_file(image_data, storage_path)
+ if not image_stored:
+ return {
+ 'success': False,
+ 'error': 'Failed to store image file'
+ }
+
+ # Store metadata
+ metadata_stored = await self._store_metadata(image_id, metadata, storage_path)
+ if not metadata_stored:
+ # Clean up image file if metadata storage fails
+ await self._cleanup_failed_storage(storage_path)
+ return {
+ 'success': False,
+ 'error': 'Failed to store image metadata'
+ }
+
+ # Update storage statistics
+ await self._update_storage_stats()
+
+ storage_time = (datetime.now() - start_time).total_seconds()
+
+ return {
+ 'success': True,
+ 'image_id': image_id,
+ 'storage_path': str(storage_path),
+ 'metadata': {
+ 'stored_at': datetime.now().isoformat(),
+ 'storage_time': storage_time,
+ 'file_size': len(image_data),
+ 'content_type': content_type
+ }
+ }
+
+ except Exception as e:
+ logger.error(f"Error storing LinkedIn image: {str(e)}")
+ return {
+ 'success': False,
+ 'error': f"Image storage failed: {str(e)}"
+ }
+
+ async def retrieve_image(self, image_id: str) -> Dict[str, Any]:
+ """
+ Retrieve stored image by ID.
+
+ Args:
+ image_id: Unique image identifier
+
+ Returns:
+ Dict containing image data and metadata
+ """
+ try:
+ # Find image file
+ image_path = await self._find_image_by_id(image_id)
+ if not image_path:
+ return {
+ 'success': False,
+ 'error': f'Image not found: {image_id}'
+ }
+
+ # Load metadata
+ metadata = await self._load_metadata(image_id)
+ if not metadata:
+ return {
+ 'success': False,
+ 'error': f'Metadata not found for image: {image_id}'
+ }
+
+ # Read image data
+ with open(image_path, 'rb') as f:
+ image_data = f.read()
+
+ return {
+ 'success': True,
+ 'image_data': image_data,
+ 'metadata': metadata,
+ 'image_path': str(image_path)
+ }
+
+ except Exception as e:
+ logger.error(f"Error retrieving LinkedIn image {image_id}: {str(e)}")
+ return {
+ 'success': False,
+ 'error': f"Image retrieval failed: {str(e)}"
+ }
+
+ async def delete_image(self, image_id: str) -> Dict[str, Any]:
+ """
+ Delete stored image and metadata.
+
+ Args:
+ image_id: Unique image identifier
+
+ Returns:
+ Dict containing deletion result
+ """
+ try:
+ # Find image file
+ image_path = await self._find_image_by_id(image_id)
+ if not image_path:
+ return {
+ 'success': False,
+ 'error': f'Image not found: {image_id}'
+ }
+
+ # Delete image file
+ if image_path.exists():
+ image_path.unlink()
+ logger.info(f"Deleted image file: {image_path}")
+
+ # Delete metadata
+ metadata_path = self.metadata_path / f"{image_id}.json"
+ if metadata_path.exists():
+ metadata_path.unlink()
+ logger.info(f"Deleted metadata file: {metadata_path}")
+
+ # Update storage statistics
+ await self._update_storage_stats()
+
+ return {
+ 'success': True,
+ 'message': f'Image {image_id} deleted successfully'
+ }
+
+ except Exception as e:
+ logger.error(f"Error deleting LinkedIn image {image_id}: {str(e)}")
+ return {
+ 'success': False,
+ 'error': f"Image deletion failed: {str(e)}"
+ }
+
+ async def list_images(
+ self,
+ content_type: Optional[str] = None,
+ limit: int = 50,
+ offset: int = 0
+ ) -> Dict[str, Any]:
+ """
+ List stored images with optional filtering.
+
+ Args:
+ content_type: Filter by content type
+ limit: Maximum number of images to return
+ offset: Number of images to skip
+
+ Returns:
+ Dict containing list of images and metadata
+ """
+ try:
+ images = []
+
+ # Scan metadata directory
+ metadata_files = list(self.metadata_path.glob("*.json"))
+
+ for metadata_file in metadata_files[offset:offset + limit]:
+ try:
+ with open(metadata_file, 'r') as f:
+ metadata = json.load(f)
+
+ # Apply content type filter
+ if content_type and metadata.get('content_type') != content_type:
+ continue
+
+ # Check if image file still exists
+ image_id = metadata_file.stem
+ image_path = await self._find_image_by_id(image_id)
+
+ if image_path and image_path.exists():
+ # Add file size and last modified info
+ stat = image_path.stat()
+ metadata['file_size'] = stat.st_size
+ metadata['last_modified'] = datetime.fromtimestamp(stat.st_mtime).isoformat()
+
+ images.append(metadata)
+
+ except Exception as e:
+ logger.warning(f"Error reading metadata file {metadata_file}: {str(e)}")
+ continue
+
+ return {
+ 'success': True,
+ 'images': images,
+ 'total_count': len(images),
+ 'limit': limit,
+ 'offset': offset
+ }
+
+ except Exception as e:
+ logger.error(f"Error listing LinkedIn images: {str(e)}")
+ return {
+ 'success': False,
+ 'error': f"Image listing failed: {str(e)}"
+ }
+
+ async def cleanup_old_images(self, days_old: Optional[int] = None) -> Dict[str, Any]:
+ """
+ Clean up old images based on retention policy.
+
+ Args:
+ days_old: Minimum age in days for cleanup (defaults to retention policy)
+
+ Returns:
+ Dict containing cleanup results
+ """
+ try:
+ if days_old is None:
+ days_old = self.image_retention_days
+
+ cutoff_date = datetime.now() - timedelta(days=days_old)
+ deleted_count = 0
+ errors = []
+
+ # Scan metadata directory
+ metadata_files = list(self.metadata_path.glob("*.json"))
+
+ for metadata_file in metadata_files:
+ try:
+ with open(metadata_file, 'r') as f:
+ metadata = json.load(f)
+
+ # Check creation date
+ created_at = metadata.get('stored_at')
+ if created_at:
+ created_date = datetime.fromisoformat(created_at)
+ if created_date < cutoff_date:
+ # Delete old image
+ image_id = metadata_file.stem
+ delete_result = await self.delete_image(image_id)
+
+ if delete_result['success']:
+ deleted_count += 1
+ else:
+ errors.append(f"Failed to delete {image_id}: {delete_result['error']}")
+
+ except Exception as e:
+ logger.warning(f"Error processing metadata file {metadata_file}: {str(e)}")
+ continue
+
+ return {
+ 'success': True,
+ 'deleted_count': deleted_count,
+ 'errors': errors,
+ 'cutoff_date': cutoff_date.isoformat()
+ }
+
+ except Exception as e:
+ logger.error(f"Error cleaning up old LinkedIn images: {str(e)}")
+ return {
+ 'success': False,
+ 'error': f"Cleanup failed: {str(e)}"
+ }
+
+ async def get_storage_stats(self) -> Dict[str, Any]:
+ """
+ Get storage statistics and usage information.
+
+ Returns:
+ Dict containing storage statistics
+ """
+ try:
+ total_size = 0
+ total_files = 0
+ content_type_counts = {}
+
+ # Calculate storage usage
+ for content_type_dir in self.images_path.iterdir():
+ if content_type_dir.is_dir():
+ content_type = content_type_dir.name
+ content_type_counts[content_type] = 0
+
+ for image_file in content_type_dir.glob("*"):
+ if image_file.is_file():
+ total_size += image_file.stat().st_size
+ total_files += 1
+ content_type_counts[content_type] += 1
+
+ # Check storage limits
+ total_size_gb = total_size / (1024 ** 3)
+ storage_limit_exceeded = total_size_gb > self.max_storage_size_gb
+
+ return {
+ 'success': True,
+ 'total_size_bytes': total_size,
+ 'total_size_gb': round(total_size_gb, 2),
+ 'total_files': total_files,
+ 'content_type_counts': content_type_counts,
+ 'storage_limit_gb': self.max_storage_size_gb,
+ 'storage_limit_exceeded': storage_limit_exceeded,
+ 'retention_days': self.image_retention_days
+ }
+
+ except Exception as e:
+ logger.error(f"Error getting storage stats: {str(e)}")
+ return {
+ 'success': False,
+ 'error': f"Failed to get storage stats: {str(e)}"
+ }
+
+ def _generate_image_id(self, image_data: bytes, metadata: Dict[str, Any]) -> str:
+ """Generate unique image ID based on content and metadata."""
+ # Create hash from image data and key metadata
+ hash_input = f"{image_data[:1000]}{metadata.get('topic', '')}{metadata.get('industry', '')}{datetime.now().isoformat()}"
+ return hashlib.sha256(hash_input.encode()).hexdigest()[:16]
+
+ async def _validate_image_for_storage(self, image_data: bytes) -> Dict[str, Any]:
+ """Validate image data before storage."""
+ try:
+ # Check file size
+ if len(image_data) > self.max_image_size_mb * 1024 * 1024:
+ return {
+ 'valid': False,
+ 'error': f'Image size {len(image_data) / (1024*1024):.2f}MB exceeds maximum {self.max_image_size_mb}MB'
+ }
+
+ # Validate image format
+ try:
+ image = Image.open(BytesIO(image_data))
+ if image.format not in ['PNG', 'JPEG', 'JPG']:
+ return {
+ 'valid': False,
+ 'error': f'Unsupported image format: {image.format}'
+ }
+ except Exception as e:
+ return {
+ 'valid': False,
+ 'error': f'Invalid image data: {str(e)}'
+ }
+
+ return {'valid': True}
+
+ except Exception as e:
+ return {
+ 'valid': False,
+ 'error': f'Validation error: {str(e)}'
+ }
+
+ def _get_storage_path(self, content_type: str, image_id: str) -> Path:
+ """Get storage path for image based on content type."""
+ # Map content types to directory names
+ content_type_map = {
+ 'post': 'posts',
+ 'article': 'articles',
+ 'carousel': 'carousels',
+ 'video_script': 'video_scripts'
+ }
+
+ directory = content_type_map.get(content_type, 'posts')
+ return self.images_path / directory / f"{image_id}.png"
+
+ async def _store_image_file(self, image_data: bytes, storage_path: Path) -> bool:
+ """Store image file to disk."""
+ try:
+ # Ensure directory exists
+ storage_path.parent.mkdir(parents=True, exist_ok=True)
+
+ # Write image data
+ with open(storage_path, 'wb') as f:
+ f.write(image_data)
+
+ logger.info(f"Stored image file: {storage_path}")
+ return True
+
+ except Exception as e:
+ logger.error(f"Error storing image file: {str(e)}")
+ return False
+
+ async def _store_metadata(self, image_id: str, metadata: Dict[str, Any], storage_path: Path) -> bool:
+ """Store image metadata to JSON file."""
+ try:
+ # Add storage metadata
+ metadata['image_id'] = image_id
+ metadata['storage_path'] = str(storage_path)
+ metadata['stored_at'] = datetime.now().isoformat()
+
+ # Write metadata file
+ metadata_path = self.metadata_path / f"{image_id}.json"
+ with open(metadata_path, 'w') as f:
+ json.dump(metadata, f, indent=2, default=str)
+
+ logger.info(f"Stored metadata: {metadata_path}")
+ return True
+
+ except Exception as e:
+ logger.error(f"Error storing metadata: {str(e)}")
+ return False
+
+ async def _find_image_by_id(self, image_id: str) -> Optional[Path]:
+ """Find image file by ID across all content type directories."""
+ for content_dir in self.images_path.iterdir():
+ if content_dir.is_dir():
+ image_path = content_dir / f"{image_id}.png"
+ if image_path.exists():
+ return image_path
+
+ return None
+
+ async def _load_metadata(self, image_id: str) -> Optional[Dict[str, Any]]:
+ """Load metadata for image ID."""
+ try:
+ metadata_path = self.metadata_path / f"{image_id}.json"
+ if metadata_path.exists():
+ with open(metadata_path, 'r') as f:
+ return json.load(f)
+ except Exception as e:
+ logger.error(f"Error loading metadata for {image_id}: {str(e)}")
+
+ return None
+
+ async def _cleanup_failed_storage(self, storage_path: Path):
+ """Clean up files if storage operation fails."""
+ try:
+ if storage_path.exists():
+ storage_path.unlink()
+ logger.info(f"Cleaned up failed storage: {storage_path}")
+ except Exception as e:
+ logger.error(f"Error cleaning up failed storage: {str(e)}")
+
+ async def _update_storage_stats(self):
+ """Update storage statistics (placeholder for future implementation)."""
+ # This could be implemented to track storage usage over time
+ pass
diff --git a/backend/services/linkedin/image_prompts/__init__.py b/backend/services/linkedin/image_prompts/__init__.py
new file mode 100644
index 00000000..21d0e010
--- /dev/null
+++ b/backend/services/linkedin/image_prompts/__init__.py
@@ -0,0 +1,18 @@
+"""
+LinkedIn Image Prompts Package
+
+This package provides AI-powered image prompt generation for LinkedIn content
+using Google's Gemini API. It creates three distinct prompt styles optimized
+for professional business image generation.
+"""
+
+from .linkedin_prompt_generator import LinkedInPromptGenerator
+
+__all__ = [
+ 'LinkedInPromptGenerator'
+]
+
+# Version information
+__version__ = "1.0.0"
+__author__ = "Alwrity Team"
+__description__ = "LinkedIn AI Image Prompt Generation Services"
diff --git a/backend/services/linkedin/image_prompts/linkedin_prompt_generator.py b/backend/services/linkedin/image_prompts/linkedin_prompt_generator.py
new file mode 100644
index 00000000..b3d46366
--- /dev/null
+++ b/backend/services/linkedin/image_prompts/linkedin_prompt_generator.py
@@ -0,0 +1,812 @@
+"""
+LinkedIn Image Prompt Generator Service
+
+This service generates AI-optimized image prompts for LinkedIn content using Gemini's
+capabilities. It creates three distinct prompt styles (professional, creative, industry-specific)
+following best practices for image generation.
+"""
+
+import asyncio
+from typing import Dict, Any, List, Optional
+from datetime import datetime
+from loguru import logger
+
+# Import existing infrastructure
+from ...api_key_manager import APIKeyManager
+from ...llm_providers.gemini_provider import gemini_text_response
+
+
+class LinkedInPromptGenerator:
+ """
+ Generates AI-optimized image prompts for LinkedIn content.
+
+ This service creates three distinct prompt styles following Gemini API best practices:
+ 1. Professional Style - Corporate aesthetics, clean lines, business colors
+ 2. Creative Style - Engaging visuals, vibrant colors, social media appeal
+ 3. Industry-Specific Style - Tailored to specific business sectors
+ """
+
+ def __init__(self, api_key_manager: Optional[APIKeyManager] = None):
+ """
+ Initialize the LinkedIn Prompt Generator.
+
+ Args:
+ api_key_manager: API key manager for Gemini authentication
+ """
+ self.api_key_manager = api_key_manager or APIKeyManager()
+ self.model = "gemini-2.0-flash-exp"
+
+ # Prompt generation configuration
+ self.max_prompt_length = 500
+ self.style_variations = {
+ 'professional': 'corporate, clean, business, professional',
+ 'creative': 'engaging, vibrant, creative, social media',
+ 'industry_specific': 'industry-tailored, specialized, contextual'
+ }
+
+ logger.info("LinkedIn Prompt Generator initialized")
+
+ async def generate_three_prompts(
+ self,
+ linkedin_content: Dict[str, Any],
+ aspect_ratio: str = "1:1"
+ ) -> List[Dict[str, Any]]:
+ """
+ Generate three AI-optimized image prompts for LinkedIn content.
+
+ Args:
+ linkedin_content: LinkedIn content context (topic, industry, content_type, content)
+ aspect_ratio: Desired image aspect ratio
+
+ Returns:
+ List of three prompt objects with style, prompt, and description
+ """
+ try:
+ start_time = datetime.now()
+ logger.info(f"Generating image prompts for LinkedIn content: {linkedin_content.get('topic', 'Unknown')}")
+
+ # Generate prompts using Gemini
+ prompts = await self._generate_prompts_with_gemini(linkedin_content, aspect_ratio)
+
+ if not prompts or len(prompts) < 3:
+ logger.warning("Gemini prompt generation failed, using fallback prompts")
+ prompts = self._get_fallback_prompts(linkedin_content, aspect_ratio)
+
+ # Ensure exactly 3 prompts
+ prompts = prompts[:3]
+
+ # Validate and enhance prompts
+ enhanced_prompts = []
+ for i, prompt in enumerate(prompts):
+ enhanced_prompt = self._enhance_prompt_for_linkedin(
+ prompt, linkedin_content, aspect_ratio, i
+ )
+ enhanced_prompts.append(enhanced_prompt)
+
+ generation_time = (datetime.now() - start_time).total_seconds()
+ logger.info(f"Generated {len(enhanced_prompts)} image prompts in {generation_time:.2f}s")
+
+ return enhanced_prompts
+
+ except Exception as e:
+ logger.error(f"Error generating LinkedIn image prompts: {str(e)}")
+ return self._get_fallback_prompts(linkedin_content, aspect_ratio)
+
+ async def _generate_prompts_with_gemini(
+ self,
+ linkedin_content: Dict[str, Any],
+ aspect_ratio: str
+ ) -> List[Dict[str, Any]]:
+ """
+ Generate image prompts using Gemini AI.
+
+ Args:
+ linkedin_content: LinkedIn content context
+ aspect_ratio: Image aspect ratio
+
+ Returns:
+ List of generated prompts
+ """
+ try:
+ # Build the prompt for Gemini
+ gemini_prompt = self._build_gemini_prompt(linkedin_content, aspect_ratio)
+
+ # Generate response using Gemini
+ response = gemini_text_response(
+ prompt=gemini_prompt,
+ temperature=0.7,
+ top_p=0.8,
+ n=1,
+ max_tokens=1000,
+ system_prompt="You are an expert AI image prompt engineer specializing in LinkedIn content optimization."
+ )
+
+ if not response:
+ logger.warning("No response from Gemini prompt generation")
+ return []
+
+ # Parse Gemini response into structured prompts
+ prompts = self._parse_gemini_response(response, linkedin_content)
+
+ return prompts
+
+ except Exception as e:
+ logger.error(f"Error in Gemini prompt generation: {str(e)}")
+ return []
+
+ def _build_gemini_prompt(
+ self,
+ linkedin_content: Dict[str, Any],
+ aspect_ratio: str
+ ) -> str:
+ """
+ Build comprehensive prompt for Gemini to generate image prompts.
+
+ Args:
+ linkedin_content: LinkedIn content context
+ aspect_ratio: Image aspect ratio
+
+ Returns:
+ Formatted prompt for Gemini
+ """
+ topic = linkedin_content.get('topic', 'business')
+ industry = linkedin_content.get('industry', 'business')
+ content_type = linkedin_content.get('content_type', 'post')
+ content = linkedin_content.get('content', '')
+
+ # Extract key content elements for better context
+ content_analysis = self._analyze_content_for_image_context(content, content_type)
+
+ prompt = f"""
+ As an expert AI image prompt engineer specializing in LinkedIn content, generate 3 distinct image generation prompts for the following LinkedIn {content_type}:
+
+ TOPIC: {topic}
+ INDUSTRY: {industry}
+ CONTENT TYPE: {content_type}
+ ASPECT RATIO: {aspect_ratio}
+
+ GENERATED CONTENT:
+ {content}
+
+ CONTENT ANALYSIS:
+ - Key Themes: {content_analysis['key_themes']}
+ - Tone: {content_analysis['tone']}
+ - Visual Elements: {content_analysis['visual_elements']}
+ - Target Audience: {content_analysis['target_audience']}
+ - Content Purpose: {content_analysis['content_purpose']}
+
+ Generate exactly 3 image prompts that directly relate to and enhance the generated content above:
+
+ 1. PROFESSIONAL STYLE:
+ - Corporate aesthetics with clean lines and geometric shapes
+ - Professional color palette (blues, grays, whites)
+ - Modern business environment or abstract business concepts
+ - Clean, minimalist design approach
+ - Suitable for B2B and professional networking
+ - MUST directly relate to the specific content themes and industry context above
+
+ 2. CREATIVE STYLE:
+ - Eye-catching and engaging visual style
+ - Vibrant colors while maintaining professional appeal
+ - Creative composition that encourages social media engagement
+ - Modern design elements with business context
+ - Optimized for LinkedIn feed visibility
+ - MUST visually represent the key themes and messages from the content above
+
+ 3. INDUSTRY-SPECIFIC STYLE:
+ - Tailored specifically to the {industry} industry
+ - Industry-relevant imagery, colors, and visual elements
+ - Professional yet creative approach
+ - Balanced design suitable for business audience
+ - Industry-specific symbolism and aesthetics
+ - MUST incorporate visual elements that directly support the content's industry context
+
+ Each prompt should:
+ - Be specific and detailed (50-100 words)
+ - Include visual composition guidance
+ - Specify color schemes and lighting
+ - Mention LinkedIn optimization
+ - Follow image generation best practices
+ - Be suitable for the {aspect_ratio} aspect ratio
+ - DIRECTLY reference and visualize the key themes, messages, and context from the generated content above
+ - Create images that would naturally accompany and enhance the specific LinkedIn content provided
+
+ Return the prompts in this exact JSON format:
+ [
+ {{
+ "style": "Professional",
+ "prompt": "Detailed prompt description that directly relates to the content above...",
+ "description": "Brief description of the visual style and how it relates to the content"
+ }},
+ {{
+ "style": "Creative",
+ "prompt": "Detailed prompt description that directly relates to the content above...",
+ "description": "Brief description of the visual style and how it relates to the content"
+ }},
+ {{
+ "style": "Industry-Specific",
+ "prompt": "Detailed prompt description that directly relates to the content above...",
+ "description": "Brief description of the visual style and how it relates to the content"
+ }}
+ ]
+
+ Focus on creating prompts that will generate high-quality, LinkedIn-optimized images that directly enhance and complement the specific content provided above.
+ """
+
+ return prompt.strip()
+
+ def _analyze_content_for_image_context(self, content: str, content_type: str) -> Dict[str, Any]:
+ """
+ Analyze the generated LinkedIn content to extract key elements for image context.
+
+ Args:
+ content: The generated LinkedIn content
+ content_type: Type of content (post, article, carousel, etc.)
+
+ Returns:
+ Dictionary containing content analysis for image generation
+ """
+ try:
+ # Basic content analysis
+ content_lower = content.lower()
+ word_count = len(content.split())
+
+ # Extract key themes based on content analysis
+ key_themes = self._extract_key_themes(content_lower, content_type)
+
+ # Determine tone based on content analysis
+ tone = self._determine_content_tone(content_lower)
+
+ # Identify visual elements that could be represented
+ visual_elements = self._identify_visual_elements(content_lower, content_type)
+
+ # Determine target audience
+ target_audience = self._determine_target_audience(content_lower, content_type)
+
+ # Determine content purpose
+ content_purpose = self._determine_content_purpose(content_lower, content_type)
+
+ return {
+ 'key_themes': ', '.join(key_themes),
+ 'tone': tone,
+ 'visual_elements': ', '.join(visual_elements),
+ 'target_audience': target_audience,
+ 'content_purpose': content_purpose,
+ 'word_count': word_count,
+ 'content_type': content_type
+ }
+
+ except Exception as e:
+ logger.error(f"Error analyzing content for image context: {str(e)}")
+ return {
+ 'key_themes': 'business, professional',
+ 'tone': 'professional',
+ 'visual_elements': 'business concepts',
+ 'target_audience': 'professionals',
+ 'content_purpose': 'informational',
+ 'word_count': len(content.split()) if content else 0,
+ 'content_type': content_type
+ }
+
+ def _extract_key_themes(self, content_lower: str, content_type: str) -> List[str]:
+ """Extract key themes from the content for image generation context."""
+ themes = []
+
+ # Industry and business themes
+ if any(word in content_lower for word in ['ai', 'artificial intelligence', 'machine learning']):
+ themes.append('AI & Technology')
+ if any(word in content_lower for word in ['marketing', 'branding', 'advertising']):
+ themes.append('Marketing & Branding')
+ if any(word in content_lower for word in ['leadership', 'management', 'strategy']):
+ themes.append('Leadership & Strategy')
+ if any(word in content_lower for word in ['innovation', 'growth', 'transformation']):
+ themes.append('Innovation & Growth')
+ if any(word in content_lower for word in ['data', 'analytics', 'insights']):
+ themes.append('Data & Analytics')
+ if any(word in content_lower for word in ['customer', 'user experience', 'engagement']):
+ themes.append('Customer Experience')
+ if any(word in content_lower for word in ['team', 'collaboration', 'workplace']):
+ themes.append('Team & Collaboration')
+ if any(word in content_lower for word in ['sustainability', 'environmental', 'green']):
+ themes.append('Sustainability')
+ if any(word in content_lower for word in ['finance', 'investment', 'economy']):
+ themes.append('Finance & Economy')
+ if any(word in content_lower for word in ['healthcare', 'medical', 'wellness']):
+ themes.append('Healthcare & Wellness')
+
+ # Content type specific themes
+ if content_type == 'post':
+ if any(word in content_lower for word in ['tip', 'advice', 'insight']):
+ themes.append('Tips & Advice')
+ if any(word in content_lower for word in ['story', 'experience', 'journey']):
+ themes.append('Personal Story')
+ if any(word in content_lower for word in ['trend', 'future', 'prediction']):
+ themes.append('Trends & Future')
+
+ elif content_type == 'article':
+ if any(word in content_lower for word in ['research', 'study', 'analysis']):
+ themes.append('Research & Analysis')
+ if any(word in content_lower for word in ['case study', 'example', 'success']):
+ themes.append('Case Studies')
+ if any(word in content_lower for word in ['guide', 'tutorial', 'how-to']):
+ themes.append('Educational Content')
+
+ elif content_type == 'carousel':
+ if any(word in content_lower for word in ['steps', 'process', 'framework']):
+ themes.append('Process & Framework')
+ if any(word in content_lower for word in ['comparison', 'vs', 'difference']):
+ themes.append('Comparison & Analysis')
+ if any(word in content_lower for word in ['checklist', 'tips', 'best practices']):
+ themes.append('Checklists & Best Practices')
+
+ # Default theme if none identified
+ if not themes:
+ themes.append('Business & Professional')
+
+ return themes[:3] # Limit to top 3 themes
+
+ def _determine_content_tone(self, content_lower: str) -> str:
+ """Determine the tone of the content for appropriate image styling."""
+ if any(word in content_lower for word in ['excited', 'amazing', 'incredible', 'revolutionary']):
+ return 'Enthusiastic & Dynamic'
+ elif any(word in content_lower for word in ['challenge', 'problem', 'issue', 'difficult']):
+ return 'Thoughtful & Analytical'
+ elif any(word in content_lower for word in ['success', 'achievement', 'win', 'victory']):
+ return 'Celebratory & Positive'
+ elif any(word in content_lower for word in ['guide', 'tutorial', 'how-to', 'steps']):
+ return 'Educational & Helpful'
+ elif any(word in content_lower for word in ['trend', 'future', 'prediction', 'forecast']):
+ return 'Forward-looking & Innovative'
+ else:
+ return 'Professional & Informative'
+
+ def _identify_visual_elements(self, content_lower: str, content_type: str) -> List[str]:
+ """Identify visual elements that could be represented in images."""
+ visual_elements = []
+
+ # Technology and digital elements
+ if any(word in content_lower for word in ['ai', 'robot', 'computer', 'digital']):
+ visual_elements.extend(['Digital interfaces', 'Technology symbols', 'Abstract tech patterns'])
+
+ # Business and professional elements
+ if any(word in content_lower for word in ['business', 'corporate', 'office', 'meeting']):
+ visual_elements.extend(['Business environments', 'Professional settings', 'Corporate aesthetics'])
+
+ # Growth and progress elements
+ if any(word in content_lower for word in ['growth', 'progress', 'improvement', 'success']):
+ visual_elements.extend(['Growth charts', 'Progress indicators', 'Success symbols'])
+
+ # Data and analytics elements
+ if any(word in content_lower for word in ['data', 'analytics', 'charts', 'metrics']):
+ visual_elements.extend(['Data visualizations', 'Charts and graphs', 'Analytics dashboards'])
+
+ # Team and collaboration elements
+ if any(word in content_lower for word in ['team', 'collaboration', 'partnership', 'network']):
+ visual_elements.extend(['Team dynamics', 'Collaboration symbols', 'Network connections'])
+
+ # Industry-specific elements
+ if 'healthcare' in content_lower:
+ visual_elements.extend(['Medical symbols', 'Healthcare imagery', 'Wellness elements'])
+ elif 'finance' in content_lower:
+ visual_elements.extend(['Financial symbols', 'Money concepts', 'Investment imagery'])
+ elif 'education' in content_lower:
+ visual_elements.extend(['Learning symbols', 'Educational elements', 'Knowledge imagery'])
+
+ # Default visual elements
+ if not visual_elements:
+ visual_elements = ['Professional business concepts', 'Modern design elements', 'Corporate aesthetics']
+
+ return visual_elements[:4] # Limit to top 4 elements
+
+ def _determine_target_audience(self, content_lower: str, content_type: str) -> str:
+ """Determine the target audience for the content."""
+ if any(word in content_lower for word in ['ceo', 'executive', 'leader', 'manager']):
+ return 'C-Suite & Executives'
+ elif any(word in content_lower for word in ['entrepreneur', 'startup', 'founder', 'business owner']):
+ return 'Entrepreneurs & Business Owners'
+ elif any(word in content_lower for word in ['marketer', 'sales', 'business development']):
+ return 'Marketing & Sales Professionals'
+ elif any(word in content_lower for word in ['developer', 'engineer', 'technical', 'it']):
+ return 'Technical Professionals'
+ elif any(word in content_lower for word in ['student', 'learner', 'aspiring', 'career']):
+ return 'Students & Career Changers'
+ else:
+ return 'General Business Professionals'
+
+ def _determine_content_purpose(self, content_lower: str, content_type: str) -> str:
+ """Determine the primary purpose of the content."""
+ if any(word in content_lower for word in ['tip', 'advice', 'how-to', 'guide']):
+ return 'Educational & Instructional'
+ elif any(word in content_lower for word in ['story', 'experience', 'journey', 'case study']):
+ return 'Storytelling & Experience Sharing'
+ elif any(word in content_lower for word in ['trend', 'prediction', 'future', 'insight']):
+ return 'Trend Analysis & Forecasting'
+ elif any(word in content_lower for word in ['challenge', 'problem', 'solution', 'strategy']):
+ return 'Problem Solving & Strategy'
+ elif any(word in content_lower for word in ['success', 'achievement', 'result', 'outcome']):
+ return 'Success Showcase & Results'
+ else:
+ return 'Informational & Awareness'
+
+ def _parse_gemini_response(
+ self,
+ response: str,
+ linkedin_content: Dict[str, Any]
+ ) -> List[Dict[str, Any]]:
+ """
+ Parse Gemini response into structured prompt objects.
+
+ Args:
+ response: Raw response from Gemini
+ linkedin_content: LinkedIn content context
+
+ Returns:
+ List of parsed prompt objects
+ """
+ try:
+ # Try to extract JSON from response
+ import json
+ import re
+
+ # Look for JSON array in the response
+ json_match = re.search(r'\[.*\]', response, re.DOTALL)
+ if json_match:
+ json_str = json_match.group(0)
+ prompts = json.loads(json_str)
+
+ # Validate prompt structure
+ if isinstance(prompts, list) and len(prompts) >= 3:
+ return prompts[:3]
+
+ # Fallback: parse response manually
+ return self._parse_response_manually(response, linkedin_content)
+
+ except Exception as e:
+ logger.error(f"Error parsing Gemini response: {str(e)}")
+ return self._parse_response_manually(response, linkedin_content)
+
+ def _parse_response_manually(
+ self,
+ response: str,
+ linkedin_content: Dict[str, Any]
+ ) -> List[Dict[str, Any]]:
+ """
+ Manually parse response if JSON parsing fails.
+
+ Args:
+ response: Raw response from Gemini
+ linkedin_content: LinkedIn content context
+
+ Returns:
+ List of parsed prompt objects
+ """
+ try:
+ prompts = []
+ lines = response.split('\n')
+
+ current_style = None
+ current_prompt = []
+ current_description = None
+
+ for line in lines:
+ line = line.strip()
+
+ if 'professional' in line.lower() and 'style' in line.lower():
+ if current_style and current_prompt:
+ prompts.append({
+ 'style': current_style,
+ 'prompt': ' '.join(current_prompt),
+ 'description': current_description or f'{current_style} style for LinkedIn'
+ })
+ current_style = 'Professional'
+ current_prompt = []
+ current_description = None
+
+ elif 'creative' in line.lower() and 'style' in line.lower():
+ if current_style and current_prompt:
+ prompts.append({
+ 'style': current_style,
+ 'prompt': ' '.join(current_prompt),
+ 'description': current_description or f'{current_style} style for LinkedIn'
+ })
+ current_style = 'Creative'
+ current_prompt = []
+ current_description = None
+
+ elif 'industry' in line.lower() and 'specific' in line.lower():
+ if current_style and current_prompt:
+ prompts.append({
+ 'style': current_style,
+ 'prompt': ' '.join(current_prompt),
+ 'description': current_description or f'{current_style} style for LinkedIn'
+ })
+ current_style = 'Industry-Specific'
+ current_prompt = []
+ current_description = None
+
+ elif line and not line.startswith('-') and current_style:
+ current_prompt.append(line)
+
+ elif line.startswith('description:') and current_style:
+ current_description = line.replace('description:', '').strip()
+
+ # Add the last prompt
+ if current_style and current_prompt:
+ prompts.append({
+ 'style': current_style,
+ 'prompt': ' '.join(current_prompt),
+ 'description': current_description or f'{current_style} style for LinkedIn'
+ })
+
+ # Ensure we have exactly 3 prompts
+ while len(prompts) < 3:
+ style_name = ['Professional', 'Creative', 'Industry-Specific'][len(prompts)]
+ prompts.append({
+ 'style': style_name,
+ 'prompt': f"Create a {style_name.lower()} LinkedIn image for {linkedin_content.get('topic', 'business')}",
+ 'description': f'{style_name} style for LinkedIn content'
+ })
+
+ return prompts[:3]
+
+ except Exception as e:
+ logger.error(f"Error in manual response parsing: {str(e)}")
+ return self._get_fallback_prompts(linkedin_content, "1:1")
+
+ def _enhance_prompt_for_linkedin(
+ self,
+ prompt: Dict[str, Any],
+ linkedin_content: Dict[str, Any],
+ aspect_ratio: str,
+ prompt_index: int
+ ) -> Dict[str, Any]:
+ """
+ Enhance individual prompt with LinkedIn-specific optimizations.
+
+ Args:
+ prompt: Individual prompt object
+ linkedin_content: LinkedIn content context
+ aspect_ratio: Image aspect ratio
+ prompt_index: Index of the prompt (0-2)
+
+ Returns:
+ Enhanced prompt object
+ """
+ try:
+ topic = linkedin_content.get('topic', 'business')
+ industry = linkedin_content.get('industry', 'business')
+ content_type = linkedin_content.get('content_type', 'post')
+
+ # Get the base prompt text
+ base_prompt = prompt.get('prompt', '')
+ style = prompt.get('style', 'Professional')
+
+ # LinkedIn-specific enhancements based on style
+ if style == 'Professional':
+ enhancements = [
+ f"Professional LinkedIn {content_type} image for {topic}",
+ "Corporate aesthetics with clean lines and geometric shapes",
+ "Professional color palette (blues, grays, whites)",
+ "Modern business environment or abstract business concepts",
+ f"Aspect ratio: {aspect_ratio}",
+ "Mobile-optimized for LinkedIn feed viewing",
+ "High-quality, professional business aesthetic"
+ ]
+ elif style == 'Creative':
+ enhancements = [
+ f"Creative LinkedIn {content_type} image for {topic}",
+ "Eye-catching and engaging visual style",
+ "Vibrant colors while maintaining professional appeal",
+ "Creative composition that encourages social media engagement",
+ f"Aspect ratio: {aspect_ratio}",
+ "Optimized for LinkedIn feed visibility and sharing",
+ "Modern design elements with business context"
+ ]
+ else: # Industry-Specific
+ enhancements = [
+ f"{industry} industry-specific LinkedIn {content_type} image for {topic}",
+ f"Industry-relevant imagery and colors for {industry}",
+ "Professional yet creative approach",
+ "Balanced design suitable for business audience",
+ f"Aspect ratio: {aspect_ratio}",
+ f"Industry-specific symbolism and {industry} aesthetics",
+ "Professional business appeal for LinkedIn"
+ ]
+
+ # Combine base prompt with enhancements
+ enhanced_prompt_text = f"{base_prompt}\n\n"
+ enhanced_prompt_text += "\n".join(enhancements)
+
+ # Ensure prompt length is within limits
+ if len(enhanced_prompt_text) > self.max_prompt_length:
+ enhanced_prompt_text = enhanced_prompt_text[:self.max_prompt_length] + "..."
+
+ return {
+ 'style': style,
+ 'prompt': enhanced_prompt_text,
+ 'description': prompt.get('description', f'{style} style for LinkedIn'),
+ 'prompt_index': prompt_index,
+ 'enhanced_at': datetime.now().isoformat(),
+ 'linkedin_optimized': True
+ }
+
+ except Exception as e:
+ logger.error(f"Error enhancing prompt: {str(e)}")
+ return prompt
+
+ def _get_fallback_prompts(
+ self,
+ linkedin_content: Dict[str, Any],
+ aspect_ratio: str
+ ) -> List[Dict[str, Any]]:
+ """
+ Generate fallback prompts if AI generation fails.
+
+ Args:
+ linkedin_content: LinkedIn content context
+ aspect_ratio: Image aspect ratio
+
+ Returns:
+ List of fallback prompt objects
+ """
+ topic = linkedin_content.get('topic', 'business')
+ industry = linkedin_content.get('industry', 'business')
+ content_type = linkedin_content.get('content_type', 'post')
+ content = linkedin_content.get('content', '')
+
+ # Analyze content for better context
+ content_analysis = self._analyze_content_for_image_context(content, content_type)
+
+ # Create context-aware fallback prompts
+ fallback_prompts = [
+ {
+ 'style': 'Professional',
+ 'prompt': f"""Create a professional LinkedIn {content_type} image for {topic} in the {industry} industry.
+
+Key Content Themes: {content_analysis['key_themes']}
+Content Tone: {content_analysis['tone']}
+Visual Elements: {content_analysis['visual_elements']}
+
+Corporate aesthetics with clean lines and geometric shapes
+Professional color palette (blues, grays, whites)
+Modern business environment or abstract business concepts
+Aspect ratio: {aspect_ratio}
+Mobile-optimized for LinkedIn feed viewing
+High-quality, professional business aesthetic
+Directly represents the content themes: {content_analysis['key_themes']}""",
+ 'description': f'Clean, business-appropriate visual for LinkedIn {content_type} about {topic}',
+ 'prompt_index': 0,
+ 'fallback': True,
+ 'content_context': content_analysis
+ },
+ {
+ 'style': 'Creative',
+ 'prompt': f"""Generate a creative LinkedIn {content_type} image for {topic} in {industry}.
+
+Key Content Themes: {content_analysis['key_themes']}
+Content Purpose: {content_analysis['content_purpose']}
+Target Audience: {content_analysis['target_audience']}
+
+Eye-catching and engaging visual style
+Vibrant colors while maintaining professional appeal
+Creative composition that encourages social media engagement
+Aspect ratio: {aspect_ratio}
+Optimized for LinkedIn feed visibility and sharing
+Modern design elements with business context
+Visually represents: {content_analysis['visual_elements']}""",
+ 'description': f'Eye-catching, shareable design for LinkedIn {content_type} about {topic}',
+ 'prompt_index': 1,
+ 'fallback': True,
+ 'content_context': content_analysis
+ },
+ {
+ 'style': 'Industry-Specific',
+ 'prompt': f"""Design a {industry} industry-specific LinkedIn {content_type} image for {topic}.
+
+Key Content Themes: {content_analysis['key_themes']}
+Content Tone: {content_analysis['tone']}
+Visual Elements: {content_analysis['visual_elements']}
+
+Industry-relevant imagery and colors for {industry}
+Professional yet creative approach
+Balanced design suitable for business audience
+Aspect ratio: {aspect_ratio}
+Industry-specific symbolism and {industry} aesthetics
+Professional business appeal for LinkedIn
+Incorporates visual elements: {content_analysis['visual_elements']}""",
+ 'description': f'Industry-tailored professional design for {industry} {content_type} about {topic}',
+ 'prompt_index': 2,
+ 'fallback': True,
+ 'content_context': content_analysis
+ }
+ ]
+
+ logger.info(f"Using context-aware fallback prompts for LinkedIn {content_type} about {topic}")
+ return fallback_prompts
+
+ async def validate_prompt_quality(
+ self,
+ prompt: Dict[str, Any]
+ ) -> Dict[str, Any]:
+ """
+ Validate the quality of a generated prompt.
+
+ Args:
+ prompt: Prompt object to validate
+
+ Returns:
+ Validation results
+ """
+ try:
+ prompt_text = prompt.get('prompt', '')
+ style = prompt.get('style', '')
+
+ # Quality metrics
+ length_score = min(len(prompt_text) / 100, 1.0) # Optimal length around 100 words
+ specificity_score = self._calculate_specificity_score(prompt_text)
+ linkedin_optimization_score = self._calculate_linkedin_optimization_score(prompt_text)
+
+ # Overall quality score
+ overall_score = (length_score + specificity_score + linkedin_optimization_score) / 3
+
+ return {
+ 'valid': overall_score >= 0.7,
+ 'overall_score': round(overall_score, 2),
+ 'metrics': {
+ 'length_score': round(length_score, 2),
+ 'specificity_score': round(specificity_score, 2),
+ 'linkedin_optimization_score': round(linkedin_optimization_score, 2)
+ },
+ 'recommendations': self._get_quality_recommendations(overall_score, prompt_text)
+ }
+
+ except Exception as e:
+ logger.error(f"Error validating prompt quality: {str(e)}")
+ return {
+ 'valid': False,
+ 'overall_score': 0.0,
+ 'error': str(e)
+ }
+
+ def _calculate_specificity_score(self, prompt_text: str) -> float:
+ """Calculate how specific and detailed the prompt is."""
+ # Count specific visual elements, colors, styles mentioned
+ specific_elements = [
+ 'wide-angle', 'close-up', 'low-angle', 'aerial',
+ 'blue', 'gray', 'white', 'red', 'green', 'yellow',
+ 'modern', 'minimalist', 'corporate', 'professional',
+ 'geometric', 'clean lines', 'sharp focus', 'soft lighting'
+ ]
+
+ element_count = sum(1 for element in specific_elements if element.lower() in prompt_text.lower())
+ return min(element_count / 8, 1.0) # Normalize to 0-1
+
+ def _calculate_linkedin_optimization_score(self, prompt_text: str) -> float:
+ """Calculate how well the prompt is optimized for LinkedIn."""
+ linkedin_keywords = [
+ 'linkedin', 'professional', 'business', 'corporate',
+ 'mobile', 'feed', 'social media', 'engagement',
+ 'networking', 'professional audience'
+ ]
+
+ keyword_count = sum(1 for keyword in linkedin_keywords if keyword.lower() in prompt_text.lower())
+ return min(keyword_count / 5, 1.0) # Normalize to 0-1
+
+ def _get_quality_recommendations(self, score: float, prompt_text: str) -> List[str]:
+ """Get recommendations for improving prompt quality."""
+ recommendations = []
+
+ if score < 0.7:
+ if len(prompt_text) < 100:
+ recommendations.append("Add more specific visual details and composition guidance")
+
+ if 'linkedin' not in prompt_text.lower():
+ recommendations.append("Include LinkedIn-specific optimization terms")
+
+ if 'aspect ratio' not in prompt_text.lower():
+ recommendations.append("Specify the desired aspect ratio")
+
+ if 'professional' not in prompt_text.lower() and 'business' not in prompt_text.lower():
+ recommendations.append("Include professional business aesthetic guidance")
+
+ return recommendations
diff --git a/backend/services/linkedin/quality_handler.py b/backend/services/linkedin/quality_handler.py
index 65d641bd..fed79a2a 100644
--- a/backend/services/linkedin/quality_handler.py
+++ b/backend/services/linkedin/quality_handler.py
@@ -54,7 +54,8 @@ class QualityHandler:
citation_coverage=quality_analysis.get('metrics', {}).get('citation_coverage', 0.0),
content_length=quality_analysis.get('content_length', 0),
word_count=quality_analysis.get('word_count', 0),
- analysis_timestamp=quality_analysis.get('analysis_timestamp', '')
+ analysis_timestamp=quality_analysis.get('analysis_timestamp', ''),
+ recommendations=quality_analysis.get('recommendations', [])
)
except Exception as e:
logger.warning(f"Quality metrics creation failed: {e}")
diff --git a/backend/services/linkedin_service.py b/backend/services/linkedin_service.py
index 4b8f96c7..86f22f2b 100644
--- a/backend/services/linkedin_service.py
+++ b/backend/services/linkedin_service.py
@@ -178,7 +178,8 @@ class LinkedInService:
research_sources=research_sources
)
else:
- content_result = await content_generator.generate_fallback_article_content(request)
+ logger.error("Grounding not enabled - cannot generate LinkedIn article without AI provider")
+ raise Exception("Grounding not enabled - cannot generate LinkedIn article without AI provider")
# Step 3-5: Use content generator for processing and response building
return await content_generator.generate_article(
@@ -235,7 +236,8 @@ class LinkedInService:
research_sources=research_sources
)
else:
- content_result = await content_generator.generate_fallback_carousel_content(request)
+ logger.error("Grounding not enabled - cannot generate LinkedIn carousel without AI provider")
+ raise Exception("Grounding not enabled - cannot generate LinkedIn carousel without AI provider")
# Step 3-5: Use content generator for processing and response building
@@ -280,7 +282,7 @@ class LinkedInService:
success=False,
error=result['error']
)
-
+
except Exception as e:
logger.error(f"Error generating LinkedIn carousel: {str(e)}")
return LinkedInCarouselResponse(
@@ -327,7 +329,8 @@ class LinkedInService:
research_sources=research_sources
)
else:
- content_result = await content_generator.generate_fallback_video_script_content(request)
+ logger.error("Grounding not enabled - cannot generate LinkedIn video script without AI provider")
+ raise Exception("Grounding not enabled - cannot generate LinkedIn video script without AI provider")
# Step 3-5: Use content generator for processing and response building
@@ -410,7 +413,8 @@ class LinkedInService:
research_sources=research_sources
)
else:
- response_result = await content_generator.generate_fallback_comment_response(request)
+ logger.error("Grounding not enabled - cannot generate LinkedIn comment response without AI provider")
+ raise Exception("Grounding not enabled - cannot generate LinkedIn comment response without AI provider")
# Step 3-5: Use content generator for processing and response building
@@ -423,11 +427,18 @@ class LinkedInService:
)
if result['success']:
- return LinkedInCommentResponseResult(
- success=True,
+ # Convert to LinkedInCommentResponseResult
+ from models.linkedin_models import CommentResponse
+ comment_response = CommentResponse(
response=result['response'],
alternative_responses=result.get('alternative_responses', []),
- tone_analysis=result.get('tone_analysis'),
+ tone_analysis=result.get('tone_analysis')
+ )
+
+ return LinkedInCommentResponseResult(
+ success=True,
+ data=comment_response,
+ research_sources=result['research_sources'],
generation_metadata=result['generation_metadata'],
grounding_status=result['grounding_status']
)
diff --git a/backend/services/llm_providers/gemini_grounded_provider.py b/backend/services/llm_providers/gemini_grounded_provider.py
index 57b863e8..34d893b9 100644
--- a/backend/services/llm_providers/gemini_grounded_provider.py
+++ b/backend/services/llm_providers/gemini_grounded_provider.py
@@ -111,11 +111,11 @@ class GeminiGroundedProvider:
Enhanced prompt for grounded generation
"""
content_type_instructions = {
- "linkedin_post": "Generate a professional LinkedIn post that is factually accurate and cites current sources. Include engaging hashtags and a call-to-action.",
- "linkedin_article": "Generate a comprehensive LinkedIn article with proper structure, factual accuracy, and source citations. Include an engaging title and conclusion.",
- "linkedin_carousel": "Generate LinkedIn carousel content with multiple slides, each containing factual information with proper source attribution.",
- "linkedin_video_script": "Generate a video script with hook, main content, and conclusion. Ensure all claims are factually grounded.",
- "linkedin_comment_response": "Generate a professional comment response that adds value to the conversation."
+ "linkedin_post": "You are an expert LinkedIn content strategist. Generate a highly engaging, professional LinkedIn post that drives meaningful engagement, establishes thought leadership, and includes compelling hooks, actionable insights, and strategic hashtags. Every element should be optimized for maximum engagement and shareability.",
+ "linkedin_article": "You are a senior content strategist and industry thought leader. Generate a comprehensive, SEO-optimized LinkedIn article with compelling headlines, structured content, data-driven insights, and practical takeaways. Include proper source citations and engagement elements throughout.",
+ "linkedin_carousel": "You are a visual content strategist specializing in LinkedIn carousels. Generate compelling, story-driven carousel content with clear visual hierarchy, actionable insights per slide, and strategic engagement elements. Each slide should provide immediate value while building anticipation for the next.",
+ "linkedin_video_script": "You are a video content strategist and LinkedIn engagement expert. Generate a compelling video script optimized for LinkedIn's algorithm with attention-grabbing hooks, strategic timing, and engagement-driven content. Include specific visual and audio recommendations for maximum impact.",
+ "linkedin_comment_response": "You are a LinkedIn engagement specialist and industry expert. Generate thoughtful, value-adding comment responses that encourage further discussion, demonstrate expertise, and build meaningful professional relationships. Focus on genuine engagement over generic responses."
}
instruction = content_type_instructions.get(content_type, "Generate professional content with factual accuracy.")
@@ -123,15 +123,29 @@ class GeminiGroundedProvider:
grounded_prompt = f"""
{instruction}
- IMPORTANT: Use current, factual information from reliable sources. Cite specific sources for any claims, statistics, or recent developments.
+ CRITICAL REQUIREMENTS FOR LINKEDIN CONTENT:
+ - Use ONLY current, factual information from reliable sources (2024-2025)
+ - Cite specific sources for ALL claims, statistics, and recent developments
+ - Ensure content is optimized for LinkedIn's algorithm and engagement patterns
+ - Include strategic hashtags and engagement elements throughout
User Request: {prompt}
- Requirements:
- - Ensure all factual claims are backed by current sources
- - Use professional, engaging language appropriate for LinkedIn
- - Include relevant industry insights and trends
- - Make content shareable and valuable for the target audience
+ CONTENT QUALITY STANDARDS:
+ - All factual claims must be backed by current, authoritative sources
+ - Use professional yet conversational language that encourages engagement
+ - Include relevant industry insights, trends, and data points
+ - Make content highly shareable with clear value proposition
+ - Optimize for LinkedIn's professional audience and engagement metrics
+
+ ENGAGEMENT OPTIMIZATION:
+ - Include thought-provoking questions and calls-to-action
+ - Use storytelling elements and real-world examples
+ - Ensure content provides immediate, actionable value
+ - Optimize for comments, shares, and professional networking
+ - Include industry-specific terminology and insights
+
+ REMEMBER: This content will be displayed on LinkedIn with full source attribution and grounding data. Every claim must be verifiable, and the content should position the author as a thought leader in their industry.
"""
return grounded_prompt.strip()
diff --git a/backend/services/llm_providers/text_to_image_generation/gen_gemini_images.py b/backend/services/llm_providers/text_to_image_generation/gen_gemini_images.py
index ddac3de9..3ee978bc 100644
--- a/backend/services/llm_providers/text_to_image_generation/gen_gemini_images.py
+++ b/backend/services/llm_providers/text_to_image_generation/gen_gemini_images.py
@@ -3,6 +3,7 @@ import sys
import time
import datetime
import base64
+import random
from typing import List, Optional, Tuple
from PIL import Image
from io import BytesIO
@@ -12,8 +13,8 @@ import logging
from ...api_key_manager import APIKeyManager
try:
- import google.generativeai as genai
- from google.generativeai import types
+ from google import genai
+ from google.genai import types
except ImportError:
genai = None
logging.getLogger('gemini_image_generator').warning(
@@ -30,6 +31,24 @@ logging.basicConfig(
)
logger = logging.getLogger('gemini_image_generator')
+# Imagen fallback configuration
+IMAGEN_FALLBACK_CONFIG = {
+ 'enabled': os.getenv('IMAGEN_FALLBACK_ENABLED', 'true').lower() == 'true', # Master switch for Imagen fallback
+ 'auto_fallback': os.getenv('IMAGEN_AUTO_FALLBACK', 'true').lower() == 'true', # Automatically fall back on Gemini failures
+ 'preferred_model': os.getenv('IMAGEN_MODEL', 'imagen-4.0-generate-001'), # Fast model for quick generation
+ 'fallback_aspect_ratios': {
+ '1:1': '1:1',
+ '3:4': '3:4',
+ '4:3': '4:3',
+ '9:16': '9:16',
+ '16:9': '16:9'
+ },
+ 'max_images': int(os.getenv('IMAGEN_MAX_IMAGES', '1')), # Generate 1 image for LinkedIn posts
+}
+
+# Log configuration on startup
+logger.info(f"đ Imagen fallback configuration: {IMAGEN_FALLBACK_CONFIG}")
+
# With image generation in Gemini, your imagination is the limit.
# Follow Google AI best practices for detailed prompts and iterative refinement.
@@ -173,13 +192,137 @@ def _ensure_client() -> Optional[object]:
api_key_manager = APIKeyManager()
api_key = api_key_manager.get_api_key("gemini")
if not api_key or genai is None:
+ if not api_key:
+ logger.warning("No Gemini API key found")
+ if genai is None:
+ logger.warning("Google Generative AI library not available")
return None
try:
- return genai.Client(api_key=api_key)
- except Exception:
+ logger.info("Creating Gemini client...")
+ # Create a client using the correct API pattern
+ # The API key is passed directly to the Client constructor
+ client = genai.Client(api_key=api_key)
+ logger.info("Gemini client created successfully")
+ return client
+ except Exception as e:
+ logger.error(f"Failed to create Gemini client: {e}")
+ import traceback
+ logger.error(f"Traceback: {traceback.format_exc()}")
return None
+def _generate_imagen_images_base64(prompt: str, aspect_ratio: str = "1:1") -> List[str]:
+ """
+ Generate images using Imagen API as a fallback method.
+
+ This function implements the Imagen API following the official documentation:
+ https://ai.google.dev/gemini-api/docs/imagen
+
+ Args:
+ prompt: Text prompt for image generation
+ aspect_ratio: Desired aspect ratio (1:1, 3:4, 4:3, 9:16, 16:9)
+
+ Returns:
+ List of base64-encoded PNG images
+ """
+ logger = logging.getLogger('gemini_image_generator')
+ logger.info("đ Falling back to Imagen API for image generation")
+
+ try:
+ # Get API key for Imagen (can use same Gemini API key)
+ api_key_manager = APIKeyManager()
+ api_key = api_key_manager.get_api_key("gemini") # Imagen uses same API key
+
+ if not api_key:
+ logger.error("No API key available for Imagen fallback")
+ return []
+
+ # Create Imagen client
+ client = genai.Client(api_key=api_key)
+
+ # Map aspect ratio to Imagen format using configuration
+ imagen_aspect_ratio = IMAGEN_FALLBACK_CONFIG['fallback_aspect_ratios'].get(aspect_ratio, "1:1")
+
+ # Optimize prompt for Imagen (remove Gemini-specific formatting)
+ imagen_prompt = _optimize_prompt_for_imagen(prompt)
+
+ logger.info(f"Generating Imagen images with prompt: {imagen_prompt[:100]}...")
+ logger.info(f"Using aspect ratio: {imagen_aspect_ratio}")
+ logger.info(f"Using model: {IMAGEN_FALLBACK_CONFIG['preferred_model']}")
+
+ # Generate images using configured Imagen model
+ # Note: sample_image_size is not supported in current library version
+ config_params = {
+ 'number_of_images': IMAGEN_FALLBACK_CONFIG['max_images'],
+ 'aspect_ratio': imagen_aspect_ratio,
+ }
+
+ # Add additional configuration options if needed
+ # config_params['guidance_scale'] = 7.5 # Optional: control image generation quality
+ # config_params['person_generation'] = 'allow_adult' # Optional: control person generation
+
+ response = client.models.generate_images(
+ model=IMAGEN_FALLBACK_CONFIG['preferred_model'],
+ prompt=imagen_prompt,
+ config=types.GenerateImagesConfig(**config_params)
+ )
+
+ # Extract base64 images from response
+ images_b64: List[str] = []
+ for generated_image in response.generated_images:
+ if hasattr(generated_image, 'image') and hasattr(generated_image.image, 'image_bytes'):
+ # Convert image bytes to base64
+ image_bytes = generated_image.image.image_bytes
+ if isinstance(image_bytes, bytes):
+ images_b64.append(base64.b64encode(image_bytes).decode('utf-8'))
+ else:
+ # If already base64 string
+ images_b64.append(str(image_bytes))
+
+ if images_b64:
+ logger.info(f"â
Imagen fallback successful! Generated {len(images_b64)} images")
+ return images_b64
+ else:
+ logger.warning("Imagen fallback returned no images")
+ return []
+
+ except Exception as e:
+ logger.error(f"â Imagen fallback failed: {e}")
+ import traceback
+ logger.error(f"Imagen error traceback: {traceback.format_exc()}")
+ return []
+
+
+def _optimize_prompt_for_imagen(prompt: str) -> str:
+ """
+ Optimize prompt for Imagen API by removing Gemini-specific formatting
+ and enhancing it with Imagen best practices.
+
+ Based on Imagen prompt guide: https://ai.google.dev/gemini-api/docs/imagen
+ """
+ # Remove Gemini-specific formatting
+ prompt = prompt.replace('\n\nEnhanced prompt:', '')
+ prompt = prompt.replace('\n\nAspect ratio:', '')
+
+ # Clean up extra whitespace
+ prompt = ' '.join(prompt.split())
+
+ # Add Imagen-specific enhancements if not present
+ if 'professional' in prompt.lower() and 'linkedin' in prompt.lower():
+ # Enhance for LinkedIn professional content
+ prompt += ", high quality, professional photography, business appropriate"
+
+ if 'digital transformation' in prompt.lower() or 'technology' in prompt.lower():
+ # Enhance for tech content
+ prompt += ", modern, innovative, clean design, corporate aesthetic"
+
+ # Ensure prompt doesn't exceed Imagen's 480 token limit
+ if len(prompt) > 400: # Leave some buffer
+ prompt = prompt[:400] + "..."
+
+ return prompt
+
+
def generate_gemini_images_base64(
prompt: str,
*,
@@ -190,17 +333,23 @@ def generate_gemini_images_base64(
aspect_ratio: str = "9:16",
max_retries: int = 2,
initial_retry_delay: float = 1.0,
+ enable_imagen_fallback: bool = True,
) -> List[str]:
"""
Return list of base64 PNG images generated from a prompt.
+
+ Primary method: Gemini API for image generation
+ Fallback method: Imagen API when Gemini fails (quota limits, API errors, etc.)
Implements best practices per Gemini docs: send text prompt, parse inline image parts,
and return base64 data suitable for API responses. No Streamlit, no printing.
- Docs: https://ai.google.dev/gemini-api/docs/image-generation
+ Docs:
+ - Gemini: https://ai.google.dev/gemini-api/docs/image-generation
+ - Imagen: https://ai.google.dev/gemini-api/docs/imagen
"""
logger = logging.getLogger('gemini_image_generator')
- logger.info("Generating image (base64) with Gemini")
+ logger.info("Generating image (base64) with Gemini (with Imagen fallback)")
if enhance_prompt and keywords:
pg = AIPromptGenerator()
@@ -215,9 +364,13 @@ def generate_gemini_images_base64(
if aspect_ratio:
prompt = f"{prompt}\n\nAspect ratio: {aspect_ratio}"
+ # Try Gemini first
client = _ensure_client()
if client is None:
logger.warning("Gemini client not available or API key missing")
+ if enable_imagen_fallback and IMAGEN_FALLBACK_CONFIG['enabled']:
+ logger.info("Falling back to Imagen API")
+ return _generate_imagen_images_base64(prompt, aspect_ratio)
return []
retry = 0
@@ -225,9 +378,10 @@ def generate_gemini_images_base64(
while retry <= max_retries:
try:
response = client.models.generate_content(
- model="gemini-2.5-flash-image-preview",
+ model="gemini-2.0-flash-exp-image-generation",
contents=[prompt],
)
+
images_b64: List[str] = []
for part in response.candidates[0].content.parts:
if getattr(part, 'inline_data', None) is not None:
@@ -239,16 +393,47 @@ def generate_gemini_images_base64(
else:
# Some SDKs may already present base64 str
images_b64.append(str(raw))
- return images_b64
+
+ if images_b64:
+ logger.info(f"â
Gemini generated {len(images_b64)} images successfully")
+ return images_b64
+ else:
+ logger.warning("Gemini returned no images, falling back to Imagen")
+ if enable_imagen_fallback and IMAGEN_FALLBACK_CONFIG['enabled']:
+ return _generate_imagen_images_base64(prompt, aspect_ratio)
+ return []
+
except Exception as e:
msg = str(e)
logger.warning(f"Gemini image gen error: {msg}")
+
+ # Check if this is a quota/API error that warrants fallback
+ if any(error_type in msg.lower() for error_type in [
+ 'quota', 'resource_exhausted', 'rate_limit', 'billing', 'api_key', '403', '429'
+ ]):
+ logger.info("Gemini quota/API error detected, falling back to Imagen")
+ if enable_imagen_fallback and IMAGEN_FALLBACK_CONFIG['enabled']:
+ return _generate_imagen_images_base64(prompt, aspect_ratio)
+ return []
+
+ # For other errors, retry if possible
if "503" in msg and retry < max_retries:
time.sleep(delay)
delay *= 2
retry += 1
continue
+
+ # Final fallback for any other errors
+ if enable_imagen_fallback and IMAGEN_FALLBACK_CONFIG['enabled']:
+ logger.info("Final fallback to Imagen due to Gemini error")
+ return _generate_imagen_images_base64(prompt, aspect_ratio)
return []
+
+ # If all retries exhausted, fall back to Imagen
+ if enable_imagen_fallback and IMAGEN_FALLBACK_CONFIG['enabled']:
+ logger.info("All Gemini retries exhausted, falling back to Imagen")
+ return _generate_imagen_images_base64(prompt, aspect_ratio)
+ return []
def generate_gemini_image(
@@ -260,9 +445,12 @@ def generate_gemini_image(
max_retries=2,
initial_retry_delay=1.0,
aspect_ratio="9:16",
+ enable_imagen_fallback=True,
):
"""
Backward-compatible wrapper that generates a single image file on disk and returns path.
+ Now includes Imagen fallback for improved reliability.
+
Prefer generate_gemini_images_base64 in new code paths.
"""
logger = logging.getLogger('gemini_image_generator')
@@ -275,20 +463,31 @@ def generate_gemini_image(
aspect_ratio=aspect_ratio,
max_retries=max_retries,
initial_retry_delay=initial_retry_delay,
+ enable_imagen_fallback=enable_imagen_fallback,
)
if not images:
return None
+
# Persist first image to file for legacy callers
img_b64 = images[0]
img_bytes = base64.b64decode(img_b64)
img = Image.open(BytesIO(img_bytes))
- out_name = f'gemini-native-image-{datetime.datetime.now().strftime("%Y%m%d-%H%M%S")}.png'
+
+ # Update filename to indicate which API was used
+ timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+ if 'imagen' in prompt.lower() or 'fallback' in prompt.lower():
+ out_name = f'imagen-fallback-image-{timestamp}.png'
+ else:
+ out_name = f'gemini-native-image-{timestamp}.png'
+
try:
img.save(out_name)
# Also call save_generated_image to reuse existing pipeline
save_generated_image({"artifacts": [{"base64": img_b64}]})
+ logger.info(f"â
Image saved successfully: {out_name}")
return out_name
- except Exception:
+ except Exception as e:
+ logger.error(f"â Failed to save image: {e}")
return None
diff --git a/backend/test_enhanced_prompt_generation.py b/backend/test_enhanced_prompt_generation.py
new file mode 100644
index 00000000..a0ff2fb5
--- /dev/null
+++ b/backend/test_enhanced_prompt_generation.py
@@ -0,0 +1,228 @@
+#!/usr/bin/env python3
+"""
+Test Script for Enhanced LinkedIn Prompt Generation
+
+This script demonstrates how the enhanced LinkedIn prompt generator analyzes
+generated content and creates context-aware image prompts.
+"""
+
+import asyncio
+import sys
+import os
+from pathlib import Path
+
+# Add the backend directory to the Python path
+backend_path = Path(__file__).parent
+sys.path.insert(0, str(backend_path))
+
+from loguru import logger
+
+# Configure logging
+logger.remove()
+logger.add(sys.stdout, colorize=True, format="{current_slide.heading}
- -{current_slide.subheading}
- -{current_slide.content}
- 0 ? 1 : 0,
+ transform: progressActive || progressSteps.length > 0 ? 'translateY(0)' : 'translateY(-10px)',
+ height: progressActive || progressSteps.length > 0 ? 'auto' : 0,
+ overflow: 'hidden'
+ }}>
+
+
+
{/* Debug: Enhanced Persistence Test Buttons (remove in production) */}
diff --git a/frontend/src/components/LinkedInWriter/RegisterLinkedInActions.tsx b/frontend/src/components/LinkedInWriter/RegisterLinkedInActions.tsx
index d7b74e43..c576d3c0 100644
--- a/frontend/src/components/LinkedInWriter/RegisterLinkedInActions.tsx
+++ b/frontend/src/components/LinkedInWriter/RegisterLinkedInActions.tsx
@@ -13,6 +13,97 @@ import { PostHITL, ArticleHITL, CarouselHITL, VideoScriptHITL, CommentResponseHI
const useCopilotActionTyped = useCopilotAction as any;
const RegisterLinkedInActions: React.FC = () => {
+ // LinkedIn Image Generation Actions
+ useCopilotActionTyped({
+ name: 'generateLinkedInImagePrompts',
+ description: 'Generate three AI-optimized image prompts for LinkedIn content',
+ parameters: [
+ { name: 'content_type', type: 'string', required: true, description: 'Type of LinkedIn content (post, article, carousel, video_script)' },
+ { name: 'topic', type: 'string', required: true, description: 'Main topic of the content' },
+ { name: 'industry', type: 'string', required: true, description: 'Industry context' },
+ { name: 'content', type: 'string', required: true, description: 'The actual content text' }
+ ],
+ handler: async (args: any) => {
+ try {
+ const response = await fetch('/api/linkedin/generate-image-prompts', {
+ method: 'POST',
+ headers: { 'Content-Type': 'application/json' },
+ body: JSON.stringify({
+ content_type: args.content_type,
+ topic: args.topic,
+ industry: args.industry,
+ content: args.content
+ })
+ });
+
+ if (!response.ok) {
+ throw new Error(`Failed to generate image prompts: ${response.status}`);
+ }
+
+ const result = await response.json();
+ return {
+ success: true,
+ prompts: result,
+ message: `Generated ${result.length} professional image prompts for your LinkedIn content. Choose one to generate the actual image.`
+ };
+ } catch (error) {
+ console.error('Error generating image prompts:', error);
+ return {
+ success: false,
+ error: 'Failed to generate image prompts. Please try again.'
+ };
+ }
+ }
+ });
+
+ useCopilotActionTyped({
+ name: 'generateLinkedInImage',
+ description: 'Generate LinkedIn-optimized image from selected prompt',
+ parameters: [
+ { name: 'prompt', type: 'string', required: true, description: 'The image generation prompt' },
+ { name: 'content_context', type: 'object', required: true, description: 'Content context including topic, industry, content_type, and style' },
+ { name: 'aspect_ratio', type: 'string', required: false, description: 'Image aspect ratio (default: 1:1)' }
+ ],
+ handler: async (args: any) => {
+ try {
+ const response = await fetch('/api/linkedin/generate-image', {
+ method: 'POST',
+ headers: { 'Content-Type': 'application/json' },
+ body: JSON.stringify({
+ prompt: args.prompt,
+ content_context: args.content_context,
+ aspect_ratio: args.aspect_ratio || '1:1'
+ })
+ });
+
+ if (!response.ok) {
+ throw new Error(`Failed to generate image: ${response.status}`);
+ }
+
+ const result = await response.json();
+ if (result.success) {
+ return {
+ success: true,
+ image_url: result.image_url,
+ image_id: result.image_id,
+ message: `â
LinkedIn image generated successfully! Your professional image is ready to use.`
+ };
+ } else {
+ return {
+ success: false,
+ error: result.error || 'Image generation failed'
+ };
+ }
+ } catch (error) {
+ console.error('Error generating image:', error);
+ return {
+ success: false,
+ error: 'Failed to generate image. Please try again.'
+ };
+ }
+ }
+ });
+
// LinkedIn Post Generation
useCopilotActionTyped({
name: 'generateLinkedInPost',
@@ -26,6 +117,19 @@ const RegisterLinkedInActions: React.FC = () => {
],
handler: async (args: any) => {
const prefs = readPrefs();
+ // Emit progress init
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressInit', { detail: {
+ steps: [
+ { id: 'personalize', label: 'Personalizing topic & context' },
+ { id: 'prepare_queries', label: 'Preparing research queries' },
+ { id: 'research', label: 'Conducting research & analysis' },
+ { id: 'grounding', label: 'Applying AI grounding' },
+ { id: 'content_generation', label: 'Generating content' },
+ { id: 'citations', label: 'Extracting citations' },
+ { id: 'quality_analysis', label: 'Quality assessment' },
+ { id: 'finalize', label: 'Finalizing & optimizing' }
+ ]
+ }}));
// If refining existing content, use the current draft as context
let existingContent = '';
@@ -38,6 +142,15 @@ const RegisterLinkedInActions: React.FC = () => {
}
}
+ // Start detailed progress tracking
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'personalize',
+ status: 'active',
+ message: 'Analyzing topic, industry context, and target audience...'
+ }
+ }));
+
const res = await linkedInWriterApi.generatePost({
topic: args?.topic || prefs.topic || 'AI transformation in business',
industry: mapIndustry(args?.industry || prefs.industry),
@@ -55,6 +168,63 @@ const RegisterLinkedInActions: React.FC = () => {
});
if (res.success && res.data) {
+ // Update progress with detailed information
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'personalize',
+ status: 'completed',
+ message: 'Topic personalized successfully'
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'prepare_queries',
+ status: 'completed',
+ message: `Prepared ${(res.data?.search_queries || []).length} research queries`
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'research',
+ status: 'completed',
+ message: `Research completed with ${(res.research_sources || []).length} sources`
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'grounding',
+ status: 'completed',
+ message: 'AI grounding applied successfully'
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'content_generation',
+ status: 'completed',
+ message: 'Content generated with industry insights'
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'citations',
+ status: 'completed',
+ message: `Extracted ${(res.data?.citations || []).length} citations`
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'quality_analysis',
+ status: 'completed',
+ message: 'Quality assessment completed'
+ }
+ }));
+
const content = res.data.content;
const hashtags = res.data.hashtags?.map(h => h.hashtag).join(' ') || '';
const cta = res.data.call_to_action || '';
@@ -82,8 +252,39 @@ const RegisterLinkedInActions: React.FC = () => {
}));
window.dispatchEvent(new CustomEvent('linkedinwriter:updateDraft', { detail: fullContent }));
- return { success: true, content: fullContent };
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'finalize',
+ status: 'completed',
+ message: 'Content finalized and optimized'
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressComplete'));
+
+ // Return recommendations message that CopilotKit can render
+ const recommendations = res.data?.quality_metrics?.recommendations || [];
+ if (recommendations.length > 0) {
+ // Create a markdown-formatted message with recommendations
+ const recommendationsMarkdown = recommendations.map((rec, index) =>
+ `${index + 1}. **${rec}**`
+ ).join('\n\n');
+
+ // Return a message that CopilotKit can render with image generation suggestion
+ return {
+ success: true,
+ message: `â
LinkedIn post generated successfully! Your content is now displayed in the preview.\n\n**đ¯ AI Content Improvement Recommendations:**\n\n${recommendationsMarkdown}\n\n**đŧī¸ Enhance Your Post with AI-Generated Images:**\n\nNow that your content is ready, you can make it even more engaging with professional LinkedIn-optimized images! Here are your options:\n\nâĸ **Professional Style**: Clean, corporate aesthetics perfect for business audiences\nâĸ **Creative Style**: Eye-catching visuals that boost social media engagement\nâĸ **Industry-Specific**: Tailored imagery for your ${mapIndustry(args?.industry || prefs.industry)} industry\n\n*To generate images, simply ask: "Generate images for my LinkedIn post" or "Create professional images for this content"*\n\n*To get specific improvement guidance for any recommendation, type: "Help me improve [specific recommendation]"*`
+ };
+ } else {
+ // Return a message with image generation suggestion even without recommendations
+ return {
+ success: true,
+ message: `â
LinkedIn post generated successfully! Your content is now displayed in the preview.\n\n**đŧī¸ Enhance Your Post with AI-Generated Images:**\n\nNow that your content is ready, you can make it even more engaging with professional LinkedIn-optimized images! Here are your options:\n\nâĸ **Professional Style**: Clean, corporate aesthetics perfect for business audiences\nâĸ **Creative Style**: Eye-catching visuals that boost social media engagement\nâĸ **Industry-Specific**: Tailored imagery for your ${mapIndustry(args?.industry || prefs.industry)} industry\n\n*To generate images, simply ask: "Generate images for my LinkedIn post" or "Create professional images for this content"*`
+ };
+ }
}
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressError', { detail: { id: 'finalize', details: res.error } }));
return { success: false, message: res.error || 'Failed to generate LinkedIn post' };
}
});
@@ -100,6 +301,29 @@ const RegisterLinkedInActions: React.FC = () => {
],
handler: async (args: any) => {
const prefs = readPrefs();
+ // Emit progress init for article
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressInit', { detail: {
+ steps: [
+ { id: 'personalize', label: 'Personalizing topic & context' },
+ { id: 'prepare_queries', label: 'Preparing research queries' },
+ { id: 'research', label: 'Conducting research & analysis' },
+ { id: 'grounding', label: 'Applying AI grounding' },
+ { id: 'content_generation', label: 'Generating article content' },
+ { id: 'citations', label: 'Extracting citations' },
+ { id: 'quality_analysis', label: 'Quality assessment' },
+ { id: 'finalize', label: 'Finalizing & optimizing' }
+ ]
+ }}));
+
+ // Start detailed progress tracking
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'personalize',
+ status: 'active',
+ message: 'Analyzing topic, industry context, and target audience...'
+ }
+ }));
+
const res = await linkedInWriterApi.generateArticle({
topic: args?.topic || prefs.topic || 'Digital transformation strategies',
industry: mapIndustry(args?.industry || prefs.industry),
@@ -116,6 +340,63 @@ const RegisterLinkedInActions: React.FC = () => {
});
if (res.success && res.data) {
+ // Update progress with detailed information
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'personalize',
+ status: 'completed',
+ message: 'Topic personalized successfully'
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'prepare_queries',
+ status: 'completed',
+ message: `Prepared ${(res.data?.search_queries || []).length} research queries`
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'research',
+ status: 'completed',
+ message: `Research completed with ${(res.research_sources || []).length} sources`
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'grounding',
+ status: 'completed',
+ message: 'AI grounding applied successfully'
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'content_generation',
+ status: 'completed',
+ message: 'Article content generated with industry insights'
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'citations',
+ status: 'completed',
+ message: `Extracted ${(res.data?.citations || []).length} citations`
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'quality_analysis',
+ status: 'completed',
+ message: 'Quality assessment completed'
+ }
+ }));
+
const content = `# ${res.data.title}\n\n${res.data.content}`;
// Debug: Log the full response structure
@@ -137,8 +418,39 @@ const RegisterLinkedInActions: React.FC = () => {
}));
window.dispatchEvent(new CustomEvent('linkedinwriter:updateDraft', { detail: content }));
- return { success: true, content };
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'finalize',
+ status: 'completed',
+ message: 'Article finalized and optimized'
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressComplete'));
+
+ // Return recommendations message that CopilotKit can render
+ const recommendations = res.data?.quality_metrics?.recommendations || [];
+ if (recommendations.length > 0) {
+ // Create a markdown-formatted message with recommendations
+ const recommendationsMarkdown = recommendations.map((rec, index) =>
+ `${index + 1}. **${rec}**`
+ ).join('\n\n');
+
+ // Return a message that CopilotKit can render with image generation suggestion
+ return {
+ success: true,
+ message: `â
LinkedIn article generated successfully! Your content is now displayed in the preview.\n\n**đ¯ AI Content Improvement Recommendations:**\n\n${recommendationsMarkdown}\n\n**đŧī¸ Enhance Your Article with AI-Generated Images:**\n\nNow that your article is ready, you can make it even more engaging with professional LinkedIn-optimized images! Here are your options:\n\nâĸ **Professional Style**: Clean, corporate aesthetics perfect for business audiences\nâĸ **Creative Style**: Eye-catching visuals that boost social media engagement\nâĸ **Industry-Specific**: Tailored imagery for your ${mapIndustry(args?.industry || prefs.industry)} industry\n\n*To generate images, simply ask: "Generate images for my LinkedIn article" or "Create professional images for this content"*\n\n*To get specific improvement guidance for any recommendation, type: "Help me improve [specific recommendation]"*`
+ };
+ } else {
+ // Return a message with image generation suggestion even without recommendations
+ return {
+ success: true,
+ message: `â
LinkedIn article generated successfully! Your content is now displayed in the preview.\n\n**đŧī¸ Enhance Your Article with AI-Generated Images:**\n\nNow that your article is ready, you can make it even more engaging with professional LinkedIn-optimized images! Here are your options:\n\nâĸ **Professional Style**: Clean, corporate aesthetics perfect for business audiences\nâĸ **Creative Style**: Eye-catching visuals that boost social media engagement\nâĸ **Industry-Specific**: Tailored imagery for your ${mapIndustry(args?.industry || prefs.industry)} industry\n\n*To generate images, simply ask: "Generate images for my LinkedIn article" or "Create professional images for this content"*`
+ };
+ }
}
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressError', { detail: { id: 'finalize', details: res.error } }));
return { success: false, message: res.error || 'Failed to generate LinkedIn article' };
}
});
@@ -154,6 +466,30 @@ const RegisterLinkedInActions: React.FC = () => {
],
handler: async (args: any) => {
const prefs = readPrefs();
+
+ // Emit progress init for carousel
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressInit', { detail: {
+ steps: [
+ { id: 'personalize', label: 'Personalizing topic & context' },
+ { id: 'prepare_queries', label: 'Preparing research queries' },
+ { id: 'research', label: 'Conducting research & analysis' },
+ { id: 'grounding', label: 'Applying AI grounding' },
+ { id: 'content_generation', label: 'Generating carousel slides' },
+ { id: 'citations', label: 'Extracting citations' },
+ { id: 'quality_analysis', label: 'Quality assessment' },
+ { id: 'finalize', label: 'Finalizing & optimizing' }
+ ]
+ }}));
+
+ // Start detailed progress tracking
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'personalize',
+ status: 'active',
+ message: 'Analyzing topic, industry context, and target audience...'
+ }
+ }));
+
const res = await linkedInWriterApi.generateCarousel({
topic: args?.topic || prefs.topic || 'Professional development tips',
industry: mapIndustry(args?.industry || prefs.industry),
@@ -167,14 +503,84 @@ const RegisterLinkedInActions: React.FC = () => {
});
if (res.success && res.data) {
+ // Update progress with detailed information
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'personalize',
+ status: 'completed',
+ message: 'Topic personalized successfully'
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'prepare_queries',
+ status: 'completed',
+ message: `Prepared research queries for carousel`
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'research',
+ status: 'completed',
+ message: `Research completed for carousel content`
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'grounding',
+ status: 'completed',
+ message: 'AI grounding applied successfully'
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'content_generation',
+ status: 'completed',
+ message: `Generated ${res.data.slides?.length || 0} carousel slides`
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'citations',
+ status: 'completed',
+ message: 'Citations extracted for carousel'
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'quality_analysis',
+ status: 'completed',
+ message: 'Quality assessment completed'
+ }
+ }));
+
let content = `# ${res.data.title}\n\n`;
res.data.slides.forEach((slide, index) => {
content += `## Slide ${index + 1}: ${slide.title}\n\n${slide.content}\n\n`;
});
window.dispatchEvent(new CustomEvent('linkedinwriter:updateDraft', { detail: content }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'finalize',
+ status: 'completed',
+ message: 'Carousel finalized and optimized'
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressComplete'));
+
return { success: true, content };
}
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressError', { detail: { id: 'finalize', details: res.error } }));
return { success: false, message: res.error || 'Failed to generate LinkedIn carousel' };
}
});
@@ -190,6 +596,30 @@ const RegisterLinkedInActions: React.FC = () => {
],
handler: async (args: any) => {
const prefs = readPrefs();
+
+ // Emit progress init for video script
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressInit', { detail: {
+ steps: [
+ { id: 'personalize', label: 'Personalizing topic & context' },
+ { id: 'prepare_queries', label: 'Preparing research queries' },
+ { id: 'research', label: 'Conducting research & analysis' },
+ { id: 'grounding', label: 'Applying AI grounding' },
+ { id: 'content_generation', label: 'Generating video script' },
+ { id: 'citations', label: 'Extracting citations' },
+ { id: 'quality_analysis', label: 'Quality assessment' },
+ { id: 'finalize', label: 'Finalizing & optimizing' }
+ ]
+ }}));
+
+ // Start detailed progress tracking
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'personalize',
+ status: 'active',
+ message: 'Analyzing topic, industry context, and target audience...'
+ }
+ }));
+
const res = await linkedInWriterApi.generateVideoScript({
topic: args?.topic || prefs.topic || 'Professional networking tips',
industry: mapIndustry(args?.industry || prefs.industry),
@@ -202,6 +632,63 @@ const RegisterLinkedInActions: React.FC = () => {
});
if (res.success && res.data) {
+ // Update progress with detailed information
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'personalize',
+ status: 'completed',
+ message: 'Topic personalized successfully'
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'prepare_queries',
+ status: 'completed',
+ message: `Prepared research queries for video script`
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'research',
+ status: 'completed',
+ message: `Research completed for video content`
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'grounding',
+ status: 'completed',
+ message: 'AI grounding applied successfully'
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'content_generation',
+ status: 'completed',
+ message: `Generated video script with ${res.data.main_content?.length || 0} scenes`
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'citations',
+ status: 'completed',
+ message: 'Citations extracted for video script'
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'quality_analysis',
+ status: 'completed',
+ message: 'Quality assessment completed'
+ }
+ }));
+
let content = `# Video Script: ${args?.topic || 'Professional Content'}\n\n`;
content += `## Hook\n${res.data.hook}\n\n`;
content += `## Main Content\n`;
@@ -216,12 +703,167 @@ const RegisterLinkedInActions: React.FC = () => {
}
window.dispatchEvent(new CustomEvent('linkedinwriter:updateDraft', { detail: content }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressStep', {
+ detail: {
+ id: 'finalize',
+ status: 'completed',
+ message: 'Video script finalized and optimized'
+ }
+ }));
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressComplete'));
+
return { success: true, content };
}
+
+ window.dispatchEvent(new CustomEvent('linkedinwriter:progressError', { detail: { id: 'finalize', details: res.error } }));
return { success: false, message: res.error || 'Failed to generate LinkedIn video script' };
}
});
+ // Content Improvement Action
+ useCopilotActionTyped({
+ name: 'improveContent',
+ description: 'Improve specific aspects of LinkedIn content based on AI recommendations',
+ parameters: [
+ { name: 'recommendation', type: 'string', required: true },
+ { name: 'current_content', type: 'string', required: false },
+ { name: 'improvement_type', type: 'string', required: false }
+ ],
+ handler: async (args: any) => {
+ const { recommendation, current_content, improvement_type } = args;
+
+ // Analyze the recommendation and provide specific improvement guidance
+ let improvementGuidance = '';
+ let actionItems = [];
+ let examples = [];
+
+ if (recommendation.toLowerCase().includes('factual accuracy') || recommendation.toLowerCase().includes('accuracy')) {
+ improvementGuidance = 'To improve factual accuracy, consider:';
+ actionItems = [
+ 'Add specific data points and statistics',
+ 'Include recent research findings',
+ 'Cite authoritative sources',
+ 'Verify all claims against reliable sources'
+ ];
+ examples = [
+ 'Instead of "AI is growing rapidly", use "AI market grew 37% in 2023 according to Gartner"',
+ 'Replace "many companies" with "73% of Fortune 500 companies"',
+ 'Add source: "According to a 2024 McKinsey report..."'
+ ];
+ } else if (recommendation.toLowerCase().includes('professional tone') || recommendation.toLowerCase().includes('tone')) {
+ improvementGuidance = 'To enhance professional tone, consider:';
+ actionItems = [
+ 'Use industry-specific terminology',
+ 'Maintain consistent formality level',
+ 'Avoid casual language and slang',
+ 'Structure content with clear headings'
+ ];
+ examples = [
+ 'Instead of "cool new features", use "innovative capabilities"',
+ 'Replace "huge impact" with "significant impact"',
+ 'Use "Furthermore" instead of "Also"'
+ ];
+ } else if (recommendation.toLowerCase().includes('citation') || recommendation.toLowerCase().includes('sources')) {
+ improvementGuidance = 'To improve citation coverage, consider:';
+ actionItems = [
+ 'Add inline citations for factual claims',
+ 'Include source references for statistics',
+ 'Link to relevant research or reports',
+ 'Provide source list at the end'
+ ];
+ examples = [
+ 'Add [1] after statistics: "The market grew 25% [1]"',
+ 'Include source links: "According to [Harvard Business Review](link)..."',
+ 'Create a numbered source list at the bottom'
+ ];
+ } else if (recommendation.toLowerCase().includes('industry relevance') || recommendation.toLowerCase().includes('relevance')) {
+ improvementGuidance = 'To increase industry relevance, consider:';
+ actionItems = [
+ 'Use industry-specific examples',
+ 'Reference current industry trends',
+ 'Include relevant case studies',
+ 'Address industry-specific challenges'
+ ];
+ examples = [
+ 'Add industry-specific metrics: "In healthcare, this translates to..."',
+ 'Reference current trends: "With the rise of telemedicine..."',
+ 'Use industry jargon appropriately: "EMR integration" vs "electronic records"'
+ ];
+ } else {
+ improvementGuidance = 'To address this recommendation, consider:';
+ actionItems = [
+ 'Review the content for clarity',
+ 'Ensure consistency in messaging',
+ 'Check for grammatical accuracy',
+ 'Verify alignment with target audience'
+ ];
+ examples = [
+ 'Break long sentences into shorter ones',
+ 'Use consistent terminology throughout',
+ 'Check subject-verb agreement',
+ 'Ensure content matches audience expertise level'
+ ];
+ }
+
+ const actionItemsMarkdown = actionItems.map((item, index) =>
+ `${index + 1}. ${item}`
+ ).join('\n');
+
+ const examplesMarkdown = examples.map((example, index) =>
+ `**Example ${index + 1}:** ${example}`
+ ).join('\n\n');
+
+ return {
+ success: true,
+ message: `**đ§ Content Improvement Guide for: "${recommendation}"**\n\n${improvementGuidance}\n\n${actionItemsMarkdown}\n\n**đĄ Practical Examples:**\n\n${examplesMarkdown}\n\n*Would you like me to help you implement any of these improvements to your content?*`
+ };
+ }
+ });
+
+ // Natural Language Content Improvement Action
+ useCopilotActionTyped({
+ name: 'helpWithContentImprovement',
+ description: 'Help users improve their LinkedIn content based on natural language requests',
+ parameters: [
+ { name: 'user_request', type: 'string', required: true }
+ ],
+ handler: async (args: any) => {
+ const { user_request } = args;
+ const request = user_request.toLowerCase();
+
+ // Handle various ways users might ask for help
+ if (request.includes('help me improve') || request.includes('how to improve') || request.includes('improve')) {
+ // Extract the specific aspect they want to improve
+ let aspect = 'content quality';
+ if (request.includes('tone')) aspect = 'professional tone';
+ else if (request.includes('accuracy') || request.includes('factual')) aspect = 'factual accuracy';
+ else if (request.includes('citation') || request.includes('source')) aspect = 'citation coverage';
+ else if (request.includes('relevance') || request.includes('industry')) aspect = 'industry relevance';
+ else if (request.includes('grammar') || request.includes('language')) aspect = 'language quality';
+
+ return {
+ success: true,
+ message: `I'd be happy to help you improve your ${aspect}! Let me provide specific guidance and examples.\n\n*Please use the "improveContent" action with the specific recommendation you'd like to address, or let me know what aspect you'd like to focus on.*`
+ };
+ }
+
+ if (request.includes('recommendation') || request.includes('suggestion')) {
+ return {
+ success: true,
+ message: `I can see you have several AI-generated recommendations for improving your content! Here's how to get specific help:\n\n**To get detailed improvement guidance:**\nâĸ Type: "Help me improve [specific recommendation]"\nâĸ Example: "Help me improve the professional tone"\nâĸ Or: "How can I improve factual accuracy?"\n\n*Which specific recommendation would you like me to help you with?*`
+ };
+ }
+
+ // Default response
+ return {
+ success: true,
+ message: `I'm here to help you improve your LinkedIn content! You can:\n\n**1. Get specific improvement guidance:**\nâĸ "Help me improve [specific recommendation]"\nâĸ "How to improve professional tone?"\nâĸ "Improve factual accuracy"\n\n**2. Ask general questions:**\nâĸ "What are the best practices for LinkedIn posts?"\nâĸ "How can I make my content more engaging?"\n\n*What would you like to improve today?*`
+ };
+ }
+ });
+
// LinkedIn Comment Response Generation
useCopilotActionTyped({
name: 'generateLinkedInCommentResponse',
diff --git a/frontend/src/components/LinkedInWriter/components/ContentRecommendations.tsx b/frontend/src/components/LinkedInWriter/components/ContentRecommendations.tsx
new file mode 100644
index 00000000..08d28fab
--- /dev/null
+++ b/frontend/src/components/LinkedInWriter/components/ContentRecommendations.tsx
@@ -0,0 +1,144 @@
+import React from 'react';
+
+interface ContentRecommendationsProps {
+ recommendations: string[];
+ onSelectRecommendation: (recommendation: string) => void;
+}
+
+export const ContentRecommendations: React.FC
+
+ );
+};
diff --git a/frontend/src/components/LinkedInWriter/components/CopilotRecommendationsMessage.tsx b/frontend/src/components/LinkedInWriter/components/CopilotRecommendationsMessage.tsx
new file mode 100644
index 00000000..7eb13d70
--- /dev/null
+++ b/frontend/src/components/LinkedInWriter/components/CopilotRecommendationsMessage.tsx
@@ -0,0 +1,183 @@
+import React from 'react';
+
+interface CopilotRecommendationsMessageProps {
+ recommendations: string[];
+ onSelectRecommendation: (recommendation: string) => void;
+}
+
+export const CopilotRecommendationsMessage: React.FC
+
+ đĄ
+
+ AI Content Improvement Recommendations
+
+
+
+ Select any recommendation below to get specific guidance on improving your content:
+
+
+
+ {recommendations.map((recommendation, index) => (
+
+ ))}
+
+
+
+ These recommendations are based on AI analysis of your content quality and can help improve engagement and credibility.
+
+
+ {/* Decorative background elements */}
+
+
+
+
+
+ );
+};
diff --git a/frontend/src/components/LinkedInWriter/components/CopilotRecommendationsRenderer.tsx b/frontend/src/components/LinkedInWriter/components/CopilotRecommendationsRenderer.tsx
new file mode 100644
index 00000000..6f676fc9
--- /dev/null
+++ b/frontend/src/components/LinkedInWriter/components/CopilotRecommendationsRenderer.tsx
@@ -0,0 +1,129 @@
+import React from 'react';
+
+interface CopilotRecommendationsRendererProps {
+ message: any;
+ onRecommendationClick: (recommendation: string) => void;
+}
+
+export const CopilotRecommendationsRenderer: React.FC
+
+
+
+
+
+ đ¯
+
+
+
+
+ AI Content Improvement Suggestions
+
+
+ Select any recommendation to get specific guidance
+
+
+ {recommendations.map((recommendation, index) => (
+
+ ))}
+
+
+
+ đĄ These recommendations are based on AI analysis of your content quality and can help improve engagement and credibility.
+
+
+ {/* Success message */}
+
+ );
+};
diff --git a/frontend/src/components/LinkedInWriter/components/CustomMessageRenderer.tsx b/frontend/src/components/LinkedInWriter/components/CustomMessageRenderer.tsx
new file mode 100644
index 00000000..a6b5b857
--- /dev/null
+++ b/frontend/src/components/LinkedInWriter/components/CustomMessageRenderer.tsx
@@ -0,0 +1,94 @@
+import React from 'react';
+import { CopilotRecommendationsMessage } from './CopilotRecommendationsMessage';
+
+interface CustomMessageRendererProps {
+ message: any;
+ onSelectRecommendation: (recommendation: string) => void;
+}
+
+export const CustomMessageRenderer: React.FC
+ {message.message}
+
+
+ {/* Recommendations as interactive buttons */}
+
+ {recommendations.map((recommendation: string, index: number) => (
+
+ ))}
+
+
+
+ đĄ Click any recommendation above to get specific improvement guidance
+
+
+ {/* Success message */}
+
+ );
+ }
+
+ // Check if this is a regular success message
+ if (message?.content?.message && !message?.content?.content) {
+ return (
+
+ {message.content.message}
+
+
+ {/* Recommendations */}
+
+ {message.content.message}
+
+ );
+ }
+
+ // Default message rendering (fallback)
+ if (message?.content?.content) {
+ return (
+
+ {message.content.content}
+
+ );
+ }
+
+ // Fallback for other message types
+ return (
+
+ {JSON.stringify(message, null, 2)}
+
+ );
+};
diff --git a/frontend/src/components/LinkedInWriter/components/ImageGenerationDemo.tsx b/frontend/src/components/LinkedInWriter/components/ImageGenerationDemo.tsx
new file mode 100644
index 00000000..e180ec85
--- /dev/null
+++ b/frontend/src/components/LinkedInWriter/components/ImageGenerationDemo.tsx
@@ -0,0 +1,110 @@
+import React from 'react';
+import { ImageGenerationSuggestions } from './index';
+
+const ImageGenerationDemo: React.FC = () => {
+ // Sample LinkedIn content for demonstration
+ const sampleContent = {
+ contentType: 'post' as const,
+ topic: 'AI in Marketing',
+ industry: 'Technology',
+ content: `đ Exciting news! Artificial Intelligence is revolutionizing how we approach marketing strategies.
+
+Here are 3 game-changing ways AI is transforming the industry:
+
+1ī¸âŖ **Predictive Analytics**: AI algorithms can now predict customer behavior with 95% accuracy, allowing marketers to create hyper-personalized campaigns.
+
+2ī¸âŖ **Content Optimization**: Machine learning models analyze engagement patterns to optimize content timing, format, and messaging for maximum impact.
+
+3ī¸âŖ **Automated Personalization**: AI-powered tools automatically adjust marketing messages based on individual user preferences and behavior.
+
+The future of marketing is here, and it's powered by AI! đ¯
+
+What's your experience with AI in marketing? Share your thoughts below! đ
+
+#AIMarketing #DigitalTransformation #MarketingInnovation #TechTrends #FutureOfMarketing`
+ };
+
+ const handleImageGenerated = (imageData: any) => {
+ console.log('Image generated successfully:', imageData);
+ // Here you would typically:
+ // 1. Update the LinkedIn preview editor
+ // 2. Store the image in your content
+ // 3. Trigger any follow-up actions
+ };
+
+ return (
+
+
+ );
+};
+
+export default ImageGenerationDemo;
diff --git a/frontend/src/components/LinkedInWriter/components/ImageGenerationSuggestions.tsx b/frontend/src/components/LinkedInWriter/components/ImageGenerationSuggestions.tsx
new file mode 100644
index 00000000..58186fa0
--- /dev/null
+++ b/frontend/src/components/LinkedInWriter/components/ImageGenerationSuggestions.tsx
@@ -0,0 +1,469 @@
+import React, { useState, useEffect } from 'react';
+import { useCopilotAction } from '@copilotkit/react-core';
+import {
+ AutoAwesome as SparklesIcon,
+ PhotoCamera as PhotoIcon,
+ ArrowForward as ArrowRightIcon,
+ CheckCircle as CheckCircleIcon,
+ Warning as ExclamationTriangleIcon
+} from '@mui/icons-material';
+
+interface ImageGenerationSuggestionsProps {
+ contentType: 'post' | 'article' | 'carousel' | 'video_script';
+ topic: string;
+ industry: string;
+ content: string;
+ onImageGenerated?: (imageData: any) => void;
+ className?: string;
+}
+
+interface ImagePrompt {
+ style: string;
+ prompt: string;
+ description: string;
+ prompt_index: number;
+}
+
+interface ImageGenerationState {
+ isGenerating: boolean;
+ selectedPrompt: ImagePrompt | null;
+ generatedImage: any | null;
+ error: string | null;
+ progress: number;
+}
+
+const ImageGenerationSuggestions: React.FC
+
+
+ LinkedIn Image Generation Demo
++ This demo showcases the ImageGenerationSuggestions component integrated with CopilotKit. + Try generating image prompts and creating images for the sample LinkedIn content below. +
+
+
+
+
+
+
+ Sample LinkedIn Content
+
+
+
+ {sampleContent.contentType}
+ {sampleContent.topic}
+ {sampleContent.industry}
+
+
+ {sampleContent.content}
+
+
+
+ Image Generation
+
+
+ How It Works
+
+
+
+
+ 1
+
+
+ Content Analysis
+The system analyzes your LinkedIn content to understand context, tone, and target audience.
+
+
+ 2
+
+
+ Prompt Generation
+AI generates three distinct image prompts: Professional, Creative, and Industry-Specific.
+
+
+ 3
+
+
+ Image Creation
+Using Gemini API, creates LinkedIn-optimized images from your selected prompt.
+
+
+ 4
+
+
+ Integration
+Generated images are ready to use in your LinkedIn content editor.
+
+ {/* Main Suggestion Card */}
+ {!showPrompts && !state.generatedImage && (
+
+
+ )}
+
+ {/* Prompt Selection */}
+ {showPrompts && !state.isGenerating && !state.generatedImage && (
+
+
+
+
+
+
+
+ + Enhance Your {contentType.charAt(0).toUpperCase() + contentType.slice(1)} with AI-Generated Images +
++ Create professional, LinkedIn-optimized images that perfectly complement your content and boost engagement. +
+
+
+
+
+
+
+
+
+
+
+
+
+ )}
+
+ {/* Image Generation Progress */}
+ {state.isGenerating && (
+
+
+
+ Choose Your Visual Style
++ Select from three AI-optimized image styles that match your content +
+
+ {prompts.map((prompt) => (
+
+
+
+ handlePromptSelect(prompt)}
+ >
+
+ ))}
+
+
+
+ {prompt.style}
+
+
+
+ {prompt.description}
+
+ {prompt.prompt.substring(0, 120)}...
+
+
+
+
+
+
+ )}
+
+ {/* Generated Image Display */}
+ {state.generatedImage && (
+
+
+
+ Generating Your Image
+
+
+
+
+
+
+ {state.selectedPrompt?.style} style âĸ {Math.round(state.progress)}% complete
+
+
+
+
+ Creating a professional, LinkedIn-optimized image...
+
+
+
+
+
+ Image Generated Successfully!
+
+ 
{
+ const handleImageGenerated = (imageData: any) => {
+ console.log('Image generated successfully:', imageData);
+ };
+
+ return (
+ ;
+ timestamp?: string;
+}
+
+interface ProgressTrackerProps {
+ steps: ProgressStep[];
+ active: boolean;
+}
+
+export const ProgressTracker: React.FC = ({ steps, active }) => {
+ if (!steps || steps.length === 0) return null;
+
+ const completedSteps = steps.filter(step => step.status === 'completed').length;
+ const progressPercentage = Math.round((completedSteps / steps.length) * 100);
+
+ return (
+ ({
+ contentType: 'post',
+ topic: '',
+ industry: '',
+ content: ''
+ });
+
+ const [generatedImage, setGeneratedImage] = useState(null);
+
+ const handleImageGenerated = (imageData: any) => {
+ setGeneratedImage(imageData);
+ // Update your content editor with the generated image
+ };
+
+ return (
+ ([]);
+ // Progress state (lightweight custom system)
+ type ProgressStatus = 'pending' | 'active' | 'completed' | 'error';
+ type ProgressStep = {
+ id: string;
+ label: string;
+ status: ProgressStatus;
+ message?: string;
+ details?: any;
+ timestamp?: string;
+ };
+ const [progressSteps, setProgressSteps] = useState([]);
+ const [progressActive, setProgressActive] = useState(false);
+
// Chat history state
const [historyVersion, setHistoryVersion] = useState(0);
const [chatHistory, setChatHistory] = useState([]);
@@ -43,6 +56,7 @@ export function useLinkedInWriter() {
const [showPreferencesModal, setShowPreferencesModal] = useState(false);
const [showContextModal, setShowContextModal] = useState(false);
const [showPreview, setShowPreview] = useState(false);
+ const [justGeneratedContent, setJustGeneratedContent] = useState(false);
// Update suggestions when context changes
const updateSuggestions = useCallback(() => {
@@ -62,6 +76,13 @@ export function useLinkedInWriter() {
savePreferences({ last_used_actions: updatedActions });
setUserPreferences(prev => ({ ...prev, last_used_actions: updatedActions }));
+ // Mark content as just generated for content creation actions
+ if (['generateLinkedInPost', 'generateLinkedInArticle', 'generateLinkedInCarousel', 'generateLinkedInVideoScript'].includes(actionName)) {
+ setJustGeneratedContent(true);
+ // Reset the flag after 30 seconds
+ setTimeout(() => setJustGeneratedContent(false), 30000);
+ }
+
// Update suggestions after action usage
setTimeout(() => updateSuggestions(), 100);
}, [updateSuggestions]);
@@ -93,6 +114,75 @@ export function useLinkedInWriter() {
loadInitialData();
}, []);
+ // Listen for lightweight progress events
+ useEffect(() => {
+ const handleProgressInit = (event: CustomEvent) => {
+ const steps: Array<{ id: string; label: string; message?: string }> = event.detail?.steps || [];
+ const initialized: ProgressStep[] = steps.map((s, index) => ({
+ id: s.id,
+ label: s.label,
+ message: s.message,
+ status: index === 0 ? 'active' : 'pending',
+ timestamp: new Date().toISOString()
+ }));
+ setProgressSteps(initialized);
+ setProgressActive(true);
+ };
+
+ const handleProgressStep = (event: CustomEvent) => {
+ const { id, status, details, message } = event.detail || {};
+ if (!id) return;
+ setProgressSteps(prev => {
+ const updated = prev.map(step => step.id === id ? {
+ ...step,
+ status: (status || 'completed') as ProgressStatus,
+ details,
+ message,
+ timestamp: new Date().toISOString()
+ } : step);
+ // Mark next pending as active if current completed
+ if ((status || 'completed') === 'completed') {
+ const nextIdx = updated.findIndex(s => s.status === 'pending');
+ if (nextIdx !== -1) {
+ updated[nextIdx] = {
+ ...updated[nextIdx],
+ status: 'active',
+ timestamp: new Date().toISOString()
+ };
+ }
+ }
+ return updated;
+ });
+ };
+
+ const handleProgressComplete = () => {
+ setProgressSteps(prev => prev.map(s => s.status === 'completed' ? s : { ...s, status: 'completed', timestamp: new Date().toISOString() }));
+ setProgressActive(false);
+ // Keep progress visible for a moment to show completion, then hide
+ setTimeout(() => {
+ setProgressSteps([]);
+ }, 1500);
+ };
+
+ const handleProgressError = (event: CustomEvent) => {
+ const { id, details } = event.detail || {};
+ setProgressSteps(prev => prev.map(s => (id ? (s.id === id) : (s.status === 'active')) ? { ...s, status: 'error', details, timestamp: new Date().toISOString() } : s));
+ setProgressActive(false);
+ };
+
+ window.addEventListener('linkedinwriter:progressInit', handleProgressInit as EventListener);
+ window.addEventListener('linkedinwriter:progressStep', handleProgressStep as EventListener);
+ window.addEventListener('linkedinwriter:progressComplete', handleProgressComplete as EventListener);
+ window.addEventListener('linkedinwriter:progressError', handleProgressError as EventListener);
+
+ return () => {
+ window.removeEventListener('linkedinwriter:progressInit', handleProgressInit as EventListener);
+ window.removeEventListener('linkedinwriter:progressStep', handleProgressStep as EventListener);
+ window.removeEventListener('linkedinwriter:progressComplete', handleProgressComplete as EventListener);
+ window.removeEventListener('linkedinwriter:progressError', handleProgressError as EventListener);
+ };
+ }, []);
+
// Listen for grounding data updates from CopilotKit actions
useEffect(() => {
const handleGroundingDataUpdate = (event: CustomEvent) => {
@@ -150,6 +240,9 @@ export function useLinkedInWriter() {
setCurrentAction(null);
// Auto-show preview when new content is generated
setShowPreview(true);
+ // Hide progress tracker when content is generated
+ setProgressActive(false);
+ setProgressSteps([]);
};
const handleAppendDraft = (event: CustomEvent) => {
@@ -271,6 +364,7 @@ export function useLinkedInWriter() {
showPreferencesModal,
showContextModal,
showPreview,
+ justGeneratedContent,
// Setters
setDraft,
@@ -288,6 +382,7 @@ export function useLinkedInWriter() {
setShowPreferencesModal,
setShowContextModal,
setShowPreview,
+ setJustGeneratedContent: setJustGeneratedContent,
// Handlers
handleDraftChange,
@@ -313,6 +408,10 @@ export function useLinkedInWriter() {
setCitations,
setQualityMetrics,
setGroundingEnabled,
- setSearchQueries
+ setSearchQueries,
+
+ // Progress (exposed to UI)
+ progressSteps,
+ progressActive
};
}
diff --git a/frontend/src/components/LinkedInWriter/hooks/useRecommendations.ts b/frontend/src/components/LinkedInWriter/hooks/useRecommendations.ts
new file mode 100644
index 00000000..e73b6b33
--- /dev/null
+++ b/frontend/src/components/LinkedInWriter/hooks/useRecommendations.ts
@@ -0,0 +1,73 @@
+import { useState, useEffect } from 'react';
+
+interface Recommendation {
+ id: string;
+ text: string;
+ category: string;
+ priority: 'high' | 'medium' | 'low';
+}
+
+export const useRecommendations = () => {
+ const [recommendations, setRecommendations] = useState([]);
+ const [showRecommendations, setShowRecommendations] = useState(false);
+
+ useEffect(() => {
+ const handleRecommendationsUpdate = (event: CustomEvent) => {
+ const { recommendations: newRecommendations } = event.detail || {};
+ if (newRecommendations && Array.isArray(newRecommendations)) {
+ // Convert string recommendations to structured format
+ const structuredRecommendations: Recommendation[] = newRecommendations.map((rec, index) => ({
+ id: `rec-${index}`,
+ text: rec,
+ category: 'content-improvement',
+ priority: 'medium' as const
+ }));
+
+ setRecommendations(structuredRecommendations);
+ setShowRecommendations(true);
+ }
+ };
+
+ window.addEventListener('linkedinwriter:recommendationsUpdate', handleRecommendationsUpdate as EventListener);
+
+ return () => {
+ window.removeEventListener('linkedinwriter:recommendationsUpdate', handleRecommendationsUpdate as EventListener);
+ };
+ }, []);
+
+ const handleRecommendationSelect = (recommendation: Recommendation) => {
+ console.log('Selected recommendation:', recommendation);
+
+ // Here you can implement specific actions for each recommendation
+ // For now, we'll just log it and could trigger specific improvement actions
+
+ // Example: Trigger specific improvement actions based on recommendation
+ if (recommendation.text.toLowerCase().includes('factual accuracy')) {
+ // Could trigger factual accuracy improvement workflow
+ console.log('Triggering factual accuracy improvement workflow');
+ } else if (recommendation.text.toLowerCase().includes('professional tone')) {
+ // Could trigger tone improvement workflow
+ console.log('Triggering professional tone improvement workflow');
+ } else if (recommendation.text.toLowerCase().includes('citation')) {
+ // Could trigger citation improvement workflow
+ console.log('Triggering citation improvement workflow');
+ }
+
+ // You could also dispatch events to trigger specific CopilotKit actions
+ window.dispatchEvent(new CustomEvent('linkedinwriter:improvementRequested', {
+ detail: { recommendation, action: 'improve' }
+ }));
+ };
+
+ const hideRecommendations = () => {
+ setShowRecommendations(false);
+ setRecommendations([]);
+ };
+
+ return {
+ recommendations,
+ showRecommendations,
+ handleRecommendationSelect,
+ hideRecommendations
+ };
+};
diff --git a/frontend/src/components/LinkedInWriter/styles/alwrity-copilot.css b/frontend/src/components/LinkedInWriter/styles/alwrity-copilot.css
index d586d312..f7a74b8e 100644
--- a/frontend/src/components/LinkedInWriter/styles/alwrity-copilot.css
+++ b/frontend/src/components/LinkedInWriter/styles/alwrity-copilot.css
@@ -1,6 +1,30 @@
-/* ALwrity Co-Pilot Custom Styling */
+/* ALwrity Co-Pilot Compact Styling - 60% Smaller & More Efficient */
-/* CopilotKit trigger button styling (without ALwrity icon) */
+/* ===== COMPACT SIDEBAR CONTAINER ===== */
+.alwrity-copilot-sidebar .copilot-sidebar-container,
+.alwrity-copilot-sidebar .copilotkit-sidebar,
+.alwrity-copilot-sidebar .copilotkit-chat-container {
+ /* Reduce overall width by 60% */
+ width: 40% !important; /* Reduced from 100% */
+ max-width: 320px !important; /* Reduced from typical 800px+ */
+ min-width: 280px !important;
+
+ /* Compact height */
+ height: 85vh !important; /* Reduced from 100vh */
+ max-height: 600px !important;
+
+ /* Reduced padding and margins */
+ padding: 8px !important; /* Reduced from 20px+ */
+ margin: 8px !important;
+
+ /* Compact border radius */
+ border-radius: 8px !important; /* Reduced from 16px+ */
+
+ /* Subtle shadow */
+ box-shadow: 0 4px 16px rgba(0, 0, 0, 0.15) !important; /* Reduced from 18px+ */
+}
+
+/* ===== COMPACT TRIGGER BUTTONS ===== */
.alwrity-copilot-sidebar .copilot-sidebar-trigger,
.alwrity-copilot-sidebar [data-testid="copilot-sidebar-trigger"],
.alwrity-copilot-sidebar .copilot-sidebar-trigger-button,
@@ -15,14 +39,17 @@
.alwrity-copilot-sidebar [data-testid*="trigger"],
.alwrity-copilot-sidebar [data-testid*="assistant"],
.alwrity-copilot-sidebar [data-testid*="copilot"] {
- width: 48px !important;
- height: 48px !important;
+ /* Reduce button size by 60% */
+ width: 32px !important; /* Reduced from 48px */
+ height: 32px !important; /* Reduced from 48px */
border-radius: 50% !important;
- box-shadow: 0 4px 12px rgba(10, 102, 194, 0.3) !important;
- transition: all 0.3s ease !important;
+
+ /* Compact shadow */
+ box-shadow: 0 2px 8px rgba(10, 102, 194, 0.25) !important; /* Reduced from 12px */
+ transition: all 0.2s ease !important; /* Reduced from 0.3s */
}
-/* Hover effects for trigger buttons */
+/* Compact hover effects */
.alwrity-copilot-sidebar .copilot-sidebar-trigger:hover,
.alwrity-copilot-sidebar [data-testid="copilot-sidebar-trigger"]:hover,
.alwrity-copilot-sidebar .copilot-sidebar-trigger-button:hover,
@@ -34,68 +61,54 @@
.alwrity-copilot-sidebar button[class*="floating"]:hover,
.alwrity-copilot-sidebar button[class*="assistant"]:hover,
.alwrity-copilot-sidebar button[class*="copilot"]:hover {
- transform: scale(1.1) !important;
- box-shadow: 0 6px 20px rgba(10, 102, 194, 0.4) !important;
+ transform: scale(1.05) !important; /* Reduced from 1.1 */
+ box-shadow: 0 3px 12px rgba(10, 102, 194, 0.35) !important; /* Reduced from 20px */
}
-/* Custom sidebar header */
+/* ===== COMPACT SIDEBAR HEADER ===== */
.alwrity-copilot-sidebar .copilot-sidebar-header {
background: linear-gradient(135deg, #0a66c2 0%, #0056b3 100%) !important;
- border-bottom: 2px solid #ffffff20 !important;
+ border-bottom: 1px solid #ffffff20 !important; /* Reduced from 2px */
+ padding: 8px 12px !important; /* Reduced from 16px+ */
+ margin-bottom: 8px !important; /* Reduced from 20px+ */
}
-/* Sidebar title styling - no icon */
+/* Compact sidebar title */
.alwrity-copilot-sidebar .copilot-sidebar-title {
color: #ffffff !important;
- font-weight: 700 !important;
- font-size: 18px !important;
+ font-weight: 600 !important; /* Reduced from 700 */
+ font-size: 14px !important; /* Reduced from 18px */
display: flex !important;
align-items: center !important;
- gap: 8px !important;
+ gap: 6px !important; /* Reduced from 8px */
}
-/* Icon display removed - no ALwrity image in chat window */
-
-/* Debug borders removed for clean appearance */
-
-/* Additional trigger button styling (without icon) */
-.alwrity-copilot-sidebar [data-testid="copilot-sidebar-trigger"],
-.alwrity-copilot-sidebar [data-testid="copilot-sidebar-trigger-button"],
-.alwrity-copilot-sidebar [data-testid="copilot-sidebar-trigger-icon"],
-.alwrity-copilot-sidebar .copilot-sidebar-trigger,
-.alwrity-copilot-sidebar .copilot-sidebar-trigger-button,
-.alwrity-copilot-sidebar .copilot-sidebar-trigger-icon,
-.alwrity-copilot-sidebar button[aria-label*="copilot"],
-.alwrity-copilot-sidebar button[aria-label*="assistant"],
-.alwrity-copilot-sidebar button[aria-label*="chat"],
-.alwrity-copilot-sidebar button[aria-label*="open"],
-.alwrity-copilot-sidebar button[aria-label*="toggle"] {
- width: 48px !important;
- height: 48px !important;
- border-radius: 50% !important;
- box-shadow: 0 4px 12px rgba(10, 102, 194, 0.3) !important;
- transition: all 0.3s ease !important;
-}
-
-/* Premium Message Styling - Enhanced Speech Bubbles */
+/* ===== COMPACT MESSAGE STYLING ===== */
.alwrity-copilot-sidebar .copilot-message,
.alwrity-copilot-sidebar [data-testid="copilot-message"],
.alwrity-copilot-sidebar .message,
.alwrity-copilot-sidebar .chat-message,
.alwrity-copilot-sidebar .assistant-message,
.alwrity-copilot-sidebar .user-message {
- border-radius: 16px !important;
- margin: 12px 0 !important;
- padding: 16px 20px !important;
- max-width: 85% !important;
- position: relative !important;
- box-shadow: 0 2px 12px rgba(0, 0, 0, 0.08) !important;
- transition: all 0.2s ease !important;
- line-height: 1.5 !important;
- font-size: 14px !important;
+ /* Reduce spacing by 60% */
+ border-radius: 8px !important; /* Reduced from 16px */
+ margin: 6px 0 !important; /* Reduced from 12px */
+ padding: 8px 12px !important; /* Reduced from 16px 20px */
+
+ /* Maximize message width for chat */
+ max-width: 95% !important; /* Increased from 85% for better space usage */
+ width: fit-content !important;
+
+ /* Compact shadows and effects */
+ box-shadow: 0 1px 4px rgba(0, 0, 0, 0.08) !important; /* Reduced from 12px */
+ transition: all 0.15s ease !important; /* Reduced from 0.2s */
+
+ /* Compact typography */
+ line-height: 1.4 !important; /* Reduced from 1.5 */
+ font-size: 13px !important; /* Reduced from 14px */
}
-/* User Message Styling */
+/* Compact user message styling */
.alwrity-copilot-sidebar .copilot-message.user,
.alwrity-copilot-sidebar [data-testid="copilot-message"].user,
.alwrity-copilot-sidebar .message.user,
@@ -109,6 +122,7 @@
font-weight: 500 !important;
}
+/* Compact user message arrow */
.alwrity-copilot-sidebar .copilot-message.user::before,
.alwrity-copilot-sidebar [data-testid="copilot-message"].user::before,
.alwrity-copilot-sidebar .message.user::before,
@@ -116,17 +130,17 @@
.alwrity-copilot-sidebar .user-message::before {
content: '' !important;
position: absolute !important;
- right: -8px !important;
+ right: -6px !important; /* Reduced from -8px */
top: 50% !important;
transform: translateY(-50%) !important;
width: 0 !important;
height: 0 !important;
- border-left: 8px solid #d1e7ff !important;
- border-top: 6px solid transparent !important;
- border-bottom: 6px solid transparent !important;
+ border-left: 6px solid #d1e7ff !important; /* Reduced from 8px */
+ border-top: 4px solid transparent !important; /* Reduced from 6px */
+ border-bottom: 4px solid transparent !important; /* Reduced from 6px */
}
-/* Assistant Message Styling */
+/* Compact assistant message styling */
.alwrity-copilot-sidebar .copilot-message.assistant,
.alwrity-copilot-sidebar [data-testid="copilot-message"].assistant,
.alwrity-copilot-sidebar .message.assistant,
@@ -140,6 +154,7 @@
font-weight: 400 !important;
}
+/* Compact assistant message arrow */
.alwrity-copilot-sidebar .copilot-message.assistant::before,
.alwrity-copilot-sidebar [data-testid="copilot-message"].assistant::before,
.alwrity-copilot-sidebar .message.assistant::before,
@@ -147,17 +162,17 @@
.alwrity-copilot-sidebar .assistant-message::before {
content: '' !important;
position: absolute !important;
- left: -8px !important;
+ left: -6px !important; /* Reduced from -8px */
top: 50% !important;
transform: translateY(-50%) !important;
width: 0 !important;
height: 0 !important;
- border-right: 8px solid #e1e8ed !important;
- border-top: 6px solid transparent !important;
- border-bottom: 6px solid transparent !important;
+ border-right: 6px solid #e1e8ed !important; /* Reduced from 8px */
+ border-top: 4px solid transparent !important; /* Reduced from 6px */
+ border-bottom: 4px solid transparent !important; /* Reduced from 6px */
}
-/* Message Hover Effects */
+/* Compact message hover effects */
.alwrity-copilot-sidebar .copilot-message:hover,
.alwrity-copilot-sidebar [data-testid="copilot-message"]:hover,
.alwrity-copilot-sidebar .message:hover,
@@ -165,18 +180,19 @@
.alwrity-copilot-sidebar .assistant-message:hover,
.alwrity-copilot-sidebar .user-message:hover {
transform: translateY(-1px) !important;
- box-shadow: 0 4px 20px rgba(0, 0, 0, 0.12) !important;
+ box-shadow: 0 2px 8px rgba(0, 0, 0, 0.12) !important; /* Reduced from 20px */
}
-/* Message Content Typography */
+/* ===== COMPACT MESSAGE CONTENT ===== */
.alwrity-copilot-sidebar .copilot-message p,
.alwrity-copilot-sidebar [data-testid="copilot-message"] p,
.alwrity-copilot-sidebar .message p,
.alwrity-copilot-sidebar .chat-message p,
.alwrity-copilot-sidebar .assistant-message p,
.alwrity-copilot-sidebar .user-message p {
- margin: 0 0 12px 0 !important;
- line-height: 1.6 !important;
+ margin: 0 0 8px 0 !important; /* Reduced from 12px */
+ line-height: 1.4 !important; /* Reduced from 1.6 */
+ font-size: 13px !important; /* Reduced from 14px */
}
.alwrity-copilot-sidebar .copilot-message p:last-child,
@@ -188,7 +204,7 @@
margin-bottom: 0 !important;
}
-/* Code Blocks in Messages */
+/* ===== COMPACT CODE BLOCKS ===== */
.alwrity-copilot-sidebar .copilot-message code,
.alwrity-copilot-sidebar [data-testid="copilot-message"] code,
.alwrity-copilot-sidebar .message code,
@@ -197,44 +213,60 @@
.alwrity-copilot-sidebar .user-message code {
background: #f7fafc !important;
border: 1px solid #e2e8f0 !important;
- border-radius: 6px !important;
- padding: 2px 6px !important;
+ border-radius: 4px !important; /* Reduced from 6px */
+ padding: 1px 4px !important; /* Reduced from 2px 6px */
font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace !important;
- font-size: 13px !important;
+ font-size: 12px !important; /* Reduced from 13px */
color: #2d3748 !important;
}
-/* Custom Button Styling */
+/* ===== COMPACT BUTTON STYLING ===== */
.alwrity-copilot-sidebar .copilot-button,
.alwrity-copilot-sidebar button,
.alwrity-copilot-sidebar .btn {
background: linear-gradient(135deg, #0a66c2 0%, #0056b3 100%) !important;
border: none !important;
- border-radius: 10px !important;
+ border-radius: 6px !important; /* Reduced from 10px */
color: white !important;
- font-weight: 600 !important;
- padding: 10px 20px !important;
- transition: all 0.2s ease !important;
- box-shadow: 0 2px 8px rgba(10, 102, 194, 0.25) !important;
+ font-weight: 500 !important; /* Reduced from 600 */
+
+ /* Reduce padding by 60% */
+ padding: 6px 12px !important; /* Reduced from 10px 20px */
+
+ /* Compact transitions */
+ transition: all 0.15s ease !important; /* Reduced from 0.2s */
+
+ /* Compact shadows */
+ box-shadow: 0 1px 4px rgba(10, 102, 194, 0.25) !important; /* Reduced from 8px */
+
+ /* Smaller font size */
+ font-size: 12px !important; /* Reduced from 14px */
}
.alwrity-copilot-sidebar .copilot-button:hover,
.alwrity-copilot-sidebar button:hover,
.alwrity-copilot-sidebar .btn:hover {
background: linear-gradient(135deg, #0056b3 0%, #004494 100%) !important;
- transform: translateY(-2px) !important;
- box-shadow: 0 4px 16px rgba(10, 102, 194, 0.35) !important;
+ transform: translateY(-1px) !important; /* Reduced from -2px */
+ box-shadow: 0 2px 8px rgba(10, 102, 194, 0.35) !important; /* Reduced from 16px */
}
-/* Custom Input Styling */
+/* ===== COMPACT INPUT STYLING ===== */
.alwrity-copilot-sidebar .copilot-input,
.alwrity-copilot-sidebar input,
.alwrity-copilot-sidebar textarea {
- border: 2px solid #e2e8f0 !important;
- border-radius: 12px !important;
- padding: 14px 18px !important;
- font-size: 14px !important;
- transition: all 0.2s ease !important;
+ border: 1px solid #e2e8f0 !important; /* Reduced from 2px */
+ border-radius: 6px !important; /* Reduced from 12px */
+
+ /* Reduce padding by 60% */
+ padding: 8px 12px !important; /* Reduced from 14px 18px */
+
+ /* Compact font size */
+ font-size: 13px !important; /* Reduced from 14px */
+
+ /* Compact transitions */
+ transition: all 0.15s ease !important; /* Reduced from 0.2s */
+
background: #ffffff !important;
color: #2d3748 !important;
}
@@ -244,25 +276,34 @@
.alwrity-copilot-sidebar textarea:focus {
border-color: #0a66c2 !important;
outline: none !important;
- box-shadow: 0 0 0 3px rgba(10, 102, 194, 0.1) !important;
+ box-shadow: 0 0 0 2px rgba(10, 102, 194, 0.1) !important; /* Reduced from 3px */
transform: translateY(-1px) !important;
}
-/* Premium Suggestion Chips */
+/* ===== COMPACT SUGGESTION CHIPS ===== */
.alwrity-copilot-sidebar .copilot-suggestion,
.alwrity-copilot-sidebar .suggestion,
.alwrity-copilot-sidebar .chip {
background: linear-gradient(135deg, #f8fafc 0%, #f1f5f9 100%) !important;
border: 1px solid #e2e8f0 !important;
- border-radius: 24px !important;
- padding: 10px 18px !important;
- margin: 6px !important;
+ border-radius: 16px !important; /* Reduced from 24px */
+
+ /* Reduce padding by 60% */
+ padding: 6px 12px !important; /* Reduced from 10px 18px */
+
+ /* Compact margins */
+ margin: 3px !important; /* Reduced from 6px */
+
cursor: pointer !important;
- transition: all 0.2s ease !important;
- font-size: 13px !important;
+ transition: all 0.15s ease !important; /* Reduced from 0.2s */
+
+ /* Compact font size */
+ font-size: 12px !important; /* Reduced from 13px */
color: #475569 !important;
font-weight: 500 !important;
- box-shadow: 0 1px 3px rgba(0, 0, 0, 0.05) !important;
+
+ /* Compact shadows */
+ box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05) !important; /* Reduced from 3px */
}
.alwrity-copilot-sidebar .copilot-suggestion:hover,
@@ -271,112 +312,1078 @@
background: linear-gradient(135deg, #0a66c2 0%, #0056b3 100%) !important;
color: white !important;
border-color: #0a66c2 !important;
- transform: translateY(-2px) !important;
- box-shadow: 0 4px 12px rgba(10, 102, 194, 0.25) !important;
+ transform: translateY(-1px) !important; /* Reduced from -2px */
+ box-shadow: 0 2px 6px rgba(10, 102, 194, 0.25) !important; /* Reduced from 12px */
}
-/* Loading States */
+/* ===== COMPACT LOADING STATES ===== */
.alwrity-copilot-sidebar .copilot-loading,
.alwrity-copilot-sidebar .loading {
color: #0a66c2 !important;
display: flex !important;
align-items: center !important;
- gap: 8px !important;
+ gap: 6px !important; /* Reduced from 8px */
+ font-size: 12px !important; /* Reduced from 14px */
}
-/* Typing Indicator */
+/* ===== COMPACT TYPING INDICATOR ===== */
.alwrity-copilot-sidebar .typing-indicator,
.alwrity-copilot-sidebar .is-typing {
display: flex !important;
align-items: center !important;
- gap: 4px !important;
- padding: 8px 16px !important;
+ gap: 3px !important; /* Reduced from 4px */
+ padding: 6px 12px !important; /* Reduced from 8px 16px */
color: #64748b !important;
font-style: italic !important;
+ font-size: 12px !important; /* Reduced from 14px */
}
.alwrity-copilot-sidebar .typing-dot {
- width: 6px !important;
- height: 6px !important;
+ width: 4px !important; /* Reduced from 6px */
+ height: 4px !important; /* Reduced from 6px */
border-radius: 50% !important;
background: #0a66c2 !important;
- animation: typing 1.4s infinite ease-in-out !important;
+ animation: typing 1.2s infinite ease-in-out !important; /* Reduced from 1.4s */
}
-.alwrity-copilot-sidebar .typing-dot:nth-child(1) { animation-delay: -0.32s !important; }
-.alwrity-copilot-sidebar .typing-dot:nth-child(2) { animation-delay: -0.16s !important; }
-
-@keyframes typing {
- 0%, 80%, 100% { transform: scale(0.8); opacity: 0.5; }
- 40% { transform: scale(1); opacity: 1; }
-}
-
-/* Scrollbar Customization */
-.alwrity-copilot-sidebar .copilot-sidebar-content::-webkit-scrollbar,
-.alwrity-copilot-sidebar .sidebar-content::-webkit-scrollbar,
-.alwrity-copilot-sidebar .chat-content::-webkit-scrollbar {
- width: 8px !important;
-}
-
-.alwrity-copilot-sidebar .copilot-sidebar-content::-webkit-scrollbar-track,
-.alwrity-copilot-sidebar .sidebar-content::-webkit-scrollbar-track,
-.alwrity-copilot-sidebar .chat-content::-webkit-scrollbar-track {
- background: #f8fafc !important;
- border-radius: 4px !important;
-}
-
-.alwrity-copilot-sidebar .copilot-sidebar-content::-webkit-scrollbar-thumb,
-.alwrity-copilot-sidebar .sidebar-content::-webkit-scrollbar-thumb,
-.alwrity-copilot-sidebar .chat-content::-webkit-scrollbar-thumb {
- background: linear-gradient(135deg, #0a66c2 0%, #0056b3 100%) !important;
- border-radius: 4px !important;
- border: 1px solid #f8fafc !important;
-}
-
-.alwrity-copilot-sidebar .copilot-sidebar-content::-webkit-scrollbar-thumb:hover,
-.alwrity-copilot-sidebar .sidebar-content::-webkit-scrollbar-thumb:hover,
-.alwrity-copilot-sidebar .chat-content::-webkit-scrollbar-thumb:hover {
- background: linear-gradient(135deg, #0056b3 0%, #004494 100%) !important;
-}
-
-/* Message Timestamps */
-.alwrity-copilot-sidebar .message-timestamp,
-.alwrity-copilot-sidebar .timestamp {
- font-size: 11px !important;
- color: #94a3b8 !important;
- margin-top: 4px !important;
- text-align: right !important;
- font-weight: 400 !important;
-}
-
-/* Tool Call Styling */
+/* ===== COMPACT TOOL CALL STYLING ===== */
.alwrity-copilot-sidebar .tool-call,
.alwrity-copilot-sidebar .function-call {
background: #f1f5f9 !important;
border: 1px solid #e2e8f0 !important;
- border-radius: 8px !important;
- padding: 12px 16px !important;
- margin: 8px 0 !important;
+ border-radius: 6px !important; /* Reduced from 8px */
+ padding: 8px 12px !important; /* Reduced from 12px 16px */
+ margin: 4px 0 !important; /* Reduced from 8px */
font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace !important;
- font-size: 12px !important;
+ font-size: 11px !important; /* Reduced from 12px */
color: #475569 !important;
- border-left: 4px solid #0a66c2 !important;
+ border-left: 3px solid #0a66c2 !important; /* Reduced from 4px */
}
-/* Error Message Styling */
+/* ===== COMPACT ERROR/SUCCESS MESSAGE STYLING ===== */
.alwrity-copilot-sidebar .error-message,
.alwrity-copilot-sidebar .error {
background: linear-gradient(135deg, #fef2f2 0%, #fee2e2 100%) !important;
border: 1px solid #fecaca !important;
color: #dc2626 !important;
- border-left: 4px solid #dc2626 !important;
+ border-left: 3px solid #dc2626 !important; /* Reduced from 4px */
+ padding: 8px 12px !important; /* Reduced from 12px 16px */
+ border-radius: 6px !important; /* Reduced from 8px */
+ font-size: 12px !important; /* Reduced from 14px */
}
-/* Success Message Styling */
.alwrity-copilot-sidebar .success-message,
.alwrity-copilot-sidebar .success {
background: linear-gradient(135deg, #f0fdf4 0%, #dcfce7 100%) !important;
border: 1px solid #bbf7d0 !important;
color: #16a34a !important;
- border-left: 4px solid #16a34a !important;
+ border-left: 3px solid #16a34a !important; /* Reduced from 4px */
+ padding: 8px 12px !important; /* Reduced from 12px 16px */
+ border-radius: 6px !important; /* Reduced from 8px */
+ font-size: 12px !important; /* Reduced from 14px */
+}
+
+/* ===== COMPACT SCROLLBAR STYLING ===== */
+.alwrity-copilot-sidebar ::-webkit-scrollbar {
+ width: 6px !important; /* Reduced from 10px */
+ height: 6px !important; /* Reduced from 10px */
+}
+
+.alwrity-copilot-sidebar ::-webkit-scrollbar-thumb {
+ background: rgba(10, 102, 194, 0.3) !important;
+ border: 1px solid rgba(0, 0, 0, 0.1) !important;
+ border-radius: 6px !important; /* Reduced from 10px */
+}
+
+.alwrity-copilot-sidebar ::-webkit-scrollbar-track {
+ background: rgba(10, 102, 194, 0.05) !important;
+ border-radius: 6px !important; /* Reduced from 10px */
+}
+
+/* ===== COMPACT SUGGESTIONS GRID ===== */
+.alwrity-copilot-sidebar .copilotkit-suggestions,
+.alwrity-copilot-sidebar .copilot-suggestions {
+ display: grid !important;
+ grid-template-columns: 1fr !important;
+ gap: 6px !important; /* Reduced from 10px */
+ margin: 8px 0 !important; /* Reduced from 16px+ */
+}
+
+@media (min-width: 420px) {
+ .alwrity-copilot-sidebar .copilotkit-suggestions,
+ .alwrity-copilot-sidebar .copilot-suggestions {
+ grid-template-columns: 1fr 1fr !important;
+ gap: 8px !important; /* Reduced from 12px */
+ }
+}
+
+/* ===== COMPACT CHAT INPUT AREA ===== */
+.alwrity-copilot-sidebar .copilotkit-input-container,
+.alwrity-copilot-sidebar .copilot-input-container {
+ padding: 8px !important; /* Reduced from 16px+ */
+ margin: 8px 0 !important; /* Reduced from 16px+ */
+ border-top: 1px solid rgba(10, 102, 194, 0.1) !important;
+}
+
+/* ===== COMPACT CHAT MESSAGES CONTAINER ===== */
+.alwrity-copilot-sidebar .copilotkit-messages,
+.alwrity-copilot-sidebar .copilot-messages,
+.alwrity-copilot-sidebar .chat-messages {
+ padding: 8px !important; /* Reduced from 16px+ */
+ margin: 0 !important;
+ max-height: 70vh !important; /* Ensure chat takes most space */
+ overflow-y: auto !important;
+}
+
+/* ===== COMPACT INITIAL MESSAGE ===== */
+.alwrity-copilot-sidebar .copilotkit-initial-message,
+.alwrity-copilot-sidebar .copilot-initial-message {
+ padding: 12px !important; /* Reduced from 20px+ */
+ margin: 8px 0 !important; /* Reduced from 16px+ */
+ font-size: 13px !important; /* Reduced from 16px+ */
+ line-height: 1.4 !important; /* Reduced from 1.6 */
+}
+
+/* ===== COMPACT SUBTITLE ===== */
+.alwrity-copilot-sidebar .copilotkit-subtitle,
+.alwrity-copilot-sidebar .copilot-subtitle {
+ font-size: 12px !important; /* Reduced from 14px+ */
+ color: #64748b !important;
+ margin: 4px 0 8px 0 !important; /* Reduced from 8px+ */
+ line-height: 1.3 !important; /* Reduced from 1.5 */
+}
+
+/* ===== COMPACT ICONS IN SUGGESTIONS ===== */
+.alwrity-copilot-sidebar .copilot-suggestion .icon,
+.alwrity-copilot-sidebar .copilotkit-suggestion .icon,
+.alwrity-copilot-sidebar .copilotkit-suggestions button .icon {
+ width: 14px !important; /* Reduced from 18px+ */
+ height: 14px !important; /* Reduced from 18px+ */
+ margin-right: 6px !important; /* Reduced from 8px+ */
+}
+
+/* ===== COMPACT CLOSE BUTTON ===== */
+.alwrity-copilot-sidebar .copilotkit-close,
+.alwrity-copilot-sidebar .copilot-close,
+.alwrity-copilot-sidebar button[aria-label*="close"],
+.alwrity-copilot-sidebar button[aria-label*="Close"] {
+ width: 24px !important; /* Reduced from 32px+ */
+ height: 24px !important; /* Reduced from 32px+ */
+ border-radius: 50% !important;
+ padding: 0 !important;
+ font-size: 12px !important; /* Reduced from 16px+ */
+}
+
+/* ===== COMPACT ANIMATIONS ===== */
+@keyframes typing {
+ 0%, 60%, 100% {
+ opacity: 0.4;
+ transform: scale(0.8); /* Reduced from 1.0 */
+ }
+ 30% {
+ opacity: 1;
+ transform: scale(1.0);
+ }
+}
+
+/* ===== COMPACT RESPONSIVE DESIGN ===== */
+@media (max-width: 768px) {
+ .alwrity-copilot-sidebar .copilot-sidebar-container,
+ .alwrity-copilot-sidebar .copilotkit-sidebar,
+ .alwrity-copilot-sidebar .copilotkit-chat-container {
+ width: 90% !important; /* Mobile: take more width */
+ max-width: none !important;
+ min-width: 280px !important;
+ height: 80vh !important;
+ }
+
+ .alwrity-copilot-sidebar .copilotkit-suggestions,
+ .alwrity-copilot-sidebar .copilot-suggestions {
+ grid-template-columns: 1fr !important; /* Single column on mobile */
+ gap: 4px !important; /* Even more compact on mobile */
+ }
+}
+
+/* ===== COMPACT FOCUS STATES ===== */
+.alwrity-copilot-sidebar .copilot-suggestion:focus-visible,
+.alwrity-copilot-sidebar .copilotkit-suggestion:focus-visible,
+.alwrity-copilot-sidebar .copilotkit-suggestions button:focus-visible,
+.alwrity-copilot-sidebar .copilot-suggestions button:focus-visible {
+ outline: none !important;
+ box-shadow: 0 0 0 2px rgba(10, 102, 194, 0.3) !important; /* Reduced from 4px */
+}
+
+/* ===== COMPACT ACCESSIBILITY ===== */
+@media (prefers-reduced-motion: reduce) {
+ .alwrity-copilot-sidebar * {
+ transition: none !important;
+ animation: none !important;
+ }
+}
+
+/* ========================================
+ IMAGE GENERATION SUGGESTIONS COMPONENT
+ ======================================== */
+
+.image-generation-suggestions {
+ font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif;
+ max-width: 100%;
+ margin: 0 auto;
+}
+
+/* Main Suggestion Card */
+.suggestion-card {
+ background: linear-gradient(135deg, #ffffff 0%, #f8fafc 100%);
+ border: 1px solid #e2e8f0;
+ border-radius: 16px;
+ padding: 24px;
+ margin-bottom: 20px;
+ box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
+ transition: all 0.3s ease;
+}
+
+.suggestion-card:hover {
+ transform: translateY(-2px);
+ box-shadow: 0 10px 25px -3px rgba(0, 0, 0, 0.1), 0 4px 6px -2px rgba(0, 0, 0, 0.05);
+}
+
+.suggestion-header {
+ display: flex;
+ align-items: flex-start;
+ gap: 16px;
+ margin-bottom: 20px;
+}
+
+.suggestion-icon {
+ flex-shrink: 0;
+ width: 48px;
+ height: 48px;
+ background: linear-gradient(135deg, #3b82f6 0%, #1d4ed8 100%);
+ border-radius: 12px;
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ color: white;
+}
+
+.suggestion-content {
+ flex: 1;
+}
+
+.suggestion-title {
+ font-size: 20px;
+ font-weight: 700;
+ color: #1e293b;
+ margin: 0 0 8px 0;
+ line-height: 1.3;
+}
+
+.suggestion-description {
+ font-size: 16px;
+ color: #64748b;
+ margin: 0;
+ line-height: 1.5;
+}
+
+.suggestion-features {
+ display: flex;
+ flex-direction: column;
+ gap: 12px;
+ margin-bottom: 24px;
+}
+
+.feature-item {
+ display: flex;
+ align-items: center;
+ gap: 12px;
+ font-size: 14px;
+ color: #475569;
+}
+
+.generate-prompts-btn {
+ width: 100%;
+ background: linear-gradient(135deg, #3b82f6 0%, #1d4ed8 100%);
+ color: white;
+ border: none;
+ border-radius: 12px;
+ padding: 16px 24px;
+ font-size: 16px;
+ font-weight: 600;
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ gap: 12px;
+ cursor: pointer;
+ transition: all 0.3s ease;
+ box-shadow: 0 4px 6px -1px rgba(59, 130, 246, 0.3);
+}
+
+.generate-prompts-btn:hover:not(:disabled) {
+ transform: translateY(-1px);
+ box-shadow: 0 8px 15px -3px rgba(59, 130, 246, 0.4);
+}
+
+.generate-prompts-btn:disabled {
+ opacity: 0.6;
+ cursor: not-allowed;
+}
+
+/* Prompt Selection */
+.prompts-selection {
+ background: white;
+ border-radius: 16px;
+ padding: 24px;
+ margin-bottom: 20px;
+}
+
+.prompts-header {
+ text-align: center;
+ margin-bottom: 24px;
+}
+
+.prompts-title {
+ font-size: 24px;
+ font-weight: 700;
+ color: #1e293b;
+ margin: 0 0 8px 0;
+}
+
+.prompts-subtitle {
+ font-size: 16px;
+ color: #64748b;
+ margin: 0;
+}
+
+.prompts-grid {
+ display: grid;
+ grid-template-columns: 1fr;
+ gap: 20px;
+ margin-bottom: 24px;
+}
+
+@media (min-width: 768px) {
+ .prompts-grid {
+ grid-template-columns: repeat(3, 1fr);
+ }
+}
+
+.prompt-card {
+ background: white;
+ border: 2px solid #e2e8f0;
+ border-radius: 12px;
+ padding: 20px;
+ cursor: pointer;
+ transition: all 0.3s ease;
+ position: relative;
+ overflow: hidden;
+}
+
+.prompt-card:hover {
+ border-color: #3b82f6;
+ transform: translateY(-2px);
+ box-shadow: 0 8px 25px -8px rgba(59, 130, 246, 0.3);
+}
+
+.prompt-card.selected {
+ border-color: #3b82f6;
+ background: linear-gradient(135deg, #f0f9ff 0%, #e0f2fe 100%);
+ box-shadow: 0 8px 25px -8px rgba(59, 130, 246, 0.3);
+}
+
+.prompt-header {
+ margin-bottom: 16px;
+}
+
+.prompt-style-badge {
+ display: inline-block;
+ background: linear-gradient(135deg, #3b82f6 0%, #1d4ed8 100%);
+ color: white;
+ padding: 6px 12px;
+ border-radius: 20px;
+ font-size: 12px;
+ font-weight: 600;
+ text-transform: uppercase;
+ letter-spacing: 0.5px;
+}
+
+.prompt-content {
+ margin-bottom: 20px;
+}
+
+.prompt-description {
+ font-size: 16px;
+ font-weight: 600;
+ color: #1e293b;
+ margin: 0 0 12px 0;
+ line-height: 1.4;
+}
+
+.prompt-preview {
+ font-size: 14px;
+ color: #64748b;
+ line-height: 1.5;
+ background: #f8fafc;
+ padding: 12px;
+ border-radius: 8px;
+ border-left: 3px solid #e2e8f0;
+}
+
+.prompt-actions {
+ text-align: center;
+}
+
+.select-prompt-btn {
+ background: linear-gradient(135deg, #10b981 0%, #059669 100%);
+ color: white;
+ border: none;
+ border-radius: 8px;
+ padding: 12px 20px;
+ font-size: 14px;
+ font-weight: 600;
+ cursor: pointer;
+ transition: all 0.3s ease;
+ width: 100%;
+}
+
+.select-prompt-btn:hover {
+ transform: translateY(-1px);
+ box-shadow: 0 4px 12px rgba(16, 185, 129, 0.3);
+}
+
+.back-btn {
+ background: transparent;
+ color: #64748b;
+ border: 1px solid #e2e8f0;
+ border-radius: 8px;
+ padding: 12px 20px;
+ font-size: 14px;
+ font-weight: 500;
+ cursor: pointer;
+ transition: all 0.3s ease;
+ width: 100%;
+}
+
+.back-btn:hover {
+ background: #f8fafc;
+ border-color: #cbd5e1;
+}
+
+/* Image Generation Progress */
+.generation-progress {
+ background: white;
+ border: 1px solid #e2e8f0;
+ border-radius: 16px;
+ padding: 24px;
+ text-align: center;
+ margin-bottom: 20px;
+}
+
+.progress-header {
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ gap: 12px;
+ margin-bottom: 20px;
+}
+
+.progress-title {
+ font-size: 20px;
+ font-weight: 600;
+ color: #1e293b;
+ margin: 0;
+}
+
+.progress-bar {
+ width: 100%;
+ height: 8px;
+ background: #e2e8f0;
+ border-radius: 4px;
+ overflow: hidden;
+ margin-bottom: 16px;
+}
+
+.progress-fill {
+ height: 100%;
+ background: linear-gradient(90deg, #3b82f6 0%, #1d4ed8 100%);
+ border-radius: 4px;
+ transition: width 0.5s ease;
+}
+
+.progress-status {
+ margin-bottom: 12px;
+}
+
+.progress-text {
+ font-size: 16px;
+ font-weight: 500;
+ color: #475569;
+}
+
+.progress-message {
+ font-size: 14px;
+ color: #64748b;
+ font-style: italic;
+}
+
+/* Generated Image Display */
+.generated-image {
+ background: white;
+ border: 1px solid #e2e8f0;
+ border-radius: 16px;
+ padding: 24px;
+ margin-bottom: 20px;
+}
+
+.image-header {
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ gap: 12px;
+ margin-bottom: 20px;
+}
+
+.image-title {
+ font-size: 20px;
+ font-weight: 600;
+ color: #1e293b;
+ margin: 0;
+}
+
+.image-preview {
+ text-align: center;
+ margin-bottom: 24px;
+}
+
+.preview-image {
+ max-width: 100%;
+ height: auto;
+ border-radius: 12px;
+ box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
+ border: 1px solid #e2e8f0;
+}
+
+.image-actions {
+ display: grid;
+ grid-template-columns: 1fr;
+ gap: 12px;
+ margin-bottom: 24px;
+}
+
+@media (min-width: 640px) {
+ .image-actions {
+ grid-template-columns: repeat(3, 1fr);
+ }
+}
+
+.action-btn {
+ padding: 12px 16px;
+ border-radius: 8px;
+ font-size: 14px;
+ font-weight: 600;
+ cursor: pointer;
+ transition: all 0.3s ease;
+ border: none;
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ gap: 8px;
+}
+
+.action-btn.primary {
+ background: linear-gradient(135deg, #3b82f6 0%, #1d4ed8 100%);
+ color: white;
+}
+
+.action-btn.primary:hover {
+ transform: translateY(-1px);
+ box-shadow: 0 4px 12px rgba(59, 130, 246, 0.3);
+}
+
+.action-btn.secondary {
+ background: white;
+ color: #475569;
+ border: 1px solid #e2e8f0;
+}
+
+.action-btn.secondary:hover {
+ background: #f8fafc;
+ border-color: #cbd5e1;
+}
+
+.image-metadata {
+ background: #f8fafc;
+ border-radius: 8px;
+ padding: 16px;
+}
+
+.metadata-item {
+ display: flex;
+ justify-content: space-between;
+ align-items: center;
+ padding: 8px 0;
+ border-bottom: 1px solid #e2e8f0;
+}
+
+.metadata-item:last-child {
+ border-bottom: none;
+}
+
+.metadata-label {
+ font-size: 14px;
+ font-weight: 500;
+ color: #64748b;
+}
+
+.metadata-value {
+ font-size: 14px;
+ font-weight: 600;
+ color: #1e293b;
+}
+
+/* Error Display */
+.error-message {
+ background: #fef2f2;
+ border: 1px solid #fecaca;
+ border-radius: 8px;
+ padding: 16px;
+ display: flex;
+ align-items: center;
+ gap: 12px;
+ margin-bottom: 20px;
+}
+
+.error-text {
+ flex: 1;
+ color: #dc2626;
+ font-size: 14px;
+ font-weight: 500;
+}
+
+.error-dismiss {
+ background: none;
+ border: none;
+ color: #dc2626;
+ font-size: 18px;
+ font-weight: bold;
+ cursor: pointer;
+ padding: 4px;
+ border-radius: 4px;
+ transition: background-color 0.2s ease;
+}
+
+.error-dismiss:hover {
+ background: rgba(220, 38, 38, 0.1);
+}
+
+/* Mobile Optimizations */
+@media (max-width: 640px) {
+ .suggestion-card,
+ .prompts-selection,
+ .generation-progress,
+ .generated-image {
+ padding: 16px;
+ margin-bottom: 16px;
+ }
+
+ .suggestion-title {
+ font-size: 18px;
+ }
+
+ .prompts-title {
+ font-size: 20px;
+ }
+
+ .suggestion-header {
+ flex-direction: column;
+ text-align: center;
+ gap: 12px;
+ }
+
+ .suggestion-icon {
+ align-self: center;
+ }
+
+ .prompts-grid {
+ gap: 16px;
+ }
+
+ .image-actions {
+ grid-template-columns: 1fr;
+ }
+
+ .action-btn {
+ padding: 14px 16px;
+ font-size: 16px;
+ }
+}
+
+/* Animation Classes */
+@keyframes fadeIn {
+ from {
+ opacity: 0;
+ transform: translateY(10px);
+ }
+ to {
+ opacity: 1;
+ transform: translateY(0);
+ }
+}
+
+.image-generation-suggestions > * {
+ animation: fadeIn 0.3s ease-out;
+}
+
+/* Loading States */
+.generate-prompts-btn:disabled,
+.select-prompt-btn:disabled {
+ opacity: 0.6;
+ cursor: not-allowed;
+}
+
+/* Focus States for Accessibility */
+.generate-prompts-btn:focus,
+.select-prompt-btn:focus,
+.back-btn:focus,
+.action-btn:focus,
+.error-dismiss:focus {
+ outline: 2px solid #3b82f6;
+ outline-offset: 2px;
+}
+
+/* Dark Mode Support (if needed) */
+@media (prefers-color-scheme: dark) {
+ .suggestion-card,
+ .prompts-selection,
+ .generation-progress,
+ .generated-image {
+ background: #1e293b;
+ border-color: #334155;
+ }
+
+ .suggestion-title,
+ .prompts-title,
+ .progress-title,
+ .image-title {
+ color: #f1f5f9;
+ }
+
+ .suggestion-description,
+ .prompts-subtitle,
+ .progress-text,
+ .progress-message {
+ color: #cbd5e1;
+ }
+
+ .prompt-card {
+ background: #334155;
+ border-color: #475569;
+ }
+
+ .prompt-description {
+ color: #f1f5f9;
+ }
+
+ .prompt-preview {
+ background: #475569;
+ border-left-color: #64748b;
+ color: #cbd5e1;
+ }
+
+ .image-metadata {
+ background: #334155;
+ }
+
+ .metadata-label {
+ color: #cbd5e1;
+ }
+
+ .metadata-value {
+ color: #f1f5f9;
+ }
+}
+
+/* ========================================
+ IMAGE GENERATION DEMO COMPONENT
+ ======================================== */
+
+.image-generation-demo {
+ max-width: 1200px;
+ margin: 0 auto;
+ padding: 24px;
+ font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif;
+}
+
+.demo-header {
+ text-align: center;
+ margin-bottom: 40px;
+ padding: 32px;
+ background: linear-gradient(135deg, #f8fafc 0%, #e2e8f0 100%);
+ border-radius: 16px;
+}
+
+.demo-title {
+ font-size: 32px;
+ font-weight: 800;
+ color: #1e293b;
+ margin: 0 0 16px 0;
+ background: linear-gradient(135deg, #1e293b 0%, #3b82f6 100%);
+ -webkit-background-clip: text;
+ -webkit-text-fill-color: transparent;
+ background-clip: text;
+}
+
+.demo-description {
+ font-size: 18px;
+ color: #64748b;
+ margin: 0;
+ line-height: 1.6;
+ max-width: 600px;
+ margin: 0 auto;
+}
+
+.demo-content {
+ display: grid;
+ grid-template-columns: 1fr;
+ gap: 32px;
+ margin-bottom: 40px;
+}
+
+@media (min-width: 1024px) {
+ .demo-content {
+ grid-template-columns: 1fr 1fr;
+ }
+}
+
+.content-preview,
+.image-generation-section {
+ background: white;
+ border: 1px solid #e2e8f0;
+ border-radius: 16px;
+ padding: 24px;
+ box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
+}
+
+.content-title,
+.section-title {
+ font-size: 20px;
+ font-weight: 700;
+ color: #1e293b;
+ margin: 0 0 20px 0;
+ padding-bottom: 12px;
+ border-bottom: 2px solid #e2e8f0;
+}
+
+.content-display {
+ background: #f8fafc;
+ border-radius: 12px;
+ padding: 20px;
+ border: 1px solid #e2e8f0;
+}
+
+.content-header {
+ display: flex;
+ flex-wrap: wrap;
+ gap: 12px;
+ margin-bottom: 16px;
+ align-items: center;
+}
+
+.content-type-badge {
+ background: linear-gradient(135deg, #3b82f6 0%, #1d4ed8 100%);
+ color: white;
+ padding: 6px 12px;
+ border-radius: 20px;
+ font-size: 12px;
+ font-weight: 600;
+ text-transform: uppercase;
+ letter-spacing: 0.5px;
+}
+
+.content-topic {
+ background: linear-gradient(135deg, #10b981 0%, #059669 100%);
+ color: white;
+ padding: 6px 12px;
+ border-radius: 20px;
+ font-size: 12px;
+ font-weight: 600;
+}
+
+.content-industry {
+ background: linear-gradient(135deg, #f59e0b 0%, #d97706 100%);
+ color: white;
+ padding: 6px 12px;
+ border-radius: 20px;
+ font-size: 12px;
+ font-weight: 600;
+}
+
+.content-text {
+ font-size: 14px;
+ line-height: 1.6;
+ color: #374151;
+ white-space: pre-wrap;
+ font-family: 'SF Mono', Monaco, 'Cascadia Code', 'Roboto Mono', Consolas, 'Courier New', monospace;
+}
+
+.demo-footer {
+ background: white;
+ border: 1px solid #e2e8f0;
+ border-radius: 16px;
+ padding: 32px;
+ box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
+}
+
+.footer-title {
+ font-size: 24px;
+ font-weight: 700;
+ color: #1e293b;
+ margin: 0 0 24px 0;
+ text-align: center;
+}
+
+.workflow-steps {
+ display: grid;
+ grid-template-columns: 1fr;
+ gap: 24px;
+}
+
+@media (min-width: 768px) {
+ .workflow-steps {
+ grid-template-columns: repeat(2, 1fr);
+ }
+}
+
+@media (min-width: 1024px) {
+ .workflow-steps {
+ grid-template-columns: repeat(4, 1fr);
+ }
+}
+
+.step {
+ display: flex;
+ align-items: flex-start;
+ gap: 16px;
+ padding: 20px;
+ background: #f8fafc;
+ border-radius: 12px;
+ border: 1px solid #e2e8f0;
+ transition: all 0.3s ease;
+}
+
+.step:hover {
+ transform: translateY(-2px);
+ box-shadow: 0 8px 25px -8px rgba(59, 130, 246, 0.2);
+ border-color: #3b82f6;
+}
+
+.step-number {
+ flex-shrink: 0;
+ width: 32px;
+ height: 32px;
+ background: linear-gradient(135deg, #3b82f6 0%, #1d4ed8 100%);
+ color: white;
+ border-radius: 50%;
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ font-weight: 700;
+ font-size: 16px;
+}
+
+.step-content h4 {
+ font-size: 16px;
+ font-weight: 600;
+ color: #1e293b;
+ margin: 0 0 8px 0;
+}
+
+.step-content p {
+ font-size: 14px;
+ color: #64748b;
+ margin: 0;
+ line-height: 1.5;
+}
+
+/* Mobile Optimizations for Demo */
+@media (max-width: 640px) {
+ .image-generation-demo {
+ padding: 16px;
+ }
+
+ .demo-header {
+ padding: 20px;
+ margin-bottom: 24px;
+ }
+
+ .demo-title {
+ font-size: 24px;
+ }
+
+ .demo-description {
+ font-size: 16px;
+ }
+
+ .demo-content {
+ gap: 20px;
+ margin-bottom: 24px;
+ }
+
+ .content-preview,
+ .image-generation-section,
+ .demo-footer {
+ padding: 16px;
+ }
+
+ .workflow-steps {
+ gap: 16px;
+ }
+
+ .step {
+ padding: 16px;
+ }
+}
+
+/* Demo-specific image suggestions styling */
+.demo-image-suggestions {
+ margin: 0;
+}
+
+.demo-image-suggestions .suggestion-card {
+ margin-bottom: 0;
+}
+
+/* Animation for demo elements */
+.image-generation-demo > * {
+ animation: fadeInUp 0.5s ease-out;
+}
+
+@keyframes fadeInUp {
+ from {
+ opacity: 0;
+ transform: translateY(20px);
+ }
+ to {
+ opacity: 1;
+ transform: translateY(0);
+ }
+}
+
+/* Stagger animation for workflow steps */
+.step:nth-child(1) { animation-delay: 0.1s; }
+.step:nth-child(2) { animation-delay: 0.2s; }
+.step:nth-child(3) { animation-delay: 0.3s; }
+.step:nth-child(4) { animation-delay: 0.4s; }
+
+/* Hover effects for interactive elements */
+.content-display:hover {
+ border-color: #3b82f6;
+ box-shadow: 0 4px 12px rgba(59, 130, 246, 0.1);
+}
+
+/* Focus states for accessibility */
+.content-preview:focus-within,
+.image-generation-section:focus-within,
+.demo-footer:focus-within {
+ outline: 2px solid #3b82f6;
+ outline-offset: 2px;
}
diff --git a/frontend/src/components/LinkedInWriter/test_enhanced_progress.tsx b/frontend/src/components/LinkedInWriter/test_enhanced_progress.tsx
new file mode 100644
index 00000000..caecaa3c
--- /dev/null
+++ b/frontend/src/components/LinkedInWriter/test_enhanced_progress.tsx
@@ -0,0 +1,162 @@
+import React, { useState } from 'react';
+import { ProgressTracker } from './components/ProgressTracker';
+
+type ProgressStatus = 'pending' | 'active' | 'completed' | 'error';
+
+interface TestProgressStep {
+ id: string;
+ label: string;
+ status: ProgressStatus;
+ message?: string;
+}
+
+// Test component to verify enhanced progress tracking
+export const TestEnhancedProgress: React.FC = () => {
+ const [testSteps, setTestSteps] = useState([
+ { id: 'personalize', label: 'Personalizing topic & context', status: 'pending' },
+ { id: 'prepare_queries', label: 'Preparing research queries', status: 'pending' },
+ { id: 'research', label: 'Conducting research & analysis', status: 'pending' },
+ { id: 'grounding', label: 'Applying AI grounding', status: 'pending' },
+ { id: 'content_generation', label: 'Generating content', status: 'pending' },
+ { id: 'citations', label: 'Extracting citations', status: 'pending' },
+ { id: 'quality_analysis', label: 'Quality assessment', status: 'pending' },
+ { id: 'finalize', label: 'Finalizing & optimizing', status: 'pending' }
+ ]);
+
+ const [isActive, setIsActive] = useState(false);
+
+ const startTest = () => {
+ setIsActive(true);
+ setTestSteps(prev => prev.map((step, index) =>
+ index === 0 ? { ...step, status: 'active', message: 'Analyzing topic, industry context, and target audience...' } : step
+ ));
+
+ // Simulate progress updates
+ let currentStep = 0;
+ const interval = setInterval(() => {
+ if (currentStep < testSteps.length) {
+ setTestSteps(prev => {
+ const updated = [...prev];
+ // Mark current step as completed
+ if (currentStep > 0) {
+ updated[currentStep - 1] = {
+ ...updated[currentStep - 1],
+ status: 'completed',
+ message: getCompletionMessage(currentStep - 1)
+ };
+ }
+ // Mark next step as active
+ if (currentStep < updated.length) {
+ updated[currentStep] = {
+ ...updated[currentStep],
+ status: 'active',
+ message: getActiveMessage(currentStep)
+ };
+ }
+ return updated;
+ });
+ currentStep++;
+ } else {
+ clearInterval(interval);
+ setIsActive(false);
+ // Mark all as completed
+ setTestSteps(prev => prev.map(step => ({
+ ...step,
+ status: 'completed',
+ message: getCompletionMessage(testSteps.findIndex(s => s.id === step.id))
+ })));
+ }
+ }, 1500);
+ };
+
+ const getActiveMessage = (stepIndex: number): string => {
+ const messages = [
+ 'Analyzing topic, industry context, and target audience...',
+ 'Preparing research queries for content generation...',
+ 'Conducting research and analyzing industry trends...',
+ 'Applying AI grounding for enhanced accuracy...',
+ 'Generating content with industry insights...',
+ 'Extracting citations and references...',
+ 'Assessing content quality and relevance...',
+ 'Finalizing and optimizing content...'
+ ];
+ return messages[stepIndex] || 'Processing...';
+ };
+
+ const getCompletionMessage = (stepIndex: number): string => {
+ const messages = [
+ 'Topic personalized successfully',
+ 'Research queries prepared',
+ 'Research completed with industry insights',
+ 'AI grounding applied successfully',
+ 'Content generated with professional quality',
+ 'Citations extracted and formatted',
+ 'Quality assessment completed',
+ 'Content finalized and optimized'
+ ];
+ return messages[stepIndex] || 'Step completed';
+ };
+
+ const resetTest = () => {
+ setTestSteps(prev => prev.map(step => ({
+ ...step,
+ status: 'pending' as const,
+ message: undefined
+ })));
+ setIsActive(false);
+ };
+
+ return (
+
+
+ );
+};
+
+export default ImageGenerationTest;
diff --git a/frontend/src/components/LinkedInWriter/components/ProgressTracker.tsx b/frontend/src/components/LinkedInWriter/components/ProgressTracker.tsx
new file mode 100644
index 00000000..07e1f3bf
--- /dev/null
+++ b/frontend/src/components/LinkedInWriter/components/ProgressTracker.tsx
@@ -0,0 +1,247 @@
+import React from 'react';
+
+type ProgressStatus = 'pending' | 'active' | 'completed' | 'error';
+
+interface ProgressStep {
+ id: string;
+ label: string;
+ status: ProgressStatus;
+ message?: string;
+ details?: RecordImage Generation Test
+Testing the ImageGenerationSuggestions component...
+ +
+
+
+ đ¯ How It Works Now:
+-
+
- Generate LinkedIn Content: Use Copilot to generate a post, article, or carousel +
- See Image Suggestions: After content generation, Copilot will automatically suggest image generation +
- Ask for Images: Type "Generate images for my LinkedIn post" or similar +
- Choose Style: Select from Professional, Creative, or Industry-Specific styles +
+ đĄ Pro Tip: The image generation suggestions now appear automatically after every successful content generation in the Copilot chat!
+
+
+ {/* Header with progress percentage */}
+
+ );
+};
+
+
diff --git a/frontend/src/components/LinkedInWriter/components/README_ImageGeneration.md b/frontend/src/components/LinkedInWriter/components/README_ImageGeneration.md
new file mode 100644
index 00000000..61699de4
--- /dev/null
+++ b/frontend/src/components/LinkedInWriter/components/README_ImageGeneration.md
@@ -0,0 +1,373 @@
+# LinkedIn Image Generation Components
+
+This document provides comprehensive documentation for the LinkedIn Image Generation components that integrate with CopilotKit to provide AI-powered image generation capabilities.
+
+## đ Overview
+
+The Image Generation components provide a seamless way to generate professional, LinkedIn-optimized images for content using Google's Gemini API. The system analyzes generated LinkedIn content and creates contextually relevant image prompts.
+
+## đ Components
+
+### 1. ImageGenerationSuggestions
+
+The main component that handles the complete image generation workflow.
+
+**Location**: `ImageGenerationSuggestions.tsx`
+
+**Features**:
+- Content-aware image prompt generation
+- Three distinct visual styles (Professional, Creative, Industry-Specific)
+- Real-time progress tracking
+- Error handling with fallback prompts
+- Mobile-optimized responsive design
+- CopilotKit integration
+
+### 2. ImageGenerationDemo
+
+A demonstration component showcasing the ImageGenerationSuggestions functionality.
+
+**Location**: `ImageGenerationDemo.tsx`
+
+**Features**:
+- Sample LinkedIn content display
+- Interactive workflow demonstration
+- Step-by-step explanation
+- Responsive layout
+
+## đ§ Installation & Setup
+
+### Prerequisites
+
+1. **CopilotKit**: Ensure CopilotKit is properly configured in your project
+2. **Heroicons**: Install Heroicons for the icon components
+3. **Backend API**: The backend image generation services must be running
+
+### Dependencies
+
+```bash
+npm install @heroicons/react
+# or
+yarn add @heroicons/react
+```
+
+### Import
+
+```typescript
+import { ImageGenerationSuggestions, ImageGenerationDemo } from './components';
+```
+
+## đ Usage
+
+### Basic Implementation
+
+```typescript
+import React from 'react';
+import { ImageGenerationSuggestions } from './components';
+
+const MyComponent: React.FC = () => {
+ const handleImageGenerated = (imageData: any) => {
+ console.log('Image generated:', imageData);
+ // Handle the generated image
+ };
+
+ return (
+
+
+
+ {/* Progress bar */}
+
+ LinkedIn Content Generation
+
+
+ {progressPercentage}% Complete
+
+
+
+
+
+ {/* Steps */}
+
+ {steps.map((step, idx) => (
+
+
+ {/* Active status indicator */}
+ {active && (
+
+ {/* Step indicator */}
+
+ ))}
+
+ {step.status === 'completed' ? 'â' :
+ step.status === 'active' ? 'â' :
+ step.status === 'error' ? 'â' : (idx + 1)}
+
+ {/* Active step glow effect */}
+ {step.status === 'active' && (
+
+ )}
+
+
+ {/* Step content */}
+
+
+
+ {/* Status indicator */}
+
+ {step.label}
+
+
+ {/* Step message */}
+ {step.message && (
+
+ {step.message}
+
+ )}
+
+ {/* Step details */}
+ {step.details && step.status === 'completed' && (
+
+ {Object.entries(step.details).map(([key, value]) => (
+
+ )}
+
+ {key}: {String(value)}
+
+ ))}
+
+ {step.status}
+
+
+
+
+ )}
+
+ {/* CSS Animations */}
+
+
+ Content generation in progress...
+
+
+ {/* Your existing content editor */}
+
+ {/* Image generation suggestions */}
+ {content.content && (
+
+ );
+};
+```
+
+### Demo Component
+
+```typescript
+import React from 'react';
+import { ImageGenerationDemo } from './components';
+
+const DemoPage: React.FC = () => {
+ return (
+
+ 
+
+ )}
+
+
+ );
+};
+```
+
+## đ¨ Props Interface
+
+### ImageGenerationSuggestions Props
+
+```typescript
+interface ImageGenerationSuggestionsProps {
+ contentType: 'post' | 'article' | 'carousel' | 'video_script';
+ topic: string;
+ industry: string;
+ content: string;
+ onImageGenerated?: (imageData: any) => void;
+ className?: string;
+}
+```
+
+**Props Description**:
+
+- **contentType**: The type of LinkedIn content (post, article, carousel, or video_script)
+- **topic**: The main topic or subject of the content
+- **industry**: The industry context for the content
+- **content**: The actual LinkedIn content text
+- **onImageGenerated**: Callback function when an image is successfully generated
+- **className**: Optional CSS class for custom styling
+
+## đ Component States
+
+The component manages several states to provide a smooth user experience:
+
+1. **Initial State**: Shows the main suggestion card
+2. **Prompt Generation**: Displays three AI-optimized image prompts
+3. **Image Generation**: Shows progress bar and status
+4. **Success State**: Displays the generated image with action buttons
+5. **Error State**: Shows error messages with retry options
+
+## đ¯ User Flow
+
+1. **Content Generation Complete**: User finishes creating LinkedIn content
+2. **Image Suggestion**: Component automatically suggests image generation
+3. **Prompt Selection**: User chooses from three visual styles
+4. **Image Creation**: AI generates LinkedIn-optimized image
+5. **Result Display**: Generated image shown with management options
+6. **Integration**: Image ready for use in LinkedIn content
+
+## đ¨ Visual Styles
+
+The component generates three distinct image styles:
+
+### 1. Professional Style
+- Corporate aesthetics and clean lines
+- Professional color scheme (blues, grays, whites)
+- Business-appropriate imagery
+- Clean typography and layout
+
+### 2. Creative Style
+- Engaging and eye-catching visuals
+- Vibrant colors while maintaining professionalism
+- Social media engagement optimization
+- Modern design elements
+
+### 3. Industry-Specific Style
+- Tailored to specific business sectors
+- Industry-relevant imagery and colors
+- Professional appeal for target audience
+- Contextual visual elements
+
+## đ CopilotKit Integration
+
+The component integrates seamlessly with CopilotKit through two main actions:
+
+### 1. generate_image_prompts
+Generates three AI-optimized image prompts based on content analysis.
+
+**Parameters**:
+- `content_type`: Type of LinkedIn content
+- `topic`: Content topic
+- `industry`: Industry context
+- `content`: Actual content text
+
+### 2. generate_linkedin_image
+Creates LinkedIn-optimized images from selected prompts.
+
+**Parameters**:
+- `prompt`: Selected image prompt
+- `content_context`: Full content context object
+- `aspect_ratio`: Image aspect ratio (default: "1:1")
+
+## đą Responsive Design
+
+The component is fully responsive and mobile-optimized:
+
+- **Desktop**: Full-width layout with side-by-side content
+- **Tablet**: Adaptive grid layouts
+- **Mobile**: Stacked layout with touch-friendly buttons
+- **Accessibility**: Proper focus states and keyboard navigation
+
+## đ¨ Customization
+
+### CSS Customization
+
+The component uses CSS custom properties and can be styled through:
+
+```css
+.image-generation-suggestions {
+ /* Custom styles */
+}
+
+.suggestion-card {
+ /* Customize suggestion card */
+}
+
+.prompt-card {
+ /* Customize prompt selection cards */
+}
+```
+
+### Theme Support
+
+The component includes built-in dark mode support and can be extended with custom themes.
+
+## đ§Ē Testing
+
+### Component Testing
+
+```typescript
+import { render, screen, fireEvent } from '@testing-library/react';
+import { ImageGenerationSuggestions } from './components';
+
+describe('ImageGenerationSuggestions', () => {
+ it('renders suggestion card initially', () => {
+ render(
+
+
+ );
+};
+
+export default TestEnhancedProgress;
diff --git a/frontend/src/components/SEODashboard/SEOCopilotKitProvider.tsx b/frontend/src/components/SEODashboard/SEOCopilotKitProvider.tsx
index 0989ed60..78f18b3d 100644
--- a/frontend/src/components/SEODashboard/SEOCopilotKitProvider.tsx
+++ b/frontend/src/components/SEODashboard/SEOCopilotKitProvider.tsx
@@ -222,60 +222,89 @@ Focus on actionable recommendations and use the registered tools.
height: 100%;
}
- /* ALwrity glassomorphic styling for Copilot sidebar */
+ /* ALwrity Compact Copilot Styling - 60% Smaller & More Efficient */
.alwrity-copilot-sidebar {
--alwrity-bg: linear-gradient(180deg, rgba(255,255,255,0.16), rgba(255,255,255,0.08));
--alwrity-border: rgba(255,255,255,0.22);
- --alwrity-shadow: 0 18px 50px rgba(0,0,0,0.35);
+ --alwrity-shadow: 0 8px 24px rgba(0,0,0,0.25); /* Reduced from 18px 50px */
--alwrity-accent: #667eea;
--alwrity-accent2: #764ba2;
--alwrity-text: rgba(255,255,255,0.92);
--alwrity-subtext: rgba(255,255,255,0.7);
}
+
.alwrity-copilot-sidebar * {
font-family: Inter, system-ui, -apple-system, Segoe UI, Roboto, Ubuntu, 'Helvetica Neue', Arial, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';
}
+
+ /* Compact sidebar container */
.alwrity-copilot-sidebar .copilot-sidebar-container,
.alwrity-copilot-sidebar .copilotkit-sidebar,
.alwrity-copilot-sidebar .copilotkit-chat-container {
background: var(--alwrity-bg) !important;
- backdrop-filter: blur(22px) !important;
- -webkit-backdrop-filter: blur(22px) !important;
+ backdrop-filter: blur(16px) !important; /* Reduced from 22px */
+ -webkit-backdrop-filter: blur(16px) !important;
border: 1px solid var(--alwrity-border) !important;
box-shadow: var(--alwrity-shadow) !important;
color: var(--alwrity-text) !important;
+
+ /* Compact dimensions */
+ width: 40% !important; /* Reduced from 100% */
+ max-width: 320px !important;
+ min-width: 280px !important;
+ height: 85vh !important;
+ max-height: 600px !important;
+
+ /* Compact spacing */
+ padding: 8px !important;
+ margin: 8px !important;
+ border-radius: 8px !important;
}
+
.alwrity-copilot-sidebar .copilotkit-title,
.alwrity-copilot-sidebar .copilot-title {
color: var(--alwrity-text) !important;
- font-weight: 700 !important;
- letter-spacing: 0.2px !important;
+ font-weight: 600 !important; /* Reduced from 700 */
+ letter-spacing: 0.1px !important; /* Reduced from 0.2px */
+ font-size: 14px !important; /* Reduced from 18px+ */
}
+
.alwrity-copilot-sidebar .copilotkit-sidebar,
.alwrity-copilot-sidebar .copilot-sidebar-container {
z-index: 1200 !important;
}
+
.alwrity-copilot-sidebar .copilotkit-subtitle,
.alwrity-copilot-sidebar .copilot-subtitle,
.alwrity-copilot-sidebar .copilotkit-initial-message {
color: var(--alwrity-subtext) !important;
+ font-size: 12px !important; /* Reduced from 14px+ */
+ line-height: 1.3 !important; /* Reduced from 1.5 */
+ margin: 4px 0 8px 0 !important;
}
- /* Suggestions: border, glow, depth, enterprise look */
+
+ /* Compact Suggestions - 60% smaller */
.alwrity-copilot-sidebar .copilot-suggestion,
.alwrity-copilot-sidebar .copilotkit-suggestion,
.alwrity-copilot-sidebar .copilotkit-suggestions button,
.alwrity-copilot-sidebar .copilot-suggestions button {
position: relative;
background: linear-gradient(180deg, rgba(255,255,255,0.16), rgba(255,255,255,0.08)) !important;
- border: 1.5px solid rgba(255,255,255,0.32) !important;
+ border: 1px solid rgba(255,255,255,0.32) !important; /* Reduced from 1.5px */
color: var(--alwrity-text) !important;
- box-shadow: 0 10px 28px rgba(0,0,0,0.25), inset 0 1px 0 rgba(255,255,255,0.2) !important;
- transition: transform 0.25s ease, box-shadow 0.25s ease, border-color 0.25s ease;
- border-radius: 12px !important;
+ box-shadow: 0 4px 12px rgba(0,0,0,0.2), inset 0 1px 0 rgba(255,255,255,0.2) !important; /* Reduced from 10px 28px */
+ transition: transform 0.15s ease, box-shadow 0.15s ease, border-color 0.15s ease; /* Reduced from 0.25s */
+ border-radius: 8px !important; /* Reduced from 12px */
overflow: hidden;
- backdrop-filter: blur(16px);
- -webkit-backdrop-filter: blur(16px);
+ backdrop-filter: blur(12px); /* Reduced from 16px */
+ -webkit-backdrop-filter: blur(12px);
+
+ /* Compact padding and margins */
+ padding: 6px 10px !important; /* Reduced from 12px+ */
+ margin: 3px !important; /* Reduced from 6px+ */
+ font-size: 12px !important; /* Reduced from 14px+ */
}
+
.alwrity-copilot-sidebar .copilot-suggestion::before,
.alwrity-copilot-sidebar .copilotkit-suggestion::before,
.alwrity-copilot-sidebar .copilotkit-suggestions button::before,
@@ -286,57 +315,69 @@ Focus on actionable recommendations and use the registered tools.
left: -120%;
width: 120%;
height: 100%;
- background: linear-gradient(90deg, transparent, rgba(255,255,255,0.18), transparent);
- transition: left 0.6s ease;
+ background: linear-gradient(90deg, transparent, rgba(255,255,255,0.15), transparent); /* Reduced from 0.18 */
+ transition: left 0.4s ease; /* Reduced from 0.6s */
}
+
.alwrity-copilot-sidebar .copilot-suggestion:hover::before,
.alwrity-copilot-sidebar .copilotkit-suggestion:hover::before,
.alwrity-copilot-sidebar .copilotkit-suggestions button:hover::before,
.alwrity-copilot-sidebar .copilot-suggestions button:hover::before {
left: 120%;
}
+
.alwrity-copilot-sidebar .copilot-suggestion:hover,
.alwrity-copilot-sidebar .copilotkit-suggestion:hover,
.alwrity-copilot-sidebar .copilotkit-suggestions button:hover,
.alwrity-copilot-sidebar .copilot-suggestions button:hover {
- transform: translateY(-4px) scale(1.015);
- box-shadow: 0 18px 44px rgba(0,0,0,0.35), 0 0 0 1px rgba(255,255,255,0.18) inset !important;
- border-color: rgba(255,255,255,0.45) !important;
+ transform: translateY(-2px) scale(1.01); /* Reduced from -4px 1.015 */
+ box-shadow: 0 8px 20px rgba(0,0,0,0.25), 0 0 0 1px rgba(255,255,255,0.15) inset !important; /* Reduced from 18px 44px */
+ border-color: rgba(255,255,255,0.4) !important; /* Reduced from 0.45 */
}
+
.alwrity-copilot-sidebar .copilot-suggestion:active,
.alwrity-copilot-sidebar .copilotkit-suggestion:active,
.alwrity-copilot-sidebar .copilotkit-suggestions button:active,
.alwrity-copilot-sidebar .copilot-suggestions button:active {
transform: translateY(-1px) scale(0.995);
- box-shadow: 0 8px 20px rgba(0,0,0,0.3) !important;
+ box-shadow: 0 4px 12px rgba(0,0,0,0.25) !important; /* Reduced from 8px 20px */
}
+
.alwrity-copilot-sidebar .copilot-suggestion:focus-visible,
.alwrity-copilot-sidebar .copilotkit-suggestion:focus-visible,
.alwrity-copilot-sidebar .copilotkit-suggestions button:focus-visible,
.alwrity-copilot-sidebar .copilot-suggestions button:focus-visible {
outline: none !important;
- box-shadow: 0 0 0 2px rgba(255,255,255,0.45), 0 0 0 4px rgba(102,126,234,0.35) !important;
+ box-shadow: 0 0 0 2px rgba(255,255,255,0.35), 0 0 0 3px rgba(102,126,234,0.3) !important; /* Reduced from 4px */
}
+
.alwrity-copilot-sidebar .copilot-suggestion .icon,
.alwrity-copilot-sidebar .copilotkit-suggestion .icon,
.alwrity-copilot-sidebar .copilotkit-suggestions button .icon {
- filter: drop-shadow(0 2px 6px rgba(0,0,0,0.25));
+ filter: drop-shadow(0 1px 3px rgba(0,0,0,0.2)); /* Reduced from 2px 6px */
+ width: 14px !important; /* Reduced from 18px+ */
+ height: 14px !important; /* Reduced from 18px+ */
+ margin-right: 6px !important; /* Reduced from 8px+ */
}
+
+ /* Compact suggestions grid */
.alwrity-copilot-sidebar .copilotkit-suggestions,
.alwrity-copilot-sidebar .copilot-suggestions {
display: grid;
grid-template-columns: 1fr;
- gap: 10px;
+ gap: 6px; /* Reduced from 10px */
+ margin: 8px 0; /* Reduced from 16px+ */
}
+
@media (min-width: 420px) {
.alwrity-copilot-sidebar .copilotkit-suggestions,
.alwrity-copilot-sidebar .copilot-suggestions {
grid-template-columns: 1fr 1fr;
- gap: 12px;
+ gap: 8px; /* Reduced from 12px */
}
}
- /* Reduce motion for users who prefer it */
+ /* Compact motion for users who prefer it */
@media (prefers-reduced-motion: reduce) {
.alwrity-copilot-sidebar .copilot-suggestion,
.alwrity-copilot-sidebar .copilotkit-suggestion,
@@ -352,32 +393,92 @@ Focus on actionable recommendations and use the registered tools.
}
}
- /* Scrollbar styling (webkit) */
+ /* Compact scrollbar styling */
.alwrity-copilot-sidebar ::-webkit-scrollbar {
- width: 10px;
- height: 10px;
+ width: 6px; /* Reduced from 10px */
+ height: 6px; /* Reduced from 10px */
}
+
.alwrity-copilot-sidebar ::-webkit-scrollbar-thumb {
- background: rgba(255,255,255,0.25);
- border: 2px solid rgba(0,0,0,0.1);
- border-radius: 10px;
+ background: rgba(255,255,255,0.2); /* Reduced from 0.25 */
+ border: 1px solid rgba(0,0,0,0.1);
+ border-radius: 6px; /* Reduced from 10px */
}
+
.alwrity-copilot-sidebar ::-webkit-scrollbar-track {
- background: rgba(255,255,255,0.08);
- border-radius: 10px;
+ background: rgba(255,255,255,0.05); /* Reduced from 0.08 */
+ border-radius: 6px; /* Reduced from 10px */
}
+
+ /* Compact primary buttons */
.alwrity-copilot-sidebar .copilot-primary,
.alwrity-copilot-sidebar .copilotkit-primary-button,
.alwrity-copilot-sidebar button[type="submit"] {
background: linear-gradient(90deg, var(--alwrity-accent), var(--alwrity-accent2)) !important;
- border: 1px solid rgba(255,255,255,0.35) !important;
+ border: 1px solid rgba(255,255,255,0.3) !important; /* Reduced from 0.35 */
color: white !important;
+ padding: 6px 12px !important; /* Reduced from 10px 20px */
+ font-size: 12px !important; /* Reduced from 14px+ */
+ border-radius: 6px !important; /* Reduced from 8px+ */
}
+
+ /* Compact input styling */
.alwrity-copilot-sidebar .copilot-input,
.alwrity-copilot-sidebar .copilotkit-input {
- background: rgba(255,255,255,0.14) !important;
+ background: rgba(255,255,255,0.12) !important; /* Reduced from 0.14 */
color: var(--alwrity-text) !important;
- border: 1px solid rgba(255,255,255,0.22) !important;
+ border: 1px solid rgba(255,255,255,0.2) !important; /* Reduced from 0.22 */
+ padding: 8px 12px !important; /* Reduced from 14px 18px */
+ font-size: 13px !important; /* Reduced from 14px+ */
+ border-radius: 6px !important; /* Reduced from 8px+ */
+ }
+
+ /* Compact chat messages container */
+ .alwrity-copilot-sidebar .copilotkit-messages,
+ .alwrity-copilot-sidebar .copilot-messages,
+ .alwrity-copilot-sidebar .chat-messages {
+ padding: 8px !important; /* Reduced from 16px+ */
+ margin: 0 !important;
+ max-height: 70vh !important; /* Ensure chat takes most space */
+ overflow-y: auto !important;
+ }
+
+ /* Compact chat input area */
+ .alwrity-copilot-sidebar .copilotkit-input-container,
+ .alwrity-copilot-sidebar .copilot-input-container {
+ padding: 8px !important; /* Reduced from 16px+ */
+ margin: 8px 0 !important; /* Reduced from 16px+ */
+ border-top: 1px solid rgba(255,255,255,0.1) !important;
+ }
+
+ /* Compact close button */
+ .alwrity-copilot-sidebar .copilotkit-close,
+ .alwrity-copilot-sidebar .copilot-close,
+ .alwrity-copilot-sidebar button[aria-label*="close"],
+ .alwrity-copilot-sidebar button[aria-label*="Close"] {
+ width: 24px !important; /* Reduced from 32px+ */
+ height: 24px !important; /* Reduced from 32px+ */
+ border-radius: 50% !important;
+ padding: 0 !important;
+ font-size: 12px !important; /* Reduced from 16px+ */
+ }
+
+ /* Compact responsive design */
+ @media (max-width: 768px) {
+ .alwrity-copilot-sidebar .copilot-sidebar-container,
+ .alwrity-copilot-sidebar .copilotkit-sidebar,
+ .alwrity-copilot-sidebar .copilotkit-chat-container {
+ width: 90% !important; /* Mobile: take more width */
+ max-width: none !important;
+ min-width: 280px !important;
+ height: 80vh !important;
+ }
+
+ .alwrity-copilot-sidebar .copilotkit-suggestions,
+ .alwrity-copilot-sidebar .copilot-suggestions {
+ grid-template-columns: 1fr !important; /* Single column on mobile */
+ gap: 4px !important; /* Even more compact on mobile */
+ }
}
.seo-copilotkit-loading {
diff --git a/frontend/src/services/linkedInWriterApi.ts b/frontend/src/services/linkedInWriterApi.ts
index 59bc06ce..c8604565 100644
--- a/frontend/src/services/linkedInWriterApi.ts
+++ b/frontend/src/services/linkedInWriterApi.ts
@@ -155,6 +155,7 @@ export interface ContentQualityMetrics {
content_length: number;
word_count: number;
analysis_timestamp: string;
+ recommendations?: string[];
}
export interface ArticleContent {
+ Enhanced LinkedIn Progress Tracker Test +
+ +
+
+
+
+
+
+
+
+
+ Test Description:
++ This test demonstrates the enhanced LinkedIn progress tracker with detailed messages, + progress percentages, and improved visual design. The tracker now shows informative + messages for each step, making it easier for users to understand what's happening + during content generation. +
+