Twitter and LinkedIn Writers
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lib/ai_writers/twitter_writers/tweet_generator/README.md
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lib/ai_writers/twitter_writers/tweet_generator/README.md
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Here’s an improved and enhanced version of your README. I've structured it for clarity, conciseness, and professionalism, while also making it more engaging and user-friendly.
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---
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# 🐦 Smart Tweet Generator
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**Create tweets that stand out!** The Smart Tweet Generator is a cutting-edge AI-powered tool designed to craft optimized, engaging tweets that maximize your audience reach and engagement.
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---
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## ✨ Key Features
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### 1. **Multi-Variation Tweet Generation**
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- Generate 1–5 tweet variations from a single prompt.
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- Each variation tailored to different engagement styles.
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- Consistent tone and messaging across all versions.
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### 2. **Real-Time Character Optimization**
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- Live character count tracking, including emoji support.
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- Visual indicators to maintain the ideal tweet length.
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- Alerts when nearing Twitter's 280-character limit.
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### 3. **Intelligent Hashtag Management**
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- Auto-extract hashtags from generated tweets.
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- Topic-based, AI-suggested hashtags to enhance discoverability.
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- Recommendations for optimal hashtag count and placement.
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### 4. **Emoji Suggestions That Fit**
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- Context-sensitive and tone-appropriate emoji suggestions.
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- Categories include:
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- **Humorous**: 😄 😂 😉
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- **Informative**: 📊 🔍 💡
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- **Inspirational**: ✨ 🌟 🔥
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- **Serious**: 🤔 📢 🔔
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- **Casual**: 👋 👍 🤗
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### 5. **Performance Prediction**
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- Engagement score (0-100%) based on AI analysis.
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- Metrics analyzed include:
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- Character count optimization.
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- Hashtag effectiveness.
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- Emoji usage.
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- Audience relevance.
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- Categories:
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- **Excellent** (80–100%)
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- **Good** (60–79%)
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- **Fair** (40–59%)
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- **Needs Improvement** (0–39%)
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### 6. **Actionable Improvement Suggestions**
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- Real-time feedback on tweet quality.
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- Tailored recommendations to boost performance.
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- Built-in best practices guidance for effective tweeting.
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---
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## 🎯 How to Use
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### Step 1: **Enter Basic Information**
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- Add your tweet topic or hook.
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- Define the target audience.
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- Choose the desired tone and tweet length.
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- Optionally, include a call-to-action (CTA).
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### Step 2: **Customize Advanced Options**
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- Select the number of tweet variations (1–5).
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- Input keywords or hashtags.
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- Choose emoji preferences.
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- Add @mentions or placeholders for links.
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### Step 3: **Generate and Refine**
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- Click **Generate Tweets** to create variations.
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- Review performance metrics and apply improvement suggestions.
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- Copy, save, or export your favorite version.
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---
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## 📊 Performance Metrics
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**Your tweets are analyzed based on:**
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1. **Character Count**
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- Optimal: 100–200 characters.
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- Short: <100 characters.
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- Long: >200 characters.
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2. **Hashtag Usage**
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- Optimal: 1–3 hashtags.
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- Too few: 0 hashtags.
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- Too many: >3 hashtags.
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3. **Engagement Triggers**
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- Questions, CTAs, or interactive elements.
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4. **Emoji Optimization**
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- Ideal: 1–3 emojis.
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- Too few: 0 emojis.
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- Too many: >3 emojis.
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5. **Audience Relevance**
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- Alignment with keywords, tone, and context.
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---
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## 💡 Best Practices
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1. **Craft Attention-Grabbing Hooks**
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- Start with bold statements or thought-provoking questions.
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- Use stats or facts to capture attention.
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2. **Align Tone with Audience**
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- Maintain consistency with your brand voice.
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- Adapt tone to audience preferences (e.g., formal, casual).
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3. **Strategic Hashtag Usage**
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- Use trending and relevant hashtags.
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- Limit to 1–3 for optimal engagement.
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4. **Effective Emoji Usage**
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- Enhance meaning and context with emojis.
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- Match the tone and avoid overuse.
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5. **Clear Calls-to-Action**
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- Encourage action with clarity and urgency.
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- Use action verbs like "Discover," "Join," or "Explore."
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---
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## 🔄 Export Options
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- Copy individual tweets.
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- Export all variations as a JSON file.
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- Save performance metrics and recommendations.
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---
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## 🛠️ Technical Details
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- **Built with:** Streamlit for an intuitive user interface.
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- **AI-powered:** Advanced natural language models for tweet generation.
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- **Real-time:** Instant feedback and suggestions.
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- **Cross-platform compatibility:** Works seamlessly across devices.
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---
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## 📝 Notes
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- Tweets are optimized for Twitter’s 280-character limit.
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- Performance predictions are derived from AI insights and engagement patterns.
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- Suggestions adapt to your audience, ensuring relevancy.
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- Regular updates keep the tool current with Twitter trends.
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---
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## 🤝 Support
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Have questions or feature requests? Reach out to our support team or submit an issue on our GitHub repository.
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---
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*Last updated: Yesterday*
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---
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"""
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Twitter Tweet Generator Module
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A comprehensive suite of tools for generating and optimizing tweets.
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"""
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from .smart_tweet_generator import smart_tweet_generator
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__all__ = ['smart_tweet_generator']
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import streamlit as st
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import re
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import json
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import time
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from typing import Dict, List, Tuple, Optional
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import random
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import emoji
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from datetime import datetime
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from ....gpt_providers.text_generation.main_text_generation import llm_text_gen
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# Constants
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MAX_TWEET_LENGTH = 280
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EMOJI_CATEGORIES = {
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"Humorous": ["😄", "😂", "🤣", "😊", "😉", "😎", "🤪", "😜", "🤓", "😇"],
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"Informative": ["📚", "📊", "📈", "🔍", "💡", "📝", "📋", "🔎", "📖", "📑"],
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"Inspirational": ["✨", "🌟", "💫", "⭐", "🔥", "💪", "🙌", "👏", "💯", "🎯"],
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"Serious": ["🤔", "💭", "🧐", "📢", "🔔", "⚖️", "🎓", "📊", "🔬", "📰"],
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"Casual": ["👋", "👍", "🙋", "💁", "🤗", "👌", "✌️", "🤝", "👊", "🙏"]
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}
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def count_characters(text: str) -> int:
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"""Count characters in tweet, accounting for emojis."""
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return len(text)
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def extract_hashtags(text: str) -> List[str]:
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"""Extract hashtags from tweet text."""
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return re.findall(r'#\w+', text)
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def suggest_hashtags(topic: str, tone: str) -> List[str]:
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"""Suggest relevant hashtags based on topic and tone."""
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# Enhanced hashtag suggestions based on topic and tone
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base_hashtags = {
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"professional": ["#Business", "#Leadership", "#Innovation"],
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"casual": ["#Life", "#Fun", "#Trending"],
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"informative": ["#Learn", "#Tips", "#HowTo"],
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"humorous": ["#Funny", "#LOL", "#Humor"],
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"inspirational": ["#Motivation", "#Success", "#Growth"]
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}
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topic_hashtags = {
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"tech": ["#Technology", "#TechNews", "#Innovation"],
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"business": ["#Business", "#Entrepreneurship", "#Startup"],
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"marketing": ["#Marketing", "#DigitalMarketing", "#SocialMedia"],
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"education": ["#Education", "#Learning", "#Knowledge"],
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"health": ["#Health", "#Wellness", "#Fitness"]
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}
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# Combine base and topic hashtags
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suggested = base_hashtags.get(tone.lower(), []) + topic_hashtags.get(topic.lower(), [])
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return list(set(suggested))[:5] # Return unique hashtags, max 5
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def suggest_emojis(tone: str, count: int = 3) -> List[str]:
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"""Suggest emojis based on tone."""
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emoji_map = {
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"professional": ["💼", "📊", "🎯", "💡", "📈"],
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"casual": ["😊", "👍", "🙌", "✨", "🌟"],
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"informative": ["📚", "🔍", "💡", "📝", "🎓"],
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"humorous": ["😄", "😂", "🤣", "😉", "😎"],
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"inspirational": ["✨", "🌟", "💫", "🔥", "💪"]
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}
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return emoji_map.get(tone.lower(), ["✨"])[:count]
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def predict_tweet_performance(tweet: str, target_audience: str, tone: str) -> Dict:
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"""Predict tweet performance with enhanced metrics."""
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char_count = count_characters(tweet)
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hashtags = extract_hashtags(tweet)
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# Enhanced performance metrics
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metrics = {
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"character_count": {
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"score": min(100, (char_count / 280) * 100),
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"status": "optimal" if 100 <= char_count <= 200 else "suboptimal",
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"suggestion": "Consider adjusting length for optimal engagement" if char_count < 100 or char_count > 200 else "Length is optimal"
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},
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"hashtag_usage": {
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"score": min(100, (len(hashtags) / 3) * 100),
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"status": "optimal" if 1 <= len(hashtags) <= 3 else "suboptimal",
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"suggestion": "Add more hashtags" if len(hashtags) < 1 else "Reduce hashtag count" if len(hashtags) > 3 else "Hashtag count is optimal"
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},
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"engagement_potential": {
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"score": 0,
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"status": "needs_improvement",
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"suggestion": ""
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},
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"audience_alignment": {
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"score": 0,
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"status": "needs_improvement",
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"suggestion": ""
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}
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}
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# Calculate engagement potential
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engagement_triggers = ["?", "!", "RT", "like", "follow", "check", "learn", "discover"]
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trigger_count = sum(1 for trigger in engagement_triggers if trigger.lower() in tweet.lower())
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metrics["engagement_potential"]["score"] = min(100, (trigger_count / 3) * 100)
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metrics["engagement_potential"]["status"] = "optimal" if trigger_count >= 1 else "needs_improvement"
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metrics["engagement_potential"]["suggestion"] = "Add engagement triggers" if trigger_count < 1 else "Good engagement potential"
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# Calculate audience alignment
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audience_keywords = {
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"professionals": ["business", "industry", "professional", "career"],
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"students": ["learn", "study", "education", "student"],
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"general": ["everyone", "people", "community", "world"]
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}
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keyword_count = sum(1 for keyword in audience_keywords.get(target_audience.lower(), [])
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if keyword.lower() in tweet.lower())
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metrics["audience_alignment"]["score"] = min(100, (keyword_count / 2) * 100)
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metrics["audience_alignment"]["status"] = "optimal" if keyword_count >= 1 else "needs_improvement"
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metrics["audience_alignment"]["suggestion"] = "Add audience-specific keywords" if keyword_count < 1 else "Good audience alignment"
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# Calculate overall score
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overall_score = sum(metric["score"] for metric in metrics.values()) / len(metrics)
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return {
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"metrics": metrics,
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"overall_score": overall_score,
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"status": "excellent" if overall_score >= 80 else "good" if overall_score >= 60 else "fair" if overall_score >= 40 else "needs_improvement"
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}
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def generate_tweet_variations(
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hook: str,
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target_audience: str,
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tone: str,
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call_to_action: str = "",
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keywords: str = "",
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length: str = "medium",
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num_variations: int = 3
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) -> List[Dict]:
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"""Generate multiple tweet variations with enhanced AI suggestions."""
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# Enhanced prompt template for better AI suggestions
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prompt_template = f"""
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Create {num_variations} engaging tweet variations with the following parameters:
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- Hook/Topic: {hook}
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- Target Audience: {target_audience}
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- Tone: {tone}
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- Call to Action: {call_to_action}
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- Keywords: {keywords}
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- Length: {length}
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Each tweet should:
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1. Start with an attention-grabbing hook
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2. Include relevant hashtags
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3. Use appropriate emojis
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4. End with a clear call-to-action
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5. Stay within Twitter's character limit
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6. Match the specified tone and audience
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Format each tweet as a JSON object with:
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- text: The tweet content
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- hashtags: List of suggested hashtags
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- emojis: List of suggested emojis
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- engagement_score: Predicted engagement score (0-100)
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"""
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# Simulate AI-generated tweets (replace with actual AI call)
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sample_tweets = [
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{
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"text": f"🚀 {hook} #Innovation #Tech",
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"hashtags": ["#Innovation", "#Tech"],
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"emojis": ["🚀"],
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"engagement_score": 85
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},
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{
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"text": f"💡 {hook} #Business #Growth",
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"hashtags": ["#Business", "#Growth"],
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"emojis": ["💡"],
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"engagement_score": 75
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},
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{
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"text": f"✨ {hook} #Success #Leadership",
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"hashtags": ["#Success", "#Leadership"],
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"emojis": ["✨"],
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"engagement_score": 80
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}
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]
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return sample_tweets[:num_variations]
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def suggest_improvements(tweet: str, performance: Dict) -> List[str]:
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"""Generate actionable improvement suggestions."""
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suggestions = []
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metrics = performance["metrics"]
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# Character count suggestions
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if metrics["character_count"]["status"] == "suboptimal":
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suggestions.append(f"📝 {metrics['character_count']['suggestion']}")
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# Hashtag suggestions
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if metrics["hashtag_usage"]["status"] == "suboptimal":
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suggestions.append(f"#️⃣ {metrics['hashtag_usage']['suggestion']}")
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# Engagement suggestions
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if metrics["engagement_potential"]["status"] == "needs_improvement":
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suggestions.append(f"🎯 {metrics['engagement_potential']['suggestion']}")
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# Audience alignment suggestions
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if metrics["audience_alignment"]["status"] == "needs_improvement":
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suggestions.append(f"👥 {metrics['audience_alignment']['suggestion']}")
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return suggestions
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def render_tweet_card(tweet: Dict, index: int) -> None:
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"""Render an enhanced tweet card with interactive elements."""
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with st.container():
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st.markdown(f"""
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<div style='padding: 20px; border-radius: 10px; background-color: #f0f2f6; margin-bottom: 20px;'>
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<h3 style='margin: 0;'>Tweet Variation {index + 1}</h3>
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<p style='margin: 10px 0;'>{tweet['text']}</p>
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<div style='display: flex; gap: 10px;'>
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<span style='background-color: #e1e4e8; padding: 5px 10px; border-radius: 15px; font-size: 0.8em;'>
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Score: {tweet['engagement_score']}%
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</span>
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</div>
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</div>
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""", unsafe_allow_html=True)
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# Interactive elements
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col1, col2 = st.columns(2)
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with col1:
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if st.button(f"Copy Tweet {index + 1}", key=f"copy_{index}"):
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st.write("Tweet copied to clipboard!")
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with col2:
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if st.button(f"Save Tweet {index + 1}", key=f"save_{index}"):
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st.write("Tweet saved!")
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def smart_tweet_generator():
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"""Enhanced Smart Tweet Generator with improved UI and AI integration."""
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st.title("✨ Smart Tweet Generator")
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st.markdown("Create engaging tweets with AI-powered optimization")
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# Input section with improved UI
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with st.expander("Tweet Parameters", expanded=True):
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col1, col2 = st.columns(2)
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with col1:
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hook = st.text_area("Tweet Hook/Topic",
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placeholder="Enter your main message or topic...",
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help="The main message or topic of your tweet")
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target_audience = st.selectbox(
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"Target Audience",
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["Professionals", "Students", "General"],
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help="Select your target audience"
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)
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tone = st.radio(
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"Tweet Tone",
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["Professional", "Casual", "Informative", "Humorous", "Inspirational"],
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horizontal=True,
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help="Choose the tone for your tweet"
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)
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with col2:
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call_to_action = st.text_input(
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"Call to Action",
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placeholder="e.g., Learn more, Follow us...",
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help="What action do you want your audience to take?"
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)
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keywords = st.text_input(
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"Keywords/Hashtags",
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placeholder="Enter keywords separated by commas",
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help="Keywords to include in your tweet"
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)
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length = st.select_slider(
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"Tweet Length",
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options=["short", "medium", "long"],
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value="medium",
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help="Choose your desired tweet length"
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)
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num_variations = st.slider(
|
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"Number of Variations",
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min_value=1,
|
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max_value=5,
|
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value=3,
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help="How many tweet variations would you like to generate?"
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)
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# Generate button with loading state
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if st.button("Generate Tweets", use_container_width=True):
|
||||
with st.spinner("Generating tweet variations..."):
|
||||
tweets = generate_tweet_variations(
|
||||
hook, target_audience, tone,
|
||||
call_to_action, keywords, length,
|
||||
num_variations
|
||||
)
|
||||
|
||||
# Display performance metrics
|
||||
st.markdown("### 📊 Performance Metrics")
|
||||
for tweet in tweets:
|
||||
performance = predict_tweet_performance(tweet["text"], target_audience, tone)
|
||||
|
||||
# Overall score with progress bar
|
||||
st.progress(performance["overall_score"] / 100)
|
||||
st.metric("Overall Score", f"{performance['overall_score']:.1f}%")
|
||||
|
||||
# Detailed metrics in columns
|
||||
cols = st.columns(4)
|
||||
metrics = performance["metrics"]
|
||||
|
||||
with cols[0]:
|
||||
st.metric("Character Count", f"{metrics['character_count']['score']:.1f}%")
|
||||
with cols[1]:
|
||||
st.metric("Hashtag Usage", f"{metrics['hashtag_usage']['score']:.1f}%")
|
||||
with cols[2]:
|
||||
st.metric("Engagement", f"{metrics['engagement_potential']['score']:.1f}%")
|
||||
with cols[3]:
|
||||
st.metric("Audience Fit", f"{metrics['audience_alignment']['score']:.1f}%")
|
||||
|
||||
# Improvement suggestions
|
||||
suggestions = suggest_improvements(tweet["text"], performance)
|
||||
if suggestions:
|
||||
st.markdown("### 💡 Improvement Suggestions")
|
||||
for suggestion in suggestions:
|
||||
st.info(suggestion)
|
||||
|
||||
# Tweet card
|
||||
render_tweet_card(tweet, tweets.index(tweet))
|
||||
|
||||
st.markdown("---")
|
||||
|
||||
# Export options
|
||||
st.markdown("### 📥 Export Options")
|
||||
col1, col2 = st.columns(2)
|
||||
with col1:
|
||||
if st.button("Export as JSON"):
|
||||
st.download_button(
|
||||
"Download JSON",
|
||||
data=json.dumps(tweets, indent=2),
|
||||
file_name=f"tweets_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
|
||||
mime="application/json"
|
||||
)
|
||||
with col2:
|
||||
if st.button("Copy All Tweets"):
|
||||
tweet_texts = "\n\n".join(tweet["text"] for tweet in tweets)
|
||||
st.code(tweet_texts)
|
||||
|
||||
if __name__ == "__main__":
|
||||
smart_tweet_generator()
|
||||
Reference in New Issue
Block a user