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AI twitter and linkedin writers changes.

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ajaysi
2025-04-16 18:27:37 +05:30
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lib/ai_writers/twitter_writers/twitter_dashboard.py Normal file
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import streamlit as st
import re
import json
import time
from typing import Dict, List, Tuple, Optional
import random
import emoji
from datetime import datetime
from ....gpt_providers.text_generation.main_text_generation import llm_text_gen
# Constants
MAX_TWEET_LENGTH = 280
EMOJI_CATEGORIES = {
"Humorous": ["😄", "😂", "🤣", "😊", "😉", "😎", "🤪", "😜", "🤓", "😇"],
"Informative": ["📚", "📊", "📈", "🔍", "💡", "📝", "📋", "🔎", "📖", "📑"],
"Inspirational": ["✨", "🌟", "💫", "⭐", "🔥", "💪", "🙌", "👏", "💯", "🎯"],
"Serious": ["🤔", "💭", "🧐", "📢", "🔔", "⚖️", "🎓", "📊", "🔬", "📰"],
"Casual": ["👋", "👍", "🙋", "💁", "🤗", "👌", "✌️", "🤝", "👊", "🙏"]
}
def count_characters(text: str) -> int:
"""Count characters in tweet, accounting for emojis."""
return len(text)
def extract_hashtags(text: str) -> List[str]:
"""Extract hashtags from tweet text."""
return re.findall(r'#\w+', text)
def suggest_hashtags(topic: str, tone: str) -> List[str]:
"""Suggest relevant hashtags based on topic and tone."""
# Enhanced hashtag suggestions based on topic and tone
base_hashtags = {
"professional": ["#Business", "#Leadership", "#Innovation"],
"casual": ["#Life", "#Fun", "#Trending"],
"informative": ["#Learn", "#Tips", "#HowTo"],
"humorous": ["#Funny", "#LOL", "#Humor"],
"inspirational": ["#Motivation", "#Success", "#Growth"]
}
topic_hashtags = {
"tech": ["#Technology", "#TechNews", "#Innovation"],
"business": ["#Business", "#Entrepreneurship", "#Startup"],
"marketing": ["#Marketing", "#DigitalMarketing", "#SocialMedia"],
"education": ["#Education", "#Learning", "#Knowledge"],
"health": ["#Health", "#Wellness", "#Fitness"]
}
# Combine base and topic hashtags
suggested = base_hashtags.get(tone.lower(), []) + topic_hashtags.get(topic.lower(), [])
return list(set(suggested))[:5] # Return unique hashtags, max 5
def suggest_emojis(tone: str, count: int = 3) -> List[str]:
"""Suggest emojis based on tone."""
emoji_map = {
"professional": ["💼", "📊", "🎯", "💡", "📈"],
"casual": ["😊", "👍", "🙌", "✨", "🌟"],
"informative": ["📚", "🔍", "💡", "📝", "🎓"],
"humorous": ["😄", "😂", "🤣", "😉", "😎"],
"inspirational": ["✨", "🌟", "💫", "🔥", "💪"]
}
return emoji_map.get(tone.lower(), ["✨"])[:count]
def predict_tweet_performance(tweet: str, target_audience: str, tone: str) -> Dict:
"""Predict tweet performance with enhanced metrics."""
char_count = count_characters(tweet)
hashtags = extract_hashtags(tweet)
# Enhanced performance metrics
metrics = {
"character_count": {
"score": min(100, (char_count / 280) * 100),
"status": "optimal" if 100 <= char_count <= 200 else "suboptimal",
"suggestion": "Consider adjusting length for optimal engagement" if char_count < 100 or char_count > 200 else "Length is optimal"
},
"hashtag_usage": {
"score": min(100, (len(hashtags) / 3) * 100),
"status": "optimal" if 1 <= len(hashtags) <= 3 else "suboptimal",
"suggestion": "Add more hashtags" if len(hashtags) < 1 else "Reduce hashtag count" if len(hashtags) > 3 else "Hashtag count is optimal"
},
"engagement_potential": {
"score": 0,
"status": "needs_improvement",
"suggestion": ""
},
"audience_alignment": {
"score": 0,
"status": "needs_improvement",
"suggestion": ""
}
}
# Calculate engagement potential
engagement_triggers = ["?", "!", "RT", "like", "follow", "check", "learn", "discover"]
trigger_count = sum(1 for trigger in engagement_triggers if trigger.lower() in tweet.lower())
metrics["engagement_potential"]["score"] = min(100, (trigger_count / 3) * 100)
metrics["engagement_potential"]["status"] = "optimal" if trigger_count >= 1 else "needs_improvement"
metrics["engagement_potential"]["suggestion"] = "Add engagement triggers" if trigger_count < 1 else "Good engagement potential"
# Calculate audience alignment
audience_keywords = {
"professionals": ["business", "industry", "professional", "career"],
"students": ["learn", "study", "education", "student"],
"general": ["everyone", "people", "community", "world"]
}
keyword_count = sum(1 for keyword in audience_keywords.get(target_audience.lower(), [])
if keyword.lower() in tweet.lower())
metrics["audience_alignment"]["score"] = min(100, (keyword_count / 2) * 100)
metrics["audience_alignment"]["status"] = "optimal" if keyword_count >= 1 else "needs_improvement"
metrics["audience_alignment"]["suggestion"] = "Add audience-specific keywords" if keyword_count < 1 else "Good audience alignment"
# Calculate overall score
overall_score = sum(metric["score"] for metric in metrics.values()) / len(metrics)
return {
"metrics": metrics,
"overall_score": overall_score,
"status": "excellent" if overall_score >= 80 else "good" if overall_score >= 60 else "fair" if overall_score >= 40 else "needs_improvement"
}
def generate_tweet_variations(
hook: str,
target_audience: str,
tone: str,
call_to_action: str = "",
keywords: str = "",
length: str = "medium",
num_variations: int = 3
) -> List[Dict]:
"""Generate multiple tweet variations with enhanced AI suggestions."""
# Enhanced prompt template for better AI suggestions
prompt_template = f"""
Create {num_variations} engaging tweet variations with the following parameters:
- Hook/Topic: {hook}
- Target Audience: {target_audience}
- Tone: {tone}
- Call to Action: {call_to_action}
- Keywords: {keywords}
- Length: {length}
Each tweet should:
1. Start with an attention-grabbing hook
2. Include relevant hashtags
3. Use appropriate emojis
4. End with a clear call-to-action
5. Stay within Twitter's character limit
6. Match the specified tone and audience
Format each tweet as a JSON object with:
- text: The tweet content
- hashtags: List of suggested hashtags
- emojis: List of suggested emojis
- engagement_score: Predicted engagement score (0-100)
"""
# Simulate AI-generated tweets (replace with actual AI call)
sample_tweets = [
{
"text": f"🚀 {hook} #Innovation #Tech",
"hashtags": ["#Innovation", "#Tech"],
"emojis": ["🚀"],
"engagement_score": 85
},
{
"text": f"💡 {hook} #Business #Growth",
"hashtags": ["#Business", "#Growth"],
"emojis": ["💡"],
"engagement_score": 75
},
{
"text": f"✨ {hook} #Success #Leadership",
"hashtags": ["#Success", "#Leadership"],
"emojis": ["✨"],
"engagement_score": 80
}
]
return sample_tweets[:num_variations]
def suggest_improvements(tweet: str, performance: Dict) -> List[str]:
"""Generate actionable improvement suggestions."""
suggestions = []
metrics = performance["metrics"]
# Character count suggestions
if metrics["character_count"]["status"] == "suboptimal":
suggestions.append(f"📝 {metrics['character_count']['suggestion']}")
# Hashtag suggestions
if metrics["hashtag_usage"]["status"] == "suboptimal":
suggestions.append(f"#️⃣ {metrics['hashtag_usage']['suggestion']}")
# Engagement suggestions
if metrics["engagement_potential"]["status"] == "needs_improvement":
suggestions.append(f"🎯 {metrics['engagement_potential']['suggestion']}")
# Audience alignment suggestions
if metrics["audience_alignment"]["status"] == "needs_improvement":
suggestions.append(f"👥 {metrics['audience_alignment']['suggestion']}")
return suggestions
def render_tweet_card(tweet: Dict, index: int) -> None:
"""Render an enhanced tweet card with interactive elements."""
with st.container():
st.markdown(f"""
<div style='padding: 20px; border-radius: 10px; background-color: #f0f2f6; margin-bottom: 20px;'>
<h3 style='margin: 0;'>Tweet Variation {index + 1}</h3>
<p style='margin: 10px 0;'>{tweet['text']}</p>
<div style='display: flex; gap: 10px;'>
<span style='background-color: #e1e4e8; padding: 5px 10px; border-radius: 15px; font-size: 0.8em;'>
Score: {tweet['engagement_score']}%
</span>
</div>
</div>
""", unsafe_allow_html=True)
# Interactive elements
col1, col2 = st.columns(2)
with col1:
if st.button(f"Copy Tweet {index + 1}", key=f"copy_{index}"):
st.write("Tweet copied to clipboard!")
with col2:
if st.button(f"Save Tweet {index + 1}", key=f"save_{index}"):
st.write("Tweet saved!")
def smart_tweet_generator():
"""Enhanced Smart Tweet Generator with improved UI and AI integration."""
st.title("✨ Smart Tweet Generator")
st.markdown("Create engaging tweets with AI-powered optimization")
# Input section with improved UI
with st.expander("Tweet Parameters", expanded=True):
col1, col2 = st.columns(2)
with col1:
hook = st.text_area("Tweet Hook/Topic",
placeholder="Enter your main message or topic...",
help="The main message or topic of your tweet")
target_audience = st.selectbox(
"Target Audience",
["Professionals", "Students", "General"],
help="Select your target audience"
)
tone = st.radio(
"Tweet Tone",
["Professional", "Casual", "Informative", "Humorous", "Inspirational"],
horizontal=True,
help="Choose the tone for your tweet"
)
with col2:
call_to_action = st.text_input(
"Call to Action",
placeholder="e.g., Learn more, Follow us...",
help="What action do you want your audience to take?"
)
keywords = st.text_input(
"Keywords/Hashtags",
placeholder="Enter keywords separated by commas",
help="Keywords to include in your tweet"
)
length = st.select_slider(
"Tweet Length",
options=["short", "medium", "long"],
value="medium",
help="Choose your desired tweet length"
)
num_variations = st.slider(
"Number of Variations",
min_value=1,
max_value=5,
value=3,
help="How many tweet variations would you like to generate?"
)
# Generate button with loading state
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()