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Content Calendar, Content Gap Analysis, and Content Optimization

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This commit is contained in:
ajaysi
2025-05-27 09:15:08 +05:30
parent 4049d19787
commit 889021c078
100 changed files with 18504 additions and 1251 deletions
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204
lib/ai_writers/twitter_writers/tweet_generator/smart_tweet_generator.py
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@@ -1,13 +1,33 @@
"""
Smart Tweet Generator with modern UI components.
"""
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
from ..twitter_streamlit_ui import (
TweetForm,
TweetCard,
Theme,
save_to_session,
get_from_session,
show_success_message,
show_error_message,
show_info_message,
validate_tweet_content,
validate_hashtags,
validate_emojis,
calculate_engagement_score,
generate_tweet_metrics,
copy_to_clipboard,
create_download_button
)
# Constants
MAX_TWEET_LENGTH = 280
@@ -19,14 +39,6 @@ EMOJI_CATEGORIES = {
"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
@@ -61,63 +73,6 @@ def suggest_emojis(tone: str, count: int = 3) -> List[str]:
}
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,
@@ -177,58 +132,14 @@ def generate_tweet_variations(
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")
# Apply theme
Theme().apply()
# Input section with improved UI
with st.expander("Tweet Parameters", expanded=True):
col1, col2 = st.columns(2)
@@ -291,34 +202,27 @@ def smart_tweet_generator():
# Display performance metrics
st.markdown("### 📊 Performance Metrics")
for tweet in tweets:
performance = predict_tweet_performance(tweet["text"], target_audience, tone)
# Calculate engagement score
engagement_score = calculate_engagement_score(
tweet["text"],
tweet["hashtags"],
tweet["emojis"],
tone
)
# Overall score with progress bar
st.progress(performance["overall_score"] / 100)
st.metric("Overall Score", f"{performance['overall_score']:.1f}%")
# Generate metrics
metrics = generate_tweet_metrics(engagement_score)
# 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))
# Display tweet card
TweetCard(
content=tweet["text"],
engagement_score=engagement_score,
hashtags=tweet["hashtags"],
emojis=tweet["emojis"],
metrics=metrics,
on_copy=lambda: copy_to_clipboard(tweet["text"]),
on_save=lambda: save_tweet(tweet)
).render()
st.markdown("---")
@@ -326,17 +230,23 @@ def smart_tweet_generator():
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"
)
create_download_button(
data=tweets,
filename=f"tweets_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
button_text="Export as JSON"
)
with col2:
if st.button("Copy All Tweets"):
tweet_texts = "\n\n".join(tweet["text"] for tweet in tweets)
st.code(tweet_texts)
copy_to_clipboard(tweet_texts)
show_success_message("All tweets copied to clipboard!")
def save_tweet(tweet: Dict):
"""Save tweet for later."""
tweets = get_from_session("tweets", [])
tweets.append(tweet)
save_to_session("tweets", tweets)
show_success_message("Tweet saved successfully!")
if __name__ == "__main__":
smart_tweet_generator()