343 lines
14 KiB
Python
343 lines
14 KiB
Python
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()
|