Getting the twitter dashboard working.
This commit is contained in:
ajaysi
@@ -1,342 +1,211 @@
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import streamlit as st
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import streamlit as st
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import re
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import streamlit.components.v1 as components
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from typing import Dict, List
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import json
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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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from .tweet_generator import smart_tweet_generator
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# Constants
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def load_feature_data() -> Dict:
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MAX_TWEET_LENGTH = 280
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"""Load feature data from a structured format."""
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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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return {
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"metrics": metrics,
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"tweet_generation": {
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"overall_score": overall_score,
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"title": "Tweet Generation & Optimization",
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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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"icon": "🐦",
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"description": "Create and optimize engaging tweets with AI assistance",
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"features": [
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{
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"name": "Smart Tweet Generator",
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"description": "Generate multiple tweet variations with optimal character count, hashtags, and emojis",
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"status": "active",
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"icon": "✨",
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"function": smart_tweet_generator
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},
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{
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"name": "Tweet Performance Predictor",
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"description": "Predict engagement rates and best posting times for maximum impact",
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"status": "coming_soon",
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"icon": "📊"
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}
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]
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},
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"content_strategy": {
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"title": "Content Strategy Tools",
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"icon": "📅",
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"description": "Plan and manage your Twitter content strategy effectively",
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"features": [
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{
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"name": "Content Calendar Generator",
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"description": "Create weekly/monthly content plans with theme-based scheduling",
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"status": "coming_soon",
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"icon": "🗓️"
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},
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{
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"name": "Hashtag Strategy Manager",
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"description": "Research and manage trending hashtags for better reach",
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"status": "coming_soon",
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"icon": "#️⃣"
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}
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]
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},
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"visual_content": {
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"title": "Visual Content Creation",
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"icon": "🎨",
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"description": "Create engaging visual content for your tweets",
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"features": [
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{
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"name": "Image Generator",
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"description": "Create tweet cards, infographics, and quote designs",
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"status": "coming_soon",
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"icon": "🖼️"
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},
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{
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"name": "Video Content Assistant",
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"description": "Generate video scripts and optimize captions",
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"status": "coming_soon",
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"icon": "🎥"
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}
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]
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},
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"engagement": {
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"title": "Engagement & Community",
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"icon": "🤝",
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"description": "Manage and enhance community engagement",
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"features": [
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{
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"name": "Reply Generator",
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"description": "Generate context-aware responses with appropriate tone",
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"status": "coming_soon",
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"icon": "💬"
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},
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{
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"name": "Community Tools",
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"description": "Create polls and plan Q&A sessions",
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"status": "coming_soon",
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"icon": "👥"
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}
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]
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},
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"analytics": {
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"title": "Analytics & Optimization",
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"icon": "📈",
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"description": "Track performance and optimize your Twitter strategy",
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"features": [
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{
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"name": "Performance Analytics",
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"description": "Track tweet performance and engagement metrics",
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"status": "coming_soon",
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"icon": "📊"
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},
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{
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"name": "A/B Testing Assistant",
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"description": "Test and optimize tweet variations for better results",
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"status": "coming_soon",
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"icon": "🔍"
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}
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]
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},
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"research": {
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"title": "Research & Intelligence",
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"icon": "🔎",
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"description": "Gain insights and stay ahead of trends",
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"features": [
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{
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"name": "Market Research",
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"description": "Analyze competitors and track industry trends",
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"status": "coming_soon",
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"icon": "📊"
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},
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{
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"name": "Content Inspiration",
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"description": "Get trending topic suggestions and content ideas",
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"status": "coming_soon",
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"icon": "💡"
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}
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]
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}
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}
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}
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def generate_tweet_variations(
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def render_feature_card(feature: Dict) -> None:
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hook: str,
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"""Render a single feature card with its details."""
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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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with st.container():
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st.markdown(f"""
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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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<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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<h3 style='margin: 0;'>{feature['icon']} {feature['name']}</h3>
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<p style='margin: 10px 0;'>{tweet['text']}</p>
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<p style='margin: 10px 0;'>{feature['description']}</p>
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<div style='display: flex; gap: 10px;'>
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<span style='background-color: {'#4CAF50' if feature['status'] == 'active' else '#ffd700'};
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<span style='background-color: #e1e4e8; padding: 5px 10px; border-radius: 15px; font-size: 0.8em;'>
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padding: 5px 10px; border-radius: 15px; font-size: 0.8em;'>
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Score: {tweet['engagement_score']}%
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{feature['status'].title()}
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</span>
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</span>
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</div>
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</div>
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</div>
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""", unsafe_allow_html=True)
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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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def render_category_section(category: Dict) -> None:
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"""Enhanced Smart Tweet Generator with improved UI and AI integration."""
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"""Render a category section with all its features."""
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st.title("✨ Smart Tweet Generator")
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st.markdown(f"### {category['icon']} {category['title']}")
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st.markdown("Create engaging tweets with AI-powered optimization")
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st.markdown(f"*{category['description']}*")
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||||||
|
|
||||||
# Input section with improved UI
|
col1, col2 = st.columns(2)
|
||||||
with st.expander("Tweet Parameters", expanded=True):
|
with col1:
|
||||||
col1, col2 = st.columns(2)
|
render_feature_card(category['features'][0])
|
||||||
|
with col2:
|
||||||
|
render_feature_card(category['features'][1])
|
||||||
|
|
||||||
|
def run_dashboard():
|
||||||
|
"""Main function to run the Twitter dashboard."""
|
||||||
|
# Header
|
||||||
|
st.title("🐦 Twitter AI Writer Dashboard")
|
||||||
|
st.markdown("""
|
||||||
|
Welcome to your all-in-one Twitter content creation and management platform.
|
||||||
|
Explore our AI-powered tools to enhance your Twitter marketing strategy.
|
||||||
|
""")
|
||||||
|
|
||||||
|
# Load feature data
|
||||||
|
features = load_feature_data()
|
||||||
|
|
||||||
|
# Create tabs for different sections
|
||||||
|
tab1, tab2, tab3 = st.tabs(["🎯 Quick Actions", "📊 Analytics", "⚙️ Settings"])
|
||||||
|
|
||||||
|
with tab1:
|
||||||
|
st.markdown("### 🚀 Quick Actions")
|
||||||
|
col1, col2, col3 = st.columns(3)
|
||||||
|
|
||||||
with col1:
|
with col1:
|
||||||
hook = st.text_area("Tweet Hook/Topic",
|
if st.button("📝 Create New Tweet", use_container_width=True):
|
||||||
placeholder="Enter your main message or topic...",
|
# Call the Smart Tweet Generator
|
||||||
help="The main message or topic of your tweet")
|
smart_tweet_generator()
|
||||||
|
|
||||||
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:
|
with col2:
|
||||||
call_to_action = st.text_input(
|
st.button("📅 Schedule Content", use_container_width=True)
|
||||||
"Call to Action",
|
with col3:
|
||||||
placeholder="e.g., Learn more, Follow us...",
|
st.button("📊 View Analytics", use_container_width=True)
|
||||||
help="What action do you want your audience to take?"
|
|
||||||
)
|
with tab2:
|
||||||
|
st.markdown("### 📈 Analytics Dashboard")
|
||||||
keywords = st.text_input(
|
st.info("Analytics features coming soon! Stay tuned for detailed insights and performance metrics.")
|
||||||
"Keywords/Hashtags",
|
|
||||||
placeholder="Enter keywords separated by commas",
|
with tab3:
|
||||||
help="Keywords to include in your tweet"
|
st.markdown("### ⚙️ Settings")
|
||||||
)
|
st.info("Settings and configuration options coming soon!")
|
||||||
|
|
||||||
length = st.select_slider(
|
# Main content area
|
||||||
"Tweet Length",
|
st.markdown("## 🛠️ Available Tools")
|
||||||
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
|
# Render each category
|
||||||
if st.button("Generate Tweets", use_container_width=True):
|
for category in features.values():
|
||||||
with st.spinner("Generating tweet variations..."):
|
render_category_section(category)
|
||||||
tweets = generate_tweet_variations(
|
|
||||||
hook, target_audience, tone,
|
# If this is the tweet generation category and the Smart Tweet Generator is active,
|
||||||
call_to_action, keywords, length,
|
# add a button to launch it
|
||||||
num_variations
|
if category["title"] == "Tweet Generation & Optimization" and category["features"][0]["status"] == "active":
|
||||||
)
|
if st.button(f"🚀 Launch {category['features'][0]['name']}", use_container_width=True):
|
||||||
|
category["features"][0]["function"]()
|
||||||
# Display performance metrics
|
|
||||||
st.markdown("### 📊 Performance Metrics")
|
# Footer
|
||||||
for tweet in tweets:
|
st.markdown("---")
|
||||||
performance = predict_tweet_performance(tweet["text"], target_audience, tone)
|
st.markdown("""
|
||||||
|
<div style='text-align: center;'>
|
||||||
# Overall score with progress bar
|
<p>Need help? Check out our <a href='#'>documentation</a> or <a href='#'>contact support</a></p>
|
||||||
st.progress(performance["overall_score"] / 100)
|
</div>
|
||||||
st.metric("Overall Score", f"{performance['overall_score']:.1f}%")
|
""", unsafe_allow_html=True)
|
||||||
|
|
||||||
# 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__":
|
if __name__ == "__main__":
|
||||||
smart_tweet_generator()
|
run_dashboard()
|
||||||
@@ -7,14 +7,6 @@ from ..manager import APIKeyManager
|
|||||||
import os
|
import os
|
||||||
from dotenv import load_dotenv # Keep if api_key_manager uses it
|
from dotenv import load_dotenv # Keep if api_key_manager uses it
|
||||||
import sys
|
import sys
|
||||||
# Corrected import: Assuming validation functions are in validation.py in the parent directory
|
|
||||||
from ..validation import (
|
|
||||||
test_serpapi_key,
|
|
||||||
test_tavily_key,
|
|
||||||
test_metaphor_key,
|
|
||||||
test_firecrawl_key
|
|
||||||
# Add others if needed later, e.g., test_bing_key, test_google_search_key
|
|
||||||
)
|
|
||||||
|
|
||||||
# Configure logger (assuming configured elsewhere or keep minimal here)
|
# Configure logger (assuming configured elsewhere or keep minimal here)
|
||||||
logger.add(sys.stderr, level="INFO") # Keep simple example if needed
|
logger.add(sys.stderr, level="INFO") # Keep simple example if needed
|
||||||
|
|||||||
@@ -199,7 +199,7 @@ def render_website_setup(api_key_manager: APIKeyManager) -> Dict[str, Any]:
|
|||||||
|
|
||||||
# --- Removed Analysis Section ---
|
# --- Removed Analysis Section ---
|
||||||
# The detailed website analysis should be a separate feature, not part of the initial setup validation.
|
# The detailed website analysis should be a separate feature, not part of the initial setup validation.
|
||||||
st.markdown("--- ---“)
|
st.markdown("---")
|
||||||
st.markdown("ℹ️ *The detailed Website Analyzer tool is available separately in the main application.*")
|
st.markdown("ℹ️ *The detailed Website Analyzer tool is available separately in the main application.*")
|
||||||
st.info("Entering your website URL is optional. Click Continue to proceed.")
|
st.info("Entering your website URL is optional. Click Continue to proceed.")
|
||||||
|
|
||||||
|
|||||||
Reference in New Issue
Block a user