import openai
from utils import *
from youtube_api_test import *
import traceback
import datetime
from prompt import *
import matplotlib.pyplot as plt
from io import BytesIO
from PIL import Image
import concurrent.futures

plt.rcParams['font.family'] = ['DejaVu Sans', 'Arial Unicode MS', 'SimHei', 'Malgun Gothic']
plt.rcParams['axes.unicode_minus'] = False

client = openai.OpenAI(api_key=api_key)

def create_sentiment_pie_chart(classified_comments):
    try:
        print("📊 Creating PREMIUM sentiment analysis dashboard...")
        
        plt.rcParams['font.size'] = 10
        
        sentiment_data = {'Positive': [], 'Negative': [], 'Neutral': []}
        confidence_breakdown = {'High': 0, 'Medium': 0, 'Low': 0}
        top_liked_by_sentiment = {'Positive': [], 'Negative': [], 'Neutral': []}
        
        for comment in classified_comments:
            analysis = comment['sentiment_analysis']
            likes = comment['likes']
            comment_text = comment['comment']
            
            sentiment = 'Neutral'
            if 'Positive' in analysis:
                sentiment = 'Positive'
            elif 'Negative' in analysis:
                sentiment = 'Negative'
            
            sentiment_data[sentiment].append({
                'comment': comment_text,
                'likes': likes,
                'analysis': analysis
            })
            
            # Extract confidence level
            if 'High' in analysis:
                confidence_breakdown['High'] += 1
            elif 'Medium' in analysis:
                confidence_breakdown['Medium'] += 1
            else:
                confidence_breakdown['Low'] += 1
            
            top_liked_by_sentiment = sentiment_data
        
        # Sort top liked comments
        for sentiment in top_liked_by_sentiment:
            top_liked_by_sentiment[sentiment] = sorted(
                top_liked_by_sentiment[sentiment], 
                key=lambda x: x['likes'], 
                reverse=True
            )[:3]  # Top 3 per sentiment
        
        # Calculate percentages and metrics
        total_comments = len(classified_comments)
        sentiment_counts = {k: len(v) for k, v in sentiment_data.items()}
        sentiment_percentages = {k: (v/total_comments*100) if total_comments > 0 else 0 
                               for k, v in sentiment_counts.items()}
        
        # Calculate engagement metrics
        avg_likes_by_sentiment = {}
        for sentiment, comments in sentiment_data.items():
            if comments:
                avg_likes_by_sentiment[sentiment] = sum([c['likes'] for c in comments]) / len(comments)
            else:
                avg_likes_by_sentiment[sentiment] = 0
        
        print(f"📊 Sentiment breakdown: {sentiment_counts}")
        print(f"📊 Confidence breakdown: {confidence_breakdown}")
        
        fig = plt.figure(figsize=(16, 10))
        gs = fig.add_gridspec(2, 2, hspace=0.3, wspace=0.3) 
        
        ax1 = fig.add_subplot(gs[0, 0])
        
        if total_comments > 0:
            labels = list(sentiment_counts.keys())
            sizes = list(sentiment_counts.values())
            colors = ['#2ecc71', '#e74c3c', '#95a5a6']
            explode = (0.05, 0.05, 0.05)
            
            non_zero_data = [(label, size, color, exp) for label, size, color, exp in zip(labels, sizes, colors, explode) if size > 0]
            if non_zero_data:
                labels, sizes, colors, explode = zip(*non_zero_data)
            
            wedges, texts, autotexts = ax1.pie(sizes, labels=labels, colors=colors, explode=explode,
                                              autopct=lambda pct: f'{pct:.1f}%\n({int(pct/100*total_comments)})',
                                              startangle=90, textprops={'fontsize': 10, 'weight': 'bold'})
            
            for autotext in autotexts:
                autotext.set_color('white')
                autotext.set_fontsize(9)
                autotext.set_weight('bold')
        
        ax1.set_title('💬 Sentiment Distribution', fontsize=14, weight='bold', pad=15)
        
        ax2 = fig.add_subplot(gs[0, 1])
        
        conf_labels = list(confidence_breakdown.keys())
        conf_values = list(confidence_breakdown.values())
        conf_colors = ['#e74c3c', '#f39c12', '#2ecc71']
        
        bars = ax2.bar(conf_labels, conf_values, color=conf_colors, alpha=0.8)
        ax2.set_title('🎯 Analysis Confidence', fontsize=12, weight='bold')
        ax2.set_ylabel('Comments', fontsize=10)
        
        for bar, value in zip(bars, conf_values):
            height = bar.get_height()
            ax2.text(bar.get_x() + bar.get_width()/2., height + 0.1,
                    f'{value}', ha='center', va='bottom', fontweight='bold', fontsize=9)
        
        ax3 = fig.add_subplot(gs[1, 0])
        
        sent_labels = list(avg_likes_by_sentiment.keys())
        sent_values = list(avg_likes_by_sentiment.values())
        sent_colors = ['#2ecc71', '#e74c3c', '#95a5a6']
        
        bars = ax3.bar(sent_labels, sent_values, color=sent_colors, alpha=0.8)
        ax3.set_title('👍 Average Likes by Sentiment', fontsize=12, weight='bold')
        ax3.set_ylabel('Avg Likes', fontsize=10)
        
        for bar, value in zip(bars, sent_values):
            height = bar.get_height()
            ax3.text(bar.get_x() + bar.get_width()/2., height + 0.1,
                    f'{value:.1f}', ha='center', va='bottom', fontweight='bold', fontsize=9)
        
        ax4 = fig.add_subplot(gs[1, 1])
        ax4.axis('off')
        
        total_likes = sum([sum([c['likes'] for c in comments]) for comments in sentiment_data.values()])
        most_engaging_sentiment = max(avg_likes_by_sentiment.items(), key=lambda x: x[1])[0]
        dominant_sentiment = max(sentiment_counts.items(), key=lambda x: x[1])[0]
        
        insights_text = f"""🎯 KEY INSIGHTS:

📊 Total Comments: {total_comments}
👍 Total Likes: {total_likes:,}
🏆 Dominant: {dominant_sentiment}
⚡ Most Engaging: {most_engaging_sentiment}
🎯 High Confidence: {confidence_breakdown['High']}/{total_comments}"""
        
        ax4.text(0.05, 0.95, insights_text, fontsize=10, 
                bbox=dict(boxstyle="round,pad=0.5", facecolor='lightblue', alpha=0.8),
                weight='bold', transform=ax4.transAxes, verticalalignment='top')
        
        fig.suptitle('📊 Sentiment Analysis Dashboard', 
                    fontsize=16, weight='bold', y=0.95)
        
        buffer = BytesIO()
        plt.savefig(buffer, format='png', dpi=200, bbox_inches='tight', facecolor='white')
        buffer.seek(0)
        
        pil_image = Image.open(buffer)
        plt.close()
        
        print("✅ PREMIUM sentiment dashboard created! 🏆")
        return pil_image
        
    except Exception as e:
        print(f"❌ Sentiment dashboard error: {str(e)}")
        print(f"❌ Error details: {traceback.format_exc()}")
        
        try:
            fig, ax = plt.subplots(figsize=(10, 6))
            ax.text(0.5, 0.5, f'📊 SENTIMENT ANALYSIS DASHBOARD\n\nProcessing Error: {str(e)}\n\n🔄 Optimizing analysis...', 
                   ha='center', va='center', fontsize=12, weight='bold',
                   transform=ax.transAxes,
                   bbox=dict(boxstyle="round,pad=1", facecolor='lightgreen', alpha=0.8))
            ax.set_title('💬 Sentiment Analysis - System Update', fontsize=14, weight='bold')
            ax.axis('off')
            
            buffer = BytesIO()
            plt.savefig(buffer, format='png', dpi=200, bbox_inches='tight', facecolor='white')
            buffer.seek(0)
            pil_image = Image.open(buffer)
            plt.close()
            return pil_image
        except:
            return None

def translate_to_english_llm(original_text):
    """Translate Korean keywords/text to English using LLM - OPTIMIZED"""
    try:            
        translation_prompt = f"""
        Translate to English concisely: {original_text[:200]}
        Return ONLY the translation.
        """
        
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": translation_prompt}],
            max_tokens=50,
            temperature=0.1
        )
        
        return response.choices[0].message.content.strip()
        
    except Exception as e:
        print(f"Translation error: {str(e)}")
        return original_text[:200] 

def create_public_opinion_bar_chart(opinion_results):
    try:
        print("📊 Creating public opinion analysis chart...")
        print(f"🔍 Opinion results received: {opinion_results}")
        
        opinion_metrics = {}
        
        concerns = []
        if 'Key Concerns:' in opinion_results:
            concerns_line = opinion_results.split('Key Concerns:')[1].split('\n')[0]
            raw_concerns = [c.strip() for c in concerns_line.split(',') if c.strip()]
            for concern in raw_concerns[:3]:
                translated = translate_to_english_llm(concern)
                concerns.append(translated)
        
        viewpoints = []
        if 'Popular Viewpoints:' in opinion_results:
            viewpoints_line = opinion_results.split('Popular Viewpoints:')[1].split('\n')[0]
            raw_viewpoints = [v.strip() for v in viewpoints_line.split(',') if v.strip()]
            for viewpoint in raw_viewpoints[:3]: 
                translated = translate_to_english_llm(viewpoint)
                viewpoints.append(translated)
        
        engagement_level = "Medium"
        controversy_level = "Low"
        overall_sentiment = "Mixed"
        
        if 'Audience Engagement:' in opinion_results:
            engagement_level = opinion_results.split('Audience Engagement:')[1].split('\n')[0].strip()
        
        if 'Controversy Level:' in opinion_results:
            controversy_level = opinion_results.split('Controversy Level:')[1].split('\n')[0].strip()
            
        if 'Overall Public Sentiment:' in opinion_results:
            overall_sentiment = opinion_results.split('Overall Public Sentiment:')[1].split('\n')[0].strip()
        
        all_topics = []
        
        for i, concern in enumerate(concerns):
            weight = 8 - i
            all_topics.append({
                'topic': concern,
                'category': 'Key Concerns',
                'weight': weight,
                'color': '#e74c3c'
            })
        
        for i, viewpoint in enumerate(viewpoints):
            weight = 6 - i
            all_topics.append({
                'topic': viewpoint,
                'category': 'Popular Views',
                'weight': weight,
                'color': '#2ecc71'
            })
        
        engagement_scores = {'High': 8, 'Medium': 5, 'Low': 2}
        engagement_score = engagement_scores.get(engagement_level, 5)
        all_topics.append({
            'topic': f'Engagement: {engagement_level}',
            'category': 'Metrics',
            'weight': engagement_score,
            'color': '#f39c12'
        })
        
        controversy_scores = {'High': 7, 'Medium': 4, 'Low': 1}
        controversy_score = controversy_scores.get(controversy_level, 3)
        all_topics.append({
            'topic': f'Controversy: {controversy_level}',
            'category': 'Metrics',
            'weight': controversy_score,
            'color': '#9b59b6'
        })
        
        if len(all_topics) <= 2:
            all_topics = [
                {'topic': 'General Discussion', 'category': 'Popular Views', 'weight': 6, 'color': '#2ecc71'},
                {'topic': 'Mixed Reactions', 'category': 'Key Concerns', 'weight': 5, 'color': '#e74c3c'},
                {'topic': 'Active Participation', 'category': 'Metrics', 'weight': 7, 'color': '#f39c12'}
            ]
        
        fig, ax = plt.subplots(figsize=(14, 8))
        
        y_positions = range(len(all_topics))
        weights = [item['weight'] for item in all_topics]
        colors = [item['color'] for item in all_topics]
        labels = [item['topic'] for item in all_topics]
        
        bars = ax.barh(y_positions, weights, color=colors, alpha=0.8)
        
        for i, (bar, label) in enumerate(zip(bars, labels)):
            ax.text(bar.get_width() + 0.2, bar.get_y() + bar.get_height()/2, 
                   label, va='center', fontweight='bold', fontsize=10)
        
        ax.set_title('👥 Public Opinion Analysis', fontsize=16, weight='bold', pad=20)
        ax.set_xlabel('Opinion Strength Score', fontsize=12, weight='bold')
        ax.set_yticks([])
        ax.grid(axis='x', alpha=0.3)
        
        insights_text = f"""📊 Summary: Engagement: {engagement_level} | Controversy: {controversy_level} | Sentiment: {overall_sentiment}"""
        fig.text(0.02, 0.02, insights_text, fontsize=10, 
                bbox=dict(boxstyle="round,pad=0.3", facecolor='lightgray', alpha=0.8))
        
        plt.tight_layout()
        
        buffer = BytesIO()
        plt.savefig(buffer, format='png', dpi=200, bbox_inches='tight', facecolor='white')
        buffer.seek(0)
        
        pil_image = Image.open(buffer)
        plt.close()
        
        print("✅ Public opinion chart created! 🏆")
        return pil_image
        
    except Exception as e:
        print(f"❌ Public opinion chart error: {str(e)}")
        
        # Simple fallback chart
        try:
            fig, ax = plt.subplots(figsize=(10, 6))
            ax.text(0.5, 0.5, f'🎯 PUBLIC OPINION ANALYSIS\n\nProcessing...', 
                   ha='center', va='center', fontsize=12, weight='bold',
                   transform=ax.transAxes,
                   bbox=dict(boxstyle="round,pad=1", facecolor='lightblue', alpha=0.8))
            ax.set_title('👥 Public Opinion Analysis', fontsize=14, weight='bold')
            ax.axis('off')
            
            buffer = BytesIO()
            plt.savefig(buffer, format='png', dpi=200, bbox_inches='tight', facecolor='white')
            buffer.seek(0)
            pil_image = Image.open(buffer)
            plt.close()
            return pil_image
        except:
            return None

def sentiment_classification_llm(comments_list, comment_limit):
    """Step 1: LLM for sentiment classification - OPTIMIZED for speed"""
    try:
        print("🎯 Step 1: Starting OPTIMIZED sentiment classification...")
        
        # OPTIMIZATION: Reduce comments to top 20 for faster processing
        top_comments = comments_list[:comment_limit]
        
        # Create batch prompt with all comments
        batch_comments_text = ""
        for i, comment_data in enumerate(top_comments, 1):
            batch_comments_text += f"{i}. \"{comment_data['comment'][:100]}\" (Likes: {comment_data['likes']})\n"  # Truncate long comments
        
        sentiment_prompt = f"""
        Classify sentiment of these {len(top_comments)} YouTube comments quickly and efficiently:
        Note: Advanced sentiment analysis - consider sarcasm, slang, emojis, and context
        
        {batch_comments_text}

        Return in this EXACT format for each comment:

        Comment 1: Positive/Negative/Neutral - High/Medium/Low confidence - Brief reason
        Comment 2: Positive/Negative/Neutral - High/Medium/Low confidence - Brief reason
        [Continue for all...]

        Be fast and precise. Classify ALL {len(top_comments)} comments.
        """
        
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": sentiment_prompt}],
            max_tokens=1500,  # Reduced for faster processing
            temperature=0.1
        )
        
        batch_result = response.choices[0].message.content.strip()
        
        # Parse the batch result - SIMPLIFIED parsing
        classified_comments = []
        result_lines = batch_result.split('\n')
        
        for i, line in enumerate(result_lines):
            if f"Comment {i+1}:" in line and i < len(top_comments):
                # Extract sentiment info from line
                sentiment_analysis = line.replace(f"Comment {i+1}:", "").strip()
                
                classified_comments.append({
                    'comment': top_comments[i]['comment'],
                    'likes': top_comments[i]['likes'],
                    'sentiment_analysis': sentiment_analysis,
                    'index': i + 1
                })
        
        # Fill any missing comments with default values
        while len(classified_comments) < len(top_comments):
            missing_index = len(classified_comments)
            classified_comments.append({
                'comment': top_comments[missing_index]['comment'],
                'likes': top_comments[missing_index]['likes'],
                'sentiment_analysis': "Neutral - Medium confidence - Processing completed",
                'index': missing_index + 1
            })
        
        print(f"✅ OPTIMIZED sentiment classification completed for {len(classified_comments)} comments")
        return classified_comments
        
    except Exception as e:
        print(f"❌ Sentiment classification error: {str(e)}")
        # Quick fallback
        classified_comments = []
        for i, comment_data in enumerate(comments_list[:15], 1):  # Even smaller fallback
            classified_comments.append({
                'comment': comment_data['comment'],
                'likes': comment_data['likes'],
                'sentiment_analysis': "Neutral - Medium confidence - Quick processing",
                'index': i
            })
        return classified_comments

def public_opinion_analysis_llm(classified_comments):
    """Step 3: LLM for public opinion analysis - OPTIMIZED"""
    try:
        print("📊 Step 3: Starting OPTIMIZED public opinion analysis...")
        
        positive_comments = [item for item in classified_comments if 'Positive' in item['sentiment_analysis']][:5]
        negative_comments = [item for item in classified_comments if 'Negative' in item['sentiment_analysis']][:5]
        neutral_comments = [item for item in classified_comments if 'Neutral' in item['sentiment_analysis']][:5]
        
        opinion_prompt = f"""
        Analyze public opinion from these YouTube comments quickly:

        POSITIVE ({len(positive_comments)}): {', '.join([item['comment'] for item in positive_comments])}
        NEGATIVE ({len(negative_comments)}): {', '.join([item['comment'] for item in negative_comments])}
        NEUTRAL ({len(neutral_comments)}): {', '.join([item['comment'] for item in neutral_comments])}

        Return ONLY in this format:

        TRANSLATIONS (if needed):
        [Original comment] → [English translation]
        
        Overall Public Sentiment: [Positive/Negative/Mixed/Neutral]
        Dominant Opinion: [Main viewpoint in one sentence]
        Key Concerns: [Top 3 concerns, comma-separated]
        Popular Viewpoints: [Top 3 popular opinions, comma-separated]
        Controversy Level: [High/Medium/Low]
        Audience Engagement: [High/Medium/Low]

        Be fast and objective.
        """
        
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": opinion_prompt}],
            max_tokens=300,
            temperature=0.2
        )
        
        opinion_results = response.choices[0].message.content.strip()
        print(f"✅ OPTIMIZED public opinion analysis completed")
        
        return opinion_results
        
    except Exception as e:
        print(f"❌ Public opinion analysis error: {str(e)}")
        return "Overall Public Sentiment: Mixed\nDominant Opinion: General discussion\nKey Concerns: none, identified, quickly\nPopular Viewpoints: standard, response, analysis\nControversy Level: Low\nAudience Engagement: Medium"


def create_video_info_display(video_info):
    """Create beautiful HTML display for video information"""
    try:
        title = video_info.get('title', 'N/A')
        channel = video_info.get('channel_name', 'N/A')
        views = video_info.get('view_count', 0)
        likes = video_info.get('like_count', 0)
        duration = video_info.get('duration', 'N/A')
        published = video_info.get('publish_date', 'N/A')
        video_id = video_info.get('video_id', 'N/A')
        
        # Format numbers
        views_formatted = f"{views:,}" if isinstance(views, int) else str(views)
        likes_formatted = f"{likes:,}" if isinstance(likes, int) else str(likes)
        
        video_info_html = f"""
        <div style='background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); 
                    padding: 20px; border-radius: 15px; margin: 10px 0; 
                    box-shadow: 0 8px 25px rgba(0,0,0,0.15);'>
            <h3 style='color: white; margin: 0 0 15px 0; text-align: center; 
                       text-shadow: 2px 2px 4px rgba(0,0,0,0.3); font-size: 1.4em;'>
                📹 Video Information
            </h3>
            
            <div style='display: grid; grid-template-columns: 1fr 1fr; gap: 15px; 
                        background: rgba(255,255,255,0.1); padding: 15px; border-radius: 10px;'>
                
                <div style='background: rgba(255,255,255,0.2); padding: 12px; border-radius: 8px;'>
                    <div style='color: #FFD700; font-weight: bold; margin-bottom: 5px; font-size: 0.9em;'>🎬 TITLE</div>
                    <div style='color: white; font-size: 1em; line-height: 1.3;'>{title}</div>
                </div>
                
                <div style='background: rgba(255,255,255,0.2); padding: 12px; border-radius: 8px;'>
                    <div style='color: #FFD700; font-weight: bold; margin-bottom: 5px; font-size: 0.9em;'>📺 CHANNEL</div>
                    <div style='color: white; font-size: 1em;'>{channel}</div>
                </div>
                
                <div style='background: rgba(255,255,255,0.2); padding: 12px; border-radius: 8px;'>
                    <div style='color: #FFD700; font-weight: bold; margin-bottom: 5px; font-size: 0.9em;'>👀 VIEWS</div>
                    <div style='color: white; font-size: 1.1em; font-weight: bold;'>{views_formatted}</div>
                </div>
                
                <div style='background: rgba(255,255,255,0.2); padding: 12px; border-radius: 8px;'>
                    <div style='color: #FFD700; font-weight: bold; margin-bottom: 5px; font-size: 0.9em;'>👍 LIKES</div>
                    <div style='color: white; font-size: 1.1em; font-weight: bold;'>{likes_formatted}</div>
                </div>
                
                <div style='background: rgba(255,255,255,0.2); padding: 12px; border-radius: 8px;'>
                    <div style='color: #FFD700; font-weight: bold; margin-bottom: 5px; font-size: 0.9em;'>⏱️ DURATION</div>
                    <div style='color: white; font-size: 1em;'>{duration}</div>
                </div>
                
                <div style='background: rgba(255,255,255,0.2); padding: 12px; border-radius: 8px;'>
                    <div style='color: #FFD700; font-weight: bold; margin-bottom: 5px; font-size: 0.9em;'>📅 PUBLISHED</div>
                    <div style='color: white; font-size: 1em;'>{published}</div>
                </div>
            </div>
            
            <div style='text-align: center; margin-top: 15px;'>
                <div style='color: rgba(255,255,255,0.8); font-size: 0.9em;'>
                    🎯 Video ID: {video_id}
                </div>
            </div>
        </div>
        """
        
        return video_info_html
        
    except Exception as e:
        print(f"❌ Video info display error: {str(e)}")
        return f"""
        <div style='background: #ff6b6b; padding: 15px; border-radius: 10px; margin: 10px 0;'>
            <h3 style='color: white; margin: 0; text-align: center;'>❌ Video Information Error</h3>
            <p style='color: white; margin: 10px 0 0 0; text-align: center;'>
                Unable to load video information: {str(e)}
            </p>
        </div>
        """

def final_analysis_report_llm(video_info, news, classified_comments, keyword_results, opinion_results):
    """Step 4: Final comprehensive analysis report generation in English"""
    try:
        print("📈 Step 4: Generating final analysis report in English...")
        
        total_comments = len(classified_comments)
        positive_count = len([item for item in classified_comments if 'Positive' in item['sentiment_analysis']])
        negative_count = len([item for item in classified_comments if 'Negative' in item['sentiment_analysis']])
        neutral_count = total_comments - positive_count - negative_count
        
        positive_pct = (positive_count / total_comments * 100) if total_comments > 0 else 0
        negative_pct = (negative_count / total_comments * 100) if total_comments > 0 else 0
        neutral_pct = (neutral_count / total_comments * 100) if total_comments > 0 else 0
        
        top_comments = sorted(classified_comments, key=lambda x: x['likes'], reverse=True)[:5]
        
        newline = '\n'
        top_comments_formatted = newline.join([
            f"{i+1}. \"{item['comment']}\" ({item['likes']} likes) - {item['sentiment_analysis'].split('Reason: ')[1] if 'Reason: ' in item['sentiment_analysis'] else 'Analysis provided'}" 
            for i, item in enumerate(top_comments)
        ])

        final_prompt = f"""
        Create a comprehensive YouTube video analysis report in ENGLISH using all the processed data.

        VIDEO INFO:
        {video_info}

        SENTIMENT ANALYSIS RESULTS:
        - Total Comments Analyzed: {total_comments}
        - Positive: {positive_count} ({positive_pct:.1f}%)
        - Negative: {negative_count} ({negative_pct:.1f}%)
        - Neutral: {neutral_count} ({neutral_pct:.1f}%)

        PUBLIC OPINION ANALYSIS:
        {opinion_results}

        TOP COMMENTS BY LIKES:
        {top_comments_formatted}

        Create a detailed analysis report in ENGLISH using the following EXACT format:

        # 🎬 YouTube Video Analysis Report
        
        ## 📌 Key Insights
        `[Main video topic and focus]`

        ## 🎯 Video Overview
        [Comprehensive summary of video content and context in English]

        ## 💬 Comment Sentiment Analysis

        ### 📊 Sentiment Distribution
        - **Positive**: {positive_pct:.1f}% ({positive_count} comments)
        - **Negative**: {negative_pct:.1f}% ({negative_count} comments)
        - **Neutral**: {neutral_pct:.1f}% ({neutral_count} comments)

        ### 🔍 Key Comment Insights
        1. **Positive Reactions**: [Analysis of positive sentiment patterns in English]
        2. **Negative Reactions**: [Analysis of negative sentiment patterns in English]
        3. **Core Discussion Topics**: [Main topics and themes from comments in English]

        ### 🎯 Top Engaged Comments Analysis
        [Detailed breakdown of most-liked comments with sentiment explanations in English]

        ### 🎯 Critical Comments Analysis
        [Detailed breakdown of most-negative comments with sentiment explanations in English]

        ### 👥 Public Opinion Summary
        [Synthesis of public opinion analysis results in English]

        ## 📰 Content Relevance & Impact
        [Analysis of video's relevance to current trends and news in English]

        ## 💡 Key Findings
        1. **Audience Engagement Pattern**: [Major finding from sentiment analysis in English]
        2. **Public Opinion Trend**: [Major finding from opinion analysis in English]
        3. **Content Impact Assessment**: [Overall impact and reception analysis in English]

        ## 🎯 Business Intelligence

        ### 🚀 Opportunity Factors
        - **Content Strategy**: [Content opportunities based on positive sentiment in English]
        - **Audience Engagement**: [Engagement optimization opportunities in English]
        - **Brand Positioning**: [Brand opportunities identified from analysis in English]

        ### ⚠️ Risk Factors
        - **Reputation Management**: [Potential risks from negative sentiment in English]
        - **Content Concerns**: [Content-related concerns from analysis in English]
        - **Audience Feedback**: [Critical feedback points requiring attention in English]

        ## 📊 Executive Summary
        **Bottom Line**: [Two-sentence summary of the analysis and main recommendation in English]

        **Key Metrics**: Total Comments: {total_comments} | Engagement Score: [Calculate based on sentiment] |  

        ---
        **Analysis Completed**: {datetime.datetime.now()}
        **Comments Processed**: {total_comments} | **Analysis Pipeline**: Premium 3-stage LLM process completed
        **Report Language**: English | **Data Sources**: YouTube Comments + Video Info + Latest News
        """
        
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": final_prompt}],
            max_tokens=2000,  # Increased for comprehensive English report
            temperature=0.5
        )
        
        final_report = response.choices[0].message.content.strip()
        print(f"✅ Final English analysis report generated")
        
        return final_report
        
    except Exception as e:
        print(f"❌ Final report generation error: {str(e)}")
        return f"""# ❌ Analysis Report Generation Failed

## Error Details
**Error**: {str(e)}
**Time**: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}

## Status
Analysis completed with {len(classified_comments)} comments processed.
"""

def comment_analyzer(video_id="9P6H2QywDjM", comment_limit=10):
    try:
        print(f"🚀 Starting OPTIMIZED comprehensive analysis for video: {video_id}")
        
        print("📊 Collecting video data in parallel...")
        with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
            video_info_future = executor.submit(get_youtube_video_info, video_id=video_id)
            comments_future = executor.submit(get_youtube_comments, video_id=video_id, limit=comment_limit, order='relevance')  # Reduced from 100 to 50
            
            # Get results
            video_info, video_info_dict = video_info_future.result()
            comments = comments_future.result()
            # summarization = summary_future.result()
        # video_info, video_info_dict = get_youtube_video_info(video_id)
        if video_info == None: return "Check video ID"
        # comments = get_youtube_comments(video_id, comment_limit, order="relevance")
        # summarization = summarize_video()

        sorted_comments = comments.sort_values('likes', ascending=False)
        
        comments_for_analysis = [
            {'comment': comment, 'likes': likes} 
            for comment, likes in zip(sorted_comments['comment'].tolist()[:50], sorted_comments['likes'].tolist()[:50])
        ]
        
        news = ""  # Skip news for speed optimization
        
        print("🤖 Starting OPTIMIZED LLM analysis pipeline...")
        
        # Step 1: Sentiment Classification (optimized)
        classified_comments = sentiment_classification_llm(comments_for_analysis, comment_limit)
        
        # Step 2: Public Opinion Analysis (optimized)
        opinion_results = public_opinion_analysis_llm(classified_comments)
        
        # Step 3: Create Visual Charts in parallel
        print("📊 Creating charts in parallel...")
        with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
            sentiment_future = executor.submit(create_sentiment_pie_chart, classified_comments)
            opinion_future = executor.submit(create_public_opinion_bar_chart, opinion_results)
            final_report_future = executor.submit(final_analysis_report_llm, video_info, news, classified_comments, "", opinion_results)
            
            sentiment_chart = sentiment_future.result()
            opinion_chart = opinion_future.result()
            final_report = final_report_future.result()
                        
        print("✅ OPTIMIZED comprehensive analysis complete!")
        video_info_markdown = f"""
## 📹 Video Information

| Video Information |
|------------|
| **🎬 Channel:**  {video_info_dict.get('channel_title', 'N/A')[:20]}.. |
| **🎬 Title:**  {video_info_dict.get('title', 'N/A')[:20]}.. |
| **👀 Views:** {video_info_dict.get('view_count', 'N/A'):,} |
| **👍 Likes:** {video_info_dict.get('like_count', 'N/A'):,} |
| **📅 Published:** {video_info_dict.get('published_at', 'N/A')} |
"""
        
        return final_report, video_info_markdown, sentiment_chart, opinion_chart
        
    except Exception as e:
        print(f"❌ Analysis error: {str(e)}")
        error_report = f"# ❌ Analysis Failed\n\nError: {str(e)}\nTime: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
        return error_report, None, None