Update app.py

app.py

@@ -1,832 +1,188 @@
-"""
-HuggingFace Spaces - Review Intelligence System (Streamlit)
-Complete app with URL input, progress tracking, and interactive dashboard
-FIXED VERSION - Better UI contrast + Proper field mapping
-"""
-
-import streamlit as st
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-import os
-from datetime import datetime
-from typing import List, Dict, Optional
-import time
-
-from gradio_pipeline import GradioPipeline
-
-
-# ============================================================================
-# PAGE CONFIGURATION
-# ============================================================================
-
-st.set_page_config(
-    page_title="Review Intelligence System",
-    page_icon="🎯",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-# FIXED Custom CSS - Better Contrast
-st.markdown("""
-    <style>
-    .main {
-        padding: 0rem 1rem;
-    }
-    
-    /* FIXED: Metric cards with better contrast */
-    .stMetric {
-        background: linear-gradient(135deg, #1e3a8a 0%, #3b82f6 100%);
-        padding: 20px;
-        border-radius: 10px;
-        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.3);
-        border: 1px solid #60a5fa;
-    }
-    
-    .stMetric label {
-        color: #dbeafe !important;
-        font-size: 14px !important;
-        font-weight: 600 !important;
-        text-transform: uppercase;
-        letter-spacing: 0.5px;
-    }
+# 🔄 AUTOMATIC RESET - Add This to Your app.py
+
+## 📍 WHERE TO ADD THE CODE
+
+Find this section in your **app.py** (around line 200-220):
+
+```python
+# After clicking "Start Analysis"
+if st.button("🚀 Start Analysis", type="primary"):
     
-    .stMetric [data-testid="stMetricValue"] {
-        color: #ffffff !important;
-        font-size: 36px !important;
-        font-weight: bold !important;
-        text-shadow: 0 2px 4px rgba(0,0,0,0.2);
-    }
+    # Set HF API key in environment
+    os.environ['HUGGINGFACE_API_KEY'] = hf_api_key
     
-    .stMetric [data-testid="stMetricDelta"] {
-        color: #93c5fd !important;
-        font-size: 14px !important;
-        font-weight: 600 !important;
-    }
+    # Scrape reviews
+    st.info("Scraping reviews...")
+    scraper.scrape_all_sources()
     
-    .big-font {
-        font-size: 24px !important;
-        font-weight: bold;
-    }
+    # Initialize database
+    db = EnhancedDatabase()
+    db.connect()
+    db.enhance_schema()
     
-    .success-box {
-        padding: 25px;
-        border-radius: 12px;
-        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-        color: white;
-        margin: 20px 0;
-        box-shadow: 0 8px 16px rgba(0, 0, 0, 0.3);
-    }
+    # ... rest of code
+```
+
+---
+
+## ✅ ADD THESE 3 LINES
+
+**BEFORE** calling `db.enhance_schema()`, add this:
+
+```python
+# After clicking "Start Analysis"
+if st.button("🚀 Start Analysis", type="primary"):
     
-    .success-box h1 {
-        color: white !important;
-        text-shadow: 0 2px 4px rgba(0,0,0,0.2);
-    }
+    # Set HF API key in environment
+    os.environ['HUGGINGFACE_API_KEY'] = hf_api_key
     
-    /* Info boxes */
-    .stAlert {
-        border-radius: 8px;
-    }
+    # Scrape reviews
+    st.info("Scraping reviews...")
+    scraper.scrape_all_sources()
     
-    /* Better table styling */
-    .dataframe {
-        border: 1px solid #e2e8f0 !important;
-    }
+    # Initialize database
+    db = EnhancedDatabase()
+    db.connect()
     
-    /* Tab styling */
-    .stTabs [data-baseweb="tab-list"] {
-        gap: 8px;
-    }
+    # ⭐ ADD THESE 3 LINES ⭐
+    # Reset the most recent 20 reviews to pending status
+    st.info("Preparing reviews for processing...")
+    db.reset_processing_status(limit=20)
     
-    .stTabs [data-baseweb="tab"] {
-        background-color: #1e293b;
-        border-radius: 8px 8px 0 0;
-        padding: 12px 24px;
-        color: #94a3b8;
-    }
+    # Continue with existing code
+    db.enhance_schema()
     
-    .stTabs [aria-selected="true"] {
-        background-color: #3b82f6;
-        color: white;
-    }
-    </style>
-""", unsafe_allow_html=True)
-
-
-# ============================================================================
-# SESSION STATE INITIALIZATION
-# ============================================================================
-
-if 'processing_complete' not in st.session_state:
-    st.session_state.processing_complete = False
-
-if 'results' not in st.session_state:
-    st.session_state.results = None
-
-if 'insights' not in st.session_state:
-    st.session_state.insights = None
+    # ... rest of code
+```
 
-if 'scraped_count' not in st.session_state:
-    st.session_state.scraped_count = 0
+---
 
+## 🎯 COMPLETE MODIFICATION
 
-# ============================================================================
-# PROCESSING FUNCTIONS
-# ============================================================================
+Here's the complete section with the fix highlighted:
 
-def process_reviews_streamlit(app_store_urls: str, play_store_urls: str, 
-                              hf_api_key: str, review_limit: int):
-    """
-    Process reviews with Streamlit progress tracking
-    """
+```python
+# After clicking "Start Analysis"
+if st.button("🚀 Start Analysis", type="primary"):
     
-    # Validate inputs
-    if not hf_api_key or not hf_api_key.strip():
-        st.error("❌ Please provide your HuggingFace API key")
-        return False
+    # Validate API key
+    if not hf_api_key or len(hf_api_key) < 10:
+        st.error("❌ Please enter a valid HuggingFace API key!")
+        st.stop()
     
-    if not app_store_urls.strip() and not play_store_urls.strip():
-        st.error("❌ Please provide at least one App Store or Play Store URL")
-        return False
+    # Set environment variable
+    os.environ['HUGGINGFACE_API_KEY'] = hf_api_key
     
-    try:
-        # Set API key
-        os.environ['HUGGINGFACE_API_KEY'] = hf_api_key.strip()
-        
-        # Progress indicators
-        progress_bar = st.progress(0)
-        status_text = st.empty()
-        
-        # Initialize pipeline
-        status_text.text("🚀 Initializing pipeline...")
-        progress_bar.progress(5)
-        pipeline = GradioPipeline(review_limit=review_limit)
-        
-        # Parse URLs
-        app_urls = [url.strip() for url in app_store_urls.split('\n') if url.strip()]
-        play_urls = [url.strip() for url in play_store_urls.split('\n') if url.strip()]
-        
-        # Stage 0: Scraping
-        status_text.text("🕷️ Scraping reviews from stores...")
-        progress_bar.progress(10)
-        
-        scraped_count = 0
-        total_apps = len(app_urls) + len(play_urls)
-        
-        for i, app_id in enumerate(app_urls, 1):
-            status_text.text(f"🍎 Scraping App Store ({i}/{total_apps}): {app_id}")
-            reviews = pipeline.scraper.scrape_app_store_rss(app_id, country="ae", limit=review_limit)
-            saved = pipeline.scraper.save_reviews_to_db(reviews)
-            scraped_count += saved
-            progress_bar.progress(10 + int(20 * i / total_apps))
-            time.sleep(1)
-        
-        for i, package in enumerate(play_urls, 1):
-            status_text.text(f"🤖 Scraping Play Store ({i}/{total_apps}): {package}")
-            reviews = pipeline.scraper.scrape_play_store_api(package, country="ae", limit=review_limit)
-            saved = pipeline.scraper.save_reviews_to_db(reviews)
-            scraped_count += saved
-            progress_bar.progress(10 + int(20 * (len(app_urls) + i) / total_apps))
-            time.sleep(1)
-        
-        if scraped_count == 0:
-            st.warning("⚠️ No reviews scraped. Please check your URLs and try again.")
-            progress_bar.empty()
-            status_text.empty()
-            return False
-        
-        st.session_state.scraped_count = scraped_count
-        
-        # Stage 1-3: Processing
-        status_text.text("🤖 Processing reviews with AI models...")
-        progress_bar.progress(30)
-        
-        reviews = pipeline.db.get_pending_reviews(limit=review_limit)
-        total_reviews = len(reviews)
-        
-        print(f"📊 DEBUG: Found {total_reviews} reviews to process")
-        
-        processed_states = []
+    # Create progress container
+    progress_container = st.container()
+    
+    with progress_container:
+        # Step 1: Scraping
+        st.info("🔍 Scraping reviews from App Store and Play Store...")
         
-        for i, review in enumerate(reviews, 1):
-            review_id = review.get('review_id', 'unknown')[:20]
-            status_text.text(f"🤖 Processing review {i}/{total_reviews}: {review_id}...")
-            progress_bar.progress(30 + int(60 * i / total_reviews))
-            
-            try:
-                from langgraph_state import create_initial_state
-                state = create_initial_state(review)
-                config = {"configurable": {"thread_id": f"review_{review.get('review_id')}"}}
-                final_state = pipeline.review_graph.invoke(state, config=config)
-                
-                # Convert to dict
-                state_dict = dict(final_state)
-                processed_states.append(state_dict)
-                
-                # DEBUG: Print what we got
-                print(f"✅ Processed {review_id}:")
-                print(f"   Type: {state_dict.get('classification_type', 'MISSING')}")
-                print(f"   Dept: {state_dict.get('department', 'MISSING')}")
-                print(f"   Sentiment: {state_dict.get('final_sentiment', 'MISSING')}")
-                
-            except Exception as e:
-                st.warning(f"⚠️ Error processing review: {str(e)}")
-                print(f"❌ ERROR: {e}")
-                import traceback
-                print(traceback.format_exc())
-                continue
+        # Initialize scraper
+        scraper = ReviewScraper()
+        scraper.scrape_all_sources()
         
-        if len(processed_states) == 0:
-            st.error("❌ No reviews were processed successfully.")
-            progress_bar.empty()
-            status_text.empty()
-            return False
+        # Step 2: Database setup
+        st.info("📁 Setting up database...")
+        db = EnhancedDatabase()
+        db.connect()
         
-        # Stage 4: Batch Analysis
-        status_text.text("📊 Generating batch insights...")
-        progress_bar.progress(90)
+        # ⭐⭐⭐ CRITICAL: ADD THESE 3 LINES ⭐⭐⭐
+        # Reset the most recent reviews to pending status
+        st.info("🔄 Preparing reviews for processing...")
+        reset_count = db.reset_processing_status(limit=20)
         
-        insights = pipeline.analyze_batch(processed_states)
+        # Continue with schema enhancement
+        db.enhance_schema()
         
-        # Store in session state
-        st.session_state.results = processed_states
-        st.session_state.insights = insights
-        st.session_state.processing_complete = True
+        # Step 3: Get pending reviews
+        reviews_to_process = db.get_pending_reviews()
         
-        # Complete
-        progress_bar.progress(100)
-        status_text.text("✅ Analysis complete!")
-        time.sleep(1)
-        progress_bar.empty()
-        status_text.empty()
+        if not reviews_to_process:
+            st.warning("⚠️ No reviews found to process!")
+            st.stop()
         
-        return True
+        st.success(f"✅ Found {len(reviews_to_process)} reviews to process!")
         
-    except Exception as e:
-        st.error(f"❌ Error during processing: {str(e)}")
-        import traceback
-        st.code(traceback.format_exc())
-        return False
+        # ... rest of your processing code
+```
 
+---
 
-# ============================================================================
-# VISUALIZATION FUNCTIONS
-# ============================================================================
+## 🎊 WHAT THIS DOES
 
-def create_summary_section(scraped_count: int, results: List[Dict], insights: Dict):
-    """Create summary metrics section"""
-    
-    total = len(results)
-    positive = insights.get('sentiment_distribution', {}).get('POSITIVE', 0)
-    neutral = insights.get('sentiment_distribution', {}).get('NEUTRAL', 0)
-    negative = insights.get('sentiment_distribution', {}).get('NEGATIVE', 0)
-    critical = insights.get('priority_distribution', {}).get('critical', 0)
-    churn_risk = insights.get('churn_risk', 0)
-    
-    # Success header
-    st.markdown(
-        f"""
-        <div class="success-box">
-            <h1 style="margin: 0;">✅ Analysis Complete!</h1>
-            <p style="margin: 10px 0 0 0; font-size: 1.2em; opacity: 0.9;">
-                Review Intelligence System Results
-            </p>
-        </div>
-        """,
-        unsafe_allow_html=True
-    )
-    
-    # Metrics with better styling
-    col1, col2, col3, col4, col5 = st.columns(5)
-    
-    with col1:
-        st.metric("📊 Total Reviews", total, f"Scraped: {scraped_count}")
-    
-    with col2:
-        pos_pct = (positive / total * 100) if total > 0 else 0
-        st.metric("😊 Positive", positive, f"{pos_pct:.1f}%")
-    
-    with col3:
-        neg_pct = (negative / total * 100) if total > 0 else 0
-        st.metric("😞 Negative", negative, f"{neg_pct:.1f}%")
-    
-    with col4:
-        st.metric("🚨 Critical", critical, "⚠️" if critical > 0 else "✅")
-    
-    with col5:
-        st.metric("📉 Churn Risk", f"{churn_risk:.1f}%", 
-                 "🔴 High" if churn_risk > 30 else "🟢 Low")
-    
-    # Recommendations
-    if insights.get('recommendations'):
-        st.markdown("### 💡 Key Recommendations")
-        for rec in insights.get('recommendations', []):
-            st.info(rec)
+1. **Scrapes reviews** from App Store and Play Store
+2. **Resets the 20 most recent reviews** to `pending` status
+3. **Processes them** through your AI pipeline
+4. **Shows results** in the dashboard
 
+Even if reviews were already processed before, they'll be reprocessed with the latest AI models!
 
-def create_sentiment_chart(insights: Dict):
-    """Create sentiment distribution donut chart"""
-    sentiment_dist = insights.get('sentiment_distribution', {})
-    
-    labels = list(sentiment_dist.keys())
-    values = list(sentiment_dist.values())
-    colors = ['#10b981', '#f59e0b', '#ef4444']
-    
-    fig = go.Figure(data=[go.Pie(
-        labels=labels,
-        values=values,
-        hole=0.5,
-        marker_colors=colors,
-        textinfo='label+percent',
-        textposition='outside',
-        textfont_size=14
-    )])
-    
-    fig.update_layout(
-        title="😊 Sentiment Distribution",
-        showlegend=True,
-        height=400
-    )
-    
-    return fig
+---
 
+## 📋 DEPLOYMENT CHECKLIST
 
-def create_priority_chart(insights: Dict):
-    """Create priority distribution bar chart"""
-    priority_dist = insights.get('priority_distribution', {})
-    
-    priority_order = ['critical', 'high', 'medium', 'low']
-    labels = [p for p in priority_order if p in priority_dist]
-    values = [priority_dist.get(p, 0) for p in labels]
-    colors = ['#dc2626', '#f59e0b', '#3b82f6', '#10b981']
-    
-    fig = go.Figure(data=[go.Bar(
-        x=labels,
-        y=values,
-        marker_color=colors[:len(labels)],
-        text=values,
-        textposition='auto'
-    )])
-    
-    fig.update_layout(
-        title="🎯 Priority Levels",
-        xaxis_title="Priority",
-        yaxis_title="Count",
-        height=400
-    )
-    
-    return fig
+- [ ] Upload `database_enhanced_UPDATED.py` to HF Spaces
+- [ ] Rename to `database_enhanced.py`
+- [ ] Add the 3 lines to your `app.py` (as shown above)
+- [ ] Upload `langgraph_nodes_FINAL.py` to HF Spaces
+- [ ] Rename to `langgraph_nodes.py`
+- [ ] Commit all changes
+- [ ] Wait for rebuild (2 min)
+- [ ] Enter your API key
+- [ ] Click "Start Analysis"
+- [ ] Watch it work! ✨
 
+---
 
-def create_department_chart(insights: Dict):
-    """Create department routing horizontal bar chart"""
-    dept_dist = insights.get('department_distribution', {})
-    
-    labels = list(dept_dist.keys())
-    values = list(dept_dist.values())
-    
-    fig = go.Figure(data=[go.Bar(
-        x=values,
-        y=labels,
-        orientation='h',
-        marker_color='#667eea',
-        text=values,
-        textposition='auto'
-    )])
-    
-    fig.update_layout(
-        title="🏢 Department Routing",
-        xaxis_title="Number of Issues",
-        yaxis_title="Department",
-        height=400
-    )
-    
-    return fig
+## 🚀 EXPECTED BEHAVIOR
 
+**Before Fix:**
+```
+✅ Scraped 20 reviews
+✅ Saved 0 new reviews  ← Nothing to process!
+⚠️ No reviews found to process
+```
 
-def create_emotion_chart(insights: Dict):
-    """Create emotion distribution chart"""
-    emotion_dist = insights.get('emotion_distribution', {})
-    
-    labels = list(emotion_dist.keys())
-    values = list(emotion_dist.values())
-    
-    fig = px.bar(
-        x=labels,
-        y=values,
-        labels={'x': 'Emotion', 'y': 'Count'},
-        color=values,
-        color_continuous_scale='Viridis'
-    )
-    
-    fig.update_layout(
-        title="😊 Emotional Analysis",
-        xaxis_title="Emotion Type",
-        yaxis_title="Number of Reviews",
-        height=300,
-        showlegend=False
-    )
-    
-    return fig
+**After Fix:**
+```
+✅ Scraped 20 reviews
+✅ Saved 0 new reviews
+🔄 Reset 20 reviews to pending status  ← Force reprocessing!
+✅ Found 20 reviews to process!
+📝 Review ID: abc123
+✅ Stage 1 complete (4.23s)
+✅ Stage 2 complete (0.83s)
+✅ Stage 3 complete (3.17s)
+```
 
+---
 
-def create_reviews_dataframe(results: List[Dict]) -> pd.DataFrame:
-    """
-    FIXED: Create DataFrame with proper field mapping
-    Checks both state field names AND database field names
-    """
-    
-    df_data = []
-    for review in results:
-        # FIXED: Check state fields FIRST, fall back to database fields
-        df_data.append({
-            'Review ID': review.get('review_id', 'N/A')[:20],
-            'Rating': review.get('rating', 0),
-            'Review': (review.get('review_text', 'N/A') or '')[:100] + '...',
-            'Sentiment': review.get('final_sentiment', review.get('stage3_final_sentiment', 'N/A')),
-            'Type': review.get('classification_type', review.get('stage1_llm1_type', 'N/A')),
-            'Department': review.get('department', review.get('stage1_llm1_department', 'N/A')),
-            'Priority': review.get('priority', review.get('stage1_llm1_priority', 'N/A')),
-            'Emotion': review.get('emotion', review.get('stage1_llm2_emotion', 'N/A')),
-            'Needs Review': '🚨 Yes' if review.get('needs_human_review', review.get('stage3_needs_human_review')) else '✅ No'
-        })
-    
-    return pd.DataFrame(df_data)
+## 💡 ALTERNATIVE: Change the Limit
 
+If you want to process **all reviews** instead of just the latest 20:
 
-# ============================================================================
-# MAIN APP
-# ============================================================================
+```python
+# Reset ALL reviews
+db.reset_processing_status()  # No limit parameter
+```
 
-def main():
-    """Main Streamlit app"""
-    
-    # Title
-    st.title("🎯 Review Intelligence System")
-    st.markdown("### Multi-Stage AI Analysis Dashboard")
-    st.markdown("Powered by **LangGraph** + **HuggingFace** • 4-Stage Processing Pipeline")
-    st.markdown("---")
-    
-    # Sidebar - Input or View Mode
-    with st.sidebar:
-        st.header("🎛️ Control Panel")
-        
-        if st.session_state.processing_complete:
-            st.success("✅ Analysis Complete!")
-            if st.button("🔄 Start New Analysis", use_container_width=True):
-                st.session_state.processing_complete = False
-                st.session_state.results = None
-                st.session_state.insights = None
-                st.rerun()
-        else:
-            st.info("👈 Enter URLs below to start")
-    
-    # Main content - Input or Results
-    if not st.session_state.processing_complete:
-        show_input_form()
-    else:
-        show_results_dashboard()
+Or process only the latest 5:
 
+```python
+# Reset only 5 most recent
+db.reset_processing_status(limit=5)
+```
 
-def show_input_form():
-    """Show input form for URLs and API key"""
-    
-    st.markdown("### 📝 Step 1: Enter Store URLs")
-    
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        st.markdown("#### 🍎 App Store IDs")
-        st.markdown(
-            """
-            **Format:** Just paste the app ID
-            - Example: `1158907446` (UAE)
-            - Example: `1234567890` (US)
-            """
-        )
-        app_store_urls = st.text_area(
-            "App Store IDs (one per line)",
-            placeholder="1158907446\n1234567890",
-            height=150,
-            key="app_urls"
-        )
-    
-    with col2:
-        st.markdown("#### 🤖 Play Store Packages")
-        st.markdown(
-            """
-            **Format:** Package name
-            - Example: `com.yas.app`
-            - Example: `com.company.app`
-            """
-        )
-        play_store_urls = st.text_area(
-            "Play Store Package Names (one per line)",
-            placeholder="com.yas.app\ncom.company.app",
-            height=150,
-            key="play_urls"
-        )
-    
-    st.markdown("---")
-    st.markdown("### 🔑 Step 2: Configure Settings")
-    
-    col1, col2 = st.columns([2, 1])
-    
-    with col1:
-        hf_api_key = st.text_input(
-            "🔑 HuggingFace API Key",
-            type="password",
-            placeholder="hf_...",
-            help="Get your key from: https://huggingface.co/settings/tokens",
-            key="hf_key"
-        )
-    
-    with col2:
-        review_limit = st.slider(
-            "📊 Reviews per App",
-            min_value=5,
-            max_value=100,
-            value=20,
-            step=5,
-            help="More reviews = longer processing time",
-            key="review_limit"
-        )
-    
-    st.markdown("---")
-    
-    # Submit button
-    col1, col2, col3 = st.columns([1, 1, 1])
-    
-    with col2:
-        if st.button("🚀 Start Analysis", use_container_width=True, type="primary"):
-            with st.spinner("Processing..."):
-                success = process_reviews_streamlit(
-                    app_store_urls,
-                    play_store_urls,
-                    hf_api_key,
-                    review_limit
-                )
-                
-                if success:
-                    st.balloons()
-                    st.rerun()
-    
-    # Documentation
-    with st.expander("📚 How to Use"):
-        st.markdown("""
-        ### 📖 Quick Guide
-        
-        **1. Get HuggingFace API Key:**
-        - Visit: https://huggingface.co/settings/tokens
-        - Create new token (Read access)
-        - Copy token (starts with `hf_`)
-        
-        **2. Enter URLs:**
-        - **App Store**: Just the ID number (e.g., `1234567890`)
-        - **Play Store**: Package name (e.g., `com.company.app`)
-        - One per line
-        
-        **3. Click Start:**
-        - Watch progress bar
-        - Wait for completion (~7 sec per review)
-        - View results automatically
-        
-        ### 🏗️ What Happens:
-        - 🕷️ **Stage 0**: Scrapes reviews from stores
-        - 🤖 **Stage 1**: Classifies with 3 AI models (Type, Department, Priority)
-        - 😊 **Stage 2**: Analyzes sentiment with dual BERT models
-        - 📊 **Stage 3**: Synthesizes insights and recommendations
-        - 💡 **Stage 4**: Generates batch analytics
-        
-        ### ⚡ Performance:
-        - ~7 seconds per review
-        - 7 AI models working together
-        - Parallel execution for speed
-        """)
+---
 
+## ⚡ Quick Summary
 
-def show_results_dashboard():
-    """Show results dashboard with charts and tables"""
-    
-    results = st.session_state.results
-    insights = st.session_state.insights
-    scraped_count = st.session_state.scraped_count
-    
-    # Summary section
-    create_summary_section(scraped_count, results, insights)
-    
-    st.markdown("---")
-    
-    # Tabs for different views
-    tab1, tab2, tab3, tab4 = st.tabs([
-        "📊 Sentiment Analysis",
-        "🚨 Critical Issues",
-        "📋 All Reviews",
-        "📥 Export"
-    ])
-    
-    # TAB 1: Sentiment Analysis
-    with tab1:
-        st.header("📊 Sentiment Analysis Overview")
-        
-        col1, col2 = st.columns(2)
-        
-        with col1:
-            fig_sentiment = create_sentiment_chart(insights)
-            st.plotly_chart(fig_sentiment, use_container_width=True)
-        
-        with col2:
-            fig_priority = create_priority_chart(insights)
-            st.plotly_chart(fig_priority, use_container_width=True)
-        
-        st.markdown("### 🏢 Department Routing")
-        fig_dept = create_department_chart(insights)
-        st.plotly_chart(fig_dept, use_container_width=True)
-        
-        st.markdown("### 😊 Emotional Analysis")
-        fig_emotion = create_emotion_chart(insights)
-        st.plotly_chart(fig_emotion, use_container_width=True)
-    
-    # TAB 2: Critical Issues
-    with tab2:
-        st.header("🚨 Critical Issues Requiring Attention")
-        
-        # Filter critical reviews
-        critical_reviews = [
-            r for r in results
-            if (r.get('priority') == 'critical' or 
-                r.get('stage1_llm1_priority') == 'critical' or
-                r.get('needs_human_review', r.get('stage3_needs_human_review')) or
-                (r.get('final_sentiment', r.get('stage3_final_sentiment')) == 'NEGATIVE' and r.get('rating', 5) <= 2))
-        ]
-        
-        if len(critical_reviews) == 0:
-            st.success("✅ No critical issues found! All reviews are in good shape.")
-        else:
-            st.warning(f"Found {len(critical_reviews)} critical issues")
-            
-            for review in critical_reviews:
-                with st.expander(
-                    f"⚠️ {review.get('review_id', 'Unknown')[:30]} - "
-                    f"Rating: {review.get('rating', 'N/A')}/5"
-                ):
-                    col1, col2 = st.columns([2, 1])
-                    
-                    with col1:
-                        st.markdown("**Review Text:**")
-                        st.write(review.get('review_text', 'No text available'))
-                        
-                        st.markdown("**Reasoning:**")
-                        reasoning = review.get('reasoning', review.get('stage3_reasoning', 'No reasoning available'))
-                        st.info(reasoning)
-                    
-                    with col2:
-                        st.markdown("**Classification:**")
-                        st.write(f"📌 Type: {review.get('classification_type', review.get('stage1_llm1_type', 'N/A'))}")
-                        st.write(f"🏢 Department: {review.get('department', review.get('stage1_llm1_department', 'N/A'))}")
-                        st.write(f"🎯 Priority: {review.get('priority', review.get('stage1_llm1_priority', 'N/A'))}")
-                        st.write(f"😔 Emotion: {review.get('emotion', review.get('stage1_llm2_emotion', 'N/A'))}")
-                        st.write(f"💭 Sentiment: {review.get('final_sentiment', review.get('stage3_final_sentiment', 'N/A'))}")
-                        
-                        st.markdown("**Action:**")
-                        action = review.get('action_recommendation', review.get('stage3_action_recommendation', 'No action specified'))
-                        st.error(action)
-    
-    # TAB 3: All Reviews
-    with tab3:
-        st.header("📋 Detailed Review Analysis")
-        
-        # Create DataFrame
-        df = create_reviews_dataframe(results)
-        
-        # Filters
-        col1, col2, col3 = st.columns(3)
-        
-        with col1:
-            sentiment_filter = st.multiselect(
-                "Filter by Sentiment",
-                options=df['Sentiment'].unique(),
-                default=df['Sentiment'].unique()
-            )
-        
-        with col2:
-            dept_filter = st.multiselect(
-                "Filter by Department",
-                options=df['Department'].unique(),
-                default=df['Department'].unique()
-            )
-        
-        with col3:
-            priority_filter = st.multiselect(
-                "Filter by Priority",
-                options=df['Priority'].unique(),
-                default=df['Priority'].unique()
-            )
-        
-        # Apply filters
-        filtered_df = df[
-            (df['Sentiment'].isin(sentiment_filter)) &
-            (df['Department'].isin(dept_filter)) &
-            (df['Priority'].isin(priority_filter))
-        ]
-        
-        st.info(f"Showing {len(filtered_df)} of {len(df)} reviews")
-        
-        # Display table
-        st.dataframe(
-            filtered_df,
-            use_container_width=True,
-            height=600
-        )
-    
-    # TAB 4: Export
-    with tab4:
-        st.header("📥 Export Results")
-        
-        st.markdown("### Download Options")
-        
-        col1, col2 = st.columns(2)
-        
-        with col1:
-            st.markdown("#### 📊 CSV Export")
-            st.write("Download complete analysis with all classifications")
-            
-            df = create_reviews_dataframe(results)
-            csv = df.to_csv(index=False)
-            
-            st.download_button(
-                label="📥 Download CSV Report",
-                data=csv,
-                file_name=f"review_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
-                mime="text/csv",
-                use_container_width=True
-            )
-        
-        with col2:
-            st.markdown("#### 📋 JSON Export")
-            st.write("Download raw data with all details")
-            
-            import json
-            json_data = json.dumps({
-                'results': results,
-                'insights': insights,
-                'scraped_count': scraped_count,
-                'export_date': datetime.now().isoformat()
-            }, indent=2)
-            
-            st.download_button(
-                label="📥 Download JSON Data",
-                data=json_data,
-                file_name=f"review_data_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
-                mime="application/json",
-                use_container_width=True
-            )
-        
-        st.markdown("---")
-        st.markdown("### 📊 Summary Statistics")
-        
-        col1, col2, col3 = st.columns(3)
-        
-        with col1:
-            st.metric("Total Reviews Analyzed", len(results))
-        
-        with col2:
-            positive = insights.get('sentiment_distribution', {}).get('POSITIVE', 0)
-            total = len(results)
-            pct = (positive / total * 100) if total > 0 else 0
-            st.metric("Positive Rate", f"{pct:.1f}%")
-        
-        with col3:
-            critical = insights.get('priority_distribution', {}).get('critical', 0)
-            st.metric("Critical Issues", critical)
-
-
-# ============================================================================
-# FOOTER
-# ============================================================================
-
-def show_footer():
-    """Show footer with credits"""
-    st.markdown("---")
-    st.markdown(
-        """
-        <div style='text-align: center'>
-            <p>🤖 Powered by Multi-Stage AI Pipeline | 
-            Stage 1: Classification (Qwen, Mistral, Llama) | 
-            Stage 2: Sentiment (Twitter-BERT) | 
-            Stage 3: Finalization (Llama 70B) | 
-            Stage 4: Batch Analysis</p>
-            <p>Built with ❤️ using LangGraph + HuggingFace + Streamlit</p>
-        </div>
-        """,
-        unsafe_allow_html=True
-    )
-
-
-# ============================================================================
-# RUN APP
-# ============================================================================
-
-if __name__ == "__main__":
-    main()
-    show_footer()
+**3 files to upload:**
+1. `database_enhanced_UPDATED.py` → rename to `database_enhanced.py`
+2. `langgraph_nodes_FINAL.py` → rename to `langgraph_nodes.py`
+3. Modify `app.py` → add 3 lines as shown above
+
+**Then it will work!** 🎉