Update app.py

app.py

@@ -1,6 +1,7 @@
 """
 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
@@ -26,27 +27,87 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 
-# Custom CSS
+# FIXED Custom CSS - Better Contrast
 st.markdown("""
     <style>
     .main {
         padding: 0rem 1rem;
     }
+    
+    /* FIXED: Metric cards with better contrast */
     .stMetric {
-        background-color: #f0f2f6;
-        padding: 15px;
-        border-radius: 5px;
+        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;
+    }
+    
+    .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);
+    }
+    
+    .stMetric [data-testid="stMetricDelta"] {
+        color: #93c5fd !important;
+        font-size: 14px !important;
+        font-weight: 600 !important;
     }
+    
     .big-font {
         font-size: 24px !important;
         font-weight: bold;
     }
+    
     .success-box {
-        padding: 20px;
-        border-radius: 10px;
+        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);
+    }
+    
+    .success-box h1 {
+        color: white !important;
+        text-shadow: 0 2px 4px rgba(0,0,0,0.2);
+    }
+    
+    /* Info boxes */
+    .stAlert {
+        border-radius: 8px;
+    }
+    
+    /* Better table styling */
+    .dataframe {
+        border: 1px solid #e2e8f0 !important;
+    }
+    
+    /* Tab styling */
+    .stTabs [data-baseweb="tab-list"] {
+        gap: 8px;
+    }
+    
+    .stTabs [data-baseweb="tab"] {
+        background-color: #1e293b;
+        border-radius: 8px 8px 0 0;
+        padding: 12px 24px;
+        color: #94a3b8;
+    }
+    
+    .stTabs [aria-selected="true"] {
+        background-color: #3b82f6;
+        color: white;
     }
     </style>
 """, unsafe_allow_html=True)
@@ -143,6 +204,8 @@ def process_reviews_streamlit(app_store_urls: str, play_store_urls: str,
         reviews = pipeline.db.get_pending_reviews(limit=review_limit)
         total_reviews = len(reviews)
         
+        print(f"📊 DEBUG: Found {total_reviews} reviews to process")
+        
         processed_states = []
         
         for i, review in enumerate(reviews, 1):
@@ -155,9 +218,22 @@ def process_reviews_streamlit(app_store_urls: str, play_store_urls: str,
                 state = create_initial_state(review)
                 config = {"configurable": {"thread_id": f"review_{review.get('review_id')}"}}
                 final_state = pipeline.review_graph.invoke(state, config=config)
-                processed_states.append(dict(final_state))
+                
+                # 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
         
         if len(processed_states) == 0:
@@ -220,32 +296,32 @@ def create_summary_section(scraped_count: int, results: List[Dict], insights: Di
         unsafe_allow_html=True
     )
     
-    # Metrics
+    # Metrics with better styling
     col1, col2, col3, col4, col5 = st.columns(5)
     
     with col1:
-        st.metric("Total Reviews", total, f"Scraped: {scraped_count}")
+        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}%")
+        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}%")
+        st.metric("😞 Negative", negative, f"{neg_pct:.1f}%")
     
     with col4:
-        st.metric("Critical Issues", critical, "🚨" if critical > 0 else "✅")
+        st.metric("🚨 Critical", critical, "⚠️" if critical > 0 else "✅")
     
     with col5:
-        delta_color = "inverse" if churn_risk > 30 else "normal"
-        st.metric("Churn Risk", f"{churn_risk:.1f}%", 
-                 "⚠️ High" if churn_risk > 30 else "✅ Low")
+        st.metric("📉 Churn Risk", f"{churn_risk:.1f}%", 
+                 "🔴 High" if churn_risk > 30 else "🟢 Low")
     
     # Recommendations
-    st.markdown("### 💡 Key Recommendations")
-    for rec in insights.get('recommendations', []):
-        st.info(rec)
+    if insights.get('recommendations'):
+        st.markdown("### 💡 Key Recommendations")
+        for rec in insights.get('recommendations', []):
+            st.info(rec)
 
 
 def create_sentiment_chart(insights: Dict):
@@ -254,7 +330,7 @@ def create_sentiment_chart(insights: Dict):
     
     labels = list(sentiment_dist.keys())
     values = list(sentiment_dist.values())
-    colors = ['#2ca02c', '#ff7f0e', '#d62728']
+    colors = ['#10b981', '#f59e0b', '#ef4444']
     
     fig = go.Figure(data=[go.Pie(
         labels=labels,
@@ -282,7 +358,7 @@ def create_priority_chart(insights: Dict):
     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 = ['#d62728', '#ff7f0e', '#1f77b4', '#2ca02c']
+    colors = ['#dc2626', '#f59e0b', #3b82f6', '#10b981']
     
     fig = go.Figure(data=[go.Bar(
         x=labels,
@@ -355,20 +431,24 @@ def create_emotion_chart(insights: Dict):
 
 
 def create_reviews_dataframe(results: List[Dict]) -> pd.DataFrame:
-    """Create DataFrame for reviews table"""
+    """
+    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('stage3_final_sentiment', 'N/A'),
-            'Type': review.get('stage1_llm1_type', 'N/A'),
-            'Department': review.get('stage1_llm1_department', 'N/A'),
-            'Priority': review.get('stage1_llm1_priority', 'N/A'),
-            'Emotion': review.get('stage1_llm2_emotion', 'N/A'),
-            'Needs Review': '🚨 Yes' if review.get('stage3_needs_human_review') else '✅ No'
+            '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)
@@ -383,7 +463,7 @@ def main():
     
     # Title
     st.title("🎯 Review Intelligence System")
-    st.markdown("### Multi-Stage AI Analysis for App Store & Play Store Reviews")
+    st.markdown("### Multi-Stage AI Analysis Dashboard")
     st.markdown("Powered by **LangGraph** + **HuggingFace** • 4-Stage Processing Pipeline")
     st.markdown("---")
     
@@ -403,10 +483,8 @@ def main():
     
     # Main content - Input or Results
     if not st.session_state.processing_complete:
-        # INPUT MODE
         show_input_form()
     else:
-        # RESULTS MODE
         show_results_dashboard()
 
 
@@ -576,9 +654,10 @@ def show_results_dashboard():
         # Filter critical reviews
         critical_reviews = [
             r for r in results
-            if (r.get('stage1_llm1_priority') == 'critical' or
-                r.get('stage3_needs_human_review') or
-                (r.get('stage3_final_sentiment') == 'NEGATIVE' and r.get('rating', 5) <= 2))
+            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:
@@ -598,18 +677,20 @@ def show_results_dashboard():
                         st.write(review.get('review_text', 'No text available'))
                         
                         st.markdown("**Reasoning:**")
-                        st.info(review.get('stage3_reasoning', 'No reasoning available'))
+                        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('stage1_llm1_type', 'N/A')}")
-                        st.write(f"🏢 Department: {review.get('stage1_llm1_department', 'N/A')}")
-                        st.write(f"🎯 Priority: {review.get('stage1_llm1_priority', 'N/A')}")
-                        st.write(f"😔 Emotion: {review.get('stage1_llm2_emotion', 'N/A')}")
-                        st.write(f"💭 Sentiment: {review.get('stage3_final_sentiment', 'N/A')}")
+                        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:**")
-                        st.error(review.get('stage3_action_recommendation', 'No action specified'))
+                        action = review.get('action_recommendation', review.get('stage3_action_recommendation', 'No action specified'))
+                        st.error(action)
     
     # TAB 3: All Reviews
     with tab3:
@@ -748,4 +829,4 @@ def show_footer():
 
 if __name__ == "__main__":
     main()
-    show_footer()
+    show_footer()