Updating Historical Analytics

visualization/pages/5_Historical_Analytics.py

@@ -1,6 +1,7 @@
 """
-Page 5: Historical Analytics
-View performance metrics and insights from all previous experiments stored in Snowflake.
+Page 5: Historical Analytics (Enhanced)
+View comprehensive performance metrics and insights from all previous experiments stored in Snowflake.
+Includes campaign-level analysis, model performance, and detailed rejection analytics.
 """
 
 import streamlit as st
@@ -25,6 +26,7 @@ else:
 try:
     import plotly.express as px
     import plotly.graph_objects as go
+    from plotly.subplots import make_subplots
 except ModuleNotFoundError:
     st.error("""
     ⚠️ **Missing Dependency: plotly**
@@ -68,6 +70,25 @@ def create_snowflake_session():
         st.error(f"Traceback: {traceback.format_exc()}")
         return None
 
+# Rejection reason mapping
+REJECTION_REASON_LABELS = {
+    "poor_header": "Poor Header",
+    "poor_body": "Poor Body/Content",
+    "grammar_issues": "Grammar Issues",
+    "emoji_problems": "Emoji Problems",
+    "recommendation_issues": "Recommendation Issues",
+    "wrong_information": "Wrong/Inaccurate Information",
+    "tone_issues": "Tone Issues",
+    "similarity": "Similar To Previous",
+    "other": "Other"
+}
+
+def get_rejection_label(reason_key):
+    """Get human-readable label for rejection reason."""
+    if reason_key is None:
+        return "Unknown"
+    return REJECTION_REASON_LABELS.get(reason_key, reason_key)
+
 # Page configuration
 st.set_page_config(
     page_title="Historical Analytics",
@@ -91,12 +112,14 @@ theme = get_brand_theme(brand)
 
 # Page Header
 emoji = get_brand_emoji(brand)
-st.title(f"📚 Historical Analytics - {emoji} {brand.title()}")
-st.markdown("**View performance metrics from all previous experiments stored in Snowflake**")
+st.title(f"📚 Enhanced Historical Analytics - {emoji} {brand.title()}")
+st.markdown("**Comprehensive insights across campaigns, models, stages, and rejection patterns**")
 
 st.markdown("---")
 
-# Load Data Button
+# ============================================================================
+# LOAD DATA SECTION
+# ============================================================================
 col1, col2, col3 = st.columns([1, 2, 1])
 with col2:
     if st.button("📊 Load Historical Data from Snowflake", use_container_width=True, type="primary"):
@@ -108,21 +131,28 @@ with col2:
                 try:
                     db_manager = UIDatabaseManager(session)
 
-                    # Load experiment summary (joins metadata + feedback)
+                    # Load campaigns summary (new enhanced view)
+                    campaigns_df = db_manager.get_all_campaigns_summary(brand=brand)
+
+                    # Load experiment summary (original view for backward compatibility)
                     experiments_df = db_manager.get_experiment_summary(brand=brand)
 
                     # Store in session_state
+                    st.session_state['historical_campaigns'] = campaigns_df
                     st.session_state['historical_experiments'] = experiments_df
                     st.session_state['historical_data_loaded'] = True
+                    st.session_state['selected_campaign'] = None  # Reset selection
 
                     # Close session
                     db_manager.close()
 
-                    st.success(f"✅ Loaded {len(experiments_df)} experiments from Snowflake!")
+                    st.success(f"✅ Loaded {len(campaigns_df)} campaigns and {len(experiments_df)} experiments from Snowflake!")
                     st.rerun()
 
                 except Exception as e:
                     st.error(f"Error loading historical data: {e}")
+                    import traceback
+                    st.error(f"Traceback: {traceback.format_exc()}")
                     if session:
                         session.close()
             else:
@@ -132,250 +162,882 @@ st.markdown("---")
 
 # Check if data is loaded
 if not st.session_state.get('historical_data_loaded', False):
-    st.info("👆 Click the button above to load historical experiment data from Snowflake")
+    st.info("👆 Click the button above to load historical data from Snowflake")
+    st.markdown("""
+    ### What's in Historical Analytics:
+    - **Campaign-Level Analysis**: View aggregated metrics for all experiments in a campaign
+    - **Model Performance**: Compare LLM models with detailed rejection metrics
+    - **Stage Analysis**: Identify which stages have the highest rejection rates
+    - **Rejection Reasons**: Interactive breakdown of why messages get rejected
+    - **Filtering**: Drill down by campaign, stage, and model
+    """)
     st.stop()
 
 # Get loaded data
+campaigns_df = st.session_state.get('historical_campaigns', pd.DataFrame())
 experiments_df = st.session_state.get('historical_experiments', pd.DataFrame())
 
-if experiments_df is None or len(experiments_df) == 0:
-    st.warning("⚠️ No historical experiments found in Snowflake. Generate and store experiments first using Campaign Builder.")
+if campaigns_df is None or len(campaigns_df) == 0:
+    st.warning("⚠️ No historical campaigns found in Snowflake. Generate and store experiments first using Campaign Builder.")
     st.stop()
 
-# Sidebar - Filters
+# ============================================================================
+# SIDEBAR - CAMPAIGN SELECTOR
+# ============================================================================
 with st.sidebar:
-    st.header("🔍 Filters")
-
-    # Date range filter
-    st.subheader("Date Range")
-    show_all = st.checkbox("Show All Experiments", value=True)
-
-    start_date = None
-    end_date = None
-
-    if not show_all:
-        # Use actual timestamps from data
-        if 'start_time' in experiments_df.columns:
-            experiments_df['start_time'] = pd.to_datetime(experiments_df['start_time'])
-            min_date = experiments_df['start_time'].min().date()
-            max_date = experiments_df['start_time'].max().date()
-
-            start_date = st.date_input("Start Date", min_date)
-            end_date = st.date_input("End Date", max_date)
+    st.header("🎯 Campaign Selector")
+
+    # Campaign selection
+    campaign_options = ["-- All Campaigns Overview --"] + list(campaigns_df['campaign_name'].unique())
+    selected_campaign = st.selectbox(
+        "Select Campaign",
+        campaign_options,
+        index=0,
+        key='campaign_selector'
+    )
 
-            # Filter by date range
-            experiments_df = experiments_df[
-                (experiments_df['start_time'].dt.date >= start_date) &
-                (experiments_df['start_time'].dt.date <= end_date)
-            ]
+    # Store selected campaign
+    if selected_campaign != "-- All Campaigns Overview --":
+        st.session_state['selected_campaign'] = selected_campaign
+    else:
+        st.session_state['selected_campaign'] = None
 
     st.markdown("---")
 
     # Refresh button
     if st.button("🔄 Reload Data", use_container_width=True):
         st.session_state['historical_data_loaded'] = False
+        st.session_state['selected_campaign'] = None
         st.rerun()
 
+    st.markdown("---")
+    st.markdown("**💡 Tip:** Select a campaign to see detailed analytics including model performance and rejection patterns.")
+
 # ============================================================================
-# OVERVIEW STATISTICS
+# MAIN CONTENT - CONDITIONAL BASED ON SELECTION
 # ============================================================================
-st.header("📊 Overview Statistics")
 
-col1, col2, col3, col4 = st.columns(4)
+if st.session_state.get('selected_campaign') is None:
+    # ========================================================================
+    # ALL CAMPAIGNS OVERVIEW
+    # ========================================================================
+    st.header("🌐 All Campaigns Overview")
 
-with col1:
-    st.metric("Total Experiments", len(experiments_df))
-
-with col2:
-    total_messages = experiments_df['total_messages'].sum() if 'total_messages' in experiments_df.columns else 0
-    st.metric("Total Messages", f"{total_messages:,}")
+    # Overview metrics
+    col1, col2, col3, col4 = st.columns(4)
 
-with col3:
-    total_rejects = experiments_df['total_rejects'].sum() if 'total_rejects' in experiments_df.columns else 0
-    st.metric("Total Rejections", f"{total_rejects:,}")
+    with col1:
+        st.metric("Total Campaigns", len(campaigns_df))
 
-with col4:
-    avg_rejection_rate = experiments_df['rejection_rate'].mean() if 'rejection_rate' in experiments_df.columns else 0
-    st.metric("Avg Rejection Rate", f"{avg_rejection_rate:.1f}%")
+    with col2:
+        total_experiments = campaigns_df['total_experiments'].sum() if 'total_experiments' in campaigns_df.columns else 0
+        st.metric("Total Experiments", f"{int(total_experiments):,}")
 
-st.markdown("---")
+    with col3:
+        total_messages = campaigns_df['total_messages'].sum() if 'total_messages' in campaigns_df.columns else 0
+        st.metric("Total Messages", f"{int(total_messages):,}")
 
-# ============================================================================
-# EXPERIMENTS SUMMARY TABLE
-# ============================================================================
-st.header("📈 All Experiments")
-
-# Display summary table
-display_df = experiments_df.copy()
-
-# Format columns for display
-if 'start_time' in display_df.columns:
-    display_df['start_time'] = pd.to_datetime(display_df['start_time']).dt.strftime('%Y-%m-%d %H:%M')
-
-# Select columns to display
-display_columns = ['campaign_name', 'config_name', 'start_time', 'total_messages',
-                   'total_users', 'total_stages', 'total_rejects', 'rejection_rate']
-
-# Only show columns that exist
-display_columns = [col for col in display_columns if col in display_df.columns]
-
-st.dataframe(
-    display_df[display_columns],
-    use_container_width=True,
-    hide_index=True,
-    column_config={
-        "campaign_name": "Campaign",
-        "config_name": "Config",
-        "start_time": "Date",
-        "total_messages": "Messages",
-        "total_users": "Users",
-        "total_stages": "Stages",
-        "total_rejects": "Rejects",
-        "rejection_rate": st.column_config.NumberColumn(
-            "Reject Rate (%)",
-            format="%.1f%%"
-        )
-    }
-)
+    with col4:
+        total_rejections = campaigns_df['total_rejections'].sum() if 'total_rejections' in campaigns_df.columns else 0
+        st.metric("Total Rejections", f"{int(total_rejections):,}")
 
-st.markdown("---")
+    st.markdown("---")
 
-# ============================================================================
-# REJECTION RATE TREND OVER TIME
-# ============================================================================
-st.header("📉 Rejection Rate Trend Over Time")
-
-if 'start_time' in experiments_df.columns and 'rejection_rate' in experiments_df.columns:
-    # Sort by time
-    trend_df = experiments_df.copy()
-    trend_df['start_time'] = pd.to_datetime(trend_df['start_time'])
-    trend_df = trend_df.sort_values('start_time')
-
-    fig = go.Figure()
-
-    fig.add_trace(go.Scatter(
-        x=trend_df['start_time'],
-        y=trend_df['rejection_rate'],
-        mode='lines+markers',
-        name='Rejection Rate',
-        line=dict(color=theme['accent'], width=3),
-        marker=dict(size=10),
-        text=trend_df['campaign_name'],
-        hovertemplate='<b>%{text}</b><br>Date: %{x}<br>Rejection Rate: %{y:.1f}%<extra></extra>'
-    ))
-
-    fig.update_layout(
-        xaxis_title="Experiment Date",
-        yaxis_title="Rejection Rate (%)",
-        template="plotly_dark",
-        paper_bgcolor='rgba(0,0,0,0)',
-        plot_bgcolor='rgba(0,0,0,0)',
-        hovermode='closest'
+    # Campaigns summary table
+    st.subheader("📊 Campaigns Summary")
+
+    display_df = campaigns_df.copy()
+
+    # Format dates
+    if 'first_experiment' in display_df.columns:
+        display_df['first_experiment'] = pd.to_datetime(display_df['first_experiment']).dt.strftime('%Y-%m-%d')
+    if 'last_experiment' in display_df.columns:
+        display_df['last_experiment'] = pd.to_datetime(display_df['last_experiment']).dt.strftime('%Y-%m-%d')
+
+    # Select and order columns for display
+    display_columns = ['campaign_name', 'total_experiments', 'total_messages', 'total_rejections',
+                      'rejection_rate', 'models_used', 'first_experiment', 'last_experiment']
+    display_columns = [col for col in display_columns if col in display_df.columns]
+
+    st.dataframe(
+        display_df[display_columns],
+        use_container_width=True,
+        hide_index=True,
+        column_config={
+            "campaign_name": "Campaign Name",
+            "total_experiments": st.column_config.NumberColumn("Experiments", format="%d"),
+            "total_messages": st.column_config.NumberColumn("Messages", format="%d"),
+            "total_rejections": st.column_config.NumberColumn("Rejections", format="%d"),
+            "rejection_rate": st.column_config.NumberColumn("Rejection Rate", format="%.1f%%"),
+            "models_used": "Models Used",
+            "first_experiment": "First Run",
+            "last_experiment": "Last Run"
+        }
     )
 
-    st.plotly_chart(fig, use_container_width=True)
+    st.markdown("---")
+
+    # Campaign comparison charts
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.subheader("📉 Rejection Rate by Campaign")
+        if 'rejection_rate' in campaigns_df.columns and len(campaigns_df) > 0:
+            fig = px.bar(
+                campaigns_df.sort_values('rejection_rate', ascending=False),
+                x='campaign_name',
+                y='rejection_rate',
+                color='rejection_rate',
+                color_continuous_scale='RdYlGn_r',
+                text='rejection_rate'
+            )
+            fig.update_traces(texttemplate='%{text:.1f}%', textposition='outside')
+            fig.update_layout(
+                template="plotly_dark",
+                paper_bgcolor='rgba(0,0,0,0)',
+                plot_bgcolor='rgba(0,0,0,0)',
+                xaxis_title="Campaign",
+                yaxis_title="Rejection Rate (%)",
+                showlegend=False
+            )
+            st.plotly_chart(fig, use_container_width=True)
+        else:
+            st.info("No rejection data available")
+
+    with col2:
+        st.subheader("📊 Messages vs Rejections")
+        if 'total_messages' in campaigns_df.columns and 'total_rejections' in campaigns_df.columns:
+            fig = go.Figure()
+            fig.add_trace(go.Bar(
+                x=campaigns_df['campaign_name'],
+                y=campaigns_df['total_messages'],
+                name='Total Messages',
+                marker_color=theme['primary']
+            ))
+            fig.add_trace(go.Bar(
+                x=campaigns_df['campaign_name'],
+                y=campaigns_df['total_rejections'],
+                name='Rejections',
+                marker_color='#ff4444'
+            ))
+            fig.update_layout(
+                template="plotly_dark",
+                paper_bgcolor='rgba(0,0,0,0)',
+                plot_bgcolor='rgba(0,0,0,0)',
+                xaxis_title="Campaign",
+                yaxis_title="Count",
+                barmode='group'
+            )
+            st.plotly_chart(fig, use_container_width=True)
+        else:
+            st.info("No message data available")
+
 else:
-    st.info("Not enough data for trend visualization")
+    # ========================================================================
+    # SELECTED CAMPAIGN DETAILED ANALYSIS
+    # ========================================================================
+    selected_campaign = st.session_state['selected_campaign']
+
+    st.header(f"🎯 Campaign Analysis: {selected_campaign}")
+
+    # Load detailed campaign analysis
+    with st.spinner(f"Loading detailed analytics for '{selected_campaign}'..."):
+        session = create_snowflake_session()
+        if session:
+            try:
+                db_manager = UIDatabaseManager(session)
+
+                # Get detailed feedback analysis
+                analysis = db_manager.get_campaign_feedback_analysis(selected_campaign, brand=brand)
+
+                # Store in session state
+                st.session_state['campaign_analysis'] = analysis
+
+                db_manager.close()
+            except Exception as e:
+                st.error(f"Error loading campaign analysis: {e}")
+                import traceback
+                st.error(f"Traceback: {traceback.format_exc()}")
+                if session:
+                    session.close()
+                st.stop()
+        else:
+            st.error("Failed to connect to Snowflake")
+            st.stop()
+
+    analysis = st.session_state.get('campaign_analysis', {})
+
+    # Get campaign summary
+    campaign_summary = campaigns_df[campaigns_df['campaign_name'] == selected_campaign].iloc[0]
+
+    # Campaign overview metrics
+    col1, col2, col3, col4, col5 = st.columns(5)
 
-st.markdown("---")
+    with col1:
+        st.metric("Experiments", int(campaign_summary.get('total_experiments', 0)))
 
-# ============================================================================
-# PERFORMANCE COMPARISON
-# ============================================================================
-st.header("📊 Performance Comparison")
+    with col2:
+        st.metric("Total Messages", f"{int(campaign_summary.get('total_messages', 0)):,}")
 
-if len(experiments_df) > 1 and 'config_name' in experiments_df.columns:
-    # Group by config and compare
-    config_summary = experiments_df.groupby('config_name').agg({
-        'rejection_rate': 'mean',
-        'total_messages': 'sum',
-        'total_rejects': 'sum'
-    }).reset_index()
+    with col3:
+        st.metric("Total Rejections", f"{int(campaign_summary.get('total_rejections', 0)):,}")
 
-    config_summary = config_summary.sort_values('rejection_rate')
+    with col4:
+        st.metric("Rejection Rate", f"{campaign_summary.get('rejection_rate', 0):.1f}%")
 
-    col1, col2 = st.columns(2)
+    with col5:
+        st.metric("Models Used", int(len(str(campaign_summary.get('models_used', '')).split(', '))))
+
+    st.markdown("---")
+
+    # ========================================================================
+    # OVERALL FEEDBACK ANALYSIS
+    # ========================================================================
+    st.header("📊 Overall Feedback Analysis")
+
+    # Get data for overall analysis
+    reasons_df = analysis.get('rejection_reasons', pd.DataFrame())
+    stage_df = analysis.get('by_stage', pd.DataFrame())
+    model_df = analysis.get('by_model', pd.DataFrame())
+
+    col1, col2, col3 = st.columns(3)
 
     with col1:
-        # Bar chart comparing configs
+        st.subheader("📋 Top Rejection Reasons")
+        if not reasons_df.empty:
+            top_reasons = reasons_df.nlargest(5, 'count')
+            for idx, row in top_reasons.iterrows():
+                reason_label = get_rejection_label(row['rejection_reason'])
+                st.metric(reason_label, f"{int(row['count']):,}",
+                         help=f"Affected {int(row.get('experiments_affected', 0))} experiments")
+        else:
+            st.info("No rejection data available")
+
+    with col2:
+        st.subheader("⚠️ Highest Rejection Rate Stages")
+        if not stage_df.empty and 'rejection_rate' in stage_df.columns:
+            top_stages = stage_df.nlargest(5, 'rejection_rate')
+            for idx, row in top_stages.iterrows():
+                st.metric(
+                    f"Stage {int(row['stage'])}",
+                    f"{row['rejection_rate']:.1f}%",
+                    help=f"{int(row.get('total_rejections', 0))} rejections out of {int(row.get('total_messages', 0))} messages"
+                )
+        else:
+            st.info("No stage rejection data available")
+
+    with col3:
+        st.subheader("🤖 Highest Rejection Rate Models")
+        if not model_df.empty and 'rejection_rate' in model_df.columns:
+            top_models = model_df.nlargest(5, 'rejection_rate')
+            for idx, row in top_models.iterrows():
+                model_name = row['llm_model']
+                # Truncate long model names
+                display_name = model_name if len(model_name) <= 20 else model_name[:17] + "..."
+                st.metric(
+                    display_name,
+                    f"{row['rejection_rate']:.1f}%",
+                    help=f"{model_name}: {int(row.get('total_rejections', 0))} rejections"
+                )
+        else:
+            st.info("No model rejection data available")
+
+    # Overall rejection reasons chart
+    if not reasons_df.empty:
+        st.subheader("🔍 Rejection Reasons Distribution")
+        reasons_display = reasons_df.copy()
+        reasons_display['reason_label'] = reasons_display['rejection_reason'].apply(get_rejection_label)
+
         fig = px.bar(
-            config_summary,
-            x='config_name',
-            y='rejection_rate',
-            title="Average Rejection Rate by Configuration",
-            color='rejection_rate',
-            color_continuous_scale='RdYlGn_r',
-            text='rejection_rate'
+            reasons_display.sort_values('count', ascending=False),
+            x='reason_label',
+            y='count',
+            color='count',
+            color_continuous_scale='Reds',
+            text='count'
         )
-
-        fig.update_traces(texttemplate='%{text:.1f}%', textposition='outside')
+        fig.update_traces(textposition='outside')
         fig.update_layout(
             template="plotly_dark",
             paper_bgcolor='rgba(0,0,0,0)',
             plot_bgcolor='rgba(0,0,0,0)',
-            xaxis_title="Configuration",
-            yaxis_title="Rejection Rate (%)"
+            xaxis_title="Rejection Reason",
+            yaxis_title="Count",
+            showlegend=False,
+            height=350
         )
-
+        fig.update_xaxes(tickangle=-45)
         st.plotly_chart(fig, use_container_width=True)
 
-    with col2:
-        # Summary metrics
-        best_config = config_summary.iloc[0]
-        worst_config = config_summary.iloc[-1]
-
-        st.markdown("### 🏆 Best Performing Config")
-        st.metric(
-            best_config['config_name'],
-            f"{best_config['rejection_rate']:.1f}%",
-            delta=f"{worst_config['rejection_rate'] - best_config['rejection_rate']:.1f}% better than worst"
-        )
+    st.markdown("---")
 
-        st.markdown("### 📊 Config Comparison")
+    # ========================================================================
+    # MODEL PERFORMANCE ANALYSIS
+    # ========================================================================
+    st.header("🤖 Model Performance Analysis")
+
+    model_df = analysis.get('by_model', pd.DataFrame())
+
+    if not model_df.empty:
+        col1, col2 = st.columns(2)
+
+        with col1:
+            st.subheader("📊 Rejection Rate by Model")
+            fig = px.bar(
+                model_df.sort_values('rejection_rate', ascending=True),
+                y='llm_model',
+                x='rejection_rate',
+                orientation='h',
+                color='rejection_rate',
+                color_continuous_scale='RdYlGn_r',
+                text='rejection_rate'
+            )
+            fig.update_traces(texttemplate='%{text:.1f}%', textposition='outside')
+            fig.update_layout(
+                template="plotly_dark",
+                paper_bgcolor='rgba(0,0,0,0)',
+                plot_bgcolor='rgba(0,0,0,0)',
+                xaxis_title="Rejection Rate (%)",
+                yaxis_title="Model",
+                showlegend=False
+            )
+            st.plotly_chart(fig, use_container_width=True)
+
+        with col2:
+            st.subheader("📈 Messages vs Rejections by Model")
+            fig = go.Figure()
+            fig.add_trace(go.Bar(
+                x=model_df['llm_model'],
+                y=model_df['total_messages_generated'],
+                name='Total Messages',
+                marker_color=theme['primary']
+            ))
+            fig.add_trace(go.Bar(
+                x=model_df['llm_model'],
+                y=model_df['total_rejections'],
+                name='Rejections',
+                marker_color='#ff4444'
+            ))
+            fig.update_layout(
+                template="plotly_dark",
+                paper_bgcolor='rgba(0,0,0,0)',
+                plot_bgcolor='rgba(0,0,0,0)',
+                xaxis_title="Model",
+                yaxis_title="Count",
+                barmode='group'
+            )
+            st.plotly_chart(fig, use_container_width=True)
+
+        # Model performance table
+        st.subheader("📋 Detailed Model Statistics")
         st.dataframe(
-            config_summary,
+            model_df,
             use_container_width=True,
             hide_index=True,
             column_config={
-                "config_name": "Configuration",
-                "rejection_rate": st.column_config.NumberColumn(
-                    "Avg Rejection Rate (%)",
-                    format="%.1f%%"
-                ),
-                "total_messages": st.column_config.NumberColumn(
-                    "Total Messages",
-                    format="%d"
-                ),
-                "total_rejects": st.column_config.NumberColumn(
-                    "Total Rejects",
-                    format="%d"
-                )
+                "llm_model": "Model",
+                "total_rejections": st.column_config.NumberColumn("Rejections", format="%d"),
+                "unique_users_rejected": st.column_config.NumberColumn("Unique Users", format="%d"),
+                "total_messages_generated": st.column_config.NumberColumn("Total Messages", format="%d"),
+                "rejection_rate": st.column_config.NumberColumn("Rejection Rate", format="%.2f%%")
             }
         )
-else:
-    st.info("Need at least 2 experiments for performance comparison")
+    else:
+        st.info("No model performance data available for this campaign")
 
-st.markdown("---")
+    st.markdown("---")
+
+    # ========================================================================
+    # COMPREHENSIVE STAGE ANALYSIS
+    # ========================================================================
+    st.header("📍 Comprehensive Stage Analysis")
+
+    stage_df = analysis.get('by_stage', pd.DataFrame())
+    reasons_by_stage_df = analysis.get('reasons_by_stage', pd.DataFrame())
+
+    if not stage_df.empty:
+        # Messages vs Rejections Chart
+        st.subheader("📊 Messages vs Rejections by Stage")
+        fig = go.Figure()
+        fig.add_trace(go.Bar(
+            x=stage_df['stage'],
+            y=stage_df['total_messages'],
+            name='Total Messages',
+            marker_color=theme['primary'],
+            text=stage_df['total_messages'],
+            textposition='outside'
+        ))
+        fig.add_trace(go.Bar(
+            x=stage_df['stage'],
+            y=stage_df['total_rejections'],
+            name='Rejections',
+            marker_color='#ff4444',
+            text=stage_df['total_rejections'],
+            textposition='outside'
+        ))
+        fig.update_layout(
+            template="plotly_dark",
+            paper_bgcolor='rgba(0,0,0,0)',
+            plot_bgcolor='rgba(0,0,0,0)',
+            xaxis_title="Stage",
+            yaxis_title="Count",
+            barmode='group',
+            height=400
+        )
+        st.plotly_chart(fig, use_container_width=True)
+
+        # Comprehensive Stage Statistics Table
+        st.subheader("📋 Detailed Stage Metrics")
+
+        # Prepare display dataframe
+        stage_display = stage_df.copy()
+
+        # Column configuration for display
+        column_config = {
+            "stage": "Stage",
+            "total_messages": st.column_config.NumberColumn("Messages", format="%d"),
+            "total_rejections": st.column_config.NumberColumn("Rejections", format="%d"),
+            "rejection_rate": st.column_config.NumberColumn("Rejection Rate", format="%.2f%%"),
+            "unique_users_rejected": st.column_config.NumberColumn("Unique Users", format="%d"),
+            "experiments_count": st.column_config.NumberColumn("Experiments", format="%d"),
+            "models_used": "Models Used"
+        }
+
+        # Only show columns that exist
+        available_columns = [col for col in ['stage', 'total_messages', 'total_rejections', 'rejection_rate',
+                                             'unique_users_rejected', 'experiments_count', 'models_used']
+                           if col in stage_display.columns]
 
-# Export options
-st.header("💾 Export Historical Data")
+        st.dataframe(
+            stage_display[available_columns],
+            use_container_width=True,
+            hide_index=True,
+            column_config=column_config
+        )
+
+        st.markdown("---")
+
+        # ========================================================================
+        # INTERACTIVE STAGE SELECTOR
+        # ========================================================================
+        st.subheader("🎯 Detailed Stage-by-Stage Analysis")
+        st.markdown("Select a stage to see detailed breakdown of models, rejection reasons, and performance metrics.")
+
+        # Stage selector
+        available_stages = sorted(stage_df['stage'].unique())
+
+        if len(available_stages) > 0:
+            selected_stage_detail = st.selectbox(
+                "Select Stage for Detailed View",
+                available_stages,
+                key='detailed_stage_selector'
+            )
+
+            # Get selected stage data
+            stage_info = stage_df[stage_df['stage'] == selected_stage_detail].iloc[0]
+
+            # Display stage metrics
+            col1, col2, col3, col4 = st.columns(4)
+
+            with col1:
+                st.metric("Total Messages", f"{int(stage_info.get('total_messages', 0)):,}")
+
+            with col2:
+                st.metric("Total Rejections", f"{int(stage_info.get('total_rejections', 0)):,}")
+
+            with col3:
+                st.metric("Rejection Rate", f"{stage_info.get('rejection_rate', 0):.2f}%")
+
+            with col4:
+                st.metric("Experiments", int(stage_info.get('experiments_count', 0)))
+
+            # Models used at this stage
+            st.markdown(f"**🤖 Models Used at Stage {selected_stage_detail}:**")
+            models_used = stage_info.get('models_used', 'N/A')
+            st.info(models_used)
+
+            # Rejection reasons for this stage
+            if not reasons_by_stage_df.empty:
+                stage_reasons = reasons_by_stage_df[reasons_by_stage_df['stage'] == selected_stage_detail].copy()
+
+                if not stage_reasons.empty:
+                    st.markdown(f"**🔍 Rejection Reasons for Stage {selected_stage_detail}:**")
+
+                    # Apply labels
+                    stage_reasons['reason_label'] = stage_reasons['rejection_reason'].apply(get_rejection_label)
+
+                    col1, col2 = st.columns(2)
+
+                    with col1:
+                        # Pie chart
+                        fig = px.pie(
+                            stage_reasons,
+                            values='count',
+                            names='reason_label',
+                            title=f"Rejection Reasons Distribution - Stage {selected_stage_detail}",
+                            color_discrete_sequence=px.colors.qualitative.Set3
+                        )
+                        fig.update_layout(
+                            template="plotly_dark",
+                            paper_bgcolor='rgba(0,0,0,0)',
+                            plot_bgcolor='rgba(0,0,0,0)',
+                            height=350
+                        )
+                        st.plotly_chart(fig, use_container_width=True)
+
+                    with col2:
+                        # Bar chart
+                        fig = px.bar(
+                            stage_reasons.sort_values('count', ascending=False),
+                            x='reason_label',
+                            y='count',
+                            color='count',
+                            color_continuous_scale='Oranges',
+                            text='count',
+                            title=f"Top Rejection Reasons - Stage {selected_stage_detail}"
+                        )
+                        fig.update_traces(textposition='outside')
+                        fig.update_layout(
+                            template="plotly_dark",
+                            paper_bgcolor='rgba(0,0,0,0)',
+                            plot_bgcolor='rgba(0,0,0,0)',
+                            xaxis_title="Rejection Reason",
+                            yaxis_title="Count",
+                            showlegend=False,
+                            height=350
+                        )
+                        fig.update_xaxes(tickangle=-45)
+                        st.plotly_chart(fig, use_container_width=True)
+
+                    # Comparison to other stages
+                    st.markdown(f"**📊 Stage {selected_stage_detail} Comparison:**")
+
+                    # Calculate average rejection rate across all stages
+                    avg_rejection_rate = stage_df['rejection_rate'].mean()
+                    stage_rejection_rate = stage_info.get('rejection_rate', 0)
+
+                    if stage_rejection_rate > avg_rejection_rate:
+                        delta_text = f"{stage_rejection_rate - avg_rejection_rate:.1f}% above average"
+                        st.warning(f"⚠️ This stage has a rejection rate **{stage_rejection_rate:.1f}%**, which is **{delta_text}**")
+                    elif stage_rejection_rate < avg_rejection_rate:
+                        delta_text = f"{avg_rejection_rate - stage_rejection_rate:.1f}% below average"
+                        st.success(f"✅ This stage has a rejection rate **{stage_rejection_rate:.1f}%**, which is **{delta_text}**")
+                    else:
+                        st.info(f"ℹ️ This stage has an average rejection rate of **{stage_rejection_rate:.1f}%**")
+
+                    # Show detailed breakdown table
+                    st.markdown("**📋 Detailed Rejection Breakdown:**")
+                    st.dataframe(
+                        stage_reasons[['reason_label', 'count']].rename(columns={'reason_label': 'Rejection Reason', 'count': 'Count'}),
+                        use_container_width=True,
+                        hide_index=True
+                    )
+                else:
+                    st.info(f"No rejection reasons data available for Stage {selected_stage_detail}")
+            else:
+                st.info("No rejection reasons data available")
+    else:
+        st.info("No stage-level data available for this campaign")
+
+    st.markdown("---")
+
+    # ========================================================================
+    # REJECTION REASONS ANALYSIS (Interactive)
+    # ========================================================================
+    st.header("🔍 Rejection Reasons Analysis")
+
+    # Get rejection reasons data
+    reasons_df = analysis.get('rejection_reasons', pd.DataFrame())
+    reasons_by_stage_df = analysis.get('reasons_by_stage', pd.DataFrame())
+    reasons_by_model_df = analysis.get('reasons_by_model', pd.DataFrame())
+
+    if not reasons_df.empty:
+        # Overall rejection reasons
+        st.subheader("📊 Overall Rejection Reasons Distribution")
+
+        # Apply labels to reasons
+        reasons_display = reasons_df.copy()
+        reasons_display['reason_label'] = reasons_display['rejection_reason'].apply(get_rejection_label)
+
+        col1, col2 = st.columns(2)
+
+        with col1:
+            # Pie chart
+            fig = px.pie(
+                reasons_display,
+                values='count',
+                names='reason_label',
+                title="Rejection Reasons Breakdown",
+                color_discrete_sequence=px.colors.qualitative.Set3
+            )
+            fig.update_layout(
+                template="plotly_dark",
+                paper_bgcolor='rgba(0,0,0,0)',
+                plot_bgcolor='rgba(0,0,0,0)'
+            )
+            st.plotly_chart(fig, use_container_width=True)
+
+        with col2:
+            # Bar chart
+            fig = px.bar(
+                reasons_display.sort_values('count', ascending=False),
+                x='reason_label',
+                y='count',
+                color='count',
+                color_continuous_scale='Reds',
+                text='count'
+            )
+            fig.update_traces(textposition='outside')
+            fig.update_layout(
+                template="plotly_dark",
+                paper_bgcolor='rgba(0,0,0,0)',
+                plot_bgcolor='rgba(0,0,0,0)',
+                xaxis_title="Rejection Reason",
+                yaxis_title="Count",
+                showlegend=False
+            )
+            fig.update_xaxes(tickangle=-45)
+            st.plotly_chart(fig, use_container_width=True)
+
+        st.markdown("---")
+
+        # Interactive filtering section
+        st.subheader("🎛️ Interactive Rejection Analysis")
+
+        tab1, tab2 = st.tabs(["📍 By Stage", "🤖 By Model"])
+
+        with tab1:
+            # Rejection reasons by stage
+            if not reasons_by_stage_df.empty:
+                # Apply labels
+                reasons_by_stage_display = reasons_by_stage_df.copy()
+                reasons_by_stage_display['reason_label'] = reasons_by_stage_display['rejection_reason'].apply(get_rejection_label)
+
+                # Stage selector
+                available_stages = sorted(reasons_by_stage_display['stage'].unique())
+
+                if len(available_stages) > 0:
+                    selected_stage = st.selectbox(
+                        "Select Stage to Analyze",
+                        available_stages,
+                        key='stage_selector'
+                    )
+
+                    # Filter by selected stage
+                    stage_reasons = reasons_by_stage_display[reasons_by_stage_display['stage'] == selected_stage]
+
+                    col1, col2 = st.columns(2)
+
+                    with col1:
+                        # Pie chart for selected stage
+                        fig = px.pie(
+                            stage_reasons,
+                            values='count',
+                            names='reason_label',
+                            title=f"Rejection Reasons - Stage {selected_stage}",
+                            color_discrete_sequence=px.colors.qualitative.Pastel
+                        )
+                        fig.update_layout(
+                            template="plotly_dark",
+                            paper_bgcolor='rgba(0,0,0,0)',
+                            plot_bgcolor='rgba(0,0,0,0)'
+                        )
+                        st.plotly_chart(fig, use_container_width=True)
+
+                    with col2:
+                        # Bar chart for selected stage
+                        fig = px.bar(
+                            stage_reasons.sort_values('count', ascending=False),
+                            x='reason_label',
+                            y='count',
+                            color='count',
+                            color_continuous_scale='Oranges',
+                            text='count'
+                        )
+                        fig.update_traces(textposition='outside')
+                        fig.update_layout(
+                            template="plotly_dark",
+                            paper_bgcolor='rgba(0,0,0,0)',
+                            plot_bgcolor='rgba(0,0,0,0)',
+                            xaxis_title="Rejection Reason",
+                            yaxis_title="Count",
+                            showlegend=False
+                        )
+                        fig.update_xaxes(tickangle=-45)
+                        st.plotly_chart(fig, use_container_width=True)
+
+                    # Heatmap across all stages
+                    st.markdown("#### 🔥 Rejection Heatmap Across All Stages")
+                    pivot_data = reasons_by_stage_display.pivot_table(
+                        index='reason_label',
+                        columns='stage',
+                        values='count',
+                        fill_value=0
+                    )
+
+                    fig = px.imshow(
+                        pivot_data,
+                        labels=dict(x="Stage", y="Rejection Reason", color="Count"),
+                        x=pivot_data.columns,
+                        y=pivot_data.index,
+                        color_continuous_scale='Reds',
+                        aspect='auto'
+                    )
+                    fig.update_layout(
+                        template="plotly_dark",
+                        paper_bgcolor='rgba(0,0,0,0)',
+                        plot_bgcolor='rgba(0,0,0,0)'
+                    )
+                    st.plotly_chart(fig, use_container_width=True)
+            else:
+                st.info("No stage-level rejection reason data available")
+
+        with tab2:
+            # Rejection reasons by model
+            if not reasons_by_model_df.empty:
+                # Apply labels
+                reasons_by_model_display = reasons_by_model_df.copy()
+                reasons_by_model_display['reason_label'] = reasons_by_model_display['rejection_reason'].apply(get_rejection_label)
+
+                # Model selector
+                available_models = sorted(reasons_by_model_display['llm_model'].unique())
+
+                if len(available_models) > 0:
+                    selected_model = st.selectbox(
+                        "Select Model to Analyze",
+                        available_models,
+                        key='model_selector'
+                    )
+
+                    # Filter by selected model
+                    model_reasons = reasons_by_model_display[reasons_by_model_display['llm_model'] == selected_model]
+
+                    col1, col2 = st.columns(2)
+
+                    with col1:
+                        # Pie chart for selected model
+                        fig = px.pie(
+                            model_reasons,
+                            values='count',
+                            names='reason_label',
+                            title=f"Rejection Reasons - {selected_model}",
+                            color_discrete_sequence=px.colors.qualitative.Set2
+                        )
+                        fig.update_layout(
+                            template="plotly_dark",
+                            paper_bgcolor='rgba(0,0,0,0)',
+                            plot_bgcolor='rgba(0,0,0,0)'
+                        )
+                        st.plotly_chart(fig, use_container_width=True)
+
+                    with col2:
+                        # Bar chart for selected model
+                        fig = px.bar(
+                            model_reasons.sort_values('count', ascending=False),
+                            x='reason_label',
+                            y='count',
+                            color='count',
+                            color_continuous_scale='Blues',
+                            text='count'
+                        )
+                        fig.update_traces(textposition='outside')
+                        fig.update_layout(
+                            template="plotly_dark",
+                            paper_bgcolor='rgba(0,0,0,0)',
+                            plot_bgcolor='rgba(0,0,0,0)',
+                            xaxis_title="Rejection Reason",
+                            yaxis_title="Count",
+                            showlegend=False
+                        )
+                        fig.update_xaxes(tickangle=-45)
+                        st.plotly_chart(fig, use_container_width=True)
+
+                    # Heatmap across all models
+                    st.markdown("#### 🔥 Rejection Heatmap Across All Models")
+                    pivot_data = reasons_by_model_display.pivot_table(
+                        index='reason_label',
+                        columns='llm_model',
+                        values='count',
+                        fill_value=0
+                    )
+
+                    fig = px.imshow(
+                        pivot_data,
+                        labels=dict(x="Model", y="Rejection Reason", color="Count"),
+                        x=pivot_data.columns,
+                        y=pivot_data.index,
+                        color_continuous_scale='Blues',
+                        aspect='auto'
+                    )
+                    fig.update_layout(
+                        template="plotly_dark",
+                        paper_bgcolor='rgba(0,0,0,0)',
+                        plot_bgcolor='rgba(0,0,0,0)'
+                    )
+                    st.plotly_chart(fig, use_container_width=True)
+            else:
+                st.info("No model-level rejection reason data available")
+    else:
+        st.info("No rejection reason data available for this campaign")
+
+# ============================================================================
+# EXPORT SECTION
+# ============================================================================
+st.markdown("---")
+st.header("💾 Export Data")
 
-col1, col2 = st.columns(2)
+col1, col2, col3 = st.columns(3)
 
 with col1:
+    if st.button("📥 Export Campaigns Summary", use_container_width=True):
+        csv = campaigns_df.to_csv(index=False, encoding='utf-8')
+        st.download_button(
+            label="⬇️ Download Campaigns CSV",
+            data=csv,
+            file_name=f"{brand}_campaigns_summary_{datetime.now().strftime('%Y%m%d')}.csv",
+            mime="text/csv",
+            use_container_width=True
+        )
+
+with col2:
     if st.button("📥 Export Experiments Summary", use_container_width=True):
         csv = experiments_df.to_csv(index=False, encoding='utf-8')
         st.download_button(
-            label="⬇️ Download Summary CSV",
+            label="⬇️ Download Experiments CSV",
             data=csv,
             file_name=f"{brand}_experiments_summary_{datetime.now().strftime('%Y%m%d')}.csv",
             mime="text/csv",
             use_container_width=True
         )
 
-with col2:
-    st.button("📥 Export Detailed Feedback", use_container_width=True, disabled=True)
-    st.caption("💡 Detailed feedback export coming soon. Use Snowflake queries to access feedback data directly.")
+with col3:
+    if st.session_state.get('selected_campaign') and st.session_state.get('campaign_analysis'):
+        if st.button("📥 Export Campaign Analysis", use_container_width=True):
+            # Combine all analysis dataframes
+            analysis = st.session_state['campaign_analysis']
+
+            # Create a writer object
+            from io import BytesIO
+            output = BytesIO()
+            with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
+                if not analysis['by_stage'].empty:
+                    analysis['by_stage'].to_excel(writer, sheet_name='By Stage', index=False)
+                if not analysis['by_model'].empty:
+                    analysis['by_model'].to_excel(writer, sheet_name='By Model', index=False)
+                if not analysis['rejection_reasons'].empty:
+                    analysis['rejection_reasons'].to_excel(writer, sheet_name='Rejection Reasons', index=False)
+                if not analysis['reasons_by_stage'].empty:
+                    analysis['reasons_by_stage'].to_excel(writer, sheet_name='Reasons by Stage', index=False)
+                if not analysis['reasons_by_model'].empty:
+                    analysis['reasons_by_model'].to_excel(writer, sheet_name='Reasons by Model', index=False)
+
+            st.download_button(
+                label="⬇️ Download Analysis Excel",
+                data=output.getvalue(),
+                file_name=f"{brand}_{selected_campaign}_analysis_{datetime.now().strftime('%Y%m%d')}.xlsx",
+                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+                use_container_width=True
+            )
 
 st.markdown("---")
-st.markdown("**💡 Tip:** Use historical analytics to track improvements over time and identify which configurations perform best!")
+st.markdown("**💡 Pro Tip:** Use the campaign selector to drill down into specific campaigns and identify patterns in model performance and rejection reasons!")

visualization/utils/db_manager.py

@@ -496,6 +496,201 @@ class UIDatabaseManager:
             st.error(f"Error generating experiment summary: {e}")
             return pd.DataFrame()
 
+    def get_campaign_feedback_analysis(self, campaign_name: str, brand: Optional[str] = None) -> Dict:
+        """
+        Get detailed feedback analysis for a specific campaign.
+        Returns comprehensive statistics including model performance, stage analysis, and rejection reasons.
+
+        Args:
+            campaign_name: Campaign name to analyze
+            brand: Optional brand filter
+
+        Returns:
+            dict: Dictionary containing multiple analysis dataframes
+        """
+        try:
+            brand_filter = f"AND m.BRAND = '{brand}'" if brand else ""
+
+            # Query 1: Enhanced rejection distribution by stage (with messages and models)
+            stage_query = f"""
+                SELECT
+                    m.STAGE,
+                    SUM(m.TOTAL_MESSAGES) as TOTAL_MESSAGES,
+                    COUNT(f.EXPERIMENT_ID) as TOTAL_REJECTIONS,
+                    ROUND((COUNT(f.EXPERIMENT_ID) * 100.0 / NULLIF(SUM(m.TOTAL_MESSAGES), 0)), 2) as REJECTION_RATE,
+                    COUNT(DISTINCT f.USER_ID) as UNIQUE_USERS_REJECTED,
+                    COUNT(DISTINCT m.EXPERIMENT_ID) as EXPERIMENTS_COUNT,
+                    LISTAGG(DISTINCT m.LLM_MODEL, ', ') WITHIN GROUP (ORDER BY m.LLM_MODEL) as MODELS_USED
+                FROM {self.METADATA_TABLE} m
+                LEFT JOIN {self.FEEDBACK_TABLE} f
+                    ON m.EXPERIMENT_ID = f.EXPERIMENT_ID AND m.STAGE = f.STAGE
+                WHERE m.CAMPAIGN_NAME = '{campaign_name}' {brand_filter}
+                GROUP BY m.STAGE
+                ORDER BY m.STAGE
+            """
+
+            # Query 2: Rejection distribution by LLM model
+            model_query = f"""
+                SELECT
+                    m.LLM_MODEL,
+                    COUNT(f.EXPERIMENT_ID) as TOTAL_REJECTIONS,
+                    COUNT(DISTINCT f.USER_ID) as UNIQUE_USERS_REJECTED,
+                    SUM(m.TOTAL_MESSAGES) as TOTAL_MESSAGES_GENERATED,
+                    ROUND((COUNT(f.EXPERIMENT_ID) * 100.0 / NULLIF(SUM(m.TOTAL_MESSAGES), 0)), 2) as REJECTION_RATE
+                FROM {self.METADATA_TABLE} m
+                LEFT JOIN {self.FEEDBACK_TABLE} f
+                    ON m.EXPERIMENT_ID = f.EXPERIMENT_ID AND m.STAGE = f.STAGE
+                WHERE m.CAMPAIGN_NAME = '{campaign_name}' {brand_filter}
+                GROUP BY m.LLM_MODEL
+                ORDER BY REJECTION_RATE DESC
+            """
+
+            # Query 3: Rejection reasons breakdown
+            reasons_query = f"""
+                SELECT
+                    f.REJECTION_REASON,
+                    COUNT(*) as COUNT,
+                    COUNT(DISTINCT f.EXPERIMENT_ID) as EXPERIMENTS_AFFECTED
+                FROM {self.FEEDBACK_TABLE} f
+                JOIN {self.METADATA_TABLE} m
+                    ON f.EXPERIMENT_ID = m.EXPERIMENT_ID
+                WHERE m.CAMPAIGN_NAME = '{campaign_name}' {brand_filter}
+                GROUP BY f.REJECTION_REASON
+                ORDER BY COUNT DESC
+            """
+
+            # Query 4: Rejection reasons by stage
+            reasons_by_stage_query = f"""
+                SELECT
+                    f.STAGE,
+                    f.REJECTION_REASON,
+                    COUNT(*) as COUNT
+                FROM {self.FEEDBACK_TABLE} f
+                JOIN {self.METADATA_TABLE} m
+                    ON f.EXPERIMENT_ID = m.EXPERIMENT_ID
+                WHERE m.CAMPAIGN_NAME = '{campaign_name}' {brand_filter}
+                GROUP BY f.STAGE, f.REJECTION_REASON
+                ORDER BY f.STAGE, COUNT DESC
+            """
+
+            # Query 5: Rejection reasons by model
+            reasons_by_model_query = f"""
+                SELECT
+                    m.LLM_MODEL,
+                    f.REJECTION_REASON,
+                    COUNT(*) as COUNT
+                FROM {self.FEEDBACK_TABLE} f
+                JOIN {self.METADATA_TABLE} m
+                    ON f.EXPERIMENT_ID = m.EXPERIMENT_ID AND f.STAGE = m.STAGE
+                WHERE m.CAMPAIGN_NAME = '{campaign_name}' {brand_filter}
+                GROUP BY m.LLM_MODEL, f.REJECTION_REASON
+                ORDER BY m.LLM_MODEL, COUNT DESC
+            """
+
+            # Execute all queries
+            stage_df = self.session.sql(stage_query).to_pandas()
+            stage_df.columns = stage_df.columns.str.lower()
+
+            model_df = self.session.sql(model_query).to_pandas()
+            model_df.columns = model_df.columns.str.lower()
+
+            reasons_df = self.session.sql(reasons_query).to_pandas()
+            reasons_df.columns = reasons_df.columns.str.lower()
+
+            reasons_by_stage_df = self.session.sql(reasons_by_stage_query).to_pandas()
+            reasons_by_stage_df.columns = reasons_by_stage_df.columns.str.lower()
+
+            reasons_by_model_df = self.session.sql(reasons_by_model_query).to_pandas()
+            reasons_by_model_df.columns = reasons_by_model_df.columns.str.lower()
+
+            print(f"✅ Generated feedback analysis for campaign '{campaign_name}'")
+
+            return {
+                'by_stage': stage_df,
+                'by_model': model_df,
+                'rejection_reasons': reasons_df,
+                'reasons_by_stage': reasons_by_stage_df,
+                'reasons_by_model': reasons_by_model_df
+            }
+
+        except Exception as e:
+            st.error(f"Error generating campaign feedback analysis: {e}")
+            import traceback
+            st.error(f"Traceback: {traceback.format_exc()}")
+            return {
+                'by_stage': pd.DataFrame(),
+                'by_model': pd.DataFrame(),
+                'rejection_reasons': pd.DataFrame(),
+                'reasons_by_stage': pd.DataFrame(),
+                'reasons_by_model': pd.DataFrame()
+            }
+
+    def get_all_campaigns_summary(self, brand: Optional[str] = None) -> pd.DataFrame:
+        """
+        Get summary statistics for all campaigns (aggregating experiments by campaign).
+
+        Args:
+            brand: Optional brand filter
+
+        Returns:
+            pd.DataFrame: Summary statistics per campaign
+        """
+        try:
+            brand_filter = f"WHERE m.BRAND = '{brand}'" if brand else ""
+
+            query = f"""
+                WITH CampaignStats AS (
+                    SELECT
+                        m.CAMPAIGN_NAME,
+                        m.BRAND,
+                        COUNT(DISTINCT m.EXPERIMENT_ID) as TOTAL_EXPERIMENTS,
+                        COUNT(DISTINCT m.STAGE) as TOTAL_STAGES,
+                        SUM(m.TOTAL_MESSAGES) as TOTAL_MESSAGES,
+                        MAX(m.TOTAL_USERS) as TOTAL_USERS,
+                        LISTAGG(DISTINCT m.LLM_MODEL, ', ') WITHIN GROUP (ORDER BY m.LLM_MODEL) as MODELS_USED,
+                        MIN(m.TIMESTAMP) as FIRST_EXPERIMENT,
+                        MAX(m.TIMESTAMP) as LAST_EXPERIMENT
+                    FROM {self.METADATA_TABLE} m
+                    {brand_filter}
+                    GROUP BY m.CAMPAIGN_NAME, m.BRAND
+                ),
+                FeedbackStats AS (
+                    SELECT
+                        m.CAMPAIGN_NAME,
+                        COUNT(*) as TOTAL_FEEDBACK,
+                        COUNT(DISTINCT f.REJECTION_REASON) as UNIQUE_REJECTION_REASONS
+                    FROM {self.FEEDBACK_TABLE} f
+                    JOIN {self.METADATA_TABLE} m
+                        ON f.EXPERIMENT_ID = m.EXPERIMENT_ID
+                    {brand_filter.replace('m.', 'm.')}
+                    GROUP BY m.CAMPAIGN_NAME
+                )
+                SELECT
+                    c.*,
+                    COALESCE(f.TOTAL_FEEDBACK, 0) as TOTAL_REJECTIONS,
+                    COALESCE(f.UNIQUE_REJECTION_REASONS, 0) as UNIQUE_REJECTION_REASONS,
+                    CASE
+                        WHEN c.TOTAL_MESSAGES > 0
+                        THEN ROUND((COALESCE(f.TOTAL_FEEDBACK, 0) * 100.0 / c.TOTAL_MESSAGES), 2)
+                        ELSE 0
+                    END as REJECTION_RATE
+                FROM CampaignStats c
+                LEFT JOIN FeedbackStats f ON c.CAMPAIGN_NAME = f.CAMPAIGN_NAME
+                ORDER BY c.LAST_EXPERIMENT DESC
+            """
+
+            result_df = self.session.sql(query).to_pandas()
+            result_df.columns = result_df.columns.str.lower()
+
+            print(f"✅ Generated summary for {len(result_df)} campaigns")
+            return result_df
+
+        except Exception as e:
+            st.error(f"Error generating campaigns summary: {e}")
+            import traceback
+            st.error(f"Traceback: {traceback.format_exc()}")
+            return pd.DataFrame()
+
     def close(self):
         """Close the Snowflake session."""
         try: