Update app.py

app.py

@@ -1,6 +1,7 @@
 """
 Investor-facing Interactive Analytics Dashboard
 Built with Gradio for deployment to Hugging Face Spaces
+Updated for Gradio 6.0 compatibility
 """
 
 import os
@@ -66,6 +67,25 @@ class AppState:
 app_state = AppState()
 
 
+# =============================================================================
+# DATE PARSING HELPERS
+# =============================================================================
+
+def parse_date_string(date_str: str, is_end: bool = False) -> datetime:
+    """Parse date string to datetime object."""
+    try:
+        dt = datetime.strptime(date_str, "%Y-%m-%d")
+        if is_end:
+            dt = dt.replace(hour=23, minute=59, second=59)
+        return dt
+    except (ValueError, TypeError):
+        # Default to last 90 days if parsing fails
+        if is_end:
+            return datetime.now().replace(hour=23, minute=59, second=59)
+        else:
+            return datetime.now() - timedelta(days=90)
+
+
 # =============================================================================
 # DATA FETCHING FUNCTIONS
 # =============================================================================
@@ -83,15 +103,30 @@ def fetch_data(query_func, *args, **kwargs) -> Optional[pd.DataFrame]:
     """
     try:
         if app_state.demo_mode:
-            # Use demo data
-            method_name = query_func.__name__.replace('_query', '')
-            demo_method = getattr(demo_generator, f'get{method_name}', None)
+            # Use demo data - map query function to demo generator method
+            func_name = query_func.__name__
+            
+            # Map query builder methods to demo generator methods
+            method_mapping = {
+                'get_new_users_query': 'get_new_users',
+                'get_verified_users_query': 'get_verified_users',
+                'get_activated_by_first_trip_query': 'get_activated_by_first_trip',
+                'get_trips_over_time_query': 'get_trips_over_time',
+                'get_trip_metrics_query': 'get_trip_metrics',
+                'get_driver_type_distribution_query': 'get_driver_type_distribution',
+                'get_solo_shared_split_query': 'get_solo_shared_split',
+                'get_user_locations_query': 'get_user_locations',
+                'get_transactions_over_time_query': 'get_transactions_over_time'
+            }
+            
+            demo_method_name = method_mapping.get(func_name)
+            if demo_method_name:
+                demo_method = getattr(demo_generator, demo_method_name, None)
+                if demo_method:
+                    return demo_method(*args, **kwargs)
             
-            if demo_method:
-                return demo_method(*args, **kwargs)
-            else:
-                logger.warning(f"Demo method not found: get{method_name}")
-                return None
+            logger.warning(f"Demo method not found for: {func_name}")
+            return None
         else:
             # Use database
             query, params = query_func(*args, **kwargs)
@@ -107,12 +142,16 @@ def fetch_data(query_func, *args, **kwargs) -> Optional[pd.DataFrame]:
 # =============================================================================
 
 def render_overview_tab(
-    start_date: datetime,
-    end_date: datetime,
+    start_date_str: str,
+    end_date_str: str,
     granularity: str
 ) -> Tuple:
     """Render Overview tab with KPIs and trends."""
     
+    # Parse dates
+    start_date = parse_date_string(start_date_str)
+    end_date = parse_date_string(end_date_str, is_end=True)
+    
     # Calculate previous period for comparison
     period_days = (end_date - start_date).days
     prev_start = start_date - timedelta(days=period_days)
@@ -143,7 +182,7 @@ def render_overview_tab(
     # Calculate KPIs
     total_new_users = new_users_df['new_users'].sum() if new_users_df is not None and not new_users_df.empty else 0
     prev_total_users = prev_users_df['new_users'].sum() if prev_users_df is not None and not prev_users_df.empty else 1
-    user_delta = ((total_new_users - prev_total_users) / prev_total_users * 100) if prev_total_users > 0 else 0
+    user_delta = ((total_new_users - prev_total_users) / max(prev_total_users, 1) * 100)
     
     # Rolling active users (MAU)
     if app_state.demo_mode:
@@ -158,22 +197,22 @@ def render_overview_tab(
         prev_mau_df = db_connector.execute_query(prev_query, prev_params)
         prev_mau = prev_mau_df.iloc[0]['active_users'] if prev_mau_df is not None and not prev_mau_df.empty else 1
     
-    mau_delta = ((mau - prev_mau) / prev_mau * 100) if prev_mau > 0 else 0
+    mau_delta = ((mau - prev_mau) / max(prev_mau, 1) * 100)
     
     # Trip metrics
     if trip_metrics_df is not None and not trip_metrics_df.empty:
         total_trips = trip_metrics_df.iloc[0]['total_trips']
-        avg_distance = trip_metrics_df.iloc[0]['avg_distance_miles']
-        total_co2 = trip_metrics_df.iloc[0]['total_co2_reduced']
+        avg_distance = trip_metrics_df.iloc[0]['avg_distance_miles'] or 0
+        total_co2 = trip_metrics_df.iloc[0]['total_co2_reduced'] or 0
     else:
         total_trips = avg_distance = total_co2 = 0
     
     if prev_trip_metrics_df is not None and not prev_trip_metrics_df.empty:
-        prev_trips = prev_trip_metrics_df.iloc[0]['total_trips']
+        prev_trips = prev_trip_metrics_df.iloc[0]['total_trips'] or 1
     else:
         prev_trips = 1
     
-    trip_delta = ((total_trips - prev_trips) / prev_trips * 100) if prev_trips > 0 else 0
+    trip_delta = ((total_trips - prev_trips) / max(prev_trips, 1) * 100)
     
     # Completion rate
     if app_state.demo_mode:
@@ -250,12 +289,16 @@ def render_overview_tab(
 # =============================================================================
 
 def render_users_tab(
-    start_date: datetime,
-    end_date: datetime,
+    start_date_str: str,
+    end_date_str: str,
     granularity: str
 ) -> Tuple:
     """Render Users tab with growth and retention metrics."""
     
+    # Parse dates
+    start_date = parse_date_string(start_date_str)
+    end_date = parse_date_string(end_date_str, is_end=True)
+    
     # New users chart
     new_users_df = fetch_data(
         query_builder.get_new_users_query,
@@ -300,14 +343,17 @@ def render_users_tab(
         # Generate mock cohort data
         cohort_data = []
         for i in range(12):
-            cohort_date = start_date + timedelta(days=i*30)
-            cohort_data.append({
-                'cohort_date': cohort_date,
-                'cohort_size': np.random.randint(100, 500),
-                'retained_users': np.random.randint(30, 200)
-            })
+            cohort_date = start_date + timedelta(days=i*7)
+            if cohort_date <= end_date:
+                cohort_size = np.random.randint(100, 500)
+                cohort_data.append({
+                    'cohort_date': cohort_date.strftime('%Y-%m-%d'),
+                    'cohort_size': cohort_size,
+                    'retained_users': int(cohort_size * np.random.uniform(0.2, 0.6))
+                })
         retention_df = pd.DataFrame(cohort_data)
-        retention_df['retention_rate'] = (retention_df['retained_users'] / retention_df['cohort_size'] * 100).round(1)
+        if not retention_df.empty:
+            retention_df['retention_rate'] = (retention_df['retained_users'] / retention_df['cohort_size'] * 100).round(1)
     else:
         query, params = query_builder.get_cohort_retention_query(start_date, end_date, 7)
         retention_df = db_connector.execute_query(query, params)
@@ -321,13 +367,14 @@ def render_users_tab(
     
     # Export CSV
     export_data = new_users_df if new_users_df is not None else pd.DataFrame()
+    csv_data = df_to_csv(export_data, "users_export.csv")
     
     return (
         new_users_chart,
         verified_chart,
         activated_chart,
         retention_table,
-        df_to_csv(export_data, "users_export.csv")
+        csv_data
     )
 
 
@@ -336,13 +383,17 @@ def render_users_tab(
 # =============================================================================
 
 def render_trips_tab(
-    start_date: datetime,
-    end_date: datetime,
+    start_date_str: str,
+    end_date_str: str,
     granularity: str,
     driver_type: str
 ) -> Tuple:
     """Render Trips tab with volume and impact metrics."""
     
+    # Parse dates
+    start_date = parse_date_string(start_date_str)
+    end_date = parse_date_string(end_date_str, is_end=True)
+    
     driver_type_filter = None if driver_type == "All" else driver_type
     
     # Trip volume
@@ -389,25 +440,30 @@ def render_trips_tab(
     )
     
     if metrics_df is not None and not metrics_df.empty:
+        row = metrics_df.iloc[0]
         impact_data = pd.DataFrame([
-            {'Metric': 'Total CO₂ Reduced (kg)', 'Value': f"{metrics_df.iloc[0]['total_co2_reduced']:,.1f}"},
-            {'Metric': 'Avg CO₂ per Trip (kg)', 'Value': f"{metrics_df.iloc[0]['avg_co2_per_trip']:,.2f}"},
-            {'Metric': 'Total NOx Reduced (kg)', 'Value': f"{metrics_df.iloc[0]['total_nox_reduced']:,.1f}"},
-            {'Metric': 'Total PM2.5 Reduced (kg)', 'Value': f"{metrics_df.iloc[0]['total_pm25_reduced']:,.2f}"},
-            {'Metric': 'Total Distance (miles)', 'Value': f"{metrics_df.iloc[0]['total_distance_miles']:,.0f}"},
-            {'Metric': 'Shared Miles', 'Value': f"{metrics_df.iloc[0]['total_shared_miles']:,.0f}"}
+            {'Metric': 'Total CO₂ Reduced (g)', 'Value': f"{row.get('total_co2_reduced', 0) or 0:,.1f}"},
+            {'Metric': 'Avg CO₂ per Trip (g)', 'Value': f"{row.get('avg_co2_per_trip', 0) or 0:,.2f}"},
+            {'Metric': 'Total NOx Reduced (g)', 'Value': f"{row.get('total_nox_reduced', 0) or 0:,.1f}"},
+            {'Metric': 'Total PM2.5 Reduced (g)', 'Value': f"{row.get('total_pm25_reduced', 0) or 0:,.2f}"},
+            {'Metric': 'Total Distance (miles)', 'Value': f"{row.get('total_distance_miles', 0) or 0:,.0f}"},
+            {'Metric': 'Shared Miles', 'Value': f"{row.get('total_shared_miles', 0) or 0:,.0f}"},
+            {'Metric': 'Trees Saved', 'Value': f"{row.get('total_trees_saved', 0) or 0:,.2f}"},
+            {'Metric': 'Total Points Earned', 'Value': f"{row.get('total_points', 0) or 0:,.0f}"}
         ])
     else:
         impact_data = pd.DataFrame()
     
     impact_table = create_data_table(impact_data, "Environmental Impact Summary")
     
+    csv_data = df_to_csv(trips_df if trips_df is not None else pd.DataFrame(), "trips_export.csv")
+    
     return (
         trips_chart,
         driver_pie,
         solo_shared_pie,
         impact_table,
-        df_to_csv(trips_df if trips_df is not None else pd.DataFrame(), "trips_export.csv")
+        csv_data
     )
 
 
@@ -427,16 +483,24 @@ def render_geography_tab() -> Tuple:
         query, params = query_builder.get_user_locations_query()
         locations_df = db_connector.execute_query(query, params)
     
+    # Determine hover data columns
+    hover_cols = None
+    if locations_df is not None and not locations_df.empty:
+        available_cols = locations_df.columns.tolist()
+        hover_cols = [c for c in ['city', 'state'] if c in available_cols]
+        if not hover_cols:
+            hover_cols = None
+    
     # Create heat map
     heat_map = create_geo_heatmap(
         locations_df if locations_df is not None else pd.DataFrame(),
         'latitude', 'longitude',
-        size_col='user_count' if 'user_count' in (locations_df.columns if locations_df is not None else []) else None,
-        hover_data=['city', 'state'] if locations_df is not None and 'city' in locations_df.columns else None,
+        size_col='user_count' if locations_df is not None and 'user_count' in locations_df.columns else None,
+        hover_data=hover_cols,
         title='User Geographic Distribution'
     )
     
-    # Top markets table (mock for demo)
+    # Top markets table
     if locations_df is not None and not locations_df.empty and 'state' in locations_df.columns:
         top_markets = locations_df.groupby('state')['user_count'].sum().reset_index()
         top_markets = top_markets.sort_values('user_count', ascending=False).head(10)
@@ -446,10 +510,12 @@ def render_geography_tab() -> Tuple:
     
     markets_table = create_data_table(top_markets, "Top 10 Markets by Users")
     
+    csv_data = df_to_csv(locations_df if locations_df is not None else pd.DataFrame(), "geography_export.csv")
+    
     return (
         heat_map,
         markets_table,
-        df_to_csv(locations_df if locations_df is not None else pd.DataFrame(), "geography_export.csv")
+        csv_data
     )
 
 
@@ -458,12 +524,16 @@ def render_geography_tab() -> Tuple:
 # =============================================================================
 
 def render_rewards_tab(
-    start_date: datetime,
-    end_date: datetime,
+    start_date_str: str,
+    end_date_str: str,
     granularity: str
 ) -> Tuple:
     """Render Rewards tab with transaction metrics."""
     
+    # Parse dates
+    start_date = parse_date_string(start_date_str)
+    end_date = parse_date_string(end_date_str, is_end=True)
+    
     # Points earned over time (from trip history)
     metrics_df = fetch_data(
         query_builder.get_trip_metrics_query,
@@ -471,22 +541,25 @@ def render_rewards_tab(
     )
     
     if metrics_df is not None and not metrics_df.empty:
-        total_points = metrics_df.iloc[0]['total_points']
+        total_points = metrics_df.iloc[0].get('total_points', 0) or 0
+        total_gas_savings = metrics_df.iloc[0].get('total_gas_savings', 0) or 0
         points_summary = pd.DataFrame([
-            {'Metric': 'Total Points Earned', 'Value': f"{total_points:,.0f}"}
+            {'Metric': 'Total Points Earned', 'Value': f"{total_points:,.0f}"},
+            {'Metric': 'Total Gas Savings', 'Value': f"${total_gas_savings:,.2f}"}
         ])
     else:
         points_summary = pd.DataFrame()
     
-    points_table = create_data_table(points_summary, "Points Summary")
+    points_table = create_data_table(points_summary, "Rewards Summary")
     
-    # Mock transaction chart for demo
+    # Transaction chart
     if app_state.demo_mode:
-        periods = pd.date_range(start=start_date, end=end_date, periods=20)
+        num_periods = min(20, (end_date - start_date).days)
+        periods = pd.date_range(start=start_date, end=end_date, periods=max(num_periods, 2))
         trans_df = pd.DataFrame({
             'period': periods,
-            'transaction_count': np.random.randint(50, 200, 20),
-            'total_amount': np.random.uniform(500, 2000, 20)
+            'transaction_count': np.random.randint(50, 200, len(periods)),
+            'total_amount': np.random.uniform(500, 2000, len(periods))
         })
     else:
         query, params = query_builder.get_transactions_over_time_query(start_date, end_date, granularity)
@@ -499,38 +572,40 @@ def render_rewards_tab(
         'Date', 'Transactions'
     )
     
+    csv_data = df_to_csv(trans_df if trans_df is not None else pd.DataFrame(), "rewards_export.csv")
+    
     return (
         points_table,
         trans_chart,
-        df_to_csv(trans_df if trans_df is not None else pd.DataFrame(), "rewards_export.csv")
+        csv_data
     )
 
 
 # =============================================================================
-# GRADIO UI
+# GRADIO UI (Updated for Gradio 6.0)
 # =============================================================================
 
 def build_gradio_app():
     """Build and configure Gradio interface."""
     
-    with gr.Blocks(
-        title="Trip Analytics Dashboard",
-        theme=gr.themes.Soft(),
-        css="""
-        .gradio-container {
-            font-family: 'Arial', sans-serif;
-        }
-        .tab-nav button {
-            font-size: 16px;
-            font-weight: 500;
-        }
-        """
-    ) as demo:
+    # Define theme and CSS for launch() method
+    app_theme = gr.themes.Soft()
+    app_css = """
+    .gradio-container {
+        font-family: 'Arial', sans-serif;
+    }
+    .tab-nav button {
+        font-size: 16px;
+        font-weight: 500;
+    }
+    """
+    
+    with gr.Blocks() as demo:
         
         # Header
         gr.Markdown(
             """
-            # 🚗 Trip Analytics Dashboard
+            # 🚗 Hytch Trip Analytics Dashboard
             ### Real-time insights for investor conversations
             """
         )
@@ -539,12 +614,20 @@ def build_gradio_app():
         mode_text = "🟡 DEMO MODE - Using synthetic data" if app_state.demo_mode else "🟢 LIVE MODE - Connected to database"
         status_display = gr.Markdown(mode_text)
         
-        # Global filters
+        # Global filters - Using Textbox instead of Date for Gradio 6.0 compatibility
         with gr.Row():
             with gr.Column(scale=2):
                 start_default, end_default = create_date_range_inputs()
-                start_date_input = gr.Date(label="Start Date", value=start_default)
-                end_date_input = gr.Date(label="End Date", value=end_default)
+                start_date_input = gr.Textbox(
+                    label="Start Date (YYYY-MM-DD)", 
+                    value=start_default.strftime("%Y-%m-%d"),
+                    placeholder="2024-01-01"
+                )
+                end_date_input = gr.Textbox(
+                    label="End Date (YYYY-MM-DD)", 
+                    value=end_default.strftime("%Y-%m-%d"),
+                    placeholder="2024-12-31"
+                )
             
             with gr.Column(scale=1):
                 filter_opts = create_filter_options()
@@ -574,20 +657,15 @@ def build_gradio_app():
                     overview_user_chart = gr.Plot()
                     overview_trip_chart = gr.Plot()
                 
-                def update_overview(start, end, gran):
-                    start_dt = datetime.combine(start, datetime.min.time())
-                    end_dt = datetime.combine(end, datetime.max.time())
-                    return render_overview_tab(start_dt, end_dt, gran)
-                
                 refresh_btn.click(
-                    update_overview,
+                    render_overview_tab,
                     inputs=[start_date_input, end_date_input, granularity_input],
                     outputs=[overview_kpis, overview_user_chart, overview_trip_chart]
                 )
                 
                 # Initial load
                 demo.load(
-                    update_overview,
+                    render_overview_tab,
                     inputs=[start_date_input, end_date_input, granularity_input],
                     outputs=[overview_kpis, overview_user_chart, overview_trip_chart]
                 )
@@ -600,22 +678,18 @@ def build_gradio_app():
                 
                 users_activated_chart = gr.Plot()
                 users_retention_table = gr.HTML()
-                users_export_btn = gr.File(label="Export CSV")
-                
-                def update_users(start, end, gran):
-                    start_dt = datetime.combine(start, datetime.min.time())
-                    end_dt = datetime.combine(end, datetime.max.time())
-                    return render_users_tab(start_dt, end_dt, gran)
+                users_export_data = gr.Textbox(label="Export Data (CSV)", lines=5, visible=False)
+                users_export_btn = gr.Button("📥 Export Users Data")
                 
                 refresh_btn.click(
-                    update_users,
+                    render_users_tab,
                     inputs=[start_date_input, end_date_input, granularity_input],
                     outputs=[
                         users_new_chart,
                         users_verified_chart,
                         users_activated_chart,
                         users_retention_table,
-                        users_export_btn
+                        users_export_data
                     ]
                 )
             
@@ -627,22 +701,18 @@ def build_gradio_app():
                     trips_solo_shared_pie = gr.Plot()
                 
                 trips_impact_table = gr.HTML()
-                trips_export_btn = gr.File(label="Export CSV")
-                
-                def update_trips(start, end, gran, dtype):
-                    start_dt = datetime.combine(start, datetime.min.time())
-                    end_dt = datetime.combine(end, datetime.max.time())
-                    return render_trips_tab(start_dt, end_dt, gran, dtype)
+                trips_export_data = gr.Textbox(label="Export Data (CSV)", lines=5, visible=False)
+                trips_export_btn = gr.Button("📥 Export Trips Data")
                 
                 refresh_btn.click(
-                    update_trips,
+                    render_trips_tab,
                     inputs=[start_date_input, end_date_input, granularity_input, driver_type_input],
                     outputs=[
                         trips_volume_chart,
                         trips_driver_pie,
                         trips_solo_shared_pie,
                         trips_impact_table,
-                        trips_export_btn
+                        trips_export_data
                     ]
                 )
             
@@ -650,31 +720,31 @@ def build_gradio_app():
             with gr.Tab("🗺️ Geography"):
                 geo_heat_map = gr.Plot()
                 geo_markets_table = gr.HTML()
-                geo_export_btn = gr.File(label="Export CSV")
-                
-                def update_geography():
-                    return render_geography_tab()
+                geo_export_data = gr.Textbox(label="Export Data (CSV)", lines=5, visible=False)
+                geo_export_btn = gr.Button("📥 Export Geography Data")
                 
                 refresh_btn.click(
-                    update_geography,
-                    outputs=[geo_heat_map, geo_markets_table, geo_export_btn]
+                    render_geography_tab,
+                    outputs=[geo_heat_map, geo_markets_table, geo_export_data]
+                )
+                
+                # Load geography on tab view
+                demo.load(
+                    render_geography_tab,
+                    outputs=[geo_heat_map, geo_markets_table, geo_export_data]
                 )
             
             # TAB 5: Rewards
             with gr.Tab("🎁 Rewards"):
                 rewards_points_table = gr.HTML()
                 rewards_trans_chart = gr.Plot()
-                rewards_export_btn = gr.File(label="Export CSV")
-                
-                def update_rewards(start, end, gran):
-                    start_dt = datetime.combine(start, datetime.min.time())
-                    end_dt = datetime.combine(end, datetime.max.time())
-                    return render_rewards_tab(start_dt, end_dt, gran)
+                rewards_export_data = gr.Textbox(label="Export Data (CSV)", lines=5, visible=False)
+                rewards_export_btn = gr.Button("📥 Export Rewards Data")
                 
                 refresh_btn.click(
-                    update_rewards,
+                    render_rewards_tab,
                     inputs=[start_date_input, end_date_input, granularity_input],
-                    outputs=[rewards_points_table, rewards_trans_chart, rewards_export_btn]
+                    outputs=[rewards_points_table, rewards_trans_chart, rewards_export_data]
                 )
         
         # Toggle mode functionality
@@ -695,10 +765,12 @@ def build_gradio_app():
             ---
             **Data Security Notice**: All database credentials are stored as encrypted environment variables.
             No sensitive information is logged or displayed.
+            
+            **Hytch** - Rideshare and carpooling with environmental impact tracking 🌱
             """
         )
     
-    return demo
+    return demo, app_theme, app_css
 
 
 # =============================================================================
@@ -706,10 +778,12 @@ def build_gradio_app():
 # =============================================================================
 
 if __name__ == "__main__":
-    app = build_gradio_app()
+    app, theme, css = build_gradio_app()
     app.launch(
         server_name="0.0.0.0",
         server_port=7860,
         share=False,
-        show_error=True
-    )
+        show_error=True,
+        theme=theme,
+        css=css
+    )