Update app.py

app.py

@@ -1,7 +1,9 @@
 import streamlit as st
 import pandas as pd
+import plotly.express as px
 import plotly.graph_objects as go
 from sqlalchemy import create_engine, text
+from datetime import datetime, timedelta
 import os
 
 # ========================== CONFIGURATION ==========================
@@ -22,6 +24,7 @@ COLOR_SAVED = "#FFD700"      # Or pour l'argent économisé
 def get_db_connection():
     """Connexion à Neon DB via Hugging Face Secret"""
     try:
+        # Récupérer l'URL depuis les secrets Hugging Face
         database_url = os.environ.get("NEON_DB_FRAUD_URL")
         if not database_url:
             st.error("❌ Variable NEON_DB_FRAUD_URL non trouvée dans les secrets Hugging Face")
@@ -34,37 +37,66 @@ def get_db_connection():
         st.stop()
 
 # ========================== REQUÊTES SQL ==========================
-@st.cache_data(ttl=60)
+@st.cache_data(ttl=60)  # Cache de 60 secondes
 def load_all_data():
+    """Charge toutes les transactions"""
     engine = get_db_connection()
     query = text("""
         SELECT 
-            trans_num, merchant, category, amt, gender, city, zip, city_pop, job,
-            hour, day, month, year, pred_is_fraud, is_fraud_ground_truth,
-            transaction_time, created_at
+            trans_num,
+            merchant,
+            category,
+            amt,
+            gender,
+            city,
+            zip,
+            city_pop,
+            job,
+            hour,
+            day,
+            month,
+            year,
+            pred_is_fraud,
+            is_fraud_ground_truth,
+            transaction_time,
+            created_at
         FROM fraud_predictions
         ORDER BY created_at DESC
     """)
+    
     with engine.connect() as conn:
         df = pd.read_sql(query, conn)
+    
+    # Convertir created_at en datetime
     df['created_at'] = pd.to_datetime(df['created_at'])
     return df
 
 def load_last_24h_data():
+    """Charge les transactions des dernières 24h"""
     engine = get_db_connection()
     query = text("""
         SELECT 
-            trans_num, merchant, category, amt, gender, city, pred_is_fraud, created_at
+            trans_num,
+            merchant,
+            category,
+            amt,
+            gender,
+            city,
+            pred_is_fraud,
+            created_at
         FROM fraud_predictions
         WHERE created_at >= NOW() - INTERVAL '24 HOURS'
         ORDER BY created_at DESC
     """)
+    
     with engine.connect() as conn:
         df = pd.read_sql(query, conn)
+    
     df['created_at'] = pd.to_datetime(df['created_at'])
     return df
 
 def load_last_7_days_stats():
+    """Charge les stats agrégées des 7 derniers jours"""
     engine = get_db_connection()
     query = text("""
         SELECT 
@@ -76,8 +108,10 @@ def load_last_7_days_stats():
         GROUP BY DATE(created_at)
         ORDER BY date ASC
     """)
+    
     with engine.connect() as conn:
         df = pd.read_sql(query, conn)
+    
     return df
 
 # ========================== PAGE: DASHBOARD ==========================
@@ -102,7 +136,7 @@ def page_dashboard():
     total_frauds = (df_all['pred_is_fraud'] == 1).sum()
     total_no_frauds = (df_all['pred_is_fraud'] == 0).sum()
     
-    # Calcul du montant économisé
+    # Calcul du montant économisé (fraude détectée * montant * 1.5)
     saved_amount = df_all[df_all['pred_is_fraud'] == 1]['amt'].sum() * 1.5
     
     col1, col2, col3 = st.columns(3)
@@ -110,24 +144,24 @@ def page_dashboard():
     with col1:
         st.markdown(f"""
         <div style="background-color: {COLOR_FRAUD}; padding: 20px; border-radius: 10px; text-align: center;">
-            <h3 style="color: white;">🚨 Frauds</h3>
-            <h1 style="color: white;">{total_frauds}</h1>
+            <h3 style="color: white; margin: 0;">🚨 Frauds</h3>
+            <h1 style="color: white; margin: 10px 0;">{total_frauds}</h1>
         </div>
         """, unsafe_allow_html=True)
     
     with col2:
         st.markdown(f"""
         <div style="background-color: {COLOR_NO_FRAUD}; padding: 20px; border-radius: 10px; text-align: center;">
-            <h3 style="color: white;">✅ No Frauds</h3>
-            <h1 style="color: white;">{total_no_frauds}</h1>
+            <h3 style="color: white; margin: 0;">✅ No Frauds</h3>
+            <h1 style="color: white; margin: 10px 0;">{total_no_frauds}</h1>
         </div>
         """, unsafe_allow_html=True)
     
     with col3:
         st.markdown(f"""
         <div style="background-color: {COLOR_SAVED}; padding: 20px; border-radius: 10px; text-align: center;">
-            <h3 style="color: white;">💰 Saved Amount</h3>
-            <h1 style="color: white;">${saved_amount:,.2f}</h1>
+            <h3 style="color: white; margin: 0;">💰 Saved Amount</h3>
+            <h1 style="color: white; margin: 10px 0;">${saved_amount:,.2f}</h1>
         </div>
         """, unsafe_allow_html=True)
     
@@ -137,6 +171,7 @@ def page_dashboard():
     col_pie, col_saved_detail = st.columns([1, 1])
     
     with col_pie:
+        # Camembert Fraud vs No Fraud
         fig_pie = go.Figure(data=[go.Pie(
             labels=['Frauds', 'No Frauds'],
             values=[total_frauds, total_no_frauds],
@@ -145,25 +180,36 @@ def page_dashboard():
             textinfo='label+percent',
             textfont_size=14
         )])
-        fig_pie.update_layout(title="Distribution Fraud vs No Fraud", showlegend=True, height=400)
+        fig_pie.update_layout(
+            title="Distribution Fraud vs No Fraud",
+            showlegend=True,
+            height=400
+        )
         st.plotly_chart(fig_pie, use_container_width=True)
     
     with col_saved_detail:
+        # Détails du montant économisé
         total_fraud_amount = df_all[df_all['pred_is_fraud'] == 1]['amt'].sum()
-        additional_costs = total_fraud_amount * 0.5
+        additional_costs = total_fraud_amount * 0.5  # 50% de frais supplémentaires
         
         st.markdown("### 💵 Breakdown of Saved Amount")
         st.markdown(f"""
         - **Total Fraud Amounts**: ${total_fraud_amount:,.2f}
-        - **Estimated Additional Costs**: ${additional_costs:,.2f}
+        - **Estimated Additional Costs** (chargebacks, fees): ${additional_costs:,.2f}
         - **Total Saved**: ${saved_amount:,.2f}
         """)
         
+        # Mini barchart
         fig_breakdown = go.Figure(data=[
             go.Bar(name='Fraud Amount', x=['Saved'], y=[total_fraud_amount], marker_color=COLOR_FRAUD),
             go.Bar(name='Additional Costs', x=['Saved'], y=[additional_costs], marker_color=COLOR_SAVED)
         ])
-        fig_breakdown.update_layout(barmode='stack', height=250, yaxis_title="Amount ($)")
+        fig_breakdown.update_layout(
+            barmode='stack',
+            showlegend=True,
+            height=250,
+            yaxis_title="Amount ($)"
+        )
         st.plotly_chart(fig_breakdown, use_container_width=True)
     
     # ========================== GRAPHIQUE EMPILÉ 7 JOURS ==========================
@@ -171,17 +217,39 @@ def page_dashboard():
     
     if not df_7days.empty:
         fig_trend = go.Figure()
-        fig_trend.add_trace(go.Bar(name='Frauds', x=df_7days['date'], y=df_7days['frauds'], marker_color=COLOR_FRAUD))
-        fig_trend.add_trace(go.Bar(name='No Frauds', x=df_7days['date'], y=df_7days['no_frauds'], marker_color=COLOR_NO_FRAUD))
-        fig_trend.update_layout(barmode='stack', height=400, hovermode='x unified')
+        
+        fig_trend.add_trace(go.Bar(
+            name='Frauds',
+            x=df_7days['date'],
+            y=df_7days['frauds'],
+            marker_color=COLOR_FRAUD
+        ))
+        
+        fig_trend.add_trace(go.Bar(
+            name='No Frauds',
+            x=df_7days['date'],
+            y=df_7days['no_frauds'],
+            marker_color=COLOR_NO_FRAUD
+        ))
+        
+        fig_trend.update_layout(
+            barmode='stack',
+            xaxis_title="Date",
+            yaxis_title="Number of Transactions",
+            height=400,
+            showlegend=True,
+            hovermode='x unified'
+        )
+        
         st.plotly_chart(fig_trend, use_container_width=True)
     else:
         st.info("Pas encore de données sur 7 jours")
 
-# ========================== PAGE: FRAUDES (24h) ==========================
+# ========================== PAGE: FRAUDES (J-1) ==========================
 def page_frauds():
     st.title("🚨 Fraudes Détectées (Dernières 24h)")
     
+    # Bouton refresh
     if st.button("🔄 Refresh Data", type="primary"):
         st.cache_data.clear()
         st.rerun()
@@ -190,21 +258,40 @@ def page_frauds():
         df = load_last_24h_data()
         df_frauds = df[df['pred_is_fraud'] == 1]
     
+    # Métrique
     st.markdown(f"""
-    <div style="background-color: {COLOR_FRAUD}; padding: 15px; border-radius: 10px; text-align: center;">
-        <h2 style="color: white;">🚨 {len(df_frauds)} Fraudes détectées</h2>
+    <div style="background-color: {COLOR_FRAUD}; padding: 15px; border-radius: 10px; text-align: center; margin-bottom: 20px;">
+        <h2 style="color: white; margin: 0;">🚨 {len(df_frauds)} Fraudes détectées</h2>
     </div>
     """, unsafe_allow_html=True)
     
     if df_frauds.empty:
         st.success("✅ Aucune fraude détectée dans les dernières 24h !")
     else:
-        st.dataframe(df_frauds.sort_values('created_at', ascending=False), use_container_width=True, height=600)
+        # Afficher le tableau
+        st.dataframe(
+            df_frauds[[
+                'trans_num', 'merchant', 'category', 'amt', 
+                'city', 'gender', 'created_at'
+            ]].sort_values('created_at', ascending=False),
+            use_container_width=True,
+            height=600
+        )
+        
+        # Stats supplémentaires
+        col1, col2, col3 = st.columns(3)
+        with col1:
+            st.metric("Montant total", f"${df_frauds['amt'].sum():,.2f}")
+        with col2:
+            st.metric("Montant moyen", f"${df_frauds['amt'].mean():,.2f}")
+        with col3:
+            st.metric("Montant max", f"${df_frauds['amt'].max():,.2f}")
 
-# ========================== PAGE: NON FRAUDES (24h) ==========================
+# ========================== PAGE: NON FRAUDES (J-1) ==========================
 def page_no_frauds():
     st.title("✅ Transactions Légitimes (Dernières 24h)")
     
+    # Bouton refresh
     if st.button("🔄 Refresh Data", type="primary"):
         st.cache_data.clear()
         st.rerun()
@@ -213,37 +300,42 @@ def page_no_frauds():
         df = load_last_24h_data()
         df_no_frauds = df[df['pred_is_fraud'] == 0]
     
+    # Métrique
     st.markdown(f"""
-    <div style="background-color: {COLOR_NO_FRAUD}; padding: 15px; border-radius: 10px; text-align: center;">
-        <h2 style="color: white;">✅ {len(df_no_frauds)} Transactions légitimes</h2>
+    <div style="background-color: {COLOR_NO_FRAUD}; padding: 15px; border-radius: 10px; text-align: center; margin-bottom: 20px;">
+        <h2 style="color: white; margin: 0;">✅ {len(df_no_frauds)} Transactions légitimes</h2>
     </div>
     """, unsafe_allow_html=True)
     
     if df_no_frauds.empty:
         st.warning("⚠️ Aucune transaction légitime dans les dernières 24h")
     else:
-        st.dataframe(df_no_frauds.sort_values('created_at', ascending=False), use_container_width=True, height=600)
-
-# ========================== ROUTING VIA QUERY PARAM ==========================
-query_params = st.query_params
-page_param = query_params.get("page", ["dashboard"])[0]
-
-page_mapping = {
-    "dashboard": page_dashboard,
-    "fraudes": page_frauds,
-    "non-fraudes": page_no_frauds
-}
-
-def open_page_from_query_param():
-    page_func = page_mapping.get(page_param.lower(), page_dashboard)
-    page_func()
+        # Afficher le tableau
+        st.dataframe(
+            df_no_frauds[[
+                'trans_num', 'merchant', 'category', 'amt', 
+                'city', 'gender', 'created_at'
+            ]].sort_values('created_at', ascending=False),
+            use_container_width=True,
+            height=600
+        )
+        
+        # Stats supplémentaires
+        col1, col2, col3 = st.columns(3)
+        with col1:
+            st.metric("Montant total", f"${df_no_frauds['amt'].sum():,.2f}")
+        with col2:
+            st.metric("Montant moyen", f"${df_no_frauds['amt'].mean():,.2f}")
+        with col3:
+            st.metric("Montant max", f"${df_no_frauds['amt'].max():,.2f}")
 
 # ========================== NAVIGATION ==========================
 def main():
+    # Sidebar
     st.sidebar.title("Navigation")
     page = st.sidebar.radio(
         "Go to",
-        ["🏠 Dashboard", "🚨 Fraudes (24h)", "✅ Non Fraudes (24h)"]
+        ["🏠 Dashboard", "🚨 Fraudes (J-1)", "✅ Non Fraudes (J-1)"]
     )
     
     st.sidebar.markdown("---")
@@ -252,11 +344,17 @@ def main():
     Dashboard de détection de fraude en temps réel.
     
     **Refresh** : Cliquez sur le bouton pour actualiser les données.
+    
     **Données** : Dernières 24h pour les pages de détail.
     """)
     
-    # Router avec query param
-    open_page_from_query_param()
+    # Router
+    if page == "🏠 Dashboard":
+        page_dashboard()
+    elif page == "🚨 Fraudes (24h)":
+        page_frauds()
+    elif page == "✅ Non Fraudes (24h)":
+        page_no_frauds()
 
 if __name__ == "__main__":
-    main()
+    main()