Update app.py

app.py

@@ -6,29 +6,6 @@ from sqlalchemy import create_engine, text
 from datetime import datetime, timedelta
 import os
 
-import streamlit as st
-
-import streamlit as st
-
-# Lire les query params avec la nouvelle API
-query_params = st.query_params
-page_param = query_params.get("page", ["dashboard"])[0]  # valeur par défaut = dashboard
-
-# Associer la query param aux fonctions de pages
-page_mapping = {
-    "dashboard": page_dashboard,
-    "fraudes": page_frauds,
-    "non-fraudes": page_no_frauds
-}
-
-# Fonction utilitaire pour appeler la page correspondante
-def open_page_from_query_param():
-    page_func = page_mapping.get(page_param.lower(), page_dashboard)
-    page_func()
-
-
-
-
 # ========================== CONFIGURATION ==========================
 st.set_page_config(
     page_title="Fraud Detection Dashboard",
@@ -47,7 +24,6 @@ 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")
@@ -60,301 +36,79 @@ def get_db_connection():
         st.stop()
 
 # ========================== REQUÊTES SQL ==========================
-@st.cache_data(ttl=60)  # Cache de 60 secondes
+@st.cache_data(ttl=60)
 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
+        SELECT 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 
-            DATE(created_at) as date,
-            SUM(CASE WHEN pred_is_fraud = 1 THEN 1 ELSE 0 END) as frauds,
-            SUM(CASE WHEN pred_is_fraud = 0 THEN 1 ELSE 0 END) as no_frauds
+        SELECT DATE(created_at) as date,
+               SUM(CASE WHEN pred_is_fraud = 1 THEN 1 ELSE 0 END) as frauds,
+               SUM(CASE WHEN pred_is_fraud = 0 THEN 1 ELSE 0 END) as no_frauds
         FROM fraud_predictions
         WHERE created_at >= NOW() - INTERVAL '7 DAYS'
         GROUP BY DATE(created_at)
         ORDER BY date ASC
     """)
-    
     with engine.connect() as conn:
         df = pd.read_sql(query, conn)
-    
     return df
 
-# ========================== PAGE: DASHBOARD ==========================
+# ========================== PAGES ==========================
 def page_dashboard():
-    st.title("🕵🏻 Fraud Detection Dashboard")
-    
-    # Bouton refresh
-    if st.button("🔄 Refresh Data", type="primary"):
-        st.cache_data.clear()
-        st.rerun()
-    
-    # Charger les données
-    with st.spinner("Chargement des données..."):
-        df_all = load_all_data()
-        df_7days = load_last_7_days_stats()
-    
-    if df_all.empty:
-        st.warning("⚠️ Aucune donnée disponible dans la base de données")
-        return
-    
-    # ========================== MÉTRIQUES ==========================
-    total_frauds = (df_all['pred_is_fraud'] == 1).sum()
-    total_no_frauds = (df_all['pred_is_fraud'] == 0).sum()
-    
-    # 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)
-    
-    with col1:
-        st.markdown(f"""
-        <div style="background-color: {COLOR_FRAUD}; padding: 20px; border-radius: 10px; text-align: center;">
-            <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; 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; margin: 0;">💰 Saved Amount</h3>
-            <h1 style="color: white; margin: 10px 0;">${saved_amount:,.2f}</h1>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    st.markdown("<br>", unsafe_allow_html=True)
-    
-    # ========================== GRAPHIQUES ==========================
-    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],
-            marker=dict(colors=[COLOR_FRAUD, COLOR_NO_FRAUD]),
-            hole=0.4,
-            textinfo='label+percent',
-            textfont_size=14
-        )])
-        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  # 50% de frais supplémentaires
-        
-        st.markdown("### 💵 Breakdown of Saved Amount")
-        st.markdown(f"""
-        - **Total Fraud Amounts**: ${total_fraud_amount:,.2f}
-        - **Estimated Additional Costs** (chargebacks, fees): ${additional_costs:,.2f}
-        - **Total Saved**: ${saved_amount:,.2f}
-        """)
-        
-        # Mini barchart
-        fig_breakdown = go.Figure(data=[
-            go.Bar(name='Fraud Amount', x=['Saved'], y=[total_fraud_amount], marker_color=COLOR_FRAUD),
-            go.Bar(name='Additional Costs', x=['Saved'], y=[additional_costs], marker_color=COLOR_SAVED)
-        ])
-        fig_breakdown.update_layout(
-            barmode='stack',
-            showlegend=True,
-            height=250,
-            yaxis_title="Amount ($)"
-        )
-        st.plotly_chart(fig_breakdown, use_container_width=True)
-    
-    # ========================== GRAPHIQUE EMPILÉ 7 JOURS ==========================
-    st.markdown("### 📊 Fraud Trend - Last 7 Days")
-    
-    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',
-            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")
+    # ... ton code existant pour le dashboard ...
+    pass
 
-# ========================== 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()
-    
-    with st.spinner("Chargement des fraudes..."):
-        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; 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:
-        # 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}")
+    # ... ton code existant pour la page fraudes ...
+    pass
 
-# ========================== 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()
-    
-    with st.spinner("Chargement des transactions légitimes..."):
-        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; 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:
-        # 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}")
+    # ... ton code existant pour la page non-fraudes ...
+    pass
+
+# ========================== 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()
 
 # ========================== NAVIGATION ==========================
 def main():
-    # Sidebar
     st.sidebar.title("Navigation")
     page = st.sidebar.radio(
         "Go to",
@@ -371,13 +125,8 @@ def main():
     **Données** : Dernières 24h pour les pages de détail.
     """)
     
-    # Router
-    if page == "🏠 Dashboard":
-        page_dashboard()
-    elif page == "🚨 Fraudes (24h)":
-        page_frauds()
-    elif page == "✅ Non Fraudes (24h)":
-        page_no_frauds()
+    # Router avec query param
+    open_page_from_query_param()
 
 if __name__ == "__main__":
-    main()
+    main()