Update app.py

app.py

@@ -1,9 +1,7 @@
 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 ==========================
@@ -55,7 +53,8 @@ def load_all_data():
 def load_last_24h_data():
     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
@@ -68,9 +67,10 @@ def load_last_24h_data():
 def load_last_7_days_stats():
     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)
@@ -80,18 +80,149 @@ def load_last_7_days_stats():
         df = pd.read_sql(query, conn)
     return df
 
-# ========================== PAGES ==========================
+# ========================== PAGE: DASHBOARD ==========================
 def page_dashboard():
-    # ... ton code existant pour le dashboard ...
-    pass
+    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
+    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;">🚨 Frauds</h3>
+            <h1 style="color: white;">{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>
+        </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>
+        </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:
+        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:
+        total_fraud_amount = df_all[df_all['pred_is_fraud'] == 1]['amt'].sum()
+        additional_costs = total_fraud_amount * 0.5
+        
+        st.markdown("### 💵 Breakdown of Saved Amount")
+        st.markdown(f"""
+        - **Total Fraud Amounts**: ${total_fraud_amount:,.2f}
+        - **Estimated Additional Costs**: ${additional_costs:,.2f}
+        - **Total Saved**: ${saved_amount:,.2f}
+        """)
+        
+        fig_breakdown = go.Figure(data=[
+            go.Bar(name='Fraud Amount', x=['Saved'], y=[total_fraud_amount], marker_color=COLOR_FRAUD),
+            go.Bar(name='Additional Costs', x=['Saved'], y=[additional_costs], marker_color=COLOR_SAVED)
+        ])
+        fig_breakdown.update_layout(barmode='stack', height=250, yaxis_title="Amount ($)")
+        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', height=400, 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) ==========================
 def page_frauds():
-    # ... ton code existant pour la page fraudes ...
-    pass
+    st.title("🚨 Fraudes Détectées (Dernières 24h)")
+    
+    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]
+    
+    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>
+    """, 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)
 
+# ========================== PAGE: NON FRAUDES (24h) ==========================
 def page_no_frauds():
-    # ... ton code existant pour la page non-fraudes ...
-    pass
+    st.title("✅ Transactions Légitimes (Dernières 24h)")
+    
+    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]
+    
+    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>
+    """, 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
@@ -121,7 +252,6 @@ 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.
     """)