Update app.py

app.py

@@ -1,3 +1,211 @@
-import trackio
+import streamlit as st
+import pandas as pd
+import numpy as np
+from prophet import Prophet
+import plotly.graph_objects as go
+from datetime import timedelta, datetime
+import os
+import math
+from sqlalchemy import create_engine, text
+import urllib.parse
+from streamlit_autorefresh import st_autorefresh
+import gc 
 
-trackio.show()
+# =====================================================
+# 1. CONFIGURATION & CONNEXION
+# =====================================================
+st.set_page_config(page_title="Master Planner - Perf & Plan", layout="wide")
+
+DB_USER = "balakibawi"
+DB_PASS = "M@tch47om_2026"
+DB_HOST = "10.228.11.110"
+DB_NAME = "wfm_reporting"
+
+@st.cache_resource
+def get_engine():
+    try:
+        safe_password = urllib.parse.quote_plus(DB_PASS)
+        return create_engine(f"mysql+pymysql://{DB_USER}:{safe_password}@{DB_HOST}/{DB_NAME}", connect_args={'connect_timeout': 5})
+    except:
+        return None
+
+# =====================================================
+# 2. FONCTIONS DE CALCUL (ERLANG & PRECISION)
+# =====================================================
+def apply_business_rules(row, acts):
+    h = row['ds'].hour + row['ds'].minute/60
+    d = row['ds'].weekday()
+    if h < 7 or h >= 21: return 0
+    if "PDV" in acts and d == 6: return 0
+    return max(0, row['yhat'])
+
+def erlang_c_besoin(calls, aht, interval_sec=1800, service_level=0.8, target_time=20):
+    calls = max(0, calls)
+    if calls <= 0 or aht <= 0: return 0
+    intensity = (calls * aht) / interval_sec
+    agents = math.ceil(intensity) + 1
+    
+    def get_service_level(n, intensity, aht, target_time):
+        try:
+            rho = intensity / n
+            if rho >= 1: return 0
+            c_part = math.exp(n * math.log(intensity) - (math.lgamma(n + 1) + math.log(1 - rho)))
+            sum_inv = sum([math.exp(i * math.log(intensity) - math.lgamma(i + 1)) for i in range(n)])
+            prob_attente = c_part / (sum_inv + c_part)
+            return 1 - (prob_attente * math.exp(-(n - intensity) * (target_time / aht)))
+        except: return 0
+
+    while agents < 500:
+        if get_service_level(agents, intensity, aht, target_time) >= service_level: break
+        agents += 1
+    return agents
+
+def calculer_precision_performance(reel, prev):
+    mask = (reel.notnull()) & (reel > 0)
+    if not mask.any(): return 0.0
+    erreur = np.sum(np.abs(reel[mask].values - prev[mask].clip(lower=0).values))
+    somme = np.sum(reel[mask].values)
+    return max(0, min(100, (1 - (erreur / somme)) * 100)) if somme > 0 else 0.0
+
+# =====================================================
+# 3. CHARGEMENT DES DONNÉES
+# =====================================================
+@st.cache_data(ttl=300) 
+def load_data_source(uploaded_file=None):
+    if uploaded_file is not None:
+        df = pd.read_csv(uploaded_file)
+    else:
+        path = "full_history.csv"
+        if os.path.exists(path):
+            df = pd.read_csv(path)
+        else:
+            return pd.DataFrame()
+            
+    df['ds'] = pd.to_datetime(df['ds'])
+    all_times = pd.date_range(start=df['ds'].min(), end=df['ds'].max(), freq='30min')
+    df_list = []
+    for act in df['activite'].unique():
+        temp = df[df['activite'] == act].set_index('ds').reindex(all_times).fillna(0).reset_index()
+        temp['activite'] = act
+        temp.rename(columns={'index': 'ds'}, inplace=True)
+        df_list.append(temp)
+    return pd.concat(df_list, ignore_index=True)
+
+# =====================================================
+# 4. MOTEUR DE PRÉVISION
+# =====================================================
+@st.cache_resource(ttl=3600)
+def train_and_forecast(_df, activities):
+    if _df.empty or not activities: return pd.DataFrame()
+    results = []
+    for act in activities:
+        gc.collect()
+        df_act = _df[_df['activite'] == act].tail(8000).copy()
+        
+        m_vol = Prophet(seasonality_mode='multiplicative', daily_seasonality=True, weekly_seasonality=True, uncertainty_samples=50)
+        m_vol.add_country_holidays(country_name='FR')
+        m_vol.fit(df_act[['ds', 'y']])
+        
+        m_aht = Prophet(daily_seasonality=True, weekly_seasonality=True, uncertainty_samples=50)
+        m_aht.fit(df_act[['ds', 'aht']].rename(columns={'aht': 'y'}))
+        
+        future = m_vol.make_future_dataframe(periods=48*14, freq="30min")
+        res_vol = m_vol.predict(future)[['ds', 'yhat']]
+        res_aht = m_aht.predict(future)[['ds', 'yhat']].rename(columns={'yhat': 'aht_hat'})
+        
+        res_act = res_vol.merge(res_aht, on='ds')
+        res_act['activite'] = act
+        results.append(res_act)
+    return pd.concat(results, ignore_index=True) if results else pd.DataFrame()
+
+# =====================================================
+# 5. UI PRINCIPALE
+# =====================================================
+st.sidebar.title("📊 Pilotage Perf & Plan")
+up_file = st.sidebar.file_uploader("Mettre à jour le CSV", type="csv")
+
+df_full = load_data_source(up_file)
+
+if df_full.empty:
+    st.info("Veuillez uploader un fichier 'full_history.csv' pour commencer.")
+    st.stop()
+
+mode = st.sidebar.selectbox("Vue", ["Rétrospective", "Planification Futur"])
+all_acts = sorted(df_full['activite'].unique().tolist())
+sel_act = st.sidebar.multiselect("Activités", options=all_acts, default=all_acts[:2])
+
+# --- NOUVEAU : SIMULATEUR DE SCÉNARIO ---
+st.sidebar.markdown("---")
+st.sidebar.header("🧪 Simulateur d'Impact")
+var_vol = st.sidebar.slider("Variation Volume (%)", -30, 50, 0)
+var_aht = st.sidebar.slider("Variation DMT (%)", -20, 30, 0)
+
+if not sel_act:
+    st.warning("Sélectionnez une activité.")
+    st.stop()
+
+with st.spinner("Calcul des prévisions..."):
+    fc_all = train_and_forecast(df_full[df_full['activite'].isin(sel_act)], sel_act)
+
+if not fc_all.empty:
+    fc_all['yhat'] = fc_all.apply(lambda r: apply_business_rules(r, [r['activite']]), axis=1)
+    fc_all['work_load_pred'] = fc_all['yhat'] * fc_all['aht_hat']
+    
+    df_filtered = df_full[df_full['activite'].isin(sel_act)][['ds', 'activite', 'y']]
+    hist = fc_all.merge(df_filtered, on=['ds', 'activite'], how='left')
+    
+    hist_agg = hist.groupby('ds').agg({'y': 'sum', 'yhat': 'sum', 'work_load_pred': 'sum'}).reset_index()
+    hist_agg['aht_hat'] = (hist_agg['work_load_pred'] / hist_agg['yhat']).fillna(180)
+    
+    d_min, d_max = hist_agg['ds'].min().date(), hist_agg['ds'].max().date()
+    sel_range = st.sidebar.date_input("Période", value=(d_min, d_max))
+    
+    if len(sel_range) == 2:
+        mask = (hist_agg['ds'].dt.date >= sel_range[0]) & (hist_agg['ds'].dt.date <= sel_range[1])
+        view = hist_agg[mask].copy()
+        
+        # --- APPLICATION SIMULATION ---
+        view['yhat_sim'] = view['yhat'] * (1 + var_vol/100)
+        view['aht_sim'] = view['aht_hat'] * (1 + var_aht/100)
+        
+        # Calcul besoins (Base vs Simulé)
+        view['besoin_agents'] = view.apply(lambda r: erlang_c_besoin(r['yhat'], r['aht_hat']), axis=1)
+        view['besoin_sim'] = view.apply(lambda r: erlang_c_besoin(r['yhat_sim'], r['aht_sim']), axis=1)
+        
+        st.title(f"🚀 Master Planner - {mode}")
+        
+        # Métriques
+        c1, c2, c3, c4 = st.columns(4)
+        c1.metric("🎯 Précision", f"{calculer_precision_performance(view['y'], view['yhat']):.1f}%")
+        c2.metric("📈 Volume Prévu", f"{int(view['yhat_sim'].sum()):,}", delta=f"{var_vol}%" if var_vol!=0 else None)
+        
+        staff_base = math.ceil(view[view['besoin_agents']>0]['besoin_agents'].mean())
+        staff_sim = math.ceil(view[view['besoin_sim']>0]['besoin_sim'].mean())
+        c3.metric("👥 Staff Requis", f"{staff_sim} agents", delta=f"{staff_sim - staff_base} agents" if staff_sim != staff_base else None)
+        
+        dmt_moy = int(view['aht_sim'].mean())
+        c4.metric("⏱️ DMT (moy)", f"{dmt_moy}s", delta=f"{var_aht}%" if var_aht!=0 else None)
+
+        # Graphique
+        fig = go.Figure()
+        if mode == "Rétrospective":
+            fig.add_trace(go.Scatter(x=view['ds'], y=view['y'], name="RÉEL", line=dict(color="#1f77b4", width=3)))
+        
+        fig.add_trace(go.Scatter(x=view['ds'], y=view['yhat_sim'], name="PRÉVISION (Simulée)", line=dict(color="#ff7f0e", dash='dot')))
+        
+        fig.update_layout(title="Courbe de Charge (Workload)", hovermode="x unified")
+        st.plotly_chart(fig, use_container_width=True)
+        
+        # Tableau de bord Staffing
+        with st.expander("📅 Détails de la planification"):
+            st.dataframe(view[['ds', 'y', 'yhat_sim', 'aht_sim', 'besoin_sim']].rename(columns={
+                'ds': 'Intervalle', 'y': 'Réel', 'yhat_sim': 'Prévu', 'aht_sim': 'DMT', 'besoin_sim': 'Agents Requis'
+            }), use_container_width=True)
+
+        st.download_button("📥 Exporter le plan de charge", view.to_csv(index=False), "planification.csv", "text/csv")
+
+# =====================================================
+# 9. FOOTER
+# =====================================================
+st.sidebar.markdown("---")
+st.sidebar.caption(f"Propulsé par Prophet & Erlang-C | MatchAtom 2026")