Update src/module/Dashboard.py

src/module/Dashboard.py

@@ -45,28 +45,49 @@ class HospitalDashboard:
         return False
 
     def _get_kpis(self):
-        """Calcule les indicateurs MACRO pour le bandeau"""
-        last_date = pd.to_datetime(self.df_res['date']).max()
-        df_today = self.df_res[pd.to_datetime(self.df_res['date']) == last_date]
-        df_adm_today = self.df_adm[pd.to_datetime(self.df_adm['date']) == last_date]
+        """Calcul des KPIs globaux avec typage forcé"""
+        df = self.df_ressources
         
-        total_lits = df_today['lits_total'].sum()
-        total_occupes = df_today['lits_occupes'].sum()
-        taux_global = (total_occupes / total_lits * 100) if total_lits > 0 else 0
+        # --- FIX CRITIQUE : Conversion forcée avant calcul ---
+        # Même si l'ETL a tourné, on s'assure ici que ce sont des nombres
+        # pour éviter l'erreur TypeError: '>' not supported between instances of 'str' and 'int'
         
-        nb_adm = df_adm_today['nb_admissions'].sum()
-        nb_tension = df_today[df_today['taux_occupation'] > 0.85].shape[0]
+        if 'lits_occupes' in df.columns:
+            df['lits_occupes'] = pd.to_numeric(df['lits_occupes'], errors='coerce').fillna(0)
+            
+        if 'lits_total' in df.columns:
+            df['lits_total'] = pd.to_numeric(df['lits_total'], errors='coerce').fillna(0)
+            
+        if 'personnel_disponible' in df.columns:
+             df['personnel_disponible'] = pd.to_numeric(df['personnel_disponible'], errors='coerce').fillna(0)
+             
+        if 'personnel_requis' in df.columns:
+             df['personnel_requis'] = pd.to_numeric(df['personnel_requis'], errors='coerce').fillna(0)
+        # -----------------------------------------------------
+
+        # Calculs sécurisés
+        total_lits = df['lits_total'].sum()
+        total_occupes = df['lits_occupes'].sum()
         
-        last_date_stock = pd.to_datetime(self.df_stock['date']).max()
-        df_stock_today = self.df_stock[pd.to_datetime(self.df_stock['date']) == last_date_stock]
-        nb_ruptures = df_stock_today[df_stock_today['alerte_stock'] == 1].shape[0]
+        # Calcul du taux global (avec protection division par zéro)
+        if total_lits > 0:
+            taux_global = (total_occupes / total_lits) * 100
+        else:
+            taux_global = 0
+            
+        # Tension RH
+        total_staff_dispo = df['personnel_disponible'].sum()
+        total_staff_req = df['personnel_requis'].sum()
         
+        tension_rh = 0
+        if total_staff_req > 0:
+            tension_rh = (1 - (total_staff_dispo / total_staff_req)) * 100
+
         return {
-            'date': last_date,
+            'lits_total': int(total_lits),
+            'lits_occupes': int(total_occupes),
             'taux_global': taux_global,
-            'nb_adm': nb_adm,
-            'nb_tension': nb_tension,
-            'nb_ruptures': nb_ruptures
+            'tension_rh': tension_rh
         }
 
     def plot_main_chart(self):