Update app.py

app.py

@@ -94,11 +94,27 @@ class EnergyMLPredictor:
             # Preprocess
             X_processed = self.threshold_preprocessor.transform(input_df)
             
-            # Make predictions
-            prob_83 = self.threshold_model_83.predict_proba(X_processed)[0][1] if len(self.threshold_model_83.classes_) > 1 else 0.0
+            # Make predictions with error handling
+            try:
+                prob_83_raw = self.threshold_model_83.predict_proba(X_processed)
+                prob_83 = prob_83_raw[0][1] if len(prob_83_raw[0]) > 1 else prob_83_raw[0][0]
+                # Ensure probability is between 0 and 1
+                prob_83 = max(0.0, min(1.0, float(prob_83)))
+            except Exception as e:
+                print(f"Error with threshold_83 prediction: {e}")
+                prob_83 = 0.0
+            
             pred_83 = int(prob_83 > 0.5)
             
-            prob_90 = self.threshold_model_90.predict_proba(X_processed)[0][1] if len(self.threshold_model_90.classes_) > 1 else 0.0
+            try:
+                prob_90_raw = self.threshold_model_90.predict_proba(X_processed)
+                prob_90 = prob_90_raw[0][1] if len(prob_90_raw[0]) > 1 else prob_90_raw[0][0]
+                # Ensure probability is between 0 and 1
+                prob_90 = max(0.0, min(1.0, float(prob_90)))
+            except Exception as e:
+                print(f"Error with threshold_90 prediction: {e}")
+                prob_90 = 0.0
+            
             pred_90 = int(prob_90 > 0.5)
             
             # Format response
@@ -212,23 +228,21 @@ class EnergyMLPredictor:
                 boosting_val = float(item['boosting'].replace(',', '.'))
                 extracao_val = float(item['extracao_forno'].replace(',', '.'))
                 
-                # Create features
+                # Create features (match training: numerical + categorical + boolean, NO day_of_month/day_of_year)
                 input_data = {
                     'boosting': boosting_val,
                     'espessura': item['espessura'],
                     'extracao_forno': extracao_val,
                     'porcentagem_caco': item['porcentagem_caco'],
                     'cor': item['cor'].lower(),
-                    'prod_e': 1,
-                    'prod_l': 1,
-                    'autoclave': 1,
                     'week_day': date_obj.weekday(),
                     'month': date_obj.month,
                     'quarter': (date_obj.month - 1) // 3 + 1,
-                    'is_weekend': int(date_obj.weekday() >= 5),
                     'week_of_year': date_obj.isocalendar()[1],
-                    'day_of_month': date_obj.day,
-                    'day_of_year': date_obj.timetuple().tm_yday
+                    'prod_e': 1,
+                    'prod_l': 1,
+                    'is_weekend': int(date_obj.weekday() >= 5),
+                    'autoclave': 1
                 }
                 
                 # Encode categorical features