Update app.py

app.py

@@ -70,7 +70,7 @@ class EnergyMLPredictor:
             color_mapping = {0: 'incolor', 1: 'verde', 2: 'cinza', 3: 'bronze'}
             cor_str = color_mapping.get(data['cor'], 'incolor')
             
-            # Create input features
+            # Create input features (with autoclave)
             input_data = {
                 'boosting': data['pot_boost'],
                 'espessura': data['espessura'],
@@ -79,13 +79,12 @@ class EnergyMLPredictor:
                 'cor': cor_str,
                 'prod_e': data['Prod_E'],
                 'prod_l': data['Prod_L'],
+                'autoclave': data.get('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
+                'week_of_year': date_obj.isocalendar()[1]
             }
             
             # Convert to DataFrame
@@ -171,8 +170,20 @@ class EnergyMLPredictor:
             for item in data:
                 # Parse input
                 date_obj = datetime.strptime(item['data'], '%Y-%m-%d')
-                boosting_val = float(item['boosting'].replace(',', '.'))
-                extracao_val = float(item['extracao_forno'].replace(',', '.'))
+                
+                # Handle different field name formats (boosting vs ext_boosting)
+                if 'boosting' in item:
+                    boosting_val = float(str(item['boosting']).replace(',', '.'))
+                elif 'ext_boosting' in item:
+                    boosting_val = float(str(item['ext_boosting']).replace(',', '.'))
+                else:
+                    boosting_val = 0.0
+                
+                # Handle extracao_forno field
+                if 'extracao_forno' in item:
+                    extracao_val = float(str(item['extracao_forno']).replace(',', '.'))
+                else:
+                    extracao_val = 800.0
                 
                 # Create features
                 input_data = {
@@ -181,8 +192,8 @@ class EnergyMLPredictor:
                     'extracao_forno': extracao_val,
                     'porcentagem_caco': item['porcentagem_caco'],
                     'cor': item['cor'].lower(),
-                    'prod_e': item.get('prod_e', 1),
-                    'prod_l': item.get('prod_l', 1),
+                    'prod_e': item.get('prod_e', item.get('Prod_E', 1)),
+                    'prod_l': item.get('prod_l', item.get('Prod_L', 1)),
                     'autoclave': item.get('autoclave', 1),
                     'week_day': date_obj.weekday(),
                     'month': date_obj.month,
@@ -225,8 +236,20 @@ class EnergyMLPredictor:
             for item in data:
                 # Parse input
                 date_obj = datetime.strptime(item['data'], '%Y-%m-%d')
-                boosting_val = float(item['boosting'].replace(',', '.'))
-                extracao_val = float(item['extracao_forno'].replace(',', '.'))
+                
+                # Handle different field name formats (boosting vs ext_boosting)
+                if 'boosting' in item:
+                    boosting_val = float(str(item['boosting']).replace(',', '.'))
+                elif 'ext_boosting' in item:
+                    boosting_val = float(str(item['ext_boosting']).replace(',', '.'))
+                else:
+                    boosting_val = 0.0
+                
+                # Handle extracao_forno field
+                if 'extracao_forno' in item:
+                    extracao_val = float(str(item['extracao_forno']).replace(',', '.'))
+                else:
+                    extracao_val = 800.0
                 
                 # Create features (match training: numerical + categorical + boolean, NO day_of_month/day_of_year)
                 input_data = {