Refactor prediction logic in historis view; prepare data for model prediction with renamed columns and improved error handling

app.py

@@ -220,12 +220,18 @@ def get_historis_view():
         df['Tanggal'] = pd.to_datetime(df['Tanggal'], format='%d/%m/%Y', errors='coerce')
         df = df.dropna(subset=['Tanggal']).sort_values('Tanggal')
         
-        # Predict using model
-        try:
-            predictions = model_mgr.predict_batch(df)
-            df['ketebalan_prediksi'] = predictions
-        except:
-            df['ketebalan_prediksi'] = df['Ketebalan BTA (mm)']
+        # Prepare data for model prediction
+        # Rename columns to match model expectations
+        df_model = pd.DataFrame({
+            'cone_depan': df['Cone Depan (°C)'],
+            'bodi_tengah': df['Bodi Tengah (°C)'],
+            'cone_belakang': df['Cone Belakang (°C)'],
+            'suhu_avg': df['average']
+        })
+        
+        # Get predictions from actual model
+        predictions = model_mgr.predict_batch(df_model)
+        df['ketebalan_prediksi'] = predictions
         
         # Create display table
         df_display = df[['Tanggal', 'average', 'ketebalan_prediksi', 'Ketebalan BTA (mm)']].copy()