Update app.py

app.py

@@ -62,11 +62,27 @@ def predict_need_maintenance_for_batch ():
     # if input is valid - return prediction
     # in case of error - return appropriate error
     try:
-        input_json = request.get_json()
-
-        # Convert JSON list → DataFrame
-        input_df = pd.DataFrame([input_json])
-
+        # Get the uploaded CSV file from the request
+        file = request.files ['file']
+    
+        # Read the file into a DataFrame
+        input_df = pd.read_csv (file)
+
+        # Process the data to clean up and make it ready for prediction
+        # mostly we will use the file with same format as given in problem statement for batch prediction
+        
+        # remove/drop engine condition column if present
+        input_df.drop(columns=['Engine Condition'], inplace=True, errors='ignore')
+
+        # update column names to replace space with _ (underscore)
+        input_df.columns = input_df.columns.str.replace(' ', '_')
+
+        # get all integer columns
+        int_columns = input_df.select_dtypes(include=['int64']).columns
+        
+        # convert integer columns to float
+        int_columns[int_columns] = int_columns[int_columns].astype('float64')
+        
         # Validate entire batch
         validated_df = validate_and_prepare_input(input_df, model)
 
@@ -80,7 +96,7 @@ def predict_need_maintenance_for_batch ():
                 "status": "success",                 # overall batch status
                 "total_records": len(prediction_list),
                 "predictions": prediction_list,     # simple list version
-            })        
+            })      
 
     except InputValidationError as e:
         return jsonify({