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app.py

@@ -2,13 +2,12 @@
 import joblib
 import pandas as pd
 from flask import Flask, request, jsonify
-from utils.validation import validate_and_prepare_input, InputValidationError
 
 # Initialize Flask app with a name
 pred_mainteanance_api = Flask ("Engine Maintenance Predictor")
 
 # Load the trained churn prediction model
-model = joblib.load ("best_eng_fail_pred_model.joblib")
+model = joblib.load ("best_eng_fail_pred_model.joblib.joblib")
 
 # Define a route for the home page
 @pred_mainteanance_api.get ('/')
@@ -21,34 +20,32 @@ def predict_need_maintenance ():
     # Get JSON data from the request
     engine_sensor_inputs = request.get_json ()
 
+    import datetime
 
-    try:
-        input_json = request.get_json()
-        input_df = pd.DataFrame([input_json])
+    current_year = datetime.datetime.now ().year   # dynamic current year
 
-        validated_df = validate_and_prepare_input(input_df, model)
+    # Extract relevant features from the input data
+    data_info = {
+        'Engine_rpm'                : engine_sensor_inputs ['Engine_rpm'],
+        'Lub_oil_pressure'          : engine_sensor_inputs ['Lub_oil_pressure'],
+        'Fuel_pressure'             : engine_sensor_inputs ['Fuel_pressure'],
+        'Coolant_pressure'          : engine_sensor_inputs ['Coolant_pressure'],
+        'lub_oil_temp'              : engine_sensor_inputs ['lub_oil_temp'],
+        'Coolant_temp'              : engine_sensor_inputs ['Coolant_temp']
+    }
 
-        prediction = model.predict(validated_df)[0]
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame ([data_info])
 
-        return jsonify({
-            "status": "success",
-            "prediction": int(prediction)
-        })
+    # Enforce types - convert all to float
+    input_data = input_data.astype (float)
 
-    except InputValidationError as e:
-        return jsonify({
-            "status": "error",
-            "error_type": "validation_error",
-            "message": str(e)
-        }), 400
+    # Make prediction using the trained model
+    predicted_sales = model.predict (input_data).tolist ()[0]
+
+    # Return the prediction as a JSON response
+    return jsonify ({'NeedsMaintenance': predicted_sales})
 
-    except Exception as e:
-        return jsonify({
-            "status": "error",
-            "error_type": "internal_error",
-            "message": "Unexpected server error"
-        }), 500
-        
 
 # Run the Flask app
 if __name__ == "__main__":