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app.py

@@ -4,6 +4,10 @@ import joblib  # For loading the serialized model
 import pandas as pd  # For data manipulation
 from flask import Flask, request, jsonify  # For creating the Flask API
 import os # For accessing environment variables if needed
+import logging # For logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
 # Initialize the Flask application
 predictive_maintenance_api = Flask("Predictive Maintenance API")
@@ -13,12 +17,12 @@ predictive_maintenance_api = Flask("Predictive Maintenance API")
 MODEL_PATH = "backend_files/Predictive_Maintenance_XGB_Tuned_model_v1_0.joblib"
 
 # Load the trained machine learning model
+model = None
 try:
     model = joblib.load(MODEL_PATH)
-    print(f"Model loaded successfully from {MODEL_PATH}")
+    logging.info(f"Model loaded successfully from {MODEL_PATH}")
 except Exception as e:
-    print(f"Error loading model: {e}")
-    model = None # Set model to None if loading fails
+    logging.error(f"Error loading model: {e}")
 
 # Define a route for the home page (GET request)
 @predictive_maintenance_api.get('/')
@@ -27,6 +31,7 @@ def home():
     This function handles GET requests to the root URL ('/') of the API.
     It returns a simple welcome message.
     """
+    logging.info("Home page accessed.")
     return "Welcome to the Predictive Maintenance API for Engine Health!"
 
 # Define an endpoint for engine condition prediction (POST request)
@@ -37,11 +42,14 @@ def predict_engine_condition():
     It expects a JSON payload containing engine sensor data and returns
     the predicted engine condition (0 for Normal, 1 for Faulty) as a JSON response.
     """
+    logging.info("Prediction request received.")
     if model is None:
+        logging.error("Model not loaded when prediction request came. Returning 500.")
         return jsonify({"error": "Model not loaded. Please check server logs."}), 500
 
     # Get the JSON data from the request body
     engine_data = request.get_json()
+    logging.info(f"Received engine data: {engine_data}")
 
     # Extract relevant features from the JSON data
     # Features: 'Engine_RPM', 'Lub_Oil_Pressure', 'Fuel_Pressure', 'Coolant_Pressure', 'Lub_Oil_Temperature', 'Coolant_Temperature'
@@ -54,18 +62,27 @@ def predict_engine_condition():
             'Lub_Oil_Temperature': [engine_data['Lub_Oil_Temperature']],
             'Coolant_Temperature': [engine_data['Coolant_Temperature']]
         }
+        logging.debug(f"Extracted sample data: {sample_data}")
     except KeyError as e:
+        logging.error(f"Missing data for feature: {e}. Returning 400.")
         return jsonify({"error": f"Missing data for feature: {e}. Please provide all required sensor readings."}), 400
+    except Exception as e:
+        logging.error(f"Unexpected error during data extraction: {e}. Returning 400.")
+        return jsonify({"error": f"An unexpected error occurred during data processing: {e}"}), 400
+
 
     # Convert the extracted data into a Pandas DataFrame
     # Ensure the order of columns matches the training data
     input_df = pd.DataFrame(sample_data)
 
     # Make prediction
-    # model.predict_proba will give probabilities for both classes [prob_class_0, prob_class_1]
-    # model.predict will give the predicted class (0 or 1)
-    prediction_proba = model.predict_proba(input_df)
-    predicted_class = model.predict(input_df)[0]
+    try:
+        prediction_proba = model.predict_proba(input_df)
+        predicted_class = model.predict(input_df)[0]
+        logging.info(f"Prediction made: class={predicted_class}, probabilities={prediction_proba}")
+    except Exception as e:
+        logging.error(f"Error during model prediction: {e}. Returning 500.")
+        return jsonify({"error": f"Error during model prediction: {e}"}), 500
 
     # Map the predicted class to a readable string
     condition_map = {0: "Normal", 1: "Faulty"}