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app.py

@@ -2,12 +2,13 @@
 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.joblib")
+model = joblib.load ("best_eng_fail_pred_model.joblib")
 
 # Define a route for the home page
 @pred_mainteanance_api.get ('/')
@@ -20,31 +21,35 @@ def predict_need_maintenance ():
     # Get JSON data from the request
     engine_sensor_inputs = request.get_json ()
 
-    import datetime
-
-    current_year = datetime.datetime.now ().year   # dynamic current year
-
-    # 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']
-    }
-
-    # Convert the extracted data into a DataFrame
-    input_data = pd.DataFrame ([data_info])
-
-    # Enforce types - convert all to float
-    input_data = input_data.astype (float)
-
-    # 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})
+    # validate request (json)
+    # if input is valid - return prediction
+    # in case of error - return appropriate error
+    try:
+        input_json = request.get_json()
+        input_df = pd.DataFrame([input_json])
+
+        validated_df = validate_and_prepare_input(input_df, model)
+
+        prediction = model.predict(validated_df)[0]
+
+        return jsonify({
+            "status": "success",
+            "prediction": int(prediction)
+        })
+
+    except InputValidationError as e:
+        return jsonify({
+            "status": "error",
+            "error_type": "validation_error",
+            "message": str(e)
+        }), 400
+
+    except Exception as e:
+        return jsonify({
+            "status": "error",
+            "error_type": "internal_error",
+            "message": "Unexpected server error"
+        }), 500
 
 
 # Run the Flask app

utils/__init__.py

@@ -0,0 +1 @@
+ 

utils/validation.py

@@ -1,3 +1,4 @@
+
 import pandas as pd
 
 
@@ -22,7 +23,7 @@ def validate_and_prepare_input(input_df: pd.DataFrame, model):
         raise InputValidationError("Unable to retrieve model feature names.")
 
     # -------------------------
-    # 1️⃣ Check missing columns
+    # 1 Check missing columns
     # -------------------------
     missing_cols = set(expected_features) - set(input_df.columns)
     if missing_cols:
@@ -31,7 +32,7 @@ def validate_and_prepare_input(input_df: pd.DataFrame, model):
         )
 
     # -------------------------
-    # 2️⃣ Check extra columns
+    # 2 Check extra columns
     # -------------------------
     extra_cols = set(input_df.columns) - set(expected_features)
     if extra_cols:
@@ -40,7 +41,7 @@ def validate_and_prepare_input(input_df: pd.DataFrame, model):
         )
 
     # -------------------------
-    # 3️⃣ Enforce numeric types
+    # 3 Enforce numeric types
     # -------------------------
     for col in expected_features:
         if not pd.api.types.is_numeric_dtype(input_df[col]):
@@ -49,8 +50,8 @@ def validate_and_prepare_input(input_df: pd.DataFrame, model):
             )
 
     # -------------------------
-    # 4️⃣ Reorder columns safely
+    # 4 Reorder columns safely
     # -------------------------
     input_df = input_df[expected_features]
 
-    return input_df
+    return input_df