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app.py

@@ -8,88 +8,54 @@ import traceback
 import numpy as np
 import os
 
-# --- Configuration ---
-MODEL_PATH = "final_xgboost_pipeline.pkl"
 
-# --- Initialize Flask App ---
-app = Flask("SuperKart Sales Predictor")
+# Initialize the Flask application
+superKart_sales_predictor_api = Flask("SuperKart Sales Predictor")
 
-# Load the model
-try:
-    model = joblib.load(MODEL_PATH)
-    print("Model loaded successfully.") # Add logging
-except Exception as e:
-    print(f"Error loading model: {e}") # Log error
+# Load the trained machine learning model
+model = joblib.load("final_xgboost_pipeline.joblib")
 
 # Define a route for the home page
-@app.get('/')
+@superKart_sales_predictor_api.get('/')
 def home():
     print("Home route accessed.") # Add logging
     return "Welcome to the SuperKart Store Product Sales Prediction API."
 
 
-@app.route("/predict", methods=['POST']) # The simple, unversioned route
+@superKart_sales_predictor_api.post("/predict") # The simple, unversioned route
 def predict_sales():
     """
     Receives product and store features, makes a sales prediction, and returns the result.
     """
 
-    if model is None:
-        return jsonify({'error': 'Prediction service unavailable. Model failed to load.'}), 503
-
-    try:
-        # 1. REMOVE force=True to enforce correct MIME type header
-        data = request.get_json() 
-        print(f"Received prediction request with data: {data}")
-
-        if not data:
-            return jsonify({'error': 'Invalid or empty JSON data provided. Ensure Content-Type is application/json.'}), 400
-
-        # --- Data Transformation ---
-        try:
-            # Create a one-row DataFrame
-            input_df = pd.DataFrame([data])
-            
-            # 2. REMOVE MANUAL TYPE CONVERSION
-            # A well-built ML pipeline should handle all feature transformations internally.
-            # input_df['Store_Establishment_Year'] = input_df['Store_Establishment_Year'].astype(str)
-            
-            # output variable removed as it was only used for the previous error
-            
-        except KeyError as ke:
-            return jsonify({'error': f'Missing required feature: {ke}. Please check all features are present.'}), 400
-
-        except Exception as e:
-            return jsonify({'error': 'Error processing input data format', 'details': str(e)}), 400
-
-
-        # --- Prediction ---
-        prediction = model.predict(input_df)[0]
-
-        # Convert NumPy type to standard Python float for JSON serialization
-        if isinstance(prediction, (np.float32, np.float64)):
-            predicted_sales = float(prediction)
-        else:
-            predicted_sales = prediction # Should ideally be a float
-
-        print(f"Prediction result: {predicted_sales:.2f}")
-
-        # --- Response ---
-        # 3. FIX: Return the actual predicted value instead of the string message
-        return jsonify({
-            'status': 'success',
-            'predicted_sales': predicted_sales # Renamed key to 'predicted_sales' for clarity
-        })
-
-    except Exception as e:
-        # Catch any unexpected runtime errors
-        print(f"An unexpected internal error occurred: {e}")
-        print(traceback.format_exc())
-        return jsonify({
-            'error': 'Internal Server Error during prediction',
-            'details': str(e)
-        }), 500
-
+    # Get the JSON data from the request body
+    input_data = request.get_json()
+
+    sample = {
+        'product_weight': input_data['Product_Weight'],
+        'product_sugar_content': input_data['Product_Sugar_Content'],
+        'product_allocated_area': input_data['Product_Allocated_Area'],
+        'product_type': input_data['Product_Type'],
+        'product_quantity': input_data[ 'Product_Quantity'],
+        'product_mrp': input_data['Product_Quantity'],
+        'store_establishment_year': input_data['Store_Establishment_Year'],
+        'store_size': input_data['Store_Size'],
+        'store_location_city_type': input_data['Store_Location_City_Type'],
+        'store_type': input_data['Store_Type']
+    }
+
+    # Convert the extracted data into a Pandas DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make prediction (get log_price)
+    predicted_sales = model.predict(input_data)[0]
+
+    # Convert predicted_price to Python float
+    predicted_sales = round(float(predicted_sales), 2)
+
+    # Return the actual price
+    return jsonify({'Predicted Sales (in dollars)': predicted_sales})
+    
 
 # --- Local Runner (Optional: Comment out for production WSGI) ---
 if __name__ == '__main__':

final_xgboost_pipeline.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d668d13238361ffedd8a48008caebc7f6ee763f4ad8b2d1c81925e7b26c4c0fd
+size 4439513