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app.py

@@ -2,46 +2,44 @@ import joblib
 import pandas as pd
 from flask import Flask, request, jsonify
 
-# Initialize Flask app with a name
+# Initialize Flask app
 sales_predictor_api = Flask("Product_Store_Total_Sales_Predictor")
 
-# Load the trained Product Store Total Sales prediction model
+# Load the trained model
 model = joblib.load("product_sales_total_prediction_v1_0.joblib")
 
-# Define a route for the home page
+# Home endpoint
 @sales_predictor_api.get('/')
 def home():
     return "Welcome to the Product Store Total Sales Prediction API!"
 
-# Define an endpoint to predict product sales for a specific store
+# Prediction endpoint
 @sales_predictor_api.post('/v1/product')
 def predict_product_sales():
-    # Get JSON data from the request
-    product_data = request.get_json()
-
-    # Extract relevant product features from the input data
-    sample = {
-        'Product_Weight': product_data['Product_Weight'],
-        'Product_Sugar_Content': product_data['Product_Sugar_Content'],
-        'Product_Allocated_Area': product_data['Product_Allocated_Area'],
-        'Product_Type': product_data['Product_Type'],
-        'Product_MRP': product_data['Product_MRP'],
-        'Store_Establishment_Year': product_data['Store_Establishment_Year'],
-        'Store_Size': product_data['Store_Size'].map(size_map),
-        'Store_Location_City_Type': product_data['Store_Location_City_Type'],
-        'Store_Type': product_data['Store_Type'],
-        'Store_Age': 2025 - product_data['Store_Establishment_Year']
-    }
-
-    # Convert the extracted data into a DataFrame
-    input_data = pd.DataFrame([sample])
-
-    # Make a churn prediction using the trained model
-    prediction = model.predict(input_data).tolist()[0]
-
-    # Return the prediction as a JSON response
-    return jsonify({'Product Store Sales Total': prediction})
-
-# Run the Flask app in debug mode
+    try:
+        product_data = request.get_json()
+
+        # Construct input DataFrame
+        input_data = pd.DataFrame([{
+            'Product_Weight': product_data['Product_Weight'],
+            'Product_Sugar_Content': product_data['Product_Sugar_Content'],
+            'Product_Allocated_Area': product_data['Product_Allocated_Area'],
+            'Product_Type': product_data['Product_Type'],
+            'Product_MRP': product_data['Product_MRP'],
+            'Store_Establishment_Year': product_data['Store_Establishment_Year'],
+            'Store_Size': product_data['Store_Size'],
+            'Store_Location_City_Type': product_data['Store_Location_City_Type'],
+            'Store_Type': product_data['Store_Type'],
+            'Store_Age': 2025 - product_data['Store_Establishment_Year']
+        }])
+
+        # Predict
+        prediction = model.predict(input_data).tolist()[0]
+        return jsonify({'Product Store Sales Total': prediction})
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+# Run the app
 if __name__ == '__main__':
-    app.run(debug=True)
+    sales_predictor_api.run(debug=True)