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app.py

@@ -33,16 +33,16 @@ def predict_rental_price():
 
     # Extract relevant features from the JSON data
     sample = {
-        'Product_Weight': property_data['Product_Weight'],
-        'Product_Sugar_Content': property_data['Product_Sugar_Content'],
-        'Product_Allocated_Area': property_data['Product_Allocated_Area'],
-        'Product_Type': property_data['Product_Type'],
-        'Product_MRP': property_data['Product_MRP'],
-        'Store_Id': property_data['Store_Id'],
-        'Store_Age': property_data['Store_Age'],
-        'Store_Size': property_data['Store_Size'],
-        'Store_Location_City_Type': property_data['Store_Location_City_Type'],
-        'Store_Type': property_data['Store_Type']
+        'Product_Weight': property_data['product_weight'],
+        'Product_Sugar_Content': property_data['product_sugar_content'],
+        'Product_Allocated_Area': property_data['product_allocated_area'],
+        'Product_Type': property_data['product_type'],
+        'Product_MRP': property_data['product_mrp'],
+        'Store_Id': property_data['store_id'],
+        'Store_Age': property_data['store_age'],
+        'Store_Size': property_data['store_size'],
+        'Store_Location_City_Type': property_data['store_location_city_type'],
+        'Store_Type': property_data['store_type']
     }
 
     # Convert the extracted data into a Pandas DataFrame
@@ -55,7 +55,7 @@ def predict_rental_price():
     predicted_price = round(float(predicted_price), 2)
     return jsonify({'Predicted Revenue (in dollars)': predicted_price})
 
-'''
+
 # Define an endpoint for batch prediction (POST request)
 @rental_price_predictor_api.post('/v1/rentalbatch')
 def predict_rental_price_batch():
@@ -88,7 +88,7 @@ def predict_rental_price_batch():
 
     # Return the predictions dictionary as a JSON response
     return jsonify(output_dict)
-'''
+
 # Run the Flask application in debug mode if this script is executed directly
 if __name__ == '__main__':
     rental_price_predictor_api.run(debug=True)