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app.py

@@ -8,7 +8,7 @@ from flask import Flask, request, jsonify  # For creating the Flask API
 store_sales_predictor_api = Flask("SuperKart Store Sales Predictor")
 
 # Load the trained machine learning model
-model = joblib.load("storel_sales_prediction_model_v1_0.joblib")
+model = joblib.load("store_sales_prediction_model_v1_0.joblib")
 
 # Define a route for the home page (GET request)
 @store_sales_predictor_api.get('/')
@@ -32,15 +32,15 @@ def predict_store_sales():
 
     # Extract relevant features from the JSON data
     sample = {
-        'Product_Weight': sales_data['Product_Weight'],
-        'Product_Sugar_Content': sales_data['Product_Sugar_Content'],
-        'Product_Allocated_Area': sales_data['Product_Allocated_Area'],
-        'Product_Type': sales_data['Product_Type'],
-        'Product_MRP': sales_data['Product_MRP'],
-        'Store_Id': sales_data['Store_Id'],
-        'Store_Size': sales_data['Store_Size'],
-        'Store_Location_City_Type': sales_data['Store_Location_City_Type'],
-        'Store_Type': sales_data['Store_Type']
+        'product_weight': sales_data['Product_Weight'],
+        'product_sugar_content': sales_data['Product_Sugar_Content'],
+        'product_allocated_area': sales_data['Product_Allocated_Area'],
+        'product_type': sales_data['Product_Type'],
+        'product_mrp': sales_data['Product_MRP'],
+        'store_id': sales_data['Store_Id'],
+        'store_size': sales_data['Store_Size'],
+        'store_location_city_type': sales_data['Store_Location_City_Type'],
+        'store_type': sales_data['Store_Type']
     }
 
     # Convert the extracted data into a Pandas DataFrame