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app.py

@@ -5,7 +5,7 @@ import pandas as pd  # For data manipulation
 from flask import Flask, request, jsonify  # For creating the Flask API
 
 # Initialize the Flask application
-superkart_sales_forecast_api = Flask("SuperKart Sales Forecast Backend V1")
+superkart_sales_forecast_api = Flask("SuperKart Sales Forecast Backend")
 
 # Load the trained machine learning model
 model = joblib.load("SuperKart_Sales_Forecast_v1_0.joblib")
@@ -17,7 +17,7 @@ def home():
     This function handles GET requests to the root URL ('/') of the API.
     It returns a simple welcome message.
     """
-    return "Welcome to the SuperKart Sales Forecast API V1!"
+    return "Welcome to the SuperKart Sales Forecast API!"
 
 # Define an endpoint for single property prediction (POST request)
 @superkart_sales_forecast_api.post('/v1/sales')
@@ -32,16 +32,18 @@ def predict_sales():
 
     # 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_establishment_year': property_data['store_establishment_year'],
-        '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_Establishment_Year': property_data['store_establishment_year'],
+            '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
     input_data = pd.DataFrame([sample])
@@ -87,4 +89,4 @@ def predict_sales_batch():
 
 # Run the Flask application in debug mode if this script is executed directly
 if __name__ == '__main__':
-    superkart_sales_forecast_api.run(debug=True)
+    rental_price_predictor_api.run(debug=True)