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@@ -13,4 +13,4 @@ RUN pip install --no-cache-dir --upgrade -r requirements.txt
 # - `-w 4`: Uses 4 worker processes for handling requests
 # - `-b 0.0.0.0:7860`: Binds the server to port 7860 on all network interfaces
 # - `app:app`: Runs the Flask app (assuming `app.py` contains the Flask instance named `app`)
-CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:rental_price_predictor_api"]
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:store_sales_predictor_api"]

app.py

@@ -24,7 +24,7 @@ def home():
 def predict_store_sales():
     """
     This function handles POST requests to the '/v1/storeSales' endpoint.
-    It expects a JSON payload containing property details and returns
+    It expects a JSON payload containing product details and returns
     the predicted store sales as a JSON response.
     """
     # Get the JSON data from the request body
@@ -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
@@ -82,7 +82,7 @@ def predict_store_sales_batch():
     predicted_sales = [round(float(np.exp(store_sales)), 2) for log_price in predicted_store_sales]
 
     # Create a dictionary of predictions with store IDs as keys
-    store_ids = input_data['id'].tolist()  # Assuming 'id' is the property ID column
+    store_ids = input_data['id'].tolist()  # Assuming 'id' is the store ID column
     output_dict = dict(zip(store_ids, predicted_sales))  # Use actual sales
 
     # Return the predictions dictionary as a JSON response