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app.py

@@ -19,13 +19,13 @@ def home():
     """
     return "Welcome to the SuperKart Sales Prediction API!"
 
-# Define an endpoint for single product prediction (POST request)
-@superkart_sales_predictor_api.post('/v1/superkart')
-def predict_sales_price():
+# Define an endpoint for single property prediction (POST request)
+@superkart_sales_predictor_api.post('/v1/sales')
+def predict_superkart_sales():
     """
-    This function handles POST requests to the '/v1/superkart' endpoint.
-    It expects a JSON payload containing product details and returns
-    the predicted superkart sales as a JSON response.
+    This function handles POST requests to the '/v1/sales' endpoint.
+    It expects a JSON payload containing property details and returns
+    the predicted rental price as a JSON response.
     """
     # Get the JSON data from the request body
     product_data = request.get_json()
@@ -34,12 +34,13 @@ def predict_sales_price():
     sample = {
         'Product_Id': product_data['Product_Id'],
         'Product_Weight': product_data['Product_Weight'],
-        'Product_Allocated_Area': product_data['Product_Allocated_Area'],
-        'Product_MRP': product_data[''],
-        'Store_Establishment_Year': product_data['Store_Establishment_Year'],
         '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_Id': product_data['Store_Id'],
+        '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']
     }
@@ -47,23 +48,23 @@ def predict_sales_price():
     # Convert the extracted data into a Pandas DataFrame
     input_data = pd.DataFrame([sample])
 
-    # Make prediction
+    # Make prediction (get log_price)
     predicted_sales = model.predict(input_data)[0]
 
     # Convert predicted_price to Python float
-    # predicted_sales = round(float(predicted_sales), 2)
+    predicted_rounded_sales = round(float(predicted_sales), 2)
 
     # Return the actual price
-    return jsonify({'Predicted Price (in dollars)': predicted_sales})
+    return jsonify({'Predicted Sales (in dollars)': predicted_rounded_sales})
 
 
 # Define an endpoint for batch prediction (POST request)
-@superkart_sales_predictor_api.post('/v1/superkartbatch')
+@superkart_sales_predictor_api.post('/v1/salesbatch')
 def predict_superkart_sales_batch():
     """
-    This function handles POST requests to the '/v1/superkartbatch' endpoint.
-    It expects a CSV file containing product details for multiple properties
-    and returns the predicted superkart sales as a dictionary in the JSON response.
+    This function handles POST requests to the '/v1/rentalbatch' endpoint.
+    It expects a CSV file containing property details for multiple properties
+    and returns the predicted rental prices as a dictionary in the JSON response.
     """
     # Get the uploaded CSV file from the request
     file = request.files['file']
@@ -74,12 +75,12 @@ def predict_superkart_sales_batch():
     # Make predictions for all properties in the DataFrame (get log_prices)
     predicted_sales = model.predict(input_data).tolist()
 
-    # Calculate actual prices
-    # predicted_sales = [round(float(sales), 2) for sales in predicted_sales]
+    # Calculate predicted sales
+    predicted_rounded_sales = [round(float(sales), 2) for sales in predicted_sales]
 
     # Create a dictionary of predictions with product IDs as keys
-    product_ids = input_data['id'].tolist()  # Assuming 'id' is the product ID column
-    output_dict = dict(zip(product_ids, predicted_sales))  # Use actual prices
+    product_ids = input_data['Product_Id'].tolist()  
+    output_dict = dict(zip(product_ids, predicted_rounded_sales))  # Use actual prices
 
     # Return the predictions dictionary as a JSON response
     return output_dict

requirements.txt

@@ -1,11 +1,11 @@
-pandas==2.2.2
-numpy==2.0.2
-scikit-learn==1.6.1
-xgboost==2.1.4
-joblib==1.4.2
+pandas==2.2.2 
+numpy==2.0.2 
+scikit-learn==1.6.1 
+xgboost==2.1.4 
+joblib==1.4.2 
 Werkzeug==2.2.2
 flask==2.2.2
 gunicorn==20.1.0
-requests==2.28.1
+requests==2.32.3 
 uvicorn[standard]
 streamlit==1.43.2