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app.py

@@ -28,11 +28,11 @@ def predict_sales():
     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
-business_data = request.get_json()
+    # Get the JSON data from the request body
+    business_data = request.get_json()
 
-# Extract relevant features from the JSON data
-sample = {
+    # Extract relevant features from the JSON data
+    sample = {
       'Product_Weight': business_data['Product_Weight'] ,
       'Product_Sugar_Content': business_data['Product_Sugar_Content'],
       'Product_Allocated_Area': business_data['Product_Allocated_Area'],
@@ -44,17 +44,17 @@ sample = {
       'Store_Type': business_data['Store_Type']
   }
   
-# Convert the extracted data into a Pandas DataFrame
-input_data = pd.DataFrame([business_data])
+    # Convert the extracted data into a Pandas DataFrame
+    input_data = pd.DataFrame([business_data])
 
-# Make a sales prediction using the sales model
-predicted_sales = model.predict(input_data)[0]
+    # Make a sales prediction using the sales model
+    predicted_sales = model.predict(input_data)[0]
 
-# Convert predicted_price to Python float
-predicted_sales = float(predicted_sales)
+    # Convert predicted_price to Python float
+    predicted_sales = float(predicted_sales)
 
-# Return the actual sales
-return jsonify({'Predicted sales': predicted_sales})
+    # Return the actual sales
+    return jsonify({'Predicted sales': predicted_sales})
 
 if __name__ == "__main__":
     sales_prediction_api.run(debug=True)
@@ -62,27 +62,26 @@ if __name__ == "__main__":
 # Define an endpoint for batch prediction (POST request)
 @sales_prediction_api.post("/v1/predict/batch")
 def predict_sales_batch():
-    """
-    This function handles POST requests to the '/v1/predict/batch' 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']
-
-    # Read the CSV file into a Pandas DataFrame
-    input_data = pd.read_csv(file)
-
-    # Make predictions for all properties in the DataFrame (get log_prices)
-    predicted_log_sales = model.predict(input_data).tolist()
+  """
+  This function handles POST requests to the '/v1/predict/batch' 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']
+  
+  # Read the CSV file into a Pandas DataFrame
+  input_data = pd.read_csv(file)
 
-    # Create a dictionary of predictions with property IDs as keys
-    Store_Type = input_data['Store_Type'].tolist() 
-    output_dict = dict(zip(Store_Type, predicted_sales))  # Use actual prices
+  # Make predictions for all properties in the DataFrame (get log_prices)
+  predicted_log_sales = model.predict(input_data).tolist()
 
-    # Return the predictions dictionary as a JSON response
-    return output_dict
+  # Create a dictionary of predictions with property IDs as keys
+  Store_Type = input_data['Store_Type'].tolist() 
+  output_dict = dict(zip(Store_Type, predicted_sales))  # Use actual prices
 
-    # Run the Flask application in debug mode if this script is executed directly
-if __name__ == '__main__':
-    sales_prediction_api.run(debug=True)
+  # Return the predictions dictionary as a JSON response
+  return output_dict
+    
+  if __name__ == '__main__':
+      sales_prediction_api.run(debug=False, host='0.0.0.0', port=7860)