Update app.py

app.py

@@ -3,6 +3,14 @@ import numpy as np
 import joblib  # For loading the serialized model
 import pandas as pd  # For data manipulation
 from flask import Flask, request, jsonify  # For creating the Flask API
+import logging
+
+# Configure the logging
+logging.basicConfig(
+    level=logging.INFO,  # You can change to DEBUG for more details
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
 
 # Initialize the Flask application
 sales_predictor_api = Flask("Sales Predictor")
@@ -28,8 +36,9 @@ def predict_rental_price():
     the predicted rental price as a JSON response.
     """
     # Get the JSON data from the request body
+    logger.debug(f"predict_rental_price called")
     property_data = request.get_json()
-
+    logger.debug(f"property data {property_data}")
 
     # Extract relevant features from the JSON data
     sample = {
@@ -47,7 +56,7 @@ def predict_rental_price():
 
     # Convert the extracted data into a Pandas DataFrame
     input_data = pd.DataFrame([sample])
-
+    logger.debug(f"input_data {input_data}")
     # Make prediction 
     predicted_sales = model.predict(input_data)[0]