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app.py

@@ -1,13 +1,33 @@
 import joblib
 import pandas as pd
 from flask import Flask, request, jsonify
+import os
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 #initialise flask app
 sales_forecast_api = Flask('Sales forecasting')
 
 # load the model
+try:
+    # Log current working directory and files
+    current_dir = os.getcwd()
+    logger.info(f"Current working directory: {current_dir}")
+    files_in_dir = os.listdir(current_dir)
+    logger.info(f"Files in current directory: {files_in_dir}")
+
+    model = joblib.load('sales_forecast_v1_0.joblib')
+    logger.info("Model loaded successfully.")
+except FileNotFoundError:
+    logger.error("Model file not found!")
+    model = None # Or handle the error as appropriate
+except Exception as e:
+    logger.error(f"Error loading model: {e}")
+    model = None # Or handle the error as appropriate
 
-model = joblib.load('sales_forecast_v1_0.joblib')
 
 #define home page
 @sales_forecast_api.get('/')
@@ -17,6 +37,9 @@ def home():
 #define an endpoint for prediction
 @sales_forecast_api.post('/v1/sales')
 def sales_predict():
+  if model is None:
+      return jsonify({"error": "Model not loaded"}), 500
+
   #get data from json request
   sales_data = request.get_json()
 
@@ -49,6 +72,9 @@ def sales_predict():
 @sales_forecast_api.post('/v1/salesbatch')
 
 def sales_batch_predict():
+  if model is None:
+      return jsonify({"error": "Model not loaded"}), 500
+
   #get the file from the request
   file = request.files['file']
   #read the file to df