Upload folder using huggingface_hub

app.py

@@ -1,14 +1,18 @@
-# Import necessary libraries
 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 joblib
+import pandas as pd
+from flask import Flask, request, jsonify
 
 # Initialize the Flask application
 super_kart_api = Flask("Super Kart Price Predictor")
 
-# Load the trained machine learning model (updated to match Super Kart model file)
-model = joblib.load("backend_files/super_kart_model_v1_0.joblib")
+# Load the trained machine learning model
+model_path = "super_kart_model_v1_0.joblib"  # Path after upload (root)
+try:
+    model = joblib.load(model_path)
+    print(f"Model loaded successfully from {model_path}")
+except FileNotFoundError:
+    raise FileNotFoundError(f"Model file not found at {model_path}. Ensure it's uploaded to the repo root.")
 
 # Define a route for the home page (GET request)
 @super_kart_api.get('/')
@@ -31,7 +35,6 @@ def predict_sales():
     input_data = request.get_json()
     
     # Extract relevant features from the JSON data
-    # Note: Exclude Product_Id and Store_Id if they are not used in prediction
     sample = {
         'Product_Weight': input_data['Product_Weight'],
         'Product_Sugar_Content': input_data['Product_Sugar_Content'],
@@ -63,7 +66,7 @@ def predict_sales():
     predicted_sales = model.predict(features_df)[0]
     
     # If your model predicts log(sales), uncomment and use this instead:
-    # predicted_log_sales = model.predict(features_df)[0]
+    # predicted_log_sales = model.predict(features_df)
     # predicted_sales = np.exp(predicted_log_sales)
     
     # Convert to Python float and round to 2 decimals