Upload folder using huggingface_hub

app.py

@@ -6,7 +6,7 @@ import pandas as pd  # For data manipulation
 from flask import Flask, request, jsonify  # For creating the Flask API
 
 # Initialize the Flask application
-rf_superkart_prediction_api = Flask("SuperKart Sales Prediction with Random Forest")
+rf_superkart_prediction_api = Flask("SuperKart Sales Prediction with XGBoost")
 
 # Load the trained machine learning model
 rf_model = joblib.load("superkart_sales_prediction_model_v1_0.joblib")
@@ -18,7 +18,7 @@ def home():
     This function handles GET requests to the root URL ('/') of the API.
     It returns a simple welcome message.
     """
-    return "Welcome to the SuperKart Sales Prediction API with Random Forest!"
+    return "Welcome to the SuperKart Sales Prediction API With Random Forest!"
 
 # Define an endpoint for single property prediction (POST request)
 @rf_superkart_prediction_api.post('/v1/predict')
@@ -52,7 +52,7 @@ def predict_sales():
       input_data = pd.DataFrame([sample])
 
       # Make prediction (get log_price)
-      sales_prediction = xgb_model.predict(input_data)[0]
+      sales_prediction = rf_model.predict(input_data)[0]
 
       # Return the prediction
       return jsonify({'Sales': sales_prediction.tolist()})
@@ -60,8 +60,6 @@ def predict_sales():
       print(f"Error in prediction: {e}")
       return jsonify({'error': str(e)})
 
-
-
 # Run the Flask application in debug mode if this script is executed directly
 if __name__ == '__main__':
     rf_superkart_prediction_api.run(debug=True)