Upload folder using huggingface_hub

SuperKart_turnOver_prediction_model_v1_0.joblib

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ac2b9b09d34a49f9f744a2cc2ce8d19bd806ccc21d31ccf24cc71145f80a6855
-size 15002707
+oid sha256:67bbe0be2e0ed379a927e8c4965c62a871ce679336d7511bd3e7a15536f86172
+size 21131427

app.py

@@ -5,7 +5,7 @@ from flask import Flask, request, jsonify
 # Initialize Flask app with a name
 SalesRevenue_predictor_api = Flask("Sales Revenue predictor")
 
-# Load the trained churn prediction model
+# Load the trained revenue prediction model
 model = joblib.load("SuperKart_turnOver_prediction_model_v1_0.joblib")
 
 # Define a route for the home page
@@ -13,7 +13,7 @@ model = joblib.load("SuperKart_turnOver_prediction_model_v1_0.joblib")
 def home():
     return "Welcome to the Sales Revenue Prediction API!"
 
-# Define an endpoint to predict churn for a single customer
+# Define an endpoint to predict revenue for a single customer
 @SalesRevenue_predictor_api.route('/v1/Sales_prediction', methods=['POST'])
 def predict_revenue():
     # Get JSON data from the request
@@ -41,9 +41,6 @@ def predict_revenue():
     #prediction = model.predict(input_data).tolist()[0]
     prediction = model.predict(input_data)[0]
 
-    # Map prediction result to a human-readable label
-    # prediction_label = "churn" if prediction == 1 else "not churn"
-
     # Return the prediction as a JSON response
     return jsonify({ 'Prediction': prediction, 'Message': 'Prediction completed' })