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app.py

@@ -14,7 +14,7 @@ def home():
     return "Welcome to the Sales Revenue Prediction API!"
 
 # Define an endpoint to predict churn for a single customer
-@SalesRevenue_predictor_api.post('/')
+@SalesRevenue_predictor_api.post('/Sales_prediction')
 def predict_revenue():
     # Get JSON data from the request
     product_data = request.get_json()
@@ -36,16 +36,16 @@ def predict_revenue():
     # Convert the extracted data into a DataFrame
     input_data = pd.DataFrame([sample])
 
-    # Make a churn prediction using the trained model
+    # Make a revenue prediction using the trained model
     prediction = model.predict(input_data).tolist()[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}),"Prediction completed"
+    return jsonify({ 'Prediction': prediction, 'Message': 'Prediction completed' })
 
 
 # Run the Flask app in debug mode
 if __name__ == '__main__':
-    app.run(debug=True)
+    SalesRevenue_predictor_api.run(debug=True)