Update app.py

app.py

@@ -4,11 +4,10 @@ import requests
 # FastAPI endpoint URL
 API_URL = "https://molinari135-product-return-prediction-api.hf.space/predict/"
 
-# Gradio Interface function
 def predict_return(selected_products, total_customer_purchases, total_customer_returns):
     # Input validation for returns (must be <= purchases)
     if total_customer_returns > total_customer_purchases:
-        return "Error: Total returns cannot be greater than total purchases."
+        return "Error: Total returns cannot be greater than total purchases.", "", ""
 
     # Prepare the request data
     models = []
@@ -31,6 +30,8 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
         "total_customer_returns": total_customer_returns
     }
 
+    print(data)
+
     try:
         # Make the POST request to the FastAPI endpoint
         response = requests.post(API_URL, json=data)
@@ -41,19 +42,18 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
         predictions = result.get('predictions', [])
 
         if not predictions:
-            return "Error: No predictions found."
+            return "Error: No predictions found.", "", ""
 
         # Format the output to display nicely
-        formatted_result = []
-        for pred in predictions:
-            formatted_result.append(f"{pred['product']} - {pred['prediction']} (Confidence: {pred['confidence']}%)")
+        formatted_result = "\n".join([f"{pred['product']} - {pred['prediction']} (Confidence: {pred['confidence']}%)" for pred in predictions])
+
+        total_products_in_cart = f"Total Products in Cart: {len(predictions)}"
         
-        # Calculate total products in cart
-        total = len(predictions)
-        return formatted_result, f"Total Products in Cart: {total}"
+        # Return all the required values
+        return formatted_result, total_products_in_cart, ""
 
     except requests.exceptions.RequestException as e:
-        return f"Error: {str(e)}", ""
+        return f"Error: {str(e)}", "", ""
 
 
 # Predefined list of model-fabric-color combinations