Update app.py

app.py

@@ -4,6 +4,7 @@ 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)
@@ -31,6 +32,8 @@ 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)
@@ -44,16 +47,11 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
             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']}%)")
-        
-        # Calculate total products in cart
-        total = len(predictions)
-        return formatted_result, f"Total Products in Cart: {total}"
+        formatted_result = "\n".join([f"Product: {pred['product']} \t Prediction: {pred['prediction']} \t Confidence: {pred['confidence']}%" for pred in predictions])
+        return formatted_result
 
     except requests.exceptions.RequestException as e:
-        return f"Error: {str(e)}", ""
+        return f"Error: {str(e)}"
 
 
 # Predefined list of model-fabric-color combinations
@@ -66,44 +64,16 @@ combinations = [
 ]
 
 # Gradio interface elements
-inventory_checkbox_group = gr.CheckboxGroup(choices=combinations, label="Select Products", type="value")
-
-# Slider elements for total purchases and returns
-total_purchases_slider = gr.Slider(0, 10, step=1, label="Total Customer Purchases", value=0)
-total_returns_slider = gr.Slider(0, 10, step=1, label="Total Customer Returns", value=0)
-
-# Output elements for predictions and cart details
-cart_output = gr.Textbox(value="", label="Cart", interactive=False)
-predictions_output = gr.Textbox(value="", label="Prediction Results", interactive=False)
-
-# User information output
-user_info_output = gr.Textbox(value="User Information\nTotal Purchases: 0\nTotal Returns: 0", label="User Info", interactive=False)
-
-# Layout with two main columns: Left (Inventory) and Right (User Info + Cart)
-with gr.Row():
-    with gr.Column():
-        inventory_checkbox_group  # Left side: Inventory
-    with gr.Column():
-        user_info_output  # Right side: User Info
-        cart_output  # Right side: Cart & Predictions
-        predictions_output  # Right side: Prediction Results
-
-
-# Gradio Interface
 interface = gr.Interface(
-    fn=predict_return,  # Function to process predictions
+    fn=predict_return,  # Function that handles the prediction logic
     inputs=[
-        inventory_checkbox_group,  # Left side: Inventory
-        total_purchases_slider,  # Total purchases
-        total_returns_slider  # Total returns
-    ],
-    outputs=[
-        predictions_output,  # Right side: Cart & Predictions
-        user_info_output,  # Right side: User Info
-        cart_output  # Right side: Cart
+        gr.CheckboxGroup(choices=combinations, label="Select Products"),  # Allow multiple product selections
+        gr.Slider(0, 10, step=1, label="Total Customer Purchases", value=0),
+        gr.Slider(0, 10, step=1, label="Total Customer Returns", value=0)
     ],
-    live=True  # Enable live interaction
+    outputs="text",  # Display predictions as text
+    live=True  # To enable the interface to interact live
 )
 
 # Launch the Gradio interface
-interface.launch()
+interface.launch()