Update app.py

app.py

@@ -4,7 +4,6 @@ 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)
@@ -32,8 +31,6 @@ 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)
@@ -47,11 +44,16 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
             return "Error: No predictions found."
 
         # Format the output to display nicely
-        formatted_result = "\n".join([f"Product: {pred['product']} \t Prediction: {pred['prediction']} \t Confidence: {pred['confidence']}%" for pred in predictions])
-        return formatted_result
+        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}"
 
     except requests.exceptions.RequestException as e:
-        return f"Error: {str(e)}"
+        return f"Error: {str(e)}", ""
 
 
 # Predefined list of model-fabric-color combinations
@@ -64,16 +66,44 @@ 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 that handles the prediction logic
+    fn=predict_return,  # Function to process predictions
     inputs=[
-        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)
+        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
     ],
-    outputs="text",  # Display predictions as text
-    live=True  # To enable the interface to interact live
+    live=True  # Enable live interaction
 )
 
 # Launch the Gradio interface
-interface.launch()
+interface.launch()