Update app.py

app.py

@@ -1,18 +1,29 @@
 import gradio as gr
 import requests
+import pandas as pd
+import os
+from datasets import load_dataset
 
 # FastAPI endpoint URL
 API_URL = "https://molinari135-product-return-prediction-api.hf.space/predict/"
 
+# Load the inventory dataset from Hugging Face
+hf_token = os.getenv("inventory_data")
+dataset = load_dataset("molinari135/armani-inventory", token=hf_token, data_files="inventory.tsv")
+inventory = pd.DataFrame(dataset['train'])
+
+# 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 = []
     fabrics = []
     colours = []
+    descriptions = []
+    total_value = 0
 
     for selected_product in selected_products:
         # Split each selected product into model, fabric, and color
@@ -20,6 +31,23 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
         models.append(model)
         fabrics.append(fabric)
         colours.append(color)
+        
+        # Get the product details from the inventory
+        product_details = inventory[
+            (inventory['Item Brand Model'] == model) & 
+            (inventory['Item Brand Fabric'] == fabric) & 
+            (inventory['Item Brand Colour'] == color)
+        ]
+        
+        if not product_details.empty:
+            # Calculate the product value and add it to the total
+            product_value = product_details['Total Order Value'].values[0]
+            total_value += product_value
+
+            # Add description to the cart
+            descriptions.append(f"{model}-{fabric}-{color}: {product_value} USD")
+        else:
+            descriptions.append(f"{model}-{fabric}-{color}: Not Found")
 
     # Prepare the data to send to the API
     data = {
@@ -30,8 +58,6 @@ 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)
@@ -42,67 +68,36 @@ def predict_return(selected_products, total_customer_purchases, total_customer_r
         predictions = result.get('predictions', [])
 
         if not predictions:
-            return "Error: No predictions found.", "", ""
-
-        # Format the output to display nicely
-        formatted_result = "\n".join([f"{pred['product']} - {pred['prediction']} (Confidence: {pred['confidence']}%)" for pred in predictions])
+            return "Error: No predictions found."
 
-        total_products_in_cart = f"Total Products in Cart: {len(predictions)}"
-        
-        # Return all the required values
-        return formatted_result, total_products_in_cart, ""
+        # Format the cart output
+        cart_output = "\n".join(descriptions) + f"\nTotal Cart Value: {total_value} USD"
 
-    except requests.exceptions.RequestException as e:
-        return f"Error: {str(e)}", "", ""
+        # Format the prediction results
+        formatted_result = "\n".join([f"Product: {pred['product']} \t Prediction: {pred['prediction']} \t Confidence: {pred['confidence']}%" for pred in predictions])
 
+        return cart_output, formatted_result
 
-# Predefined list of model-fabric-color combinations
-combinations = [
-    "01CA9T-0130C-922",
-    "0NG3DT-02003-999",
-    "3R1F67-1JCYZ-0092",
-    "211740-3R419-06935",
-    "6R1J75-1DQSZ-0943"
-]
+    except requests.exceptions.RequestException as e:
+        return f"Error: {str(e)}"
 
 # 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
+        gr.CheckboxGroup(
+            choices=[f"{row['Item Brand Model']}-{row['Item Brand Fabric']}-{row['Item Brand Colour']}" 
+                     for _, row in inventory.iterrows()], 
+            label="Select Products"
+        ),
+        gr.Slider(0, 10, step=1, label="Total Customer Purchases", value=0),
+        gr.Slider(0, 10, step=1, label="Total Customer Returns", value=0)
     ],
     outputs=[
-        predictions_output,  # Right side: Cart & Predictions
-        user_info_output,  # Right side: User Info
-        cart_output  # Right side: Cart
+        gr.Textbox(label="Cart Details"),  # Display cart details
+        gr.Textbox(label="Prediction Results")  # Display prediction results
     ],
-    live=True  # Enable live interaction
+    live=True  # To enable the interface to interact live
 )
 
 # Launch the Gradio interface