Update app.py

app.py

@@ -13,57 +13,95 @@ dataset = load_dataset("molinari135/armani-inventory", token=hf_token, data_file
 inventory = pd.DataFrame(dataset['train']).head(50)
 
 # Gradio Interface function
-def predict_return(selected_indices, total_customer_purchases, total_customer_returns):
+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."
 
-    # Selezionare i prodotti scelti usando gli indici
-    selected_products = inventory.iloc[selected_indices]
-    
-    # Preparare i dati per la predizione
-    models = selected_products['Item Brand Model'].tolist()
-    fabrics = selected_products['Item Brand Fabric'].tolist()
-    colours = selected_products['Item Brand Colour'].tolist()
-    
-    # Preparazione per il calcolo del carrello
-    cart_details = "\n".join([ 
-        f"{row['Item Brand Model']} - {row['Product Type']}, {row['Main Material']}, Sales: {row['Net Sales (FA)']} USD" 
-        for _, row in selected_products.iterrows()
-    ])
-
-    # Calcolare il totale del carrello
-    total_value = selected_products['Net Sales (FA)'].sum()
-
-    # Simula la predizione
-    # (In un'applicazione reale, invia questi dati a un endpoint API per fare la predizione)
-    predictions = [
-        f"{model} - No Return (Confidence: 0.95%)" for model in models
-    ]
-    
-    # Formatta il risultato per visualizzazione
-    formatted_result = "\n".join(predictions)
-    total_cart = f"Total Cart Value: {total_value} USD"
-
-    return cart_details, formatted_result, total_cart
+    # Prepare the request data
+    models = []
+    fabrics = []
+    colours = []
+    descriptions = []
+    total_value = 0
 
-# Gradio interface elements
-checkboxes = [
-    gr.Checkbox(label=f"{row['Item Brand Model']} - {row['Item Brand Fabric']} - {row['Item Brand Colour']}", value=False)
-    for _, row in inventory.iterrows()
-]
+    for selected_product in selected_products:
+        # Split each selected product into model, fabric, and color
+        model, fabric, color = selected_product.split("-")
+        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['Net Sales (FA)'].values[0]
+            total_value += product_value
+
+            # Add description to the cart
+            description = (
+                f"Model: {model}, Fabric: {fabric}, Colour: {color}, "
+                f"Product Type: {product_details['Product Type'].values[0]}, "
+                f"Material: {product_details['Main Material'].values[0]}, "
+                f"Sales Value: {product_value} USD"
+            )
+            descriptions.append(description)
+        else:
+            descriptions.append(f"{model}-{fabric}-{color}: Not Found")
+
+    # Prepare the data to send to the API
+    data = {
+        "models": models,
+        "fabrics": fabrics,
+        "colours": colours,
+        "total_customer_purchases": total_customer_purchases,
+        "total_customer_returns": total_customer_returns
+    }
+
+    try:
+        # Make the POST request to the FastAPI endpoint
+        response = requests.post(API_URL, json=data)
+        response.raise_for_status()  # Raise an error for bad responses
+
+        # Get the predictions and return them
+        result = response.json()
+        predictions = result.get('predictions', [])
+
+        if not predictions:
+            return "Error: No predictions found."
 
+        # Format the cart output
+        cart_output = "\n".join(descriptions) + f"\nTotal Cart Value: {total_value} USD"
+
+        # 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
+
+    except requests.exceptions.RequestException as e:
+        return f"Error: {str(e)}"
+
+# Gradio interface elements
 interface = gr.Interface(
     fn=predict_return,  # Funzione per la logica di predizione
     inputs=[
-        gr.Column([*checkboxes], label="Select Products"),  # Aggiungi tutte le checkbox
+        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=[
         gr.Textbox(label="Cart Details"),  # Dettagli del carrello
-        gr.Textbox(label="Prediction Results"),  # Risultati della predizione
-        gr.Textbox(label="Total Cart")  # Totale del carrello
+        gr.Textbox(label="Prediction Results")  # Risultati della predizione
     ],
     live=True  # Interattività in tempo reale
 )