Update app.py

app.py

@@ -1,79 +1,79 @@
-import gradio as gr
-import requests
-
-# FastAPI endpoint URL
-API_URL = "https://molinari135-product-return-prediction.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."
-
-    # Prepare the request data
-    models = []
-    fabrics = []
-    colours = []
-
-    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)
-
-    # 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
-    }
-
-    print(data)
-
-    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 output to display nicely
-        formatted_result = "\n".join([f"Product: {pred['product']} | Prediction: {pred['prediction']} | Confidence: {pred['confidence']}%" for pred in predictions])
-        return formatted_result
-
-    except requests.exceptions.RequestException as e:
-        return f"Error: {str(e)}"
-
-
-# Predefined list of model-fabric-color combinations
-combinations = [
-    "01CA9T-0130C-922",
-    "0NG3DT-02003-999",
-    "3R1F67-1JCYZ-0092",
-    "211740-3R419-06935",
-    "6R1J75-1DQSZ-0943"
-]
-
-# Gradio interface elements
-interface = gr.Interface(
-    fn=predict_return,  # Function that handles the prediction logic
-    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)
-    ],
-    outputs="text",  # Display predictions as text
-    live=True  # To enable the interface to interact live
-)
-
-# Launch the Gradio interface
-interface.launch()
+import gradio as gr
+import requests
+
+# FastAPI endpoint URL
+API_URL = "https://molinari135-product-return-prediction-api.hf.space"
+
+
+# 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."
+
+    # Prepare the request data
+    models = []
+    fabrics = []
+    colours = []
+
+    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)
+
+    # 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
+    }
+
+    print(data)
+
+    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 output to display nicely
+        formatted_result = "\n".join([f"Product: {pred['product']} | Prediction: {pred['prediction']} | Confidence: {pred['confidence']}%" for pred in predictions])
+        return formatted_result
+
+    except requests.exceptions.RequestException as e:
+        return f"Error: {str(e)}"
+
+
+# Predefined list of model-fabric-color combinations
+combinations = [
+    "01CA9T-0130C-922",
+    "0NG3DT-02003-999",
+    "3R1F67-1JCYZ-0092",
+    "211740-3R419-06935",
+    "6R1J75-1DQSZ-0943"
+]
+
+# Gradio interface elements
+interface = gr.Interface(
+    fn=predict_return,  # Function that handles the prediction logic
+    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)
+    ],
+    outputs="text",  # Display predictions as text
+    live=True  # To enable the interface to interact live
+)
+
+# Launch the Gradio interface
+interface.launch()