Added Gradio

README.md

@@ -4,6 +4,7 @@ emoji: 🏃
 colorFrom: purple
 colorTo: red
 sdk: docker
+app_file: app.py
 pinned: false
 ---
 

app.py

@@ -0,0 +1,79 @@
+import gradio as gr
+import requests
+
+# FastAPI endpoint URL
+API_URL = "http://localhost:7860/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()

requirements.txt

@@ -2,6 +2,7 @@ black
 codecarbon
 fastapi
 flake8
+gradio
 ipython
 isort
 jupyterlab