create app.py

app.py

@@ -0,0 +1,62 @@
+import gradio as gr
+from huggingface_hub import InferenceClient
+import PIL.Image
+import io
+
+# Initialize the Hugging Face client
+HF_TOKEN = "your_token_here"  # Replace with your token
+
+client = InferenceClient(
+    model="Kwai-Kolors/Kolors-Virtual-Try-On",
+    token=HF_TOKEN
+)
+
+def virtual_try_on(person_image, garment_image):
+    """
+    Process the virtual try-on request
+    Args:
+        person_image: PIL Image of the person
+        garment_image: PIL Image of the garment
+    Returns:
+        PIL Image of the result
+    """
+    try:
+        # Convert images to bytes
+        person_bytes = io.BytesIO()
+        garment_bytes = io.BytesIO()
+        person_image.save(person_bytes, format='PNG')
+        garment_image.save(garment_bytes, format='PNG')
+        
+        # Make API request
+        response = client.post(
+            json={
+                "inputs": [
+                    {"image": person_bytes.getvalue()},
+                    {"image": garment_bytes.getvalue()}
+                ]
+            }
+        )
+        
+        # Convert response to image
+        result_image = PIL.Image.open(io.BytesIO(response))
+        return result_image, "Success"
+    except Exception as e:
+        return None, f"Error: {str(e)}"
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=virtual_try_on,
+    inputs=[
+        gr.Image(type="pil", label="Person Image"),
+        gr.Image(type="pil", label="Garment Image")
+    ],
+    outputs=[
+        gr.Image(type="pil", label="Result"),
+        gr.Text(label="Status")
+    ],
+    title="Virtual Try-On API",
+    description="Upload a person image and a garment image to see how the garment would look on the person."
+)
+
+if __name__ == "__main__":
+    demo.launch()