import gradio as gr
from huggingface_hub import InferenceClient
import PIL.Image
import io
import base64

client = InferenceClient(
    model="Kwai-Kolors/Kolors-Virtual-Try-On"
)

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 base64
        person_bytes = io.BytesIO()
        garment_bytes = io.BytesIO()
        person_image.save(person_bytes, format='PNG')
        garment_image.save(garment_bytes, format='PNG')
        
        person_base64 = base64.b64encode(person_bytes.getvalue()).decode('utf-8')
        garment_base64 = base64.b64encode(garment_bytes.getvalue()).decode('utf-8')
        
        # Make API request
        response = client.post(
            json={
                "inputs": [
                    {"image": person_base64},
                    {"image": garment_base64}
                ]
            }
        )
        
        # Eğer response bytes ise doğrudan kullan, değilse base64'ten decode et
        if isinstance(response, bytes):
            result_bytes = response
        else:
            result_bytes = base64.b64decode(response)
            
        # Convert response to image
        result_image = PIL.Image.open(io.BytesIO(result_bytes))
        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()