app.py

import gradio as gr
from huggingface_hub import InferenceClient
import os

# Get the API token from environment variables
api_token = os.getenv("HUGGINGFACE_API_TOKEN")

# Initialize the Inference Client for your model
client = InferenceClient(
    model="SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net",
    token=api_token
)

def predict(image):
    """
    Process the uploaded image and return the segmentation result.
    
    Args:
        image: PIL Image object from Gradio input
    
    Returns:
        The segmentation result (assumed to be an image) or an error message
    """
    try:
        # TODO: Add any necessary preprocessing here (e.g., resizing, normalization)
        # Send the image to the model via the Inference API
        result = client.post(data={"inputs": image})
        # TODO: Add any necessary postprocessing here (e.g., converting to image, overlaying on original)
        # For now, assuming the result is directly the segmentation image
        return result
    except Exception as e:
        return f"Error: {str(e)}"

# Create the Gradio interface
iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="Upload Panoramic X-ray Image"),
    outputs=gr.Image(type="pil", label="Segmentation Result"),
    title="Teeth Segmentation in Panoramic X-rays",
    description="Upload an X-ray image to see the segmented teeth."
)

# Launch the interface
iface.launch()