app.py

import gradio as gr
from transformers import AutoProcessor, AutoModelForImageClassification
import torch
from PIL import Image

model_name = 'e1010101/vit-384-tongue-image'
processor = AutoProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)

def classify_image(image):
    inputs = processor(images=image, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    # Apply sigmoid for multi-label classification
    probs = torch.sigmoid(logits)[0].numpy()
    # Get label names
    labels = model.config.id2label.values()
    # Create a dictionary of labels and probabilities
    result = {label: float(prob) for label, prob in zip(labels, probs)}
    # Sort results by probability
    result = dict(sorted(result.items(), key=lambda item: item[1], reverse=True))
    return result

interface = gr.Interface(
    fn=classify_image,
    inputs=gr.inputs.Image(type="pil"),
    outputs=gr.outputs.Label(num_top_classes=None),
    title="Multi-Label Image Classification",
    description="Upload an image to get classification results."
)


if __name__ == "__main__":
    interface.launch()