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import gradio as gr | |
from transformers import pipeline | |
pipeline = pipeline(task="image-classification", model="Fu-chiang/bit-50-skin-lesions") | |
def predict(image): | |
predictions = pipeline(image) | |
return {p["label"]: p["score"] for p in predictions} | |
gr.Interface( | |
predict, | |
inputs=gr.inputs.Image(label="Upload skin lesion picture", type="filepath"), | |
outputs=gr.outputs.Label(num_top_classes=2), | |
title="Melanoma or Nevus?", | |
).launch() | |