import gradio as gr
from transformers import pipeline, AutoConfig, AutoModelForImageClassification, AutoImageProcessor


config = AutoConfig.from_pretrained("./model/checkpoint-9730/config.json")
model = AutoModelForImageClassification.from_pretrained(
    "./model/checkpoint-9730/", config=config
)
image_processor  = AutoImageProcessor.from_pretrained("./model/checkpoint-9730/")


pipe = pipeline("image-classification", model=model, feature_extractor=image_processor)
def predict(image):
    predictions = pipe(image)
    return {p["label"]: p["score"] for p in predictions}

gr.Interface(
    predict,
    inputs=gr.inputs.Image(label="Upload Skin Disease Image", type="filepath"),
    outputs=gr.outputs.Label(num_top_classes=3),
    title="Detect your skin disease",
).launch()