| 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() |