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# coding: utf8
import gradio as gr
from transformers import pipeline
pipeline = pipeline(task="image-classification", model="dqnguyen/Diabetic_Foot_Ulcer_Image_Classification")
def predict(image):
predictions = pipeline(image)
#return {p["label"]: p["score"] for p in predictions}
results = {}
for p in predictions:
if p["label"] == "MoHat":
results["Granulation tissue"] = p["score"]
elif p["label"] == "MoGiaMacNhiemKhuan":
results["Pseudomembranous tissue with a bacterial infection"] = p["score"]
elif p["label"] == "MoHoaiTu":
results["Necrotic tissue"] = p["score"]
return results
gr.Interface(
predict,
inputs=gr.inputs.Image(label="Upload a diabetic foot ulcer image", type="filepath"),
outputs=gr.outputs.Label(num_top_classes=5),
title="Diabetic Foot Ulcer Image Classification",
).launch() |