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import gradio as gr
import torch
from huggingface_hub import from_pretrained_fastai
from pathlib import Path
examples = ["akiec.jpg",
"mel.jpg",]
# ⚠️ Type of model/library unknown.
repo_id = "Saim8250/Skin-Diseases-Classification"
path = Path("./")
def get_y(r):
return r["label"]
def get_x(r):
return path/r["fname"]
learner = from_pretrained_fastai(repo_id)
labels = learner.dls.vocab
def inference(image):
label_predict,_,probs = learner.predict(image)
labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
return labels_probs
gr.Interface(
fn=inference,
title="Skin Diseases classification",
description = "Predict which type of skin disease",
inputs="image",
examples=examples,
outputs=gr.outputs.Label(num_top_classes=5, label='Prediction'),
cache_examples=False,
ssl_context = create_ssl_context(verify=verify, cert=cert, trust_env=trust_env)
).launch(debug=True, enable_queue=True) |