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from fastai.learner import *
from fastai.vision.all import *
import gradio as gr
learn = load_learner("export.pkl")
labels = learn.dls.vocab
def predict(img):
img = PILImage.create(img)
pred,pred_idx,probs = learn.predict(img)
return {labels[i]: float(probs[i]) for i in range(len(labels))}
title = "Skin Lesion Classifier [RESNET 50]"
description = "A skin lesion classifier trained on the ISIC2019 dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
article="<p style='text-align: center'><a href='https://challenge.isic-archive.com/data/' target='_blank'>Link to ISIC Dataset</a></p>"
interpretation='default'
enable_queue=True
examples = examples=['img1.jpg','img2.jpg','img3.jpg']
gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()
# import gradio as gr
# from fastai.vision.all import *
# import skimage
# #Importing necessary libraries
# import gradio as gr
# #import scikit-learn as sklearn
# from fastai.vision.all import *
# from sklearn.metrics import roc_auc_score
# learn = load_learner('export.pkl')
# labels = learn.dls.vocab
# def predict(img):
# img = PILImage.create(img)
# pred,pred_idx,probs = learn.predict(img)
# return {labels[i]: float(probs[i]) for i in range(len(labels))}
# examples = ['img1.jpg','img2.jpg','img3.jpg']
# #Launching the gradio application
# gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),
# outputs=gr.outputs.Label(num_top_classes=1),
# title=title,
# description=description,article=article,
# examples=examples,
# enable_queue=enable_queue).launch(inline=False)
# #gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(224, 224)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch() |