app.py

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()