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))}

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."

interpretation='default'
enable_queue=True

examples = ['img1.jpg','img2.jpg','img3.jpg']

#Launching the gradio application
gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(224, 224)),
             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()