from fastai.vision.all import *
learn = load_learner('cig_detector_2.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))}

import gradio as gr

title = "Smoking Classifier"
description = "A smoking, not smoking classifier, resnet14 trained with custom dataset using fastai."
examples = ['smoking2.jpg', 'not_smoking.jpg']
gr.Interface(title=title, description=description, examples=examples,fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch()