#!/usr/bin/env python
# coding: utf-8

import gradio as gr
from fastai.vision.all import *
import skimage


import pathlib
plt = platform.system()
if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath

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learn = load_learner('export.pkl')


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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))}


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import gradio as gr

title = "Pet Breed Classifier"
description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."

gr.Interface(fn=predict,
             inputs=gr.inputs.Image(shape=(512, 512)),
             theme="dark-peach",
             title = "?תפוח או עגבניה",
             examples=[["example1.jpg"], ["example2.png"], ["example3.jpg"]],
             description = "Tomato / Apple classifier trained on images from the internet with fastai. Created as a demo for Gradio and HuggingFace Spaces.",
             outputs=gr.outputs.Label(num_top_classes=3)).launch(share=False)


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