import gradio as gr
from fastai.vision.all import *
import pathlib
temp = pathlib.PosixPath
pathlib.PosixPath = pathlib.WindowsPath

examples = ["apple.jpg", "avocado.jpg", 
        "mixed_fruit.jpg", 
        "nectarine.jpg", "passion_fruit.jpg", 
        "peach.jpg"]
title = "A fruit classification app"
description = "A fruit classficiation app using fastai and a pretrained-Resnet50 model."

learn = load_learner("model.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))}

gr_interface = gr.Interface(fn=predict, 
                    inputs = gr.inputs.Image(shape = (512, 512)), 
                    outputs = gr.outputs.Label(num_top_classes = 3),
                    title = title,description=description,
                    examples = examples
                    )
    
gr_interface.launch(share=True)