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

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

path_model = "model_trash.pkl"
learn = load_learner(path_model)


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 = "Kind of trash identifier"
description = "Simple (and not very accurate) model for identifying category of trash for recycling purposes. The model was finetuned version of resnet with 34 layers with trash dataset found on github <https://github.com/garythung/trashnet>"

path_example = "szkl.jpg"
examples = [[path_example]]

#interpretation function
interpretation='default'

#queueing traffic
enable_queue=True



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, # I haven't created that
             examples=examples,
             interpretation=interpretation,
             enable_queue=enable_queue).launch()