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

__all__ = ['is_rock', 'learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf'] 

def is_rock(x): return x[0].issuper() 

import torch

import pathlib

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

learn = load_learner('RPS_model.pkl')


categories = ('paper', 'rock', 'scissors')

def classify_images(img):
    pred, idx, probs = learn.predict(img)
    return dict(zip(categories, map(float, probs)))

image = gr.inputs.Image(shape=(192,192))
label = gr.outputs.Label()
examples = ['rock.jpg', 'paper.jpg', 'scissor.jpg']

intf = gr.Interface(fn = classify_images, inputs = image, outputs = label, examples = examples)
intf.launch(inline=False)