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

class GetX(ItemTransform):
    def encodes(self, x): return (x[0])


class GetY(ItemTransform):
    def encodes(self, x): return (x[1])


pathlib.WindowsPath = pathlib.PosixPath

learn = load_learner("model.pkl")

# categories = ('Disturbed Galaxies',
#         'Merging Galaxies',
#         'Round Smooth Galaxies',
#         'In-between Round Smooth Galaxies',
#         'Cigar Shaped Smooth Galaxies',
#         'Barred Spiral Galaxies',
#         'Unbarred Tight Spiral Galaxies',
#         'Unbarred Loose Spiral Galaxies',
#         'Edge-on Galaxies without Bulge',
#         'Edge-on Galaxies with Bulge')
#
categories = (
        'Round Smooth Galaxies', 'Barred Spiral Galaxies',
        )

def classify_image(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()

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