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


learn = load_learner('petronet_101.pth')
categories = ('Andalusite', 'Argillaceous_siltstone', 'Bioturbated_siltstone', 'Massive_calcareous_siltstone', 'Massive_calcite-cemented_siltstone', 'Porous_calcareous_siltstone', 'actinolite', 'biotite', 'carbonate', 'coal', 'debris_rock', 'granite', 'hornblende', 'olivine', 'oolites', 'plagioclase', 'pyroxene', 'sandstone', 'staurolite ')

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

image = gr.inputs.Image(shape=(250,250))
label = gr.outputs.Label()
examples = ['granite.jpg', 'andalusite.jpg', 'actinolite.jpg']

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