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

title = "Interstellar"
description = (
    "Experimental Astronomical Classifier built for the fast.ai 'Deep Learning' "
    "course by fine tuning ResNet50 (1 + 3 epochs) with a custom dataset "
    "of images (150 per label)."
)
inputs = gr.components.Image()
outputs = gr.components.Label()
examples = "examples"

model = load_learner("model/model.pkl")
labels = model.dls.vocab


def predict(img):
    pred, pred_idx, probs = model.predict(img)
    return dict(zip(labels, map(float, probs)))


demo = gr.Interface(
    fn=predict,
    inputs=inputs,
    outputs=outputs,
    examples=examples,
    title=title,
    description=description,
).queue(default_concurrency_limit=5)
demo.launch()