app.py

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 with augmentation)."
)
inputs = gr.components.Image()
outputs = gr.components.Label()
examples = "examples"

model_class = load_learner("models/model.class.pkl")
labels_class = model_class.dls.vocab

model_object = load_learner("models/model.object.pkl")
labels_object = model_object.dls.vocab


def predict_class(img):
    pred, pred_idx, probs = model_class.predict(img)
    return dict(zip(labels_class, map(float, probs)))


def predict_object(img):
    pred, pred_idx, probs = model_object.predict(img)
    return dict(zip(labels_object, map(float, probs)))


with gr.Blocks() as demo:
    with gr.Tab("Class Prediction"):
        gr.Interface(
            fn=predict_class,
            inputs=inputs,
            outputs=outputs,
            examples=examples,
            title=title,
            description=description,
        ).queue(default_concurrency_limit=5)
    with gr.Tab("Object Prediction"):
        gr.Interface(
            fn=predict_object,
            inputs=inputs,
            outputs=outputs,
            examples=examples,
            title=title,
            description=description,
        ).queue(default_concurrency_limit=5)

demo.launch()