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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_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()
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