Spaces:
Sleeping
Sleeping
File size: 1,453 Bytes
e5a756f b32ee1e e5a756f a22f15d 956d1f2 a22f15d 6dcd719 956d1f2 42e90e2 b32ee1e e5a756f d68a05b b32ee1e d68a05b b32ee1e d68a05b b32ee1e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
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()
|