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import gradio as gr | |
from fastai.vision.all import * | |
import sys | |
import subprocess | |
# Install fasttransform for model compatibility | |
try: | |
import fasttransform # This makes the Pipeline class available for unpickling | |
except ImportError: | |
subprocess.check_call([sys.executable, "-m", "pip", "install", "fasttransform"]) | |
learn = load_learner('model.pkl') | |
labels = learn.dls.vocab | |
def predict(img): | |
img = PILImage.create(img) | |
pred, pred_idx, probs = learn.predict(img) | |
return {labels[i]: float(probs[i]) for i in range(len(labels))} | |
title = "Pet Breed Classifier" | |
description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces." | |
article = "<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>" | |
examples = ['siamese.png'] | |
gr.Interface( | |
fn=predict, | |
inputs=gr.Image(shape=(512, 512)), # Updated to newer Gradio syntax | |
outputs=gr.Label(num_top_classes=3), # Updated to newer Gradio syntax | |
title=title, | |
description=description, | |
article=article, | |
examples=examples | |
).launch() |