first commit

README.md

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 ---
-title: Fast Ai Pet Classifier
-emoji: 👀
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
 license: apache-2.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+learn = load_learner('export.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.jpg']
+interpretation='default'
+enable_queue=True
+
+gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()

export.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f6ccd7c029767fe800266d73becd777080c804f66d64c285721c9fd553e33ed9
+size 47061419

requirements.txt

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+fastai
+scikit-image