feat: init pet classifier repo

.vscode/settings.json

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+{
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter"
+    },
+    "python.formatting.provider": "none"
+}

app.py

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+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+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 = ["black_bear.jpg", "grizzly_bear.jpg", "teddy_bear.jpg", "unsure_bear.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()

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:962b06b82bade1abec3b71a412285b314135f8f9e2fd9b3adecb6091596a356f
+size 47061419