Initial deploy: added fastai model, app.py, and examples

app.py

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+
+import gradio as gr
+from fastai.vision.all import *
+import os
+
+# --- Model Loading (Assumes model.pkl exists in the root) ---
+try:
+    learn = load_learner('export.pkl')
+except Exception:
+    print("Error loading export.pkl. Check file path/existence.")
+    raise
+
+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))}
+
+# --- Interface Setup ---
+examples = ["birman.jpg", "pomerian.jpg", "british.jpg"]
+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>"
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title=title,
+    description=description,
+    article=article,
+    examples=examples
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+
+fastai
+scikit-image
+gradio
+torch