Upload 3 files

app.py

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+import gradio as gr
+from fastai.vision.all import load_learner, PILImage
+
+# Load the trained model
+learn = load_learner('model.pkl')
+
+# Define the prediction function
+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))}
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    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.",
+    examples=["example1.jpg", "example2.jpg", "example3.jpg"]  # Replace with actual filenames
+)
+
+# Launch the interface
+interface.launch()

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8a3aa1a7266adeda9675fab1c94f1e61be40822674bb14f47db464a28aafb926
+size 103057644

requirements.txt

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+fastai
+gradio