Add application file

app.py

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+import gradio as gr
+from fastai.vision.all import *
+
+learn = load_learner('bears_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 = "Bears Classifier"
+description = "A bear classifier trained on the resnet18 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>"
+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,interpretation=interpretation,enable_queue=enable_queue).launch()

bears_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8d0fc78f205e73440f98736d24c713542a360361684ac0e154907cfc6ee8af5e
+size 46965720

requirements.txt

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+fastai
+git+https://github.com/huggingface/huggingface_hub
+gradio
+scikit-image