first hugging space deployment

app.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../weed_classifier.ipynb.
+
+# %% auto 0
+__all__ = ['learn', 'labels', 'title', 'description', 'article', 'examples', 'interpretation', 'enable_queue', 'predict']
+
+# %% ../weed_classifier.ipynb 1
+from fastai.vision.all import *
+import gradio as gr
+import skimage
+
+# %% ../weed_classifier.ipynb 2
+learn = load_learner('export.pkl')
+
+# %% ../weed_classifier.ipynb 3
+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))}
+
+# %% ../weed_classifier.ipynb 5
+title = "Weed Classifier"
+description = "A weed classifier trained on the Kaggle V2 Plant Seedling dataset with fastai. Dataset has mostly african weeds in it at the moment."
+article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
+examples = ['SugarBeet.png']
+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).launch(share=True, enable_queue=enable_queue)

export.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8611976c26b9bb1e6deedf03ae034d9be1992786048915b8b9cfed4c4ad2d9f5
+size 87656638

requirements.txt

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+fastai
+scikit-image