let's deploy to huggingface spaces

app.py

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+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+imported_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 = "Redacted Document Classifier"
+description = "A classifier trained on publicly released redacted (and unredacted) FOIA documents, using fastai."
+article = "<p style='text-align: center'><a href='https://mlops.systems/fastai/redactionmodel/computervision/datalabelling/2021/09/06/redaction-classification-chapter-2.html' target='_blank'>Blog post</a></p>"
+examples = [
+    "test1.jpg",
+    "test2.jpg",
+    "test3.jpg",
+    "test4.jpg",
+    "test5.jpg",
+]
+interpretation = "default"
+# interpretation='shap'
+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:f1f259b0d96a72e65b72fea92226109aebf275adeec7e1dcddda00e8cb5b8ec7
+size 87538219

requirements.txt

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+fastai
+scikit-image