deploy to huggingface spaces

app.py

@@ -0,0 +1,19 @@
+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+learn = load_learner('export.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 = "Pet Breed Classifier"
+description = "A pet breed classifier (both cats and dogs) trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces by Dan Waldie."
+examples = ['wheaten.jpeg', 'havanese.jpeg', 'cockerspaniel.jpg', 'pyrenees.jpeg']
+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,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()

export.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a56a40ae7cef6f4e0670dfbb614f9bbc6f617972a9fa295d5e4966de627a8302
+size 103040765

requirements.txt

@@ -0,0 +1,2 @@
+fastai
+scikit-image