let's deploy to huggingface spaces

app.py

@@ -1,7 +1,28 @@
 import gradio as gr
+from fastai.vision.all import *
+import skimage
 
-def greet(name):
-    return "Hello " + name + "!!"
+learn = load_learner('export.pkl')
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+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 = "Dog Cat Classifier"
+description = "A dog cat classifier. Created as a demo for Gradio and HuggingFace Spaces."
+article=""
+examples = ['siamese.jpg']
+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,
+    enable_queue=enable_queue).launch()

export.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:04c637a459dcd50e3c490ab22ea340bc9af78e66c0139c862d5673a098e13729
+size 87555051

requirements.txt

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