git add requirements.txt

app.py

@@ -1,9 +1,8 @@
-!pip install -Uqq fastai
-from fastai.vision.all import *
-
 import gradio as gr
+from fastai.vision.all import *
+import skimage
 
-learn = load_learner('model.pkl')
+learn = load_learner('export.pkl')
 
 labels = learn.dls.vocab
 def predict(img):
@@ -11,6 +10,11 @@ def predict(img):
     pred,pred_idx,probs = learn.predict(img)
     return {labels[i]: float(probs[i]) for i in range(len(labels))}
 
-iface = gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)
+title = "Pet Breed Classifier"
+description = "A pet breed classifier trained on the Oxford Pets 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>"
+examples = ['siamese.jpg']
+interpretation='default'
+enable_queue=True
 
-iface.launch()
+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()

requirements.txt

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