updated files

app.py

@@ -1,21 +1,23 @@
 import gradio as gr
 from fastai.vision.all import *
+from fastai.vision.widgets import *
+from fastai.callback.preds import load_learner
 import skimage
 
-learn = load_learner('model.pkl')
+learner = 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))}
-
+def classify_image(img):
+    pred, idx, probs = learner.predict(img)
+    return dict(zip(learner.dls.vocab, map(float, probs)))
 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', 'dog.jpg']
 interpretation='default'
 enable_queue=True
-
-demo = 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)
-demo.launch()
+image = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label()
+out_pl = widgets.Output()
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch()

requirements.txt

@@ -2,4 +2,5 @@ fastbook
 fastai
 gradio
 scikit-image
+
 numpy<1.24