Made debugging edits

app.py

@@ -1,20 +1,18 @@
 import gradio as gr
 from fastai.vision.all import *
 import skimage
+def is_cat(x): return x[0].isupper()
 
 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))}
+categories = ('Dog', 'Cat')
 
-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
+def classify_image(img):
+        pred,idx,probs = learn.predict(img)
+        return dict(zip(categories, map(float,probs)))
+image = gr.Image(height=192, width=192)
+label = gr.Label()
+examples:list = ['./assets/dog.png', './assets/cat.png', './assets/dunno.png']
 
-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()
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)