feat: cat dog model

app.py

@@ -1,12 +1,29 @@
+import gradio as gr
 from fastai.vision.all import *
 
+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)
+    pred, pred_idx, probs = learn.predict(img)
     return {labels[i]: float(probs[i]) for i in range(len(labels))}
 
-import gradio as gr
-gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)
+
+title = "Cat or Dog 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
+
+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()