let's see why mine isn't working

app copy.py

@@ -0,0 +1,29 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: drive/MyDrive/Colab Notebooks/fastbook/fastcourse/ch2minimal/tmabrahamgradio/gradiotest/gradiotest.ipynb.
+
+# %% auto 0
+__all__ = ['learn', 'labels', 'predict']
+
+# %% drive/MyDrive/Colab Notebooks/fastbook/fastcourse/ch2minimal/tmabrahamgradio/gradiotest/gradiotest.ipynb 1
+from fastai.vision.all import *
+import skimage
+
+# %% drive/MyDrive/Colab Notebooks/fastbook/fastcourse/ch2minimal/tmabrahamgradio/gradiotest/gradiotest.ipynb 4
+learn = load_learner('export.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))}
+
+
+
+
+# %% drive/MyDrive/Colab Notebooks/fastbook/fastcourse/ch2minimal/tmabrahamgradio/gradiotest/gradiotest.ipynb 7
+import gradio as gr
+#gr.Interface(fn=predict, inputs=gr.Image(shape=(512,512)), outputs=gr.outputs.Label(num_top_classes=3), title="Pet Breed Classifier", description="A pet breed classifier trained on the Oxford Pets dataset using the fastai library (5 epochs) as a proof of concept for Gradio.", article="<p style='text-align: center'><a href='https://gameologist.com/portfolio' target='_blank'>see more of my things here</a></p>", examples=['abys.jpg', 'download (3).jpg'], enable_queue=True).launch()
+
+iface = gr.Interface(fn=predict, inputs=gr.Image(shape=(512,512)), outputs=gr.outputs.Label(num_top_classes=3))
+iface.launch();
+

app.py

@@ -1,29 +1,20 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: drive/MyDrive/Colab Notebooks/fastbook/fastcourse/ch2minimal/tmabrahamgradio/gradiotest/gradiotest.ipynb.
-
-# %% auto 0
-__all__ = ['learn', 'labels', 'predict']
-
-# %% drive/MyDrive/Colab Notebooks/fastbook/fastcourse/ch2minimal/tmabrahamgradio/gradiotest/gradiotest.ipynb 1
+import gradio as gr
 from fastai.vision.all import *
 import skimage
 
-# %% drive/MyDrive/Colab Notebooks/fastbook/fastcourse/ch2minimal/tmabrahamgradio/gradiotest/gradiotest.ipynb 4
 learn = load_learner('export.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))}
-
-
-
-
-# %% drive/MyDrive/Colab Notebooks/fastbook/fastcourse/ch2minimal/tmabrahamgradio/gradiotest/gradiotest.ipynb 7
-import gradio as gr
-#gr.Interface(fn=predict, inputs=gr.Image(shape=(512,512)), outputs=gr.outputs.Label(num_top_classes=3), title="Pet Breed Classifier", description="A pet breed classifier trained on the Oxford Pets dataset using the fastai library (5 epochs) as a proof of concept for Gradio.", article="<p style='text-align: center'><a href='https://gameologist.com/portfolio' target='_blank'>see more of my things here</a></p>", examples=['abys.jpg', 'download (3).jpg'], enable_queue=True).launch()
-
-iface = gr.Interface(fn=predict, inputs=gr.Image(shape=(512,512)), outputs=gr.outputs.Label(num_top_classes=3))
-iface.launch();
-
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+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 = ['abys.jpg', 'download (3).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()