Update app.py

app.py

@@ -1,30 +1,22 @@
+
 import gradio as gr
-from gradio.components import Image, Label
 from fastai.vision.all import *
+import skimage
 
 learn = load_learner('ConvNext_RmsProps.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))}
-
-title = "European Mushroom Common Genus Image Classifier"
-description = "European Mushroom Common Genus Image Classifier trained on the Kaggle dataset https://www.kaggle.com/maysee/mushrooms-classification-common-genuss-imagest with fastai. Created as a demo for Gradio and HuggingFace Spaces."
+ 
+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 = ['Amanita.jpg']
 interpretation='default'
-allow_queue=True  
+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()
 
-gr.Interface(fn=predict,
-             inputs=Image(shape=(224, 224)),  
-             outputs=Label(num_top_classes=3),
-             title=title,
-             description=description,
-             article=article,
-             examples=examples,
-             interpretation=interpretation,
-             allow_queue=allow_queue
-             ).launch()