update

app.py

@@ -1,18 +1,33 @@
 import gradio as gr
 from fastai.vision.all import *
+import sys
+import subprocess
 
+# Install fasttransform for model compatibility
+try:
+    import fasttransform  # This makes the Pipeline class available for unpickling
+except ImportError:
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "fasttransform"])
 
 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))}
 
 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>"
+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.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()
+gr.Interface(
+    fn=predict,
+    inputs=gr.Image(shape=(512, 512)),  # Updated to newer Gradio syntax
+    outputs=gr.Label(num_top_classes=3),  # Updated to newer Gradio syntax
+    title=title,
+    description=description,
+    article=article,
+    examples=examples
+).launch()

requirements.txt

@@ -1 +1,3 @@
 fastai
+gradio
+fasttransform