Update app.py

app.py

@@ -1,26 +1,28 @@
-import gradio as gr
 from fastai.vision.all import *
 from fastai.vision.widgets import *
 from fastai.vision.core import PILImage
 from fastai.learner import load_learner
-import skimage
-
-# Define the is_cat function that was used when creating the model
-def is_cat(x): return x[0].isupper()  # This is a common implementation, adjust if yours was different
+def is_cat(x):
+    return x[0].isupper()
 
+# Load the learner
 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')
+def classify_image(img):
+  pred,idx,probs = learn.predict(img)
+  return dict(zip(categories, map(float,probs)))
+
+#gradio interface
+import gradio as gr
+from gradio import Interface, Image as grImage, Label
+from PIL import Image
 
-title = "Cat v/s Dog Classifier"
-description = "Cat v/s Dog classifier "
-article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
+# Create input and output components
+image = gr.Image()
+label = gr.Label()
 examples = ['siamese.jpg']
-interpretation='default'
-enable_queue=True
+# Create Gradio interface
+intf = Interface(fn=classify_image, inputs=image, outputs=label, title="CAT and DOG Classifier", examples = examples)
 
-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.launch(inline=False)