Create app.py

app.py

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+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+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))}
+
+import os
+for root, dirs, files in os.walk(r'sample_images/'):
+    for filename in files:
+        print(filename)
+
+title = "Pet Breed Classifier"
+description = "A pet breed classifier trained on the Oxford Pets dataset"
+interpretation='default'
+examples = ["sample_images/"+file for file in files] 
+article="<p style='text-align: center'><a href='https://dicksonneoh.com/pet-classifier/' target='_blank'>Blog post</a></p>"
+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()