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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))}
+ 
+title = "Liveness Classification using Photo"
+description = "Liveness classification using Adobe Antialiased model 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 = ['modelo_cropped1.jpg']
+interpretation='default'
+enable_queue=True
+
+gr.Interface(fn=predict,inputs=gr.inputs.Image(source="upload",shape=(224, 224), tool="editor"),
+                        outputs=gr.Label(num_top_classes=2),
+                        title=title,description=description,article=article,enable_queue=enable_queue)
+                        .launch()