Code

app.py

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+from fastai.vision.all import *
+import skimage
+import gradio as gr
+
+def predict(img):
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+
+learn = load_learner('export.pkl')
+labels = learn.dls.vocab
+
+title = "Demodex Blepharitis Classifier"
+description = "A classifier trained on public images to detect Demodex Blepharitis. (Demo)"
+# article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
+interpretation = "default"
+examples = ['projectO/Experiments/phase0/demodex/blepharitis.jpg',
+            'projectO/Experiments/phase0/demodex/demodex.jpg',
+            'projectO/Experiments/phase0/no_demodex/10485-coconut_oil_for_eyelashes_732x549-thumbnail-Recovered-732x549.jpg']
+
+gr.Interface(fn=predict, 
+             inputs=gr.inputs.Image(shape=(512, 512)), 
+             outputs=gr.outputs.Label(num_top_classes=2),
+             title = title,
+             description = description,
+            #  article=article,
+             examples=examples,
+             interpretation=interpretation,
+             enable_queue=True).launch(share=True)