Create app.py

app.py

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+import gradio as gr
+from fastai.vision.all import *
+
+# import skimage
+
+learn = load_learner("models/learner.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 = "NSWX Electrode Classifier"
+description = "Ok, KIV, NG"
+interpretation = "default"
+examples = ["sample_images/" + file for file in files]
+article = "<p style='text-align: center'><a href='https://dicksonneoh.com/' target='_blank'>Blog post</a></p>"
+enable_queue = True
+
+gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(shape=(460, 460)),
+    outputs=gr.outputs.Label(num_top_classes=5),
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+    interpretation=interpretation,
+    enable_queue=enable_queue,
+    capture_session=True
+).launch(share=True, server_name="0.0.0.0")