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app.py

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+import gradio as gr
+from fastai.vision.all import *
+from huggingface_hub import from_pretrained_fastai
+
+repo_id = "hugginglearners/brain-tumor-detection-mri"
+learn = from_pretrained_fastai(repo_id)
+labels = learn.dls.vocab
+
+def predict(img):
+    img = PILImage.create(img)
+    _pred, _pred_w_idx, probs = learn.predict(img)
+    # gradio doesn't support tensors, so converting to float
+    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
+    return labels_probs
+
+interface_options = {
+    "title": "Brain tumor detection for MRI images",
+    "description": "For reference only. Should **not** be used for medical diagnosis",
+    "interpretation": "default",
+    "layout": "horizontal",
+    # Audio from validation file
+#    "examples": [
+#        "100098.jpg",
+#        "100002.jpg",
+#        "100048.jpg"
+#    ],
+    "allow_flagging": "never",
+}
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(shape=(480, 480)),
+    outputs=gr.outputs.Label(num_top_classes=3),
+    **interface_options,
+)
+
+launch_options = {
+    "enable_queue": True,
+    "share": False,
+}
+
+demo.launch(**launch_options)