Created based on fastai tutorial

.gitattributes

@@ -32,3 +32,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,2 @@
+*.png
+*.jpg

app.py

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+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+learn = load_learner('model.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))}
+
+# Create the gradio interface with title, description, default interpretation, and examples (car.jpg and scooter.png) that shows only the top 1 prediction
+
+gr.Interface(fn=predict,
+        title="Car or Scooter",
+        description="Upload an image of a car or scooter and we'll tell you which one it is.",
+        inputs=gr.inputs.Image(shape=(192, 192)),
+        outputs=gr.outputs.Label(num_top_classes=1),
+        interpretation="default",
+        examples=[["car.jpg"], ["scooter.png"]]
+    ).launch()

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:092c988452ea874f98140ca906003abc6585755f313a106c8f7ce3d5864287fc
+size 46955567

requirements.txt

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+fastai
+scikit-image
+