initial commit

app.py

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+import gradio as gr
+import os
+
+from fastai.vision.all import *
+from utils import get_example_images
+
+learn = load_learner("vegetable-classifier.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 = "Classify vegetables"
+description = "A vegetable classifier trained on the vegetable-image-dataset from Kaggle. Made for the fastai deep learning course."
+
+
+p = "../01_getting-started-image-classifier/Vegetable Images/test"
+dst_path = "./gradio_example_images"
+
+get_example_images(p, dst_path)
+
+_examples = [f"{dst_path}/{i}" for i in os.listdir(dst_path)]
+
+gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(shape=(512, 512)),
+    outputs=gr.outputs.Label(num_top_classes=3),
+    title=title,
+    description=description,
+    examples=_examples,
+    interpretation="default",
+    enable_queue=True,
+).launch()

requirements.txt

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+fastai
+scikit-image

vegetable-classifier.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:453dd81f03b5d1ac82ef04b1245163f7d3d832cf36dcfbe306c1f2f60676b03a
+size 47246740