upload files

app.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../../lesson_2.ipynb.
+
+# %% auto 0
+import gradio as gr
+__all__ = ['learn_inf', 'labels', 'title', 'description', 'article',
+           'examples', 'interpretation', 'enable_queue', 'predict']
+
+# %% ../../lesson_2.ipynb 0
+import fastai
+
+# %% ../../lesson_2.ipynb 1
+import pandas
+
+# %% ../../lesson_2.ipynb 2
+from fastai.vision.widgets import *
+
+# %% ../../lesson_2.ipynb 3
+from fastai.vision.all import *
+
+# %% ../../lesson_2.ipynb 4
+learn_inf = load_learner("./export.pkl")
+
+# %% ../../lesson_2.ipynb 6
+labels = learn_inf.dls.vocab
+
+# %% ../../lesson_2.ipynb 7
+
+
+def predict(img):
+    img = PILImage.create(img)
+    pred, pred_idx, probs = learn_inf.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+
+# %% ../../lesson_2.ipynb 8
+
+# %% ../../lesson_2.ipynb 9
+title = "Car Classifier"
+description = "Upload the image of a car to get its type. The model uses the resnet18 trained on a variety of images of cars."
+article = "<p style='text-align: center'><a href='https://github.com/aar2dee2' target='_blank'>Made by aar2dee2</a></p>"
+examples = ['car2.jpeg', 'car3.jpeg', 'car4.jpeg',
+            'car5.jpg', 'car6.jpg', 'car7.jpg']
+interpretation = 'default'
+enable_queue = True
+
+# %% ../../lesson_2.ipynb 10
+gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(shape=(512, 512)),
+    outputs=gr.outputs.Label(num_top_classes=3),
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+    interpretation=interpretation,
+    enable_queue=enable_queue).launch(share=True)

export.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:03e957b52ed457b7dc7bef736871089df633e0a0c7174099b89e7f37f0bc5caa
+size 47023631

requirements.txt

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+fastai
+scikit-image