Created interface and app file.

app.py

@@ -0,0 +1,42 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
+
+# %% auto 0
+__all__ = ['learner', 'cats', 'img', 'lbl', 'examples', 'title', 'description', 'article', 'interface', 'classify_img']
+
+# %% app.ipynb 3
+from fastai.vision.all import *
+import gradio as gr
+
+# %% app.ipynb 6
+learner = load_learner('model/export.pkl')
+
+# %% app.ipynb 9
+cats = ('Grizzly Bear', 'Black Bear', 'Teddy Bear',)
+
+def classify_img(img):
+    preds, idx, probs = learner.predict(img)
+    return dict(zip(cats, map(float, probs)))
+
+# %% app.ipynb 12
+img = gr.Image()
+lbl = gr.Label()
+examples = [str(img_path) for img_path in Path('example_images/').rglob('*.jpg')]
+
+title = 'Bear Classifier'
+description = 'My first AI model that can tell you whether an image contains a grizzly bear, a black bear, or a teddy bear. This model was trained on the ' \
+              'ResNet18 architecture and used the fastai library.'
+article = "<p style='text-align: center; font-size: 36px'><a href='https://forbo7.github.io/forblog/posts/2_bear_classifier_model.html' " \
+          "targets='_blank'><Blog Post</a></p>"
+
+# %% app.ipynb 15
+interface = gr.Interface(
+    fn=classify_img,
+    inputs='image',
+    outputs='label',
+    examples=examples,
+    title=title,
+    description=description,
+    article=article
+)
+
+interface.launch(inline=False, enable_queue=True)