Added more examples.

README.md

@@ -12,6 +12,8 @@ license: apache-2.0
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
-Trying to reproduce Lesson #2 fast.ai
-inspiration: https://huggingface.co/spaces/jph00/testing
-https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial
+Reproduced Lesson #2 fast.ai Practical Deep Learning for Coders.
+
+Inspiration: 
+- https://huggingface.co/spaces/jph00/testing
+- https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial

app.py

@@ -1,25 +1,25 @@
 from fastai.vision.all import *
 import gradio as gr
 import PIL
+import glob
 
+# very careful here, order matters, and it should be the same as when training
+categories = ('black', 'grizzly', 'teddy')
+learner = load_learner('bear_model.pkl')
 
-categories = ('black', 'grizzly', 'teddy')  # very careful here, order matters and it should be the same as when training
 
-learn = load_learner('bear_model.pkl')
-
-
-def classify_image(img):
-    pred, idx, probs = learn.predict(img)
+def classify_image(img: PILImage):
+    pred, idx, probs = learner.predict(img)
     return dict(zip(categories, map(float, probs)))
 
 
 image = gr.inputs.Image(shape=(192, 192))
 label = gr.outputs.Label()
-examples = ['sample_images/black_bear.jpeg', 'sample_images/grizzly_bear.jpeg', 'sample_images/grizzly_bear2.jpeg']
+examples = glob.glob('sample_images/*.*')
 
 intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples, title='Bear Classifier',
                     description='Predicts between black, grizzly, and teddy bears.')
-                    # article="<p style='text-align: center'><a href='some URL' target='_blank'>Bear classifier</a></p>"
+# article="<p style='text-align: center'><a href='some URL' target='_blank'>Bear classifier</a></p>"
 
 
 intf.launch(inline=False)