Updating app.py per Gradio tutorial

app.py

@@ -1,15 +1,25 @@
 from fastai.vision.all import *
 import gradio as gr
+import skimage
 
 learn = load_learner('bearmodel.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 = "Bear Breed Classifier"
+description = "A bear breed classifier trained on a custom dataset from DDG images with fastai. Created as a demo for Gradio and HuggingFace Spaces."
+interpretation='default'
 image = gr.Image(height=192, width=192)
 label = gr.Label()
+examples = ['black bear.jpg', 'grizzly bear.jpg']
+
+
+enable_queue=True
 
 intf = gr.Interface(fn=predict, inputs=image, outputs=label)
 intf.launch(inline=False)

requirements.txt

@@ -1 +1,2 @@
-fastai
+fastai
+scikit-image