refactor

app.py

@@ -1,6 +1,7 @@
 from fastai.vision.all import *
 import gradio as gr
 import logging
+EXAMPLES_PATH = Path('./examples')
 
 learn = load_learner('watersports.pkl')
 categories = learn.dls.vocab
@@ -11,14 +12,21 @@ def classify_image(img):
     pred,idx,probs = learn.predict(img)
     return( dict(zip(categories, map(float,probs))))
 
+interface_options = {
+    "title": "Which Watersport? Compare your guess to the machine inferencing.z",
+    "description": "Click an image and click submit. Drag an image into the classifier.  Take a close look and guess it yourself, before hitting **\<Submit\>**. \n \
+You need to **\<Clear\>** before dragging next image.  Other images can be dragged directly from google search.",
+    "examples" : [f'{EXAMPLES_PATH}/{f.name}' for f in EXAMPLES_PATH.iterdir()],
+}                   
+                      
 title = "Which Watersport? Compare your guess to the machine inferencing.y"
-description = "CLick an image and click submit. Drag an image into the classifier.  Take a close look and guess it yourself, before hitting **\<Submit\>**. \n \
+description = "Click an image and click submit. Drag an image into the classifier.  Take a close look and guess it yourself, before hitting **\<Submit\>**. \n \
 You need to **\<Clear\>** before dragging next image.  Other images can be dragged directly from google search."
-image = gr.inputs.Image(shape=(192,192))
-label = gr.outputs.Label()
-examples = './examples'
 
-iface = gr.Interface(fn=classify_image, inputs=image, outputs=label, \
-        examples=examples, examples_per_page=100, title=title, description=description)
+iface = gr.Interface(fn=classify_image, 
+            inputs=gr.inputs.Image(shape=(192,192)), 
+            outputs=gr.outputs.Label(),
+            **interface_options)            
+
 iface.launch(inline=False)