minor fixes for app.py

app.py

@@ -3,27 +3,13 @@ from utils.predict import predict_action
 import os
 import glob
 
-##Create Dataset for loading examples
+##Create list of examples to be loaded
 example_list = glob.glob("examples/*")
 example_list = list(map(lambda el:[el], example_list))
 
 
-# def load_example(video):
-#     return video[0]
-
 demo = gr.Blocks()
 
-# input_video = gr.Video(label="Input Video", show_label=True)
-# output_label = gr.Label(label="Model Output", show_label=True)
-# output_gif = gr.Image(label="Video Gif", show_label=True)
-# title = "Video Classification with Transformers"
-# description = "This space demonstrates the use of a hybrid (CNN-Transformer based) model for video classification. \n The model can classify videos belonging to the following action categories: CricketShot, Punch, ShavingBeard, TennisSwing, PlayingCello. \n Upload a video and try out 🤗 "
-
-# article = '\n Demo created by: <a href=\"https://www.linkedin.com/in/shivalika-singh/\">Shivalika Singh</a> <br> Based on this <a href=\"https://keras.io/examples/vision/video_transformers/\">Keras example</a> by <a href=\"https://twitter.com/RisingSayak\">Sayak Paul</a> <br> Demo Powered by this <a href=\"https://huggingface.co/shivi/video-transformers/\"> Video Classification</a> model'
-
-# gr.Interface(predict_action, input_video, [output_label, output_gif], examples=example_list, allow_flagging=False, analytics_enabled=False,
-#   title=title, description=description, cache_examples=True, article=article).launch(enable_queue=True,share=True)
-
 
 with demo:
     
@@ -47,16 +33,14 @@ with demo:
                     submit_button = gr.Button("Submit")
             
             gr.Markdown("**Examples:**")
-            gr.Markdown("The model is trained to classify videos belonging to the following classes:")
-            gr.Markdown("CricketShot, PlayingCello, Punch, ShavingBeard, TennisSwing")
+            gr.Markdown("The model is trained to classify videos belonging to the following classes: CricketShot, PlayingCello, Punch, ShavingBeard, TennisSwing")
+            # gr.Markdown("CricketShot, PlayingCello, Punch, ShavingBeard, TennisSwing")
 
             with gr.Column():
                 gr.Examples(example_list, [input_video], [output_label,output_gif], predict_action, cache_examples=True)
-                # examples = gr.components.Dataset(components=[input_video], samples=example_list, type='values')
-                # examples.click(load_example, examples, input_video)
         
     submit_button.click(predict_action, inputs=input_video, outputs=[output_label,output_gif])
     
-    gr.Markdown('\n Author: <a href=\"https://www.linkedin.com/in/shivalika-singh/\">Shivalika Singh</a> <br> Based on this <a href=\"https://keras.io/examples/vision/video_transformers/\">Keras example</a> by <a href=\"https://twitter.com/RisingSayak\">Sayak Paul</a> <br> Demo Powered by this <a href=\"https://huggingface.co/shivi/video-transformers/\"> Video Classification</a> model')
+    gr.Markdown('\n Demo created by: <a href=\"https://www.linkedin.com/in/shivalika-singh/\">Shivalika Singh</a> <br> Based on this <a href=\"https://keras.io/examples/vision/video_transformers/\">Keras example</a> by <a href=\"https://twitter.com/RisingSayak\">Sayak Paul</a> <br> Demo Powered by this <a href=\"https://huggingface.co/shivi/video-transformers/\"> Video Classification</a> model')
     
 demo.launch()