changed blocks app to interface based

app.py

@@ -8,45 +8,56 @@ example_list = glob.glob("examples/*")
 example_list = list(map(lambda el:[el], example_list))
 
 
-def load_example(video):
-    return video[0]
+# def load_example(video):
+#     return video[0]
 
-demo = gr.Blocks()
+# 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'
 
-with demo:
+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:
     
-    gr.Markdown("# **<p align='center'>Video Classification with Transformers</p>**")
-    gr.Markdown("This space demonstrates the use of hybrid Transformer-based models for video classification that operate on CNN feature maps.")
+#     gr.Markdown("# **<p align='center'>Video Classification with Transformers</p>**")
+#     gr.Markdown("This space demonstrates the use of hybrid Transformer-based models for video classification that operate on CNN feature maps.")
     
-    with gr.Tabs():
+#     with gr.Tabs():
                 
-        with gr.TabItem("Upload & Predict"):
-            with gr.Box():
+#         with gr.TabItem("Upload & Predict"):
+#             with gr.Box():
                 
-                with gr.Row():
-                    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)
+#                 with gr.Row():
+#                     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)
             
-            gr.Markdown("**Predict**")
+#             gr.Markdown("**Predict**")
             
-            with gr.Box():
-                with gr.Row():
-                    submit_button = gr.Button("Submit")
+#             with gr.Box():
+#                 with gr.Row():
+#                     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")
-
-            with gr.Column():
-                # gr.Examples("examples", [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)
+#             gr.Markdown("**Examples:**")
+#             gr.Markdown("The model is trained to classify videos belonging to the following classes:")
+#             gr.Markdown("CricketShot, PlayingCello, Punch, ShavingBeard, TennisSwing")
+
+#             with gr.Column():
+#                 # gr.Examples("examples", [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])
+#     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 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')
     
-demo.launch()
+# demo.launch()
+