Update app.py

app.py

@@ -17,7 +17,7 @@ def load_agent(model_id_1, model_id_2):
     results_1 = parse_metrics_accuracy(metadata_1)
     
     # Load the video
-    video_path_1 = hf_hub_download(model_id_1, filename="replay.mp4")
+    #video_path_1 = hf_hub_download(model_id_1, filename="replay.mp4")
     
     # Load the metrics
     metadata_2 = get_metadata(model_id_2)
@@ -26,9 +26,9 @@ def load_agent(model_id_1, model_id_2):
     results_2 = parse_metrics_accuracy(metadata_2)
     
     # Load the video
-    video_path_2 = hf_hub_download(model_id_2, filename="replay.mp4")
+    #video_path_2 = hf_hub_download(model_id_2, filename="replay.mp4")
     
-    return model_id_1, video_path_1, results_1, model_id_2, video_path_2, results_2
+    return model_id_1 results_1, model_id_2, results_2
 
 def parse_metrics_accuracy(meta):
     if "model-index" not in meta:
@@ -58,7 +58,7 @@ def get_metadata(model_id):
 with app:
     gr.Markdown(
     """
-    # Compare Deep Reinforcement Learning Agents 🤖
+    # Compare Sentiment Analysis Models 
     
     Type two models id you want to compare or check examples below.
     """)
@@ -70,19 +70,17 @@ with app:
     with gr.Row():
       with gr.Column():
         model1_name = gr.Markdown()
-        model1_video_output = gr.Video()
+        #model1_video_output = gr.Video()
         model1_score_output = gr.Textbox(label="Mean Reward +/- Std Reward")
       with gr.Column():
         model2_name = gr.Markdown()
-        model2_video_output = gr.Video()
+        #model2_video_output = gr.Video()
         model2_score_output = gr.Textbox(label="Mean Reward +/- Std Reward")
 
-    app_button.click(load_agent, inputs=[model1_input, model2_input], outputs=[model1_name,  model1_video_output, model1_score_output, model2_name, model2_video_output, model2_score_output])
+    app_button.click(load_agent, inputs=[model1_input, model2_input], outputs=[model1_name, model1_score_output, model2_name, model2_score_output])
     
-    examples = gr.Examples(examples=[["sb3/a2c-AntBulletEnv-v0","sb3/ppo-AntBulletEnv-v0"],
-        ["ThomasSimonini/a2c-AntBulletEnv-v0", "sb3/a2c-AntBulletEnv-v0"],
-        ["sb3/dqn-SpaceInvadersNoFrameskip-v4", "sb3/a2c-SpaceInvadersNoFrameskip-v4"], 
-        ["ThomasSimonini/ppo-QbertNoFrameskip-v4","sb3/ppo-QbertNoFrameskip-v4"]],
+    examples = gr.Examples(examples=[["scikit-learn/sentiment-analysis","microsoft/Multilingual-MiniLM-L12-H384"],
+        ["distilbert-base-uncased-finetuned-sst-2-english", "microsoft/Multilingual-MiniLM-L12-H384"],
                            inputs=[model1_input, model2_input])