Modify to show only 1 model

app.py

@@ -5,7 +5,7 @@ from huggingface_hub.repocard import metadata_load
 
 app = gr.Blocks()
 
-def load_agent(model_id_1, model_id_2):
+def load_agent(model_id_1):
     """
     This function load the agent's video and results
     :return: video_path
@@ -19,16 +19,7 @@ def load_agent(model_id_1, model_id_2):
     # Load the video
     video_path_1 = hf_hub_download(model_id_1, filename="replay.mp4")
     
-    # Load the metrics
-    metadata_2 = get_metadata(model_id_2)
-    
-    # Get the accuracy
-    results_2 = parse_metrics_accuracy(metadata_2)
-    
-    # Load the video
-    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, video_path_1, results_1
 
 def parse_metrics_accuracy(meta):
     if "model-index" not in meta:
@@ -58,32 +49,26 @@ def get_metadata(model_id):
 with app:
     gr.Markdown(
     """
-    # Compare Deep Reinforcement Learning Agents 🤖
+    # Observa los agentes de Lagomorph en acción 🤖
     
-    Type two models id you want to compare or check examples below.
+    Selecciona el modelo a observar.
     """)
     with gr.Row():
-      model1_input = gr.Textbox(label="Model 1")
-      model2_input = gr.Textbox(label="Model 2")
+      model1_input = gr.Textbox(label="Modelo")
     with gr.Row():
-      app_button = gr.Button("Compare models")
+      app_button = gr.Button("Mostrar modelo")
     with gr.Row():
       with gr.Column():
         model1_name = gr.Markdown()
         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_score_output = gr.Textbox(label="Mean Reward +/- Std Reward")
+        model1_score_output = gr.Textbox(label="Recompensa +/- Variación")
 
-    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], outputs=[model1_name,  model1_video_output, model1_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"]],
-                           inputs=[model1_input, model2_input])
+    examples = gr.Examples(examples=[["odiaz1066/CartPoleGoal-Lagomorph-seed42"],
+        ["odiaz1066/BreakoutNoFrameskip-v4-dqn_atari-seed1"],
+        ["odiaz1066/PongNoFrameskip-v4-dqn_atari-seed1"]],
+        inputs=[model1_input])
 
     
 app.launch()