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()