Create app.py

app.py

@@ -0,0 +1,89 @@
+import gradio as gr
+import requests.exceptions
+from huggingface_hub import HfApi, hf_hub_download
+from huggingface_hub.repocard import metadata_load
+
+app = gr.Blocks()
+
+def load_agent(model_id_1, model_id_2):
+    """
+    This function load the agent's video and results
+    :return: video_path
+    """
+    # Load the metrics
+    metadata_1 = get_metadata(model_id_1)
+    
+    # Get the accuracy
+    results_1 = parse_metrics_accuracy(metadata_1)
+    
+    # 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
+
+def parse_metrics_accuracy(meta):
+    if "model-index" not in meta:
+        return None
+    result = meta["model-index"][0]["results"]
+    metrics = result[0]["metrics"]
+    accuracy = metrics[0]["value"]
+    return accuracy
+
+def get_metadata(model_id):
+    """
+    Get the metadata of the model repo
+    :param model_id:
+    :return: metadata
+    """
+    try:
+        readme_path = hf_hub_download(model_id, filename="README.md")
+        metadata = metadata_load(readme_path)
+        print(metadata)
+        return metadata
+    except requests.exceptions.HTTPError:
+        return None
+
+
+
+
+with app:
+    gr.Markdown(
+    """
+    # Compare Deep Reinforcement Learning Agents 🤖
+    
+    Type two models id you want to compare or check examples below.
+    """)
+    with gr.Row():
+      model1_input = gr.Textbox(label="Model 1")
+      model2_input = gr.Textbox(label="Model 2")
+    with gr.Row():
+      app_button = gr.Button("Compare models")
+    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")
+
+    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])
+    
+    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])
+
+    
+app.launch()