Update Space

app.py

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+import gradio as gr
+from huggingface_hub import hf_hub_download
+from huggingface_hub import HfApi, login
+import os
+import yaml
+from pathlib import Path
+from loguru import logger
+
+SPACE_REPO = "c-gohlke/litrl"
+SPACE_REPO_TYPE = "space"
+MODEL_REPO = "c-gohlke/litrl"
+MODEL_REPO_TYPE = "model"
+
+ENV_RESULTS_FILE_DEPTH = 3
+
+hf_api = HfApi()
+login(  # type: ignore[no-untyped-call]
+    token=os.environ.get("HUGGINGFACE_TOKEN"),
+    add_to_git_credential=True,
+    new_session=False,
+)
+
+def get_best_gif(env: str) -> Path:
+    hf_env_results_path = f"models/{env}/results.yaml"
+    local_env_results_path = hf_hub_download(repo_id=MODEL_REPO, repo_type=MODEL_REPO_TYPE, filename=hf_env_results_path)
+    with open(local_env_results_path, "r") as f:
+        env_results = yaml.load(f, Loader=yaml.FullLoader)
+    best_model_type = max(env_results, key=lambda model: env_results[model])
+    model_results_path = f"models/{env}/{best_model_type}/results.yaml"
+    local_model_results_path = hf_hub_download(repo_id=MODEL_REPO, repo_type=MODEL_REPO_TYPE, filename=model_results_path)
+    with open(local_model_results_path, "r") as f:
+        model_results = yaml.load(f, Loader=yaml.FullLoader)
+
+    best_model = max(model_results, key=lambda model: model_results[model])
+    hf_gif_path = f"models/{env}/{best_model_type}/{best_model}/demo.gif"
+    return Path(hf_hub_download(repo_id=MODEL_REPO, repo_type=MODEL_REPO_TYPE, filename=hf_gif_path))
+
+
+
+def get_environments() -> list[str]:
+    environments = []
+    files = hf_api.list_repo_files(MODEL_REPO, repo_type=MODEL_REPO_TYPE)
+    for file in files:
+        vals = file.split("/")
+        # e.g. ['models', 'CartPole-v1', 'results.yaml']
+        if len(vals) == ENV_RESULTS_FILE_DEPTH and vals[2] == "results.yaml" and vals[0] == "models":
+            environments.append(vals[1])
+    return environments
+
+def get_gif_paths(environments: list[str]) -> dict[str, str]:
+    gif_paths = {}
+    for env in environments:
+        gif_paths[env] = get_best_gif(env)
+
+def run_demo() -> None:
+    environments = get_environments()
+    gif_paths = get_gif_paths(environments)
+
+    def api_get_text(env_id: str)-> str:
+        logger.info(f"Getting text for {env_id}")
+        return  gr.Markdown("# Greetings from LitRL!")
+
+    def api_predict(env_id: str)-> gr.Image|None:
+        if env_id not in gif_paths:
+            logger.error(f"Environment {env_id} not found in {gif_paths}")
+            return None
+        return gr.Image(gif_paths[env_id], type="filepath")
+
+    with gr.Blocks() as demo:
+        md = gr.Markdown("# Greetings from LitRL!")
+        env = gr.Dropdown(choices=gif_paths.keys(), value="CartPole-v1", label="Environment")
+        button = gr.Button(value="Submit")
+        cartpole_out = gr.Image(gif_paths[env.value], type="filepath")
+
+        button.click(
+            fn=api_get_text,
+            inputs=env,
+            outputs=md,
+            api_name="get_text",
+        )
+        button.click(
+            fn=api_predict,
+            inputs=env,
+            outputs=cartpole_out,
+            api_name="predict",
+        )
+        demo.launch()
+
+if __name__ == "__main__":
+    run_demo()

requirements.txt

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+loguru==0.7.2