Download smoldataenv_opencode/ui.py from FineEnvs/smoldataenv-multi-harness-opencode: direct link, hf CLI and curl.
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https://huggingface.co/spaces/FineEnvs/smoldataenv-multi-harness-opencode/resolve/main/smoldataenv_opencode/ui.py
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hf download hf://spaces/FineEnvs/smoldataenv-multi-harness-opencode/smoldataenv_opencode/ui.py
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curl -L -o ui.py https://huggingface.co/spaces/FineEnvs/smoldataenv-multi-harness-opencode/resolve/main/smoldataenv_opencode/ui.py
1.33 kB
| """Use OpenEnv's playground with connection examples for this environment.""" | |
| from openenv.core.env_server.gradio_ui import build_gradio_app | |
| QUICK_START = """ | |
| This environment runs native OpenCode in Daytona and returns its grade and | |
| training trace. Configure `SANDBOX_VLLM_URL` and `SANDBOX_VLLM_KEY` on the server | |
| before a rollout. The endpoint must return token IDs and logprobs for training. | |
| Click **Reset**, set Type to `call_tool`, and use Tool Name `run_rollout`. | |
| Arguments require `split`, `task_name`, `model` and a `sampling` object. | |
| Use the Task API at `/docs` to find a task name. | |
| From Python: | |
| ```python | |
| import requests | |
| from openenv.core.mcp_client import MCPToolClient | |
| url = "https://fineenvs-smoldataenv-multi-harness-opencode.hf.space" | |
| task = requests.post(url + "/smoldataenv_opencode/task", | |
| json={"split": "test", "index": 0}).json()["task"] | |
| with MCPToolClient(url, message_timeout_s=1800).sync() as env: | |
| env.reset() | |
| result = env.call_tool("run_rollout", split="test", task_name=task["name"], | |
| model="YOUR_SERVED_MODEL", sampling={"temperature": 0.8}) | |
| ``` | |
| """ | |
| def build_ui(manager, fields, metadata, is_chat, title, quick_start): | |
| return build_gradio_app( | |
| manager, fields, metadata, is_chat, title=title, quick_start_md=QUICK_START | |
| ) | |