| """Hub Scout — an AGENTS.md-driven agent. |
| |
| The agent's entire instruction set lives in AGENTS.md, loaded verbatim at |
| startup and passed as the system prompt. app.py only wires tools and the |
| chat loop; edit AGENTS.md to change behavior. |
| |
| Run locally: |
| export HF_TOKEN=hf_... # token with Inference access |
| pip install -r requirements.txt |
| python app.py # opens http://127.0.0.1:7860 |
| """ |
|
|
| import json |
| import os |
|
|
| import gradio as gr |
| from huggingface_hub import HfApi, InferenceClient |
|
|
| HERE = os.path.dirname(os.path.abspath(__file__)) |
| MODEL_ID = os.environ.get("AGENT_MODEL", "Qwen/Qwen2.5-72B-Instruct") |
| HF_TOKEN = os.environ.get("HF_TOKEN") |
|
|
| with open(os.path.join(HERE, "AGENTS.md"), encoding="utf-8") as f: |
| AGENTS_MD = f.read() |
| assert AGENTS_MD.strip(), "AGENTS.md is empty — the agent would have no instructions" |
|
|
| client = InferenceClient(model=MODEL_ID, provider="auto", token=HF_TOKEN) |
| hf_api = HfApi(token=HF_TOKEN) |
|
|
| TOOLS = [ |
| { |
| "type": "function", |
| "function": { |
| "name": "search_hf_models", |
| "description": ( |
| "Search the Hugging Face Hub for model repos matching keywords, " |
| "ranked by likes. Returns repo ids with likes and downloads." |
| ), |
| "parameters": { |
| "type": "object", |
| "properties": { |
| "query": { |
| "type": "string", |
| "description": "Search keywords, e.g. 'multilingual embeddings'", |
| }, |
| "limit": { |
| "type": "integer", |
| "description": "How many results to return (default 5, max 10)", |
| }, |
| "task": { |
| "type": "string", |
| "description": ( |
| "Optional pipeline_tag filter, e.g. 'text-generation', " |
| "'feature-extraction', 'text-classification'" |
| ), |
| }, |
| }, |
| "required": ["query"], |
| }, |
| }, |
| }, |
| { |
| "type": "function", |
| "function": { |
| "name": "get_model_stats", |
| "description": ( |
| "Get current stats for one model repo: downloads, likes, " |
| "pipeline tag and last-modified date." |
| ), |
| "parameters": { |
| "type": "object", |
| "properties": { |
| "repo_id": { |
| "type": "string", |
| "description": "Model repo id, e.g. 'Qwen/Qwen2.5-72B-Instruct'", |
| } |
| }, |
| "required": ["repo_id"], |
| }, |
| }, |
| }, |
| ] |
|
|
|
|
| def _search_hf_models(query, limit=5, task=None): |
| limit = max(1, min(int(limit or 5), 10)) |
| kwargs = {"search": query, "limit": limit, "sort": "likes", "direction": -1} |
| if task: |
| kwargs["pipeline_tag"] = task |
| lines = [ |
| f"- {m.id} (likes={getattr(m, 'likes', '?')}, downloads={getattr(m, 'downloads', '?')})" |
| for m in hf_api.list_models(**kwargs) |
| ] |
| return "\n".join(lines) if lines else "No models matched that query." |
|
|
|
|
| def _get_model_stats(repo_id): |
| info = hf_api.model_info(repo_id) |
| return ( |
| f"{info.id}: pipeline_tag={getattr(info, 'pipeline_tag', None)}, " |
| f"downloads={getattr(info, 'downloads', None)}, likes={getattr(info, 'likes', None)}, " |
| f"lastModified={getattr(info, 'lastModified', None)}" |
| ) |
|
|
|
|
| def run_tool(name, arguments): |
| try: |
| args = json.loads(arguments or "{}") |
| except json.JSONDecodeError: |
| return f"Invalid JSON arguments: {arguments!r}" |
| try: |
| if name == "search_hf_models": |
| return _search_hf_models(args["query"], args.get("limit", 5), args.get("task")) |
| if name == "get_model_stats": |
| return _get_model_stats(args["repo_id"]) |
| return f"Unknown tool: {name}" |
| except Exception as exc: |
| return f"Tool '{name}' failed: {type(exc).__name__}: {exc}" |
|
|
|
|
| MAX_TOOL_ROUNDS = 3 |
|
|
|
|
| def run_agent(message, history): |
| """history is OpenAI-style dicts: {"role": "user"|"assistant", "content": str}.""" |
| messages = [{"role": "system", "content": AGENTS_MD}] |
| messages.extend(history) |
| messages.append({"role": "user", "content": message}) |
|
|
| reply = "" |
| msg = None |
| for _ in range(MAX_TOOL_ROUNDS): |
| resp = client.chat_completion( |
| messages=messages, tools=TOOLS, tool_choice="auto", max_tokens=1024 |
| ) |
| msg = resp.choices[0].message |
| calls = getattr(msg, "tool_calls", None) or [] |
| if not calls: |
| reply = msg.content or "" |
| break |
| |
| messages.append( |
| { |
| "role": "assistant", |
| "content": msg.content or "", |
| "tool_calls": [ |
| { |
| "id": tc.id, |
| "type": "function", |
| "function": { |
| "name": tc.function.name, |
| "arguments": tc.function.arguments, |
| }, |
| } |
| for tc in calls |
| ], |
| } |
| ) |
| for tc in calls: |
| result = run_tool(tc.function.name, tc.function.arguments) |
| messages.append({"role": "tool", "tool_call_id": tc.id, "content": result}) |
| else: |
| reply = msg.content or "(I stopped after 3 tool rounds.)" |
| return reply |
|
|
|
|
| demo = gr.ChatInterface( |
| fn=run_agent, |
| title="Hub Scout — an AGENTS.md agent", |
| description=( |
| f"Instructions live in <code>AGENTS.md</code> and are loaded verbatim at startup. " |
| f"Model: <code>{MODEL_ID}</code>. Tools: search_hf_models, get_model_stats." |
| ), |
| examples=[ |
| "Find me a small chat model that runs on a laptop CPU.", |
| "What's the most downloaded sentence-embedding model right now?", |
| "How actively used is Qwen/Qwen2.5-72B-Instruct?", |
| ], |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
