Autarch Will Control The Internet
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DigiJ
283
web/routes/chat.py
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283
web/routes/chat.py
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"""Chat and Agent API routes — Hal chat with Agent system for module creation."""
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import json
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import threading
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import time
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import uuid
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from pathlib import Path
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from flask import Blueprint, request, jsonify, Response
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from web.auth import login_required
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chat_bp = Blueprint('chat', __name__, url_prefix='/api')
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_agent_runs: dict = {} # run_id -> {'steps': [], 'done': bool, 'stop': threading.Event}
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_system_prompt = None
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def _get_system_prompt():
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"""Load the Hal system prompt from data/hal_system_prompt.txt."""
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global _system_prompt
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if _system_prompt is None:
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prompt_path = Path(__file__).parent.parent.parent / 'data' / 'hal_system_prompt.txt'
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if prompt_path.exists():
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_system_prompt = prompt_path.read_text(encoding='utf-8')
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else:
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_system_prompt = (
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"You are Hal, the AI agent for AUTARCH. You can create new modules, "
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"run shell commands, read and write files. When asked to create a module, "
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"use the create_module tool."
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)
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return _system_prompt
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def _ensure_model_loaded():
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"""Load the LLM model if not already loaded. Returns (llm, error)."""
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from core.llm import get_llm, LLMError
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llm = get_llm()
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if not llm.is_loaded:
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try:
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llm.load_model(verbose=False)
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except LLMError as e:
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return None, str(e)
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return llm, None
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@chat_bp.route('/chat', methods=['POST'])
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@login_required
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def chat():
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"""Handle chat messages — direct chat or agent mode based on user toggle.
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Streams response via SSE."""
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data = request.get_json(silent=True) or {}
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message = data.get('message', '').strip()
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mode = data.get('mode', 'chat') # 'chat' (default) or 'agent'
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if not message:
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return jsonify({'error': 'No message provided'})
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if mode == 'agent':
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return _handle_agent_chat(message)
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else:
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return _handle_direct_chat(message)
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def _handle_direct_chat(message):
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"""Direct chat mode — streams tokens from the LLM without the Agent system."""
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def generate():
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from core.llm import get_llm, LLMError
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llm = get_llm()
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if not llm.is_loaded:
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yield f"data: {json.dumps({'type': 'status', 'content': 'Loading model...'})}\n\n"
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try:
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llm.load_model(verbose=False)
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except LLMError as e:
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yield f"data: {json.dumps({'type': 'error', 'content': f'Failed to load model: {e}'})}\n\n"
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yield f"data: {json.dumps({'done': True})}\n\n"
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return
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system_prompt = _get_system_prompt()
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try:
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token_gen = llm.chat(message, system_prompt=system_prompt, stream=True)
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for token in token_gen:
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yield f"data: {json.dumps({'token': token})}\n\n"
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except LLMError as e:
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yield f"data: {json.dumps({'type': 'error', 'content': str(e)})}\n\n"
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yield f"data: {json.dumps({'done': True})}\n\n"
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return Response(generate(), mimetype='text/event-stream',
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headers={'Cache-Control': 'no-cache', 'X-Accel-Buffering': 'no'})
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def _handle_agent_chat(message):
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"""Agent mode — uses the Agent system with tools for complex tasks."""
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run_id = str(uuid.uuid4())
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stop_event = threading.Event()
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steps = []
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_agent_runs[run_id] = {'steps': steps, 'done': False, 'stop': stop_event}
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def worker():
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try:
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from core.agent import Agent
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from core.tools import get_tool_registry
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from core.llm import get_llm, LLMError
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llm = get_llm()
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if not llm.is_loaded:
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steps.append({'type': 'status', 'content': 'Loading model...'})
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try:
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llm.load_model(verbose=False)
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except LLMError as e:
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steps.append({'type': 'error', 'content': f'Failed to load model: {e}'})
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return
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tools = get_tool_registry()
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agent = Agent(llm=llm, tools=tools, max_steps=20, verbose=False)
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# Inject system prompt into agent
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system_prompt = _get_system_prompt()
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agent.SYSTEM_PROMPT = system_prompt + "\n\n{tools_description}"
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def on_step(step):
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if step.thought:
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steps.append({'type': 'thought', 'content': step.thought})
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if step.tool_name and step.tool_name not in ('task_complete', 'ask_user'):
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steps.append({'type': 'action', 'content': f"{step.tool_name}({json.dumps(step.tool_args or {})})"})
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if step.tool_result:
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result = step.tool_result
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if len(result) > 800:
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result = result[:800] + '...'
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steps.append({'type': 'result', 'content': result})
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result = agent.run(message, step_callback=on_step)
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if result.success:
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steps.append({'type': 'answer', 'content': result.summary})
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else:
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steps.append({'type': 'error', 'content': result.error or result.summary})
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except Exception as e:
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steps.append({'type': 'error', 'content': str(e)})
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finally:
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_agent_runs[run_id]['done'] = True
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threading.Thread(target=worker, daemon=True).start()
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# Stream the agent steps as SSE
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def generate():
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run = _agent_runs.get(run_id)
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if not run:
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yield f"data: {json.dumps({'error': 'Run not found'})}\n\n"
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return
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sent = 0
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while True:
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current_steps = run['steps']
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while sent < len(current_steps):
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yield f"data: {json.dumps(current_steps[sent])}\n\n"
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sent += 1
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if run['done']:
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yield f"data: {json.dumps({'done': True})}\n\n"
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return
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time.sleep(0.15)
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return Response(generate(), mimetype='text/event-stream',
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headers={'Cache-Control': 'no-cache', 'X-Accel-Buffering': 'no'})
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@chat_bp.route('/chat/reset', methods=['POST'])
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@login_required
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def chat_reset():
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"""Clear LLM conversation history."""
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try:
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from core.llm import get_llm
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llm = get_llm()
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if hasattr(llm, 'clear_history'):
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llm.clear_history()
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elif hasattr(llm, 'reset'):
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llm.reset()
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elif hasattr(llm, 'conversation_history'):
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llm.conversation_history = []
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except Exception:
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pass
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return jsonify({'ok': True})
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@chat_bp.route('/chat/status')
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@login_required
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def chat_status():
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"""Get LLM model status."""
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try:
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from core.llm import get_llm
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llm = get_llm()
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return jsonify({
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'loaded': llm.is_loaded,
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'model': llm.model_name if llm.is_loaded else None,
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})
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except Exception as e:
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return jsonify({'loaded': False, 'error': str(e)})
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@chat_bp.route('/agent/run', methods=['POST'])
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@login_required
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def agent_run():
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"""Start an autonomous agent run in a background thread. Returns run_id."""
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data = request.get_json(silent=True) or {}
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task = data.get('task', '').strip()
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if not task:
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return jsonify({'error': 'No task provided'})
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run_id = str(uuid.uuid4())
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stop_event = threading.Event()
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steps = []
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_agent_runs[run_id] = {'steps': steps, 'done': False, 'stop': stop_event}
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def worker():
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try:
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from core.agent import Agent
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from core.tools import get_tool_registry
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from core.llm import get_llm, LLMError
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llm = get_llm()
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if not llm.is_loaded:
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try:
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llm.load_model(verbose=False)
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except LLMError as e:
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steps.append({'type': 'error', 'content': f'Failed to load model: {e}'})
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return
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tools = get_tool_registry()
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agent = Agent(llm=llm, tools=tools, verbose=False)
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# Inject system prompt
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system_prompt = _get_system_prompt()
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agent.SYSTEM_PROMPT = system_prompt + "\n\n{tools_description}"
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def on_step(step):
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steps.append({'type': 'thought', 'content': step.thought})
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if step.tool_name and step.tool_name not in ('task_complete', 'ask_user'):
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steps.append({'type': 'action', 'content': f"{step.tool_name}({json.dumps(step.tool_args or {})})"})
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if step.tool_result:
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steps.append({'type': 'result', 'content': step.tool_result[:800]})
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agent.run(task, step_callback=on_step)
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except Exception as e:
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steps.append({'type': 'error', 'content': str(e)})
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finally:
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_agent_runs[run_id]['done'] = True
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threading.Thread(target=worker, daemon=True).start()
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return jsonify({'run_id': run_id})
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@chat_bp.route('/agent/stream/<run_id>')
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@login_required
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def agent_stream(run_id):
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"""SSE stream of agent steps for a given run_id."""
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def generate():
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run = _agent_runs.get(run_id)
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if not run:
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yield f"data: {json.dumps({'error': 'Run not found'})}\n\n"
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return
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sent = 0
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while True:
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current_steps = run['steps']
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while sent < len(current_steps):
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yield f"data: {json.dumps(current_steps[sent])}\n\n"
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sent += 1
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if run['done']:
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yield f"data: {json.dumps({'done': True})}\n\n"
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return
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time.sleep(0.15)
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return Response(generate(), mimetype='text/event-stream',
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headers={'Cache-Control': 'no-cache', 'X-Accel-Buffering': 'no'})
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@chat_bp.route('/agent/stop/<run_id>', methods=['POST'])
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@login_required
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def agent_stop(run_id):
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"""Signal a running agent to stop."""
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run = _agent_runs.get(run_id)
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if run:
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run['stop'].set()
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run['done'] = True
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return jsonify({'stopped': bool(run)})
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