from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from supervisor_agent import process_query
import logging
import sys

logging.basicConfig(level=logging.INFO, stream=sys.stdout)
logger = logging.getLogger(__name__)

app = FastAPI(title="LangGraph Supervisor Service")

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

class QueryRequest(BaseModel):
    query: str

class QueryResponse(BaseModel):
    response: str
    agent_used: str
    context: dict

@app.get("/health")
async def health():
    return {"status": "healthy", "service": "langgraph-supervisor"}

@app.post("/query", response_model=QueryResponse)
async def query_supervisor(request: QueryRequest):
    """Main entry point for agent orchestration."""
    logger.info(f"Received query: {request.query}")
    
    try:
        result = await process_query(request.query)
        
        return QueryResponse(
            response=result["response"],
            agent_used=result["context"].get("source", "unknown"),
            context=result["context"]
        )
    except Exception as e:
        logger.error(f"Error processing query: {e}")
        return QueryResponse(
            response="Error processing your request",
            agent_used="error",
            context={"error": str(e)}
        )

@app.get("/agents")
async def list_agents():
    """List available specialist agents."""
    return {
        "agents": [
            {
                "name": "librarian",
                "description": "Queries the knowledge base for semantic information",
                "triggers": ["repo", "code", "git", "hobby", "about", "skill"]
            },
            {
                "name": "opencode",
                "description": "Handles coding tasks and file modifications",
                "triggers": ["write", "edit", "create", "fix", "implement"]
            },
            {
                "name": "brain",
                "description": "General LLM for reasoning and generation",
                "triggers": ["default", "general questions"]
            }
        ]
    }

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8090)