# =============================================================
# File: backend/api/routes/agent.py
# =============================================================

from fastapi import APIRouter
from pydantic import BaseModel
import os
import sys
from pathlib import Path

# Add backend to path for imports
backend_dir = Path(__file__).parent.parent.parent
sys.path.insert(0, str(backend_dir))

from api.services.agent_orchestrator import AgentOrchestrator
from api.models.agent import AgentRequest, AgentResponse


router = APIRouter()


orchestrator = AgentOrchestrator(
    rag_mcp_url=os.getenv("RAG_MCP_URL", "http://localhost:8001"),
    web_mcp_url=os.getenv("WEB_MCP_URL", "http://localhost:8002"),
    admin_mcp_url=os.getenv("ADMIN_MCP_URL", "http://localhost:8003"),
    llm_backend=os.getenv("LLM_BACKEND", "ollama")
)


class ChatRequest(BaseModel):
    tenant_id: str
    user_id: str | None = None
    message: str
    conversation_history: list[dict] = []
    temperature: float = 0.0


@router.post("/message", response_model=AgentResponse)
async def agent_chat(req: ChatRequest):
    agent_req = AgentRequest(
        tenant_id=req.tenant_id,
        user_id=req.user_id,
        message=req.message,
        conversation_history=req.conversation_history,
        temperature=req.temperature
    )
    return await orchestrator.handle(agent_req)


@router.post("/debug")
async def agent_debug(req: ChatRequest):
    """
    Returns detailed debugging information about agent reasoning.
    Includes intent classification, tool selection, reasoning trace, and tool traces.
    """
    agent_req = AgentRequest(
        tenant_id=req.tenant_id,
        user_id=req.user_id,
        message=req.message,
        conversation_history=req.conversation_history,
        temperature=req.temperature
    )
    response = await orchestrator.handle(agent_req)
    
    return {
        "request": {
            "tenant_id": req.tenant_id,
            "user_id": req.user_id,
            "message": req.message[:200],
            "temperature": req.temperature
        },
        "response": {
            "text": response.text[:500] + "..." if len(response.text) > 500 else response.text,
            "decision": response.decision.dict() if response.decision else None,
            "tool_traces": response.tool_traces,
            "reasoning_trace": response.reasoning_trace
        },
        "debug_info": {
            "intent": response.reasoning_trace[1].get("intent") if len(response.reasoning_trace) > 1 else None,
            "tool_selection": next((t for t in response.reasoning_trace if t.get("step") == "tool_selection"), None),
            "tool_scores": next((t for t in response.reasoning_trace if t.get("step") == "tool_scoring"), None),
            "redflag_check": next((t for t in response.reasoning_trace if t.get("step") == "redflag_check"), None),
            "total_steps": len(response.reasoning_trace)
        }
    }


@router.post("/plan")
async def agent_plan(req: ChatRequest):
    """
    Returns only the agent's planning output (tool selection decision).
    Useful for understanding what tools the agent would use without executing them.
    """
    from ..services.intent_classifier import IntentClassifier
    from ..services.tool_selector import ToolSelector
    from ..services.tool_scoring import ToolScoringService
    import os
    
    # Create minimal orchestrator components for planning only
    llm = orchestrator.llm
    intent_classifier = IntentClassifier(llm_client=llm)
    tool_selector = ToolSelector(llm_client=llm)
    tool_scorer = ToolScoringService()
    
    # Classify intent
    intent = await intent_classifier.classify(req.message)
    
    # Pre-fetch RAG for context (optional)
    rag_results = []
    try:
        rag_prefetch = await orchestrator.mcp.call_rag(req.tenant_id, req.message)
        if isinstance(rag_prefetch, dict):
            rag_results = rag_prefetch.get("results") or rag_prefetch.get("hits") or []
    except Exception:
        pass
    
    # Score tools
    tool_scores = tool_scorer.score(req.message, intent, rag_results)
    
    # Select tools
    ctx = {
        "tenant_id": req.tenant_id,
        "rag_results": rag_results,
        "tool_scores": tool_scores
    }
    decision = await tool_selector.select(intent, req.message, ctx)
    
    return {
        "tenant_id": req.tenant_id,
        "message": req.message,
        "intent": intent,
        "tool_scores": tool_scores,
        "plan": decision.dict(),
        "steps": decision.tool_input.get("steps", []) if decision.tool_input else [],
        "reason": decision.reason
    }