"""
Ukweli — Agent Streaming Endpoint
POST /agent/stream — Server-Sent Events (SSE) streaming for real-time RAG responses.
Architecture Section 4.2 — Agent Endpoint (Streaming).
"""

from __future__ import annotations

import json
import logging
import time
import uuid
from collections.abc import AsyncGenerator

from fastapi import APIRouter, Depends
from sse_starlette.sse import EventSourceResponse
from sqlalchemy.ext.asyncio import AsyncSession

from app.api.auth import AuthContext, require_auth
from app.db.session import get_db_session
from app.dependencies import get_citation_formatter, get_llm_gateway, get_retrieval_orchestrator
from app.models.database import QueryLog
from app.models.schemas import AgentStreamRequest
from app.services.citation.formatter import CitationFormatter
from app.services.llm.gateway import LLMGateway
from app.services.llm.guardrails import check_query_safety
from app.services.llm.prompts import build_rag_prompt
from app.services.retrieval.orchestrator import RetrievalOrchestrator

logger = logging.getLogger("ukweli.api.agent")
router = APIRouter(tags=["Agent"])


@router.post("/agent/stream")
async def agent_stream(
    request: AgentStreamRequest,
    db: AsyncSession = Depends(get_db_session),
    auth: AuthContext = Depends(require_auth),
    retriever: RetrievalOrchestrator = Depends(get_retrieval_orchestrator),
    llm: LLMGateway = Depends(get_llm_gateway),
    citation_fmt: CitationFormatter = Depends(get_citation_formatter),
):
    """
    SSE streaming endpoint for agent integrations (WhatsApp, mobile, etc.).

    Event types emitted:
      - retrieval: search status and source count
      - citation: individual citation as discovered
      - delta: incremental answer text
      - done: final payload with all citations and confidence
      - error: if something goes wrong
    """

    async def event_generator() -> AsyncGenerator[dict, None]:
        start_time = time.monotonic()
        query_id = uuid.uuid4()

        # Safety check
        safety = check_query_safety(request.query)
        if safety.blocked:
            yield {
                "event": "error",
                "data": json.dumps({"message": safety.message}),
            }
            return

        # Emit retrieval start
        yield {
            "event": "retrieval",
            "data": json.dumps({"status": "searching", "query_id": str(query_id)}),
        }

        # Retrieve context
        try:
            retrieval_result = await retriever.retrieve(
                query=request.query,
                language=request.language,
                filters=request.filters,
                tier=auth.tier,
            )
        except Exception as exc:
            logger.error("Retrieval failed during stream: %s", exc)
            yield {
                "event": "error",
                "data": json.dumps({"message": "Retrieval failed. Please try again."}),
            }
            return

        yield {
            "event": "retrieval",
            "data": json.dumps({
                "status": "found",
                "sources": len(retrieval_result.context_blocks),
            }),
        }

        # Emit individual citations
        for block in retrieval_result.context_blocks:
            yield {
                "event": "citation",
                "data": json.dumps({
                    "document": block.get("document_title", ""),
                    "page": block.get("page_range_start"),
                    "section": block.get("section_heading", ""),
                }),
            }

        # Build prompt
        prompt_messages = build_rag_prompt(
            query=request.query,
            context_chunks=retrieval_result.context_blocks,
            language=request.language,
            mode="concise",
        )

        # Stream LLM response
        full_answer = ""
        model_used = ""
        tokens_used = 0

        try:
            async for chunk in llm.stream(messages=prompt_messages):
                full_answer += chunk.text
                model_used = chunk.model_used
                tokens_used = chunk.tokens_used
                yield {
                    "event": "delta",
                    "data": json.dumps({"content": chunk.text}),
                }
        except Exception as exc:
            logger.error("LLM streaming failed: %s", exc)
            yield {
                "event": "error",
                "data": json.dumps({"message": "Generation failed. Please try again."}),
            }
            return

        # Format final citations
        formatted = citation_fmt.format_response(
            raw_answer=full_answer,
            retrieved_chunks=retrieval_result.context_blocks,
        )

        latency_ms = int((time.monotonic() - start_time) * 1000)

        # Log to audit trail
        try:
            query_log = QueryLog(
                id=query_id,
                query_text=request.query,
                language=request.language,
                mode="concise",
                user_tier=auth.tier,
                user_id=auth.user.id if auth.user else None,
                api_key_id=auth.api_key.id if auth.api_key else None,
                fingerprint=auth.fingerprint,
                filters=request.filters.model_dump() if request.filters else None,
                answer=full_answer,
                citations=[c.model_dump() for c in formatted.citations] if formatted.citations else None,
                chunks_considered=retrieval_result.total_candidates,
                latency_ms=latency_ms,
                confidence="high" if retrieval_result.top_score > 0.8 else "medium",
                llm_model_used=model_used,
                llm_tokens_used=tokens_used,
            )
            db.add(query_log)
            await db.commit()
        except Exception as exc:
            logger.error("Failed to log stream query: %s", exc)

        # Emit done event
        yield {
            "event": "done",
            "data": json.dumps({
                "query_id": str(query_id),
                "citations": [c.model_dump() for c in formatted.citations],
                "confidence": "high" if retrieval_result.top_score > 0.8 else "medium",
                "latency_ms": latency_ms,
            }),
        }

    return EventSourceResponse(event_generator())