"""
FastAPI application with chat, RAG, and document ingestion endpoints.

Routes:
- POST /chat       — streaming chat via SSE (or JSON if stream=false)
- POST /chat/rag   — RAG-augmented chat (retrieve context, then generate)
- POST /ingest     — upload .txt/.md files, chunk + embed + store
- GET  /health     — model status, device, chunk count

The LLM runs synchronously in PyTorch, so generation is wrapped in
run_in_executor to avoid blocking the async event loop. An asyncio
Lock serialises generation calls since PyTorch is not thread-safe.
"""

import asyncio
from contextlib import asynccontextmanager

from fastapi import FastAPI, UploadFile, File
from fastapi.responses import JSONResponse
from sse_starlette.sse import EventSourceResponse

from src.guardrails import GuardrailsMiddleware, PIICheck, ToxicityCheck, Severity
from src.serving.engine import GenerationEngine
from src.serving.models import (
    ChatRequest,
    HealthResponse,
    IngestResponse,
    RAGChatRequest,
)
from src.serving.rag import RAGPipeline

# Global state populated at startup
_engine: GenerationEngine | None = None
_rag: RAGPipeline | None = None
_lock: asyncio.Lock | None = None
_guardrails: GuardrailsMiddleware | None = None


@asynccontextmanager
async def lifespan(app: FastAPI):
    global _engine, _rag, _lock, _guardrails
    model_size = app.state.model_size if hasattr(app.state, "model_size") else "0.6B"
    print(f"Loading GenerationEngine (Qwen3-{model_size})...")
    _engine = GenerationEngine(model_size=model_size)
    print("Loading RAG pipeline (BGE-small-en-v1.5)...")
    _rag = RAGPipeline()
    _lock = asyncio.Lock()
    print("Loading guardrails (PII + toxicity)...")
    _guardrails = GuardrailsMiddleware([
        PIICheck(severity=Severity.WARN),
        ToxicityCheck(),
    ])
    print("Ready.")
    yield
    print("Shutting down.")


app = FastAPI(title="LLM Hub API", lifespan=lifespan)


def _messages_to_dicts(messages) -> list[dict]:
    return [{"role": m.role, "content": m.content} for m in messages]


async def _generate_sse(messages: list[dict], request: ChatRequest):
    """Wrap the sync generator into an async SSE stream."""
    loop = asyncio.get_event_loop()

    async def event_generator():
        async with _lock:
            gen = _engine.generate_stream(
                messages,
                max_tokens=request.max_tokens,
                temperature=request.temperature,
                top_k=request.top_k,
                top_p=request.top_p,
            )
            while True:
                chunk = await loop.run_in_executor(None, lambda: next(gen, None))
                if chunk is None:
                    break
                yield {"data": chunk}

    return EventSourceResponse(event_generator())


async def _generate_full(messages: list[dict], request: ChatRequest):
    """Collect all tokens and return as a single JSON response."""
    loop = asyncio.get_event_loop()

    def _run():
        parts = []
        gen = _engine.generate_stream(
            messages,
            max_tokens=request.max_tokens,
            temperature=request.temperature,
            top_k=request.top_k,
            top_p=request.top_p,
        )
        for chunk in gen:
            parts.append(chunk)
        return "".join(parts)

    async with _lock:
        content = await loop.run_in_executor(None, _run)

    return JSONResponse({"role": "assistant", "content": content})


@app.post("/chat")
async def chat(request: ChatRequest):
    messages = _messages_to_dicts(request.messages)
    # Scan the latest user message.
    if _guardrails and messages:
        last_user = next((m["content"] for m in reversed(messages) if m["role"] == "user"), "")
        scan = _guardrails.scan_input(last_user)
        if scan.blocked:
            return JSONResponse(
                status_code=400,
                content={"error": "Request blocked by safety filters.",
                         "flags": [f.description for f in scan.flags]},
            )
    if request.stream:
        return await _generate_sse(messages, request)
    return await _generate_full(messages, request)


@app.post("/chat/rag")
async def chat_rag(request: RAGChatRequest):
    messages = _messages_to_dicts(request.messages)
    # Scan the latest user message.
    if _guardrails and messages:
        last_user = next((m["content"] for m in reversed(messages) if m["role"] == "user"), "")
        scan = _guardrails.scan_input(last_user)
        if scan.blocked:
            return JSONResponse(
                status_code=400,
                content={"error": "Request blocked by safety filters.",
                         "flags": [f.description for f in scan.flags]},
            )
    augmented = _rag.build_rag_messages(messages, top_k=request.top_k_docs)
    if request.stream:
        return await _generate_sse(augmented, request)
    return await _generate_full(augmented, request)


@app.post("/ingest")
async def ingest(file: UploadFile = File(...)):
    if not file.filename.endswith((".txt", ".md")):
        return JSONResponse(
            status_code=400,
            content={"error": "Only .txt and .md files are supported"},
        )
    content = (await file.read()).decode("utf-8")
    num_chunks = _rag.ingest(content)
    return IngestResponse(
        filename=file.filename,
        num_chunks=num_chunks,
        message=f"Ingested {num_chunks} chunks from {file.filename}",
    )


@app.get("/health")
async def health():
    return HealthResponse(
        status="ok" if _engine is not None else "loading",
        model=f"Qwen3-{_engine.model_size}" if _engine else "none",
        device=str(_engine.device) if _engine else "none",
        num_chunks=_rag.store.num_chunks if _rag else 0,
    )