from __future__ import annotations

import json
import logging
from pathlib import Path
from typing import AsyncIterator

import httpx
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel, Field
from starlette.responses import FileResponse

from app.config import settings

LOG = logging.getLogger(__name__)

STATIC_DIR = Path(__file__).resolve().parent.parent / "static"
logging.basicConfig(level=logging.INFO)

app = FastAPI(title="Ask Jerry API")

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

JERRY_SYSTEM_BASE = (
    "You are AI Jerry, a cybersecurity-focused assistant running on the BrainForge Security model. "
    "You give clear, practical guidance; you distinguish facts from speculation; you flag risks and "
    "compliance considerations when relevant. You are friendly and professional."
)

MODEL_CONTEXT_WINDOW = 8192
SUMMARIZE_SYSTEM = (
    "You are a concise summarizer. Condense the following conversation into a short summary "
    "that preserves the key topics discussed, any conclusions reached, important facts shared, "
    "and the overall tone. Keep it under 300 words. Write in third person narrative form."
)

_STATEMENT_MAX_CHARS = 4000

SEARCH_REF_SYSTEM = """You help a user find web sources to **research** an AI assistant's cybersecurity answer.

You will receive the full text of that answer. Do this in order:

1. **Facts** — Identify the main factual claims (CVEs, standards, protocols, vendor names, regulations, definitions, and procedural steps).
2. **Meaning** — In a few words, capture the overall gist: what the answer is explaining or recommending.
3. **Search query** — Compose one concise web search query (or two short queries separated by `; `) optimized to find **authoritative references** that could verify or deepen those facts—e.g. NIST, CISA, vendor docs, RFCs, CWE/CVE pages, or reputable security guidance.

Rules:
- Prefer concrete, verifiable keywords from the text.
- The query should help someone **research** the topic, not merely restate the answer in different words.
- Do not include meta-commentary, labels, bullets, or step numbers in your output.
- Return **only** the search query string (or two `; `-separated queries), with no quotes around the whole thing and no preamble."""


class ChatMessage(BaseModel):
    role: str
    content: str


class ChatStreamBody(BaseModel):
    messages: list[ChatMessage] = Field(..., min_length=1)
    extra_persona: str = ""
    temperature: float | None = None
    max_tokens: int | None = None
    summary: str | None = None


class SummarizeBody(BaseModel):
    messages: list[ChatMessage] = Field(..., min_length=1)
    extra_persona: str = ""


class SearchRefBody(BaseModel):
    statement: str = ""


def _build_system_prompt(extra_persona: str) -> str:
    extra = (extra_persona or "").strip()
    if not extra:
        return JERRY_SYSTEM_BASE
    return f"{JERRY_SYSTEM_BASE}\n\nAdditional instructions from the user:\n{extra}"


def _estimate_tokens(text: str) -> int:
    return max(1, len(text) // 4)


def _estimate_messages_tokens(msgs: list[dict]) -> int:
    total = 0
    for m in msgs:
        total += _estimate_tokens(m.get("content", "")) + 4
    return total


def _build_api_messages(
    system: str,
    body_messages: list[ChatMessage],
    summary: str | None,
) -> list[dict]:
    msgs: list[dict] = [{"role": "system", "content": system}]
    if summary:
        msgs.append({
            "role": "system",
            "content": f"Summary of earlier conversation:\n{summary}",
        })
    for m in body_messages:
        if m.role in ("user", "assistant") and m.content.strip():
            msgs.append({"role": m.role, "content": m.content})
    return msgs


def _delta_text(delta: dict) -> str:
    c = delta.get("content")
    if c is None:
        return ""
    if isinstance(c, str):
        return c
    if isinstance(c, list):
        parts: list[str] = []
        for p in c:
            if isinstance(p, str):
                parts.append(p)
            elif isinstance(p, dict):
                t = p.get("text")
                if isinstance(t, str):
                    parts.append(t)
        return "".join(parts)
    return str(c)


def _is_context_overflow(error_text: str) -> bool:
    indicators = ["context length", "max_tokens", "too large", "too many tokens"]
    lower = error_text.lower()
    return any(ind in lower for ind in indicators)


async def _yield_sse_tokens(line_iter: AsyncIterator[str]) -> AsyncIterator[str]:
    async for line in line_iter:
        if not line:
            continue
        if not line.startswith("data: "):
            continue
        payload = line[6:].strip()
        if payload == "[DONE]":
            yield f"data: {json.dumps({'type': 'done'})}\n\n"
            return
        try:
            obj = json.loads(payload)
        except json.JSONDecodeError:
            continue
        err = obj.get("error")
        if err:
            yield f"data: {json.dumps({'type': 'error', 'detail': str(err)})}\n\n"
            return
        choices = obj.get("choices") or []
        if not choices:
            continue
        ch0 = choices[0] if isinstance(choices[0], dict) else {}
        delta = ch0.get("delta") or {}
        if not isinstance(delta, dict):
            delta = {}
        piece = _delta_text(delta)
        if not piece and isinstance(ch0.get("message"), dict):
            piece = _delta_text(ch0["message"])
        if piece:
            yield f"data: {json.dumps({'type': 'token', 'content': piece})}\n\n"
    yield f"data: {json.dumps({'type': 'done'})}\n\n"


def _vllm_headers() -> dict[str, str]:
    headers: dict[str, str] = {"Content-Type": "application/json"}
    if settings.vllm_api_key:
        headers["Authorization"] = f"Bearer {settings.vllm_api_key}"
    return headers


@app.get("/health")
async def health():
    return {
        "status": "ok",
        "model": settings.chat_model_id,
        "context_window": MODEL_CONTEXT_WINDOW,
        "max_tokens": settings.max_tokens,
    }


@app.post("/api/chat/stream")
async def chat_stream(body: ChatStreamBody):
    system = _build_system_prompt(body.extra_persona)
    msgs = _build_api_messages(system, body.messages, body.summary)
    input_tokens = _estimate_messages_tokens(msgs)
    reply_budget = body.max_tokens if body.max_tokens is not None else settings.max_tokens

    if input_tokens + reply_budget > MODEL_CONTEXT_WINDOW:
        reply_budget = max(256, MODEL_CONTEXT_WINDOW - input_tokens - 64)
        if reply_budget < 256:
            detail = (
                f"Context too large: ~{input_tokens} input tokens with a {MODEL_CONTEXT_WINDOW} "
                f"token window leaves no room for a reply."
            )
            async def overflow_gen():
                yield f"data: {json.dumps({'type': 'context_overflow', 'detail': detail, 'input_tokens': input_tokens, 'context_window': MODEL_CONTEXT_WINDOW})}\n\n"
            return StreamingResponse(overflow_gen(), media_type="text/event-stream", headers={"Cache-Control": "no-cache"})

    url = f"{settings.vllm_base_url.rstrip('/')}/chat/completions"
    req_body: dict = {
        "model": settings.chat_model_id,
        "messages": msgs,
        "stream": True,
        "temperature": body.temperature if body.temperature is not None else settings.temperature,
        "max_tokens": reply_budget,
        "stop": ["<|user|>", "<|end|>", "<|endoftext|>", "<|im_end|>", "</s>"],
    }

    async def event_gen():
        try:
            async with httpx.AsyncClient(timeout=httpx.Timeout(120.0, connect=15.0)) as client:
                async with client.stream("POST", url, json=req_body, headers=_vllm_headers()) as resp:
                    if resp.status_code >= 400:
                        text = (await resp.aread()).decode("utf-8", errors="replace")[:2000]
                        LOG.warning("vLLM error %s: %s", resp.status_code, text)
                        if _is_context_overflow(text):
                            yield f"data: {json.dumps({'type': 'context_overflow', 'detail': text})}\n\n"
                        else:
                            yield f"data: {json.dumps({'type': 'error', 'detail': text or resp.reason_phrase})}\n\n"
                        return
                    async for chunk in _yield_sse_tokens(resp.aiter_lines()):
                        yield chunk
        except httpx.RequestError as e:
            LOG.exception("vLLM request failed")
            yield f"data: {json.dumps({'type': 'error', 'detail': str(e)})}\n\n"

    return StreamingResponse(
        event_gen(),
        media_type="text/event-stream",
        headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
    )


@app.post("/api/chat/summarize")
async def chat_summarize(body: SummarizeBody):
    transcript_lines: list[str] = []
    for m in body.messages:
        label = "User" if m.role == "user" else "AI Jerry"
        transcript_lines.append(f"{label}: {m.content}")
    transcript = "\n".join(transcript_lines)

    msgs = [
        {"role": "system", "content": SUMMARIZE_SYSTEM},
        {"role": "user", "content": transcript},
    ]

    url = f"{settings.vllm_base_url.rstrip('/')}/chat/completions"
    req_body: dict = {
        "model": settings.chat_model_id,
        "messages": msgs,
        "stream": False,
        "temperature": 0.3,
        "max_tokens": 1024,
        "stop": ["<|user|>", "<|end|>", "<|endoftext|>", "<|im_end|>", "</s>"],
    }

    try:
        async with httpx.AsyncClient(timeout=httpx.Timeout(60.0, connect=15.0)) as client:
            resp = await client.post(url, json=req_body, headers=_vllm_headers())
            if resp.status_code >= 400:
                text = resp.text[:500]
                LOG.warning("Summarize error %s: %s", resp.status_code, text)
                return {"summary": "", "error": text}
            data = resp.json()
            choices = data.get("choices") or []
            if choices:
                msg = choices[0].get("message") or {}
                return {"summary": (msg.get("content") or "").strip()}
            return {"summary": "", "error": "No choices returned"}
    except Exception as e:
        LOG.exception("Summarize request failed")
        return {"summary": "", "error": str(e)}


@app.post("/api/chat/estimate")
async def chat_estimate(body: ChatStreamBody):
    system = _build_system_prompt(body.extra_persona)
    msgs = _build_api_messages(system, body.messages, body.summary)
    input_tokens = _estimate_messages_tokens(msgs)
    reply_budget = body.max_tokens if body.max_tokens is not None else settings.max_tokens
    return {
        "input_tokens": input_tokens,
        "reply_budget": reply_budget,
        "context_window": MODEL_CONTEXT_WINDOW,
        "headroom": MODEL_CONTEXT_WINDOW - input_tokens - reply_budget,
    }


def _extract_message_content(data: dict) -> str:
    choices = data.get("choices") or []
    if not choices:
        return ""
    msg = choices[0].get("message") or {}
    return (msg.get("content") or "").strip()


@app.post("/api/search-references")
async def search_references(body: SearchRefBody):
    """Generate a web search query from an assistant answer (for Perplexity / copy)."""
    text = (body.statement or "")[:_STATEMENT_MAX_CHARS]
    if not text.strip():
        return {"search_query": ""}

    msgs = [
        {"role": "system", "content": SEARCH_REF_SYSTEM},
        {
            "role": "user",
            "content": f"Assistant answer to analyze for research search terms:\n\n{text}",
        },
    ]
    url = f"{settings.vllm_base_url.rstrip('/')}/chat/completions"
    req_body: dict = {
        "model": settings.chat_model_id,
        "messages": msgs,
        "stream": False,
        "temperature": 0.25,
        "max_tokens": 200,
        "stop": ["<|user|>", "<|end|>", "<|endoftext|>", "<|redacted_im_end|>", "</s>"],
    }

    try:
        async with httpx.AsyncClient(timeout=httpx.Timeout(60.0, connect=15.0)) as client:
            resp = await client.post(url, json=req_body, headers=_vllm_headers())
            if resp.status_code >= 400:
                LOG.warning("search-references error %s: %s", resp.status_code, resp.text[:500])
                return {"search_query": text[:100].strip()}
            data = resp.json()
            q = _extract_message_content(data)
            return {"search_query": q or text[:100].strip()}
    except Exception as e:
        LOG.exception("search-references failed")
        return {"search_query": text[:100].strip()}


# Production / Hugging Face Spaces: Vite build copied to ./static (see Dockerfile)
if STATIC_DIR.is_dir():
    assets_dir = STATIC_DIR / "assets"
    if assets_dir.is_dir():
        app.mount("/assets", StaticFiles(directory=str(assets_dir)), name="vite-assets")

    @app.get("/")
    async def spa_index():
        return FileResponse(STATIC_DIR / "index.html")

    @app.get("/{full_path:path}")
    async def spa_fallback(full_path: str):
        if full_path.startswith("api"):
            raise HTTPException(status_code=404, detail="Not found")
        file_path = STATIC_DIR / full_path
        if full_path and file_path.is_file():
            return FileResponse(file_path)
        return FileResponse(STATIC_DIR / "index.html")