from __future__ import annotations

import json
import os
import uuid
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Optional

import numpy as np
from fastapi import FastAPI, Request
from fastapi.responses import FileResponse, HTMLResponse, JSONResponse
from fastapi.templating import Jinja2Templates
from fastapi.staticfiles import StaticFiles


try:
    from sentence_transformers import SentenceTransformer
except Exception:
    SentenceTransformer = None


APP_TITLE = "Human Intelligence"
DATA_DIR = Path(os.environ.get("DATA_DIR", "/data"))
CONVERSATIONS_FILE = DATA_DIR / "conversations.json"
EMBED_FILE = DATA_DIR / "embeddings.json"
TEMPLATES_DIR = Path("/app/templates")

SIMILARITY_THRESHOLD = float(os.environ.get("SIMILARITY_THRESHOLD", "0.62"))
EMBED_MODEL_NAME = os.environ.get(
    "EMBED_MODEL_NAME",
    "sentence-transformers/paraphrase-MiniLM-L6-v2",
)

DATA_DIR.mkdir(parents=True, exist_ok=True)
TEMPLATES_DIR.mkdir(parents=True, exist_ok=True)

app = FastAPI(title=APP_TITLE)
templates = Jinja2Templates(directory=str(TEMPLATES_DIR))

_embed_model = None

app.mount("/templates", StaticFiles(directory=str(TEMPLATES_DIR)), name="templates")

# ────────────────────── Utilities ──────────────────────

def now_iso() -> str:
    return datetime.now(timezone.utc).isoformat(timespec="seconds")


def read_json(path: Path, default: Any):
    if not path.exists():
        return default
    try:
        return json.loads(path.read_text(encoding="utf-8"))
    except Exception:
        return default


def write_json(path: Path, data: Any) -> None:
    tmp = path.with_suffix(path.suffix + ".tmp")
    tmp.write_text(
        json.dumps(data, ensure_ascii=False, indent=2, default=str),
        encoding="utf-8",
    )
    tmp.replace(path)


def get_client_id(request: Request, payload: dict | None = None) -> str:
    header_value = request.headers.get("x-client-id", "").strip()
    if header_value:
        return header_value
    if payload:
        payload_value = str(payload.get("client_id", "")).strip()
        if payload_value:
            return payload_value
    return "anon"


def anon_label(client_id: str) -> str:
    return "Anonymous"


# ────────────────────── Embeddings ──────────────────────

def load_embed_model():
    global _embed_model
    if _embed_model is None:
        if SentenceTransformer is None:
            return None
        _embed_model = SentenceTransformer(EMBED_MODEL_NAME)
    return _embed_model


def embed_text(text: str) -> list[float]:
    model = load_embed_model()
    if model is None:
        return []
    vec = model.encode(text, normalize_embeddings=True)
    if hasattr(vec, "tolist"):
        return vec.tolist()
    return list(vec)


def load_embed_index() -> dict[str, dict[str, Any]]:
    data = read_json(EMBED_FILE, {})
    return data if isinstance(data, dict) else {}


def save_embed_index(idx: dict[str, dict[str, Any]]) -> None:
    write_json(EMBED_FILE, idx)


# ────────────────────── Conversations CRUD ──────────────────────

def load_conversations() -> list[dict[str, Any]]:
    data = read_json(CONVERSATIONS_FILE, [])
    if isinstance(data, dict) and "conversations" in data:
        data = data["conversations"]
    return data if isinstance(data, list) else []


def save_conversations(conversations: list[dict[str, Any]]) -> None:
    write_json(CONVERSATIONS_FILE, conversations)


def normalize_version(version: dict[str, Any]) -> dict[str, Any]:
    v = dict(version or {})
    v.setdefault("id", uuid.uuid4().hex)
    v.setdefault("text", "")
    v.setdefault("author", "Anonymous")
    v.setdefault("created_at", now_iso())
    v.setdefault("votes", 0)
    v.setdefault("votes_by_client", {})
    if not isinstance(v["votes_by_client"], dict):
        v["votes_by_client"] = {}
    v["votes"] = int(v.get("votes", 0))
    return v


def normalize_answer(answer: dict[str, Any]) -> dict[str, Any]:
    a = dict(answer or {})
    a.setdefault("id", uuid.uuid4().hex)
    a.setdefault("versions", [])
    a.setdefault("active_version", "")
    a.setdefault("created_at", now_iso())
    a.setdefault("updated_at", a["created_at"])

    versions = [
        normalize_version(v)
        for v in a.get("versions", [])
        if isinstance(v, dict)
    ]
    a["versions"] = versions

    if versions:
        version_ids = {v["id"] for v in versions}
        if a["active_version"] not in version_ids:
            a["active_version"] = max(
                versions,
                key=lambda v: (int(v.get("votes", 0)), str(v.get("created_at", ""))),
            )["id"]
    return a


def normalize_conversation(conversation: dict[str, Any]) -> dict[str, Any]:
    c = dict(conversation or {})
    c.setdefault("id", uuid.uuid4().hex)
    c.setdefault("question", "")
    c.setdefault("author", "Anonymous")
    c.setdefault("created_at", now_iso())
    c.setdefault("updated_at", c["created_at"])
    c.setdefault("turns", [])
    c.setdefault("answers", [])

    turns: list[dict[str, Any]] = []
    for turn in c.get("turns", []):
        if not isinstance(turn, dict):
            continue
        t = dict(turn)
        t.setdefault("id", uuid.uuid4().hex)
        t.setdefault("role", "user")
        t.setdefault("text", "")
        t.setdefault("author", "Anonymous")
        t.setdefault("ts", now_iso())
        turns.append(t)
    c["turns"] = turns

    c["answers"] = [
        normalize_answer(a)
        for a in c.get("answers", [])
        if isinstance(a, dict)
    ]

    if not c["turns"] and c["question"]:
        c["turns"].append({
            "id": uuid.uuid4().hex,
            "role": "user",
            "text": c["question"],
            "author": c.get("author", "Anonymous"),
            "ts": c["created_at"],
        })
    return c


def load_conversation(conversation_id: str) -> Optional[dict[str, Any]]:
    if not conversation_id:
        return None
    for conv in load_conversations():
        if str(conv.get("id")) == conversation_id:
            return normalize_conversation(conv)
    return None


def save_conversation(conversation: dict[str, Any]) -> dict[str, Any]:
    conversation = normalize_conversation(conversation)
    conversation["updated_at"] = now_iso()

    conversations = [normalize_conversation(c) for c in load_conversations()]
    replaced = False
    for i, existing in enumerate(conversations):
        if str(existing.get("id")) == str(conversation["id"]):
            conversations[i] = conversation
            replaced = True
            break
    if not replaced:
        conversations.insert(0, conversation)

    save_conversations(conversations)
    return conversation


def ensure_embedding(conversation: dict[str, Any]) -> None:
    question = str(conversation.get("question", "")).strip()
    if not question:
        return
    vec = embed_text(question)
    if not vec:
        return
    idx = load_embed_index()
    idx[str(conversation["id"])] = {
        "question": question,
        "vector": vec,
    }
    save_embed_index(idx)


# ────────────────────── Semantic search ──────────────────────

def find_similar_conversation(
    question: str,
    exclude_id: str | None = None,
) -> Optional[dict[str, Any]]:
    idx = load_embed_index()
    if not idx or SentenceTransformer is None:
        return None

    q_vec = np.array(embed_text(question), dtype=float)
    if q_vec.size == 0:
        return None

    ids = [cid for cid in idx if cid != exclude_id]
    if not ids:
        return None

    try:
        vecs = np.array([idx[cid]["vector"] for cid in ids], dtype=float)
    except Exception:
        return None

    if vecs.size == 0 or vecs.ndim != 2:
        return None
    if vecs.shape[1] != q_vec.shape[0]:
        return None

    sims = vecs @ q_vec
    best_i = int(np.argmax(sims))
    score = float(sims[best_i])

    if score < SIMILARITY_THRESHOLD:
        return None

    conv = load_conversation(ids[best_i])
    if conv is None:
        return None

    return {"conversation": conv, "score": score}


def find_top_k_similar(question: str, k: int = 3) -> list[dict[str, Any]]:
    idx = load_embed_index()
    if not idx or SentenceTransformer is None:
        return []

    q_vec = np.array(embed_text(question), dtype=float)
    if q_vec.size == 0:
        return []

    results: list[tuple[str, float]] = []
    for cid, data in idx.items():
        try:
            vec = np.array(data["vector"], dtype=float)
        except Exception:
            continue
        if vec.shape != q_vec.shape:
            continue
        score = float(vec @ q_vec)
        results.append((cid, score))

    results.sort(key=lambda x: x[1], reverse=True)

    out: list[dict[str, Any]] = []
    for cid, score in results[:k]:
        conv = load_conversation(cid)
        if not conv:
            continue
        best = best_answer_payload(conv)
        if not best:
            continue
        out.append({
            "conversation_id": cid,
            "question": conv.get("question"),
            "answer": best["text"],
            "score": score,
        })
    return out


# ────────────────────── Actions ──────────────────────

def create_conversation(question: str, author: str = "Anonymous") -> dict[str, Any]:
    question = question.strip()
    now = now_iso()
    conversation = {
        "id": uuid.uuid4().hex,
        "question": question,
        "author": author,
        "created_at": now,
        "updated_at": now,
        "turns": [{
            "id": uuid.uuid4().hex,
            "role": "user",
            "text": question,
            "author": author,
            "ts": now,
        }],
        "answers": [],
    }
    conversation = save_conversation(conversation)
    ensure_embedding(conversation)
    return conversation


def active_version(answer: dict[str, Any]) -> Optional[dict[str, Any]]:
    versions = answer.get("versions", [])
    if not versions:
        return None
    active_id = answer.get("active_version")
    for version in versions:
        if version.get("id") == active_id:
            return version
    return max(
        versions,
        key=lambda v: (int(v.get("votes", 0)), str(v.get("created_at", ""))),
    )


def answer_score(answer: dict[str, Any]) -> tuple[int, str]:
    av = active_version(answer)
    if av is None:
        return 0, str(answer.get("created_at", ""))
    return int(av.get("votes", 0)), str(answer.get("created_at", ""))


def best_answer_payload(conversation: dict[str, Any]) -> Optional[dict[str, Any]]:
    answers = conversation.get("answers", [])
    if not answers:
        return None
    best = max(answers, key=answer_score)
    av = active_version(best)
    if av is None:
        return None
    return {
        "answer_id": best["id"],
        "version_id": av["id"],
        "text": av["text"],
        "votes": int(av.get("votes", 0)),
        "author": av.get("author", "Anonymous"),
        "created_at": av.get("created_at", ""),
    }


def add_answer(
    conversation_id: str,
    text: str,
    author: str = "Anonymous",
    question_if_new: str | None = None,
) -> tuple[Optional[dict[str, Any]], str]:
    text = text.strip()
    if not text:
        return None, "empty answer"

    conversation = load_conversation(conversation_id)

    # If user is answering a NEW question (even if matched),
    # create a new conversation instead of polluting the matched one.
    if question_if_new:
        if conversation is None or conversation.get("question") != question_if_new:
            conversation = create_conversation(question_if_new, author)
    elif conversation is None:
        return None, "conversation not found"

    now = now_iso()
    version = normalize_version({
        "text": text,
        "author": author,
        "created_at": now,
        "votes": 0,
        "votes_by_client": {},
    })
    answer = normalize_answer({
        "id": uuid.uuid4().hex,
        "versions": [version],
        "active_version": version["id"],
        "created_at": now,
        "updated_at": now,
    })

    conversation["answers"].append(answer)
    conversation["turns"].append({
        "id": uuid.uuid4().hex,
        "role": "assistant",
        "text": text,
        "author": author,
        "answer_id": answer["id"],
        "version_id": version["id"],
        "ts": now,
    })

    save_conversation(conversation)
    return conversation, "ok"


def propose_version(
    conversation_id: str,
    answer_id: str,
    text: str,
    author: str = "Anonymous",
) -> tuple[Optional[dict[str, Any]], str]:
    text = text.strip()
    if not text:
        return None, "empty proposal"

    conversation = load_conversation(conversation_id)
    if conversation is None:
        return None, "conversation not found"

    for answer in conversation["answers"]:
        if str(answer.get("id")) != answer_id:
            continue

        now = now_iso()
        version = normalize_version({
            "text": text,
            "author": author,
            "created_at": now,
            "votes": 0,
            "votes_by_client": {},
        })
        answer["versions"].append(version)
        answer["updated_at"] = now
        save_conversation(conversation)
        return conversation, "ok"

    return None, "answer not found"


def vote_version(
    conversation_id: str,
    answer_id: str,
    version_id: str,
    client_id: str,
    delta: int,
) -> tuple[Optional[dict[str, Any]], str]:
    conversation = load_conversation(conversation_id)
    if conversation is None:
        return None, "conversation not found"

    delta = 1 if int(delta) >= 0 else -1

    for answer in conversation["answers"]:
        if str(answer.get("id")) != answer_id:
            continue
        for version in answer.get("versions", []):
            if str(version.get("id")) != version_id:
                continue

            votes_by_client = version.setdefault("votes_by_client", {})
            if not isinstance(votes_by_client, dict):
                votes_by_client = {}
                version["votes_by_client"] = votes_by_client

            current = int(votes_by_client.get(client_id, 0))
            if current == delta:
                return conversation, "already_voted"

            votes_by_client[client_id] = delta
            version["votes"] = int(sum(int(v) for v in votes_by_client.values()))

            if answer.get("versions"):
                answer["active_version"] = max(
                    answer["versions"],
                    key=lambda v: (int(v.get("votes", 0)), str(v.get("created_at", ""))),
                )["id"]

            conversation["updated_at"] = now_iso()
            save_conversation(conversation)
            return conversation, "ok"

    return None, "version not found"


# ────────────────────── Routes ──────────────────────

@app.get("/", response_class=HTMLResponse)
def home(request: Request):
    init = {
        "ok": True,
        "client_id": get_client_id(request),
        "conversation": None,
    }
    return templates.TemplateResponse(
        "index.html",
        {
            "request": request,
            "app_title": APP_TITLE,
            "init_json": json.dumps(init, ensure_ascii=False),
        },
    )


@app.get("/logo.png")
def logo():
    logo_path = Path(__file__).with_name("logo.png")
    if logo_path.exists():
        return FileResponse(logo_path)
    return JSONResponse({"ok": False, "error": "logo not found"}, status_code=404)


@app.get("/health")
def health():
    return {"ok": True}


@app.post("/api")
async def api(request: Request):
    try:
        payload = await request.json()
    except Exception:
        return JSONResponse({"ok": False, "error": "bad payload"})

    action = str(payload.get("action", ""))
    client_id = get_client_id(request, payload)
    author = anon_label(client_id)

    # ── init ──
    if action == "init":
        conversation_id = str(payload.get("conversation_id", "")).strip()
        conversation = load_conversation(conversation_id) if conversation_id else None
        return JSONResponse({
            "ok": True,
            "client_id": client_id,
            "conversation": conversation,
        })

    # ── get_conversation ──
    if action == "get_conversation":
        conversation_id = str(payload.get("conversation_id", "")).strip()
        conversation = load_conversation(conversation_id)
        if conversation is None:
            return JSONResponse({"ok": False, "error": "not found"})
        return JSONResponse({"ok": True, "conversation": conversation})

    # ── ask ──
    if action == "ask":
        question = str(payload.get("question", "")).strip()
        if not question:
            return JSONResponse({"ok": False, "error": "empty question"})

        # 1) Search FIRST — before creating anything
        match = find_similar_conversation(question)
        if match and match.get("conversation"):
            conversation = match["conversation"]
            best = best_answer_payload(conversation)
            related = find_top_k_similar(question, k=3)
            return JSONResponse({
                "ok": True,
                "matched": True,
                "similarity": match["score"],
                "conversation": conversation,
                "assistant_text": best["text"] if best else "No answer yet. You can write one.",
                "best_answer": best,
                "related": related,
            })

        # 2) No match — create new
        conversation = create_conversation(question, author)
        return JSONResponse({
            "ok": True,
            "matched": False,
            "conversation": conversation,
            "assistant_text": "No answer yet. You can write one.",
            "best_answer": None,
            "related": [],
        })

    # ── answer ──
    if action == "answer":
        conversation_id = str(payload.get("conversation_id", "")).strip()
        text = str(payload.get("text", "")).strip()
        question = str(payload.get("question", "")).strip() or None

        conversation, msg = add_answer(
            conversation_id=conversation_id,
            text=text,
            author=author,
            question_if_new=question,
        )
        if conversation is None:
            return JSONResponse({"ok": False, "error": msg})
        return JSONResponse({"ok": True, "conversation": conversation})

    # ── propose ──
    if action == "propose":
        conversation_id = str(payload.get("conversation_id", "")).strip()
        answer_id = str(payload.get("answer_id", "")).strip()
        text = str(payload.get("text", "")).strip()

        conversation, msg = propose_version(
            conversation_id=conversation_id,
            answer_id=answer_id,
            text=text,
            author=author,
        )
        if conversation is None:
            return JSONResponse({"ok": False, "error": msg})
        return JSONResponse({"ok": True, "conversation": conversation})

    # ── vote ──
    if action == "vote":
        conversation_id = str(payload.get("conversation_id", "")).strip()
        answer_id = str(payload.get("answer_id", "")).strip()
        version_id = str(payload.get("version_id", "")).strip()
        delta = int(payload.get("delta", 1))

        conversation, msg = vote_version(
            conversation_id=conversation_id,
            answer_id=answer_id,
            version_id=version_id,
            client_id=client_id,
            delta=delta,
        )
        if conversation is None:
            return JSONResponse({"ok": False, "error": msg})
        if msg == "already_voted":
            return JSONResponse({"ok": False, "error": "already voted"})
        return JSONResponse({"ok": True, "conversation": conversation})

    return JSONResponse({"ok": False, "error": f"unknown action: {action}"})


if __name__ == "__main__":
    import uvicorn
    uvicorn.run("main:app", host="0.0.0.0", port=7860, reload=False)