# api/server.py
import os
import time
from typing import Dict, List, Optional

from fastapi import FastAPI, UploadFile, File, Form, Request
from fastapi.responses import FileResponse, JSONResponse
from fastapi.staticfiles import StaticFiles
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel

from api.config import DEFAULT_COURSE_TOPICS, DEFAULT_MODEL
from api.syllabus_utils import extract_course_topics_from_file
from api.rag_engine import build_rag_chunks_from_file, retrieve_relevant_chunks
from api.clare_core import (
    detect_language,
    chat_with_clare,
    update_weaknesses_from_message,
    update_cognitive_state_from_message,
    render_session_status,
    export_conversation,
    summarize_conversation,
)

# ✅ LangSmith (same idea as your Gradio app.py)
try:
    from langsmith import Client
except Exception:
    Client = None

# ----------------------------
# Paths / Constants
# ----------------------------
API_DIR = os.path.dirname(__file__)

MODULE10_PATH = os.path.join(API_DIR, "module10_responsible_ai.pdf")
MODULE10_DOC_TYPE = "Literature Review / Paper"

WEB_DIST = os.path.abspath(os.path.join(API_DIR, "..", "web", "build"))
WEB_INDEX = os.path.join(WEB_DIST, "index.html")
WEB_ASSETS = os.path.join(WEB_DIST, "assets")

# ✅ LangSmith dataset name (match what you used before)
LS_DATASET_NAME = os.getenv("LS_DATASET_NAME", "clare_user_events").strip()
LS_PROJECT = os.getenv("LANGSMITH_PROJECT", os.getenv("LANGCHAIN_PROJECT", "")).strip()  # optional

# ----------------------------
# App
# ----------------------------
app = FastAPI(title="Clare API")

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

# ----------------------------
# Static hosting (Vite build)
# ----------------------------
if os.path.isdir(WEB_ASSETS):
    app.mount("/assets", StaticFiles(directory=WEB_ASSETS), name="assets")

if os.path.isdir(WEB_DIST):
    app.mount("/static", StaticFiles(directory=WEB_DIST), name="static")


@app.get("/")
def index():
    if os.path.exists(WEB_INDEX):
        return FileResponse(WEB_INDEX)
    return JSONResponse(
        {"detail": "web/build not found. Build frontend first (web/build/index.html)."},
        status_code=500,
    )


# ----------------------------
# In-memory session store (MVP)
# ----------------------------
SESSIONS: Dict[str, Dict] = {}


def _preload_module10_chunks():
    if os.path.exists(MODULE10_PATH):
        try:
            return build_rag_chunks_from_file(MODULE10_PATH, MODULE10_DOC_TYPE) or []
        except Exception as e:
            print(f"[preload] module10 parse failed: {repr(e)}")
            return []
    return []


MODULE10_CHUNKS_CACHE = _preload_module10_chunks()


def _get_session(user_id: str) -> Dict:
    if user_id not in SESSIONS:
        SESSIONS[user_id] = {
            "user_id": user_id,
            "name": "",
            "history": [],
            "weaknesses": [],
            "cognitive_state": {"confusion": 0, "mastery": 0},
            "course_outline": DEFAULT_COURSE_TOPICS,
            "rag_chunks": list(MODULE10_CHUNKS_CACHE),
            "model_name": DEFAULT_MODEL,
        }
    return SESSIONS[user_id]


# ----------------------------
# LangSmith helpers
# ----------------------------
_ls_client = None
if Client is not None:
    try:
        _ls_client = Client()
    except Exception as e:
        print("[langsmith] init failed:", repr(e))
        _ls_client = None


def _log_event_to_langsmith(data: Dict):
    """
    Create an Example in LangSmith Dataset (clare_user_events).
    Mimic your previous Gradio log_event behavior.

    Inputs/Outputs show up as "Inputs" / "Reference Outputs".
    Everything else goes into metadata columns.
    """
    if _ls_client is None:
        return

    try:
        inputs = {
            "question": data.get("question", ""),
            "student_id": data.get("student_id", ""),
            "student_name": data.get("student_name", ""),
        }
        outputs = {"answer": data.get("answer", "")}
        metadata = {k: v for k, v in data.items() if k not in ("question", "answer")}

        # helpful for filtering in UI
        if LS_PROJECT:
            metadata.setdefault("langsmith_project", LS_PROJECT)

        _ls_client.create_example(
            inputs=inputs,
            outputs=outputs,
            metadata=metadata,
            dataset_name=LS_DATASET_NAME,
        )
    except Exception as e:
        print("[langsmith] log failed:", repr(e))


# ----------------------------
# Schemas
# ----------------------------
class LoginReq(BaseModel):
    name: str
    user_id: str


class ChatReq(BaseModel):
    user_id: str
    message: str
    learning_mode: str
    language_preference: str = "Auto"
    doc_type: str = "Syllabus"


class ExportReq(BaseModel):
    user_id: str
    learning_mode: str


class SummaryReq(BaseModel):
    user_id: str
    learning_mode: str
    language_preference: str = "Auto"


class FeedbackReq(BaseModel):
    user_id: str
    rating: str  # "helpful" | "not_helpful"
    assistant_message_id: Optional[str] = None

    # what the user is rating
    assistant_text: str
    user_text: Optional[str] = ""

    # optional free-text comment
    comment: Optional[str] = ""

    # context for analysis
    refs: Optional[List[str]] = []
    learning_mode: Optional[str] = None
    doc_type: Optional[str] = None
    timestamp_ms: Optional[int] = None


# ----------------------------
# API Routes
# ----------------------------
@app.post("/api/login")
def login(req: LoginReq):
    user_id = (req.user_id or "").strip()
    name = (req.name or "").strip()
    if not user_id or not name:
        return JSONResponse({"ok": False, "error": "Missing name/user_id"}, status_code=400)

    sess = _get_session(user_id)
    sess["name"] = name
    return {"ok": True, "user": {"name": name, "user_id": user_id}}


@app.post("/api/chat")
def chat(req: ChatReq):
    user_id = (req.user_id or "").strip()
    msg = (req.message or "").strip()
    if not user_id:
        return JSONResponse({"error": "Missing user_id"}, status_code=400)

    sess = _get_session(user_id)

    if not msg:
        return {
            "reply": "",
            "session_status_md": render_session_status(
                req.learning_mode, sess["weaknesses"], sess["cognitive_state"]
            ),
            "refs": [],
            "latency_ms": 0.0,
        }

    resolved_lang = detect_language(msg, req.language_preference)

    sess["weaknesses"] = update_weaknesses_from_message(msg, sess["weaknesses"])
    sess["cognitive_state"] = update_cognitive_state_from_message(msg, sess["cognitive_state"])

    rag_context_text, rag_used_chunks = retrieve_relevant_chunks(msg, sess["rag_chunks"])

    start_ts = time.time()
    try:
        answer, new_history = chat_with_clare(
            message=msg,
            history=sess["history"],
            model_name=sess["model_name"],
            language_preference=resolved_lang,
            learning_mode=req.learning_mode,
            doc_type=req.doc_type,
            course_outline=sess["course_outline"],
            weaknesses=sess["weaknesses"],
            cognitive_state=sess["cognitive_state"],
            rag_context=rag_context_text,
        )
    except Exception as e:
        print(f"[chat] error: {repr(e)}")
        return JSONResponse({"error": f"chat failed: {repr(e)}"}, status_code=500)

    latency_ms = (time.time() - start_ts) * 1000.0
    sess["history"] = new_history

    refs = [
        {"source_file": c.get("source_file"), "section": c.get("section")}
        for c in (rag_used_chunks or [])
    ]

    # ✅ log chat_turn to LangSmith (uses login name/id; NO hardcoding)
    _log_event_to_langsmith(
        {
            "experiment_id": "RESP_AI_W10",
            "student_id": user_id,
            "student_name": sess.get("name", ""),
            "event_type": "chat_turn",
            "timestamp": time.time(),
            "latency_ms": latency_ms,
            "question": msg,
            "answer": answer,
            "model_name": sess["model_name"],
            "language": resolved_lang,
            "learning_mode": req.learning_mode,
            "doc_type": req.doc_type,
            "refs": refs,
        }
    )

    return {
        "reply": answer,
        "session_status_md": render_session_status(
            req.learning_mode, sess["weaknesses"], sess["cognitive_state"]
        ),
        "refs": refs,
        "latency_ms": latency_ms,
    }


@app.post("/api/upload")
async def upload(
    user_id: str = Form(...),
    doc_type: str = Form(...),
    file: UploadFile = File(...),
):
    user_id = (user_id or "").strip()
    doc_type = (doc_type or "").strip()

    if not user_id:
        return JSONResponse({"ok": False, "error": "Missing user_id"}, status_code=400)
    if not file or not file.filename:
        return JSONResponse({"ok": False, "error": "Missing file"}, status_code=400)

    sess = _get_session(user_id)

    safe_name = os.path.basename(file.filename).replace("..", "_")
    tmp_path = os.path.join("/tmp", safe_name)

    content = await file.read()
    with open(tmp_path, "wb") as f:
        f.write(content)

    if doc_type == "Syllabus":
        class _F:
            pass
        fo = _F()
        fo.name = tmp_path
        try:
            sess["course_outline"] = extract_course_topics_from_file(fo, doc_type)
        except Exception as e:
            print(f"[upload] syllabus parse error: {repr(e)}")

    try:
        new_chunks = build_rag_chunks_from_file(tmp_path, doc_type) or []
        sess["rag_chunks"] = (sess["rag_chunks"] or []) + new_chunks
    except Exception as e:
        print(f"[upload] rag build error: {repr(e)}")
        new_chunks = []

    status_md = f"✅ Loaded base reading + uploaded {doc_type} file."

    # ✅ optional: log upload event
    _log_event_to_langsmith(
        {
            "experiment_id": "RESP_AI_W10",
            "student_id": user_id,
            "student_name": sess.get("name", ""),
            "event_type": "upload",
            "timestamp": time.time(),
            "doc_type": doc_type,
            "filename": safe_name,
            "added_chunks": len(new_chunks),
            "question": f"[upload] {safe_name}",
            "answer": status_md,
        }
    )

    return {"ok": True, "added_chunks": len(new_chunks), "status_md": status_md}


@app.post("/api/feedback")
def api_feedback(req: FeedbackReq):
    user_id = (req.user_id or "").strip()
    if not user_id:
        return JSONResponse({"ok": False, "error": "Missing user_id"}, status_code=400)

    sess = _get_session(user_id)
    student_name = sess.get("name", "")

    rating = (req.rating or "").strip().lower()
    if rating not in ("helpful", "not_helpful"):
        return JSONResponse({"ok": False, "error": "Invalid rating"}, status_code=400)

    # ✅ record feedback as its own event row in the SAME dataset
    _log_event_to_langsmith(
        {
            "experiment_id": "RESP_AI_W10",
            "student_id": user_id,
            "student_name": student_name,
            "event_type": "feedback",
            "timestamp": time.time(),
            "rating": rating,
            "assistant_message_id": req.assistant_message_id,
            "question": (req.user_text or "").strip(),
            "answer": (req.assistant_text or "").strip(),
            "comment": (req.comment or "").strip(),
            "refs": req.refs or [],
            "learning_mode": req.learning_mode,
            "doc_type": req.doc_type,
            "timestamp_ms": req.timestamp_ms,
        }
    )

    return {"ok": True}


@app.post("/api/export")
def api_export(req: ExportReq):
    user_id = (req.user_id or "").strip()
    if not user_id:
        return JSONResponse({"error": "Missing user_id"}, status_code=400)

    sess = _get_session(user_id)
    md = export_conversation(
        sess["history"],
        sess["course_outline"],
        req.learning_mode,
        sess["weaknesses"],
        sess["cognitive_state"],
    )
    return {"markdown": md}


@app.post("/api/summary")
def api_summary(req: SummaryReq):
    user_id = (req.user_id or "").strip()
    if not user_id:
        return JSONResponse({"error": "Missing user_id"}, status_code=400)

    sess = _get_session(user_id)
    md = summarize_conversation(
        sess["history"],
        sess["course_outline"],
        sess["weaknesses"],
        sess["cognitive_state"],
        sess["model_name"],
        req.language_preference,
    )
    return {"markdown": md}


@app.get("/api/memoryline")
def memoryline(user_id: str):
    _ = _get_session((user_id or "").strip())
    return {"next_review_label": "T+7", "progress_pct": 0.4}


# ----------------------------
# SPA Fallback
# ----------------------------
@app.get("/{full_path:path}")
def spa_fallback(full_path: str, request: Request):
    if (
        full_path.startswith("api/")
        or full_path.startswith("assets/")
        or full_path.startswith("static/")
    ):
        return JSONResponse({"detail": "Not Found"}, status_code=404)

    if os.path.exists(WEB_INDEX):
        return FileResponse(WEB_INDEX)

    return JSONResponse(
        {"detail": "web/build not found. Build frontend first (web/build/index.html)."},
        status_code=500,
    )