api/server.py

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

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,
)

# ✅ NEW: course directory + workspace schema routes
from api.routes_directory import router as directory_router

# ✅ LangSmith (optional)
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")

LS_DATASET_NAME = os.getenv("LS_DATASET_NAME", "clare_user_events").strip()
LS_PROJECT = os.getenv("LANGSMITH_PROJECT", os.getenv("LANGCHAIN_PROJECT", "")).strip()

EXPERIMENT_ID = os.getenv("CLARE_EXPERIMENT_ID", "RESP_AI_W10").strip()

# ----------------------------
# Health / Warmup (cold start mitigation)
# ----------------------------
APP_START_TS = time.time()

WARMUP_DONE = False
WARMUP_ERROR: Optional[str] = None
WARMUP_STARTED = False

CLARE_ENABLE_WARMUP = os.getenv("CLARE_ENABLE_WARMUP", "1").strip() == "1"
CLARE_WARMUP_BLOCK_READY = os.getenv("CLARE_WARMUP_BLOCK_READY", "0").strip() == "1"

# Dataset logging (create_example)
CLARE_ENABLE_LANGSMITH_LOG = os.getenv("CLARE_ENABLE_LANGSMITH_LOG", "0").strip() == "1"
CLARE_LANGSMITH_ASYNC = os.getenv("CLARE_LANGSMITH_ASYNC", "1").strip() == "1"

# Feedback logging (create_feedback -> attach to run_id)
CLARE_ENABLE_LANGSMITH_FEEDBACK = os.getenv("CLARE_ENABLE_LANGSMITH_FEEDBACK", "1").strip() == "1"

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

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

# ✅ NEW: include directory/workspace APIs BEFORE SPA fallback
app.include_router(directory_router)

# ----------------------------
# 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[str, Any]] = {}


def _preload_module10_chunks() -> List[Dict[str, Any]]:
    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[str, Any]:
    if user_id not in SESSIONS:
        SESSIONS[user_id] = {
            "user_id": user_id,
            "name": "",
            "history": [],  # List[Tuple[str, str]]
            "weaknesses": [],
            "cognitive_state": {"confusion": 0, "mastery": 0},
            "course_outline": DEFAULT_COURSE_TOPICS,
            "rag_chunks": list(MODULE10_CHUNKS_CACHE),
            "model_name": DEFAULT_MODEL,
            "uploaded_files": [],
            #  NEW: profile init (MVP in-memory)
            "profile_bio": "",
            "init_answers": {},
            "init_dismiss_until": 0,
        }

    if "uploaded_files" not in SESSIONS[user_id]:
        SESSIONS[user_id]["uploaded_files"] = []

    #  NEW backfill
    SESSIONS[user_id].setdefault("profile_bio", "")
    SESSIONS[user_id].setdefault("init_answers", {})
    SESSIONS[user_id].setdefault("init_dismiss_until", 0)

    return SESSIONS[user_id]



# NEW: helper to build a deterministic “what files are loaded” hint for the LLM
def _build_upload_hint(sess: Dict[str, Any]) -> str:
    files = sess.get("uploaded_files") or []
    if not files:
        # Still mention that base reading is available
        return (
            "Files available to you in this session:\n"
            "- Base reading: module10_responsible_ai.pdf (pre-loaded)\n"
            "If the student asks about an uploaded file but none exist, ask them to upload."
        )
    lines = [
        "Files available to you in this session:",
        "- Base reading: module10_responsible_ai.pdf (pre-loaded)",
    ]
    # show last few only to keep prompt small
    for f in files[-5:]:
        fn = (f.get("filename") or "").strip()
        dt = (f.get("doc_type") or "").strip()
        chunks = f.get("added_chunks")
        lines.append(f"- Uploaded: {fn} (doc_type={dt}, added_chunks={chunks})")
    lines.append(
        "When the student asks to summarize/read 'the uploaded file', interpret it as the MOST RECENT uploaded file unless specified."
    )
    return "\n".join(lines)

#  NEW: force RAG on short "document actions" so refs exist
def _should_force_rag(message: str) -> bool:
    m = (message or "").lower()
    if not m:
        return False
    triggers = [
        "summarize", "summary", "read", "analyze", "explain",
        "the uploaded file", "uploaded", "file", "document", "pdf",
        "slides", "ppt", "syllabus", "lecture",
        "总结", "概括", "阅读", "读一下", "解析", "分析", "这份文件", "上传", "文档", "课件", "讲义",
    ]
    return any(t in m for t in triggers)

def _extract_filename_hint(message: str) -> Optional[str]:
    m = (message or "").strip()
    if not m:
        return None
    # 极简：如果用户直接提到了 .pdf/.ppt/.docx 文件名，就用它
    for token in m.replace("“", '"').replace("”", '"').split():
        if any(token.lower().endswith(ext) for ext in [".pdf", ".ppt", ".pptx", ".doc", ".docx"]):
            return os.path.basename(token.strip('"').strip("'").strip())
    return None


def _resolve_rag_scope(sess: Dict[str, Any], msg: str) -> Tuple[Optional[List[str]], Optional[List[str]]]:
    """
    Return (allowed_source_files, allowed_doc_types)
    - If user is asking about "uploaded file"/document action -> restrict to latest uploaded file.
    - If message contains an explicit filename -> restrict to that filename if we have it.
    - Else no restriction (None, None).
    """
    files = sess.get("uploaded_files") or []
    msg_l = (msg or "").lower()

    # 1) explicit filename mentioned
    hinted = _extract_filename_hint(msg)
    if hinted:
        # only restrict if that file exists in session uploads
        known = {os.path.basename(f.get("filename", "")) for f in files if f.get("filename")}
        if hinted in known:
            return ([hinted], None)

    # 2) generic "uploaded file" intent
    uploaded_intent = any(t in msg_l for t in [
        "uploaded file", "uploaded files", "the uploaded file", "this file", "this document",
        "上传的文件", "这份文件", "这个文件", "文档", "课件", "讲义"
    ])
    if uploaded_intent and files:
        last = files[-1]
        fn = os.path.basename(last.get("filename", "")).strip() or None
        dt = (last.get("doc_type") or "").strip() or None
        allowed_files = [fn] if fn else None
        allowed_doc_types = [dt] if dt else None
        return (allowed_files, allowed_doc_types)

    return (None, None)


# ----------------------------
# Warmup
# ----------------------------
def _do_warmup_once():
    global WARMUP_DONE, WARMUP_ERROR, WARMUP_STARTED
    if WARMUP_STARTED:
        return
    WARMUP_STARTED = True

    try:
        from api.config import client
        client.models.list()
        _ = MODULE10_CHUNKS_CACHE
        WARMUP_DONE = True
        WARMUP_ERROR = None
    except Exception as e:
        WARMUP_DONE = False
        WARMUP_ERROR = repr(e)


def _start_warmup_background():
    if not CLARE_ENABLE_WARMUP:
        return
    threading.Thread(target=_do_warmup_once, daemon=True).start()


@app.on_event("startup")
def _on_startup():
    _start_warmup_background()


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


def _log_event_to_langsmith(data: Dict[str, Any]):
    """
    Dataset logging: create_example into LS_DATASET_NAME
    """
    if _ls_client is None:
        return

    def _do():
        try:
            inputs = {
                "question": data.get("question", ""),
                "student_id": data.get("student_id", ""),
                "student_name": data.get("student_name", ""),
            }
            outputs = {"answer": data.get("answer", "")}

            # keep metadata clean and JSON-serializable
            metadata = {k: v for k, v in data.items() if k not in ("question", "answer")}

            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))

    if CLARE_LANGSMITH_ASYNC:
        threading.Thread(target=_do, daemon=True).start()
    else:
        _do()


def _write_feedback_to_langsmith_run(
    run_id: str,
    rating: str,
    comment: str = "",
    tags: Optional[List[str]] = None,
    metadata: Optional[Dict[str, Any]] = None,
) -> bool:
    """
    Run-level feedback: create_feedback attached to a specific run_id.
    This is separate from dataset create_example logging.
    """
    if not CLARE_ENABLE_LANGSMITH_FEEDBACK:
        return False
    if Client is None:
        return False

    rid = (run_id or "").strip()
    if not rid:
        return False

    try:
        ls = Client()
        score = 1 if rating == "helpful" else 0

        meta = metadata or {}
        if tags is not None:
            meta["tags"] = tags

        if LS_PROJECT:
            meta.setdefault("langsmith_project", LS_PROJECT)

        ls.create_feedback(
            run_id=rid,
            key="ui_rating",
            score=score,
            comment=comment or "",
            metadata=meta,
        )
        return True
    except Exception as e:
        print("[langsmith] create_feedback failed:", repr(e))
        return False


# ----------------------------
# Health endpoints
# ----------------------------
@app.get("/health")
def health():
    return {
        "ok": True,
        "uptime_s": round(time.time() - APP_START_TS, 3),
        "warmup_enabled": CLARE_ENABLE_WARMUP,
        "warmup_started": bool(WARMUP_STARTED),
        "warmup_done": bool(WARMUP_DONE),
        "warmup_error": WARMUP_ERROR,
        "ready": bool(WARMUP_DONE) if CLARE_WARMUP_BLOCK_READY else True,
        "langsmith_enabled": bool(CLARE_ENABLE_LANGSMITH_LOG),
        "langsmith_async": bool(CLARE_LANGSMITH_ASYNC),
        "langsmith_feedback_enabled": bool(CLARE_ENABLE_LANGSMITH_FEEDBACK),
        "ts": int(time.time()),
    }


@app.get("/ready")
def ready():
    if not CLARE_ENABLE_WARMUP or not CLARE_WARMUP_BLOCK_READY:
        return {"ready": True}
    if WARMUP_DONE:
        return {"ready": True}
    return JSONResponse({"ready": False, "error": WARMUP_ERROR}, status_code=503)


# ----------------------------
# Quiz (Micro-Quiz) Instruction
# ----------------------------
MICRO_QUIZ_INSTRUCTION = (
    "We are running a short micro-quiz session based ONLY on **Module 10 – "
    "Responsible AI (Alto, 2024, Chapter 12)** and the pre-loaded materials.\n\n"
    "Step 1 – Before asking any content question:\n"
    "• First ask me which quiz style I prefer right now:\n"
    "  - (1) Multiple-choice questions\n"
    "  - (2) Short-answer / open-ended questions\n"
    "• Ask me explicitly: \"Which quiz style do you prefer now: 1) Multiple-choice or 2) Short-answer? "
    "Please reply with 1 or 2.\"\n"
    "• Do NOT start a content question until I have answered 1 or 2.\n\n"
    "Step 2 – After I choose the style:\n"
    "• If I choose 1 (multiple-choice):\n"
    "  - Ask ONE multiple-choice question at a time, based on Module 10 concepts "
    "(Responsible AI definition, risk types, mitigation layers, EU AI Act, etc.).\n"
    "  - Provide 3–4 options (A, B, C, D) and make only one option clearly correct.\n"
    "• If I choose 2 (short-answer):\n"
    "  - Ask ONE short-answer question at a time, also based on Module 10 concepts.\n"
    "  - Do NOT show the answer when you ask the question.\n\n"
    "Step 3 – For each answer I give:\n"
    "• Grade my answer (correct / partially correct / incorrect).\n"
    "• Give a brief explanation and the correct answer.\n"
    "• Then ask if I want another question of the SAME style.\n"
    "• Continue this pattern until I explicitly say to stop.\n\n"
    "Please start by asking me which quiz style I prefer (1 = multiple-choice, 2 = short-answer). "
    "Do not ask any content question before I choose."
)


# ----------------------------
# 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 QuizStartReq(BaseModel):
    user_id: str
    language_preference: str = "Auto"
    doc_type: str = MODULE10_DOC_TYPE
    learning_mode: str = "quiz"


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):
    class Config:
        extra = "ignore"

    user_id: str
    rating: str  # "helpful" | "not_helpful"
    run_id: Optional[str] = None
    assistant_message_id: Optional[str] = None
    assistant_text: str
    user_text: Optional[str] = ""
    comment: Optional[str] = ""
    tags: Optional[List[str]] = []
    refs: Optional[List[str]] = []
    learning_mode: Optional[str] = None
    doc_type: Optional[str] = None
    timestamp_ms: Optional[int] = None


class ProfileStatusResp(BaseModel):
    need_init: bool
    bio_len: int
    dismissed_until: int

class ProfileDismissReq(BaseModel):
    user_id: str
    days: int = 7


class ProfileInitSubmitReq(BaseModel):
    user_id: str
    answers: Dict[str, Any]
    language_preference: str = "Auto"


def _generate_profile_bio_with_clare(
    sess: Dict[str, Any],
    answers: Dict[str, Any],
    language_preference: str = "Auto",
) -> str:
    """
    Generates an English Profile Bio. Keep it neutral/supportive and non-judgmental.
    IMPORTANT: Do not contaminate user's normal chat history; use empty history.
    """
    student_name = (sess.get("name") or "").strip()

    prompt = f"""
You are Clare, an AI teaching assistant.

Task:
Generate a concise English Profile Bio for the student using ONLY the initialization answers provided below.

Hard constraints:
- Output language: English.
- Tone: neutral, supportive, non-judgmental.
- No medical/psychological diagnosis language.
- Do not infer sensitive attributes (race, religion, political views, health status, sexuality, immigration status).
- Length: 60–120 words.
- Structure (4 short sentences max):
  1) background & current context
  2) learning goal for this course
  3) learning preferences (format + pace)
  4) how Clare will support them going forward (practical and concrete)

Student name (if available): {student_name}

Initialization answers (JSON):
{answers}

Return ONLY the bio text. Do not add a title.
""".strip()

    resolved_lang = "English"  # force English regardless of UI preference

    try:
        bio, _unused_history, _run_id = chat_with_clare(
            message=prompt,
            history=[],
            model_name=sess["model_name"],
            language_preference=resolved_lang,
            learning_mode="summary",
            doc_type="Other Course Document",
            course_outline=sess["course_outline"],
            weaknesses=sess["weaknesses"],
            cognitive_state=sess["cognitive_state"],
            rag_context="",
        )
        return (bio or "").strip()
    except Exception as e:
        print("[profile_bio] generate failed:", repr(e))
        return ""


# ----------------------------
# 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,
            "run_id": None,
        }

    t0 = time.time()
    marks_ms: Dict[str, float] = {"start": 0.0}

    resolved_lang = detect_language(msg, req.language_preference)
    marks_ms["language_detect_done"] = (time.time() - t0) * 1000.0

    sess["weaknesses"] = update_weaknesses_from_message(msg, sess["weaknesses"])
    marks_ms["weakness_update_done"] = (time.time() - t0) * 1000.0

    sess["cognitive_state"] = update_cognitive_state_from_message(msg, sess["cognitive_state"])
    marks_ms["cognitive_update_done"] = (time.time() - t0) * 1000.0

    # NEW: do NOT bypass RAG for document actions (so UI refs are preserved)
    force_rag = _should_force_rag(msg)

    allowed_files, allowed_doc_types = _resolve_rag_scope(sess, msg)

    if (len(msg) < 20 and ("?" not in msg)) and (not force_rag):
        rag_context_text, rag_used_chunks = "", []
    else:
        rag_context_text, rag_used_chunks = retrieve_relevant_chunks(
            msg,
            sess["rag_chunks"],
            allowed_source_files=allowed_files,
            allowed_doc_types=allowed_doc_types,
        )



        
    marks_ms["rag_retrieve_done"] = (time.time() - t0) * 1000.0

    #  NEW: prepend deterministic upload/file-state hint so the model never says “no file”
    upload_hint = _build_upload_hint(sess)
    if upload_hint:
        rag_context_text = (upload_hint + "\n\n---\n\n" + (rag_context_text or "")).strip()

    try:
        answer, new_history, run_id = 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)

    marks_ms["llm_done"] = (time.time() - t0) * 1000.0
    total_ms = marks_ms["llm_done"]

    ordered = [
        "start",
        "language_detect_done",
        "weakness_update_done",
        "cognitive_update_done",
        "rag_retrieve_done",
        "llm_done",
    ]
    segments_ms: Dict[str, float] = {}
    for i in range(1, len(ordered)):
        a = ordered[i - 1]
        b = ordered[i]
        segments_ms[b] = max(0.0, marks_ms.get(b, 0.0) - marks_ms.get(a, 0.0))

    latency_breakdown = {"marks_ms": marks_ms, "segments_ms": segments_ms, "total_ms": total_ms}

    sess["history"] = new_history

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

    rag_context_chars = len((rag_context_text or ""))
    rag_used_chunks_count = len(rag_used_chunks or [])
    history_len = len(sess["history"])

    _log_event_to_langsmith(
        {
            "experiment_id": EXPERIMENT_ID,
            "student_id": user_id,
            "student_name": sess.get("name", ""),
            "event_type": "chat_turn",
            "timestamp": time.time(),
            "latency_ms": total_ms,
            "latency_breakdown": latency_breakdown,
            "rag_context_chars": rag_context_chars,
            "rag_used_chunks_count": rag_used_chunks_count,
            "history_len": history_len,
            "question": msg,
            "answer": answer,
            "model_name": sess["model_name"],
            "language": resolved_lang,
            "learning_mode": req.learning_mode,
            "doc_type": req.doc_type,
            "refs": refs,
            "run_id": run_id,
        }
    )

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


@app.post("/api/quiz/start")
def quiz_start(req: QuizStartReq):
    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)

    quiz_instruction = MICRO_QUIZ_INSTRUCTION
    t0 = time.time()

    resolved_lang = detect_language(quiz_instruction, req.language_preference)

    rag_context_text, rag_used_chunks = retrieve_relevant_chunks(
        "Module 10 quiz", sess["rag_chunks"]
    )

    # ✅ NEW: same hint for quiz start as well
    upload_hint = _build_upload_hint(sess)
    if upload_hint:
        rag_context_text = (upload_hint + "\n\n---\n\n" + (rag_context_text or "")).strip()

    try:
        answer, new_history, run_id = chat_with_clare(
            message=quiz_instruction,
            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"[quiz_start] error: {repr(e)}")
        return JSONResponse({"error": f"quiz_start failed: {repr(e)}"}, status_code=500)

    total_ms = (time.time() - t0) * 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_event_to_langsmith(
        {
            "experiment_id": EXPERIMENT_ID,
            "student_id": user_id,
            "student_name": sess.get("name", ""),
            "event_type": "micro_quiz_start",
            "timestamp": time.time(),
            "latency_ms": total_ms,
            "question": "[micro_quiz_start] " + quiz_instruction[:200],
            "answer": answer,
            "model_name": sess["model_name"],
            "language": resolved_lang,
            "learning_mode": req.learning_mode,
            "doc_type": req.doc_type,
            "refs": refs,
            "rag_used_chunks_count": len(rag_used_chunks or []),
            "history_len": len(sess["history"]),
            "run_id": run_id,
        }
    )

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


@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 = []

    # ✅ NEW: record upload metadata for prompting/debug
    try:
        sess["uploaded_files"] = sess.get("uploaded_files") or []
        sess["uploaded_files"].append(
            {
                "filename": safe_name,
                "doc_type": doc_type,
                "added_chunks": len(new_chunks),
                "ts": int(time.time()),
            }
        )
    except Exception as e:
        print(f"[upload] uploaded_files record error: {repr(e)}")

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

    _log_event_to_langsmith(
        {
            "experiment_id": EXPERIMENT_ID,
            "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)

    assistant_text = (req.assistant_text or "").strip()
    user_text = (req.user_text or "").strip()
    comment = (req.comment or "").strip()
    refs = req.refs or []
    tags = req.tags or []
    timestamp_ms = int(req.timestamp_ms or int(time.time() * 1000))

    _log_event_to_langsmith(
        {
            "experiment_id": EXPERIMENT_ID,
            "student_id": user_id,
            "student_name": student_name,
            "event_type": "feedback",
            "timestamp": time.time(),
            "timestamp_ms": timestamp_ms,
            "rating": rating,
            "assistant_message_id": req.assistant_message_id,
            "run_id": req.run_id,
            "question": user_text,
            "answer": assistant_text,
            "comment": comment,
            "tags": tags,
            "refs": refs,
            "learning_mode": req.learning_mode,
            "doc_type": req.doc_type,
        }
    )

    wrote_run_feedback = False
    if req.run_id:
        wrote_run_feedback = _write_feedback_to_langsmith_run(
            run_id=req.run_id,
            rating=rating,
            comment=comment,
            tags=tags,
            metadata={
                "experiment_id": EXPERIMENT_ID,
                "student_id": user_id,
                "student_name": student_name,
                "assistant_message_id": req.assistant_message_id,
                "learning_mode": req.learning_mode,
                "doc_type": req.doc_type,
                "refs": refs,
                "timestamp_ms": timestamp_ms,
            },
        )

    return {"ok": True, "run_feedback_written": wrote_run_feedback}


@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}

@app.get("/api/profile/status")
def profile_status(user_id: str):
    user_id = (user_id or "").strip()
    if not user_id:
        return JSONResponse({"error": "Missing user_id"}, status_code=400)

    sess = _get_session(user_id)
    bio = (sess.get("profile_bio") or "").strip()
    bio_len = len(bio)

    now = int(time.time())
    dismissed_until = int(sess.get("init_dismiss_until") or 0)

    # 触发条件：bio <= 50 且不在 dismiss 窗口内
    need_init = (bio_len <= 50) and (now >= dismissed_until)

    return {
        "need_init": need_init,
        "bio_len": bio_len,
        "dismissed_until": dismissed_until,
    }




@app.get("/api/profile/status")
def profile_status(user_id: str):
    user_id = (user_id or "").strip()
    if not user_id:
        return JSONResponse({"error": "Missing user_id"}, status_code=400)

    sess = _get_session(user_id)
    bio = (sess.get("profile_bio") or "").strip()
    bio_len = len(bio)

    now = int(time.time())
    dismissed_until = int(sess.get("init_dismiss_until") or 0)

    # Trigger if bio is too short and not within dismiss window
    need_init = (bio_len <= 50) and (now >= dismissed_until)

    return {
        "need_init": need_init,
        "bio_len": bio_len,
        "dismissed_until": dismissed_until,
    }


@app.post("/api/profile/dismiss")
def profile_dismiss(req: ProfileDismissReq):
    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)
    days = max(1, min(int(req.days or 7), 30))  # 1–30 days
    sess["init_dismiss_until"] = int(time.time()) + days * 24 * 3600
    return {"ok": True, "dismissed_until": sess["init_dismiss_until"]}


@app.post("/api/profile/init_submit")
def profile_init_submit(req: ProfileInitSubmitReq):
    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)
    answers = req.answers or {}

    sess["init_answers"] = answers

    bio = _generate_profile_bio_with_clare(sess, answers, req.language_preference)
    if not bio:
        return JSONResponse({"error": "Failed to generate bio"}, status_code=500)

    sess["profile_bio"] = bio

    return {"ok": True, "bio": bio}

# ----------------------------
# 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,
    )