# app.py — DynBot (tenant-scoped)

import os
import re
import uuid
import logging
import json
import random

from time import perf_counter
from contextlib import contextmanager

from flask import Flask, g, request, jsonify, render_template, abort
from sqlalchemy.sql import func, text
from openai import OpenAI

from db import SessionLocal, init_db
from rag_service import generate_rag_addendum
from utils.prompt_manager import build_messages_payload
from models import (
    Tenant,
    Session as ChatSession,
    User,
    Case,
    Message,
    Document,
    Topic,
)

# ------------------------------------------------------------------------------
# App + DB init
# ------------------------------------------------------------------------------
app = Flask(__name__)
init_db()

# ------------------------------------------------------------------------------
# Helpers
# ------------------------------------------------------------------------------
def current_tenant_id() -> int:
    """Single source of truth: bind from systemd Environment=TENANT_ID=4"""
    return int(os.getenv("TENANT_ID", "4"))

@app.before_request
def bind_tenant_and_session():
    g.tenant_id = current_tenant_id()
    g.db = SessionLocal()
    app.logger.info(f"[TENANT] {g.tenant_id} {request.method} {request.path}")

@app.teardown_request
def teardown_session(exc):
    db = getattr(g, "db", None)
    if db is not None:
        try:
            if exc:
                db.rollback()
        finally:
            db.close()

def ensure_case(case_id: int) -> Case:
    """Ensure the case belongs to the current tenant, otherwise 404."""
    case = g.db.query(Case).filter_by(id=case_id, tenant_id=g.tenant_id).first()
    if not case:
        abort(404)
    return case

def get_openai_client() -> OpenAI:
    """Use env OPENAI_API_KEY only. Never hardcode keys."""
    api_key = (os.getenv("OPENAI_API_KEY") or "").strip()
    if not api_key:
        raise RuntimeError("OPENAI_API_KEY not set")
    logging.getLogger().info("🔑 OPENAI_API_KEY ends with …%s", api_key[-4:])
    return OpenAI(api_key=api_key)

@contextmanager
def timed(label, bag):
    t0 = perf_counter()
    try:
        yield
    finally:
        bag[label] = int((perf_counter() - t0) * 1000)  # ms

def truncate_numbered_blocks(text: str, max_items: int = 3) -> str:
    """Keep only the first N numbered list items (1., 2., …) to avoid overlong replies."""
    m = re.search(r'(?m)^\s*\d+\.\s+', text)
    if not m:
        return text
    head = text[:m.start()].rstrip("\n")
    body = text[m.start():]
    blocks = re.findall(r'(?ms)^\s*(\d+)\.\s+(.*?)(?=^\s*\d+\.|\Z)', body)
    if not blocks:
        return text
    out = []
    for idx, (_num, content) in enumerate(blocks[:max_items], start=1):
        out.append(f"{idx}. {content.strip()}")
    return (head + "\n\n" if head else "") + "\n\n".join(out)

# ------------------------------------------------------------------------------
# Routes
# ------------------------------------------------------------------------------
@app.route("/")
def index():
    return render_template("index.html")

@app.get("/__whoami")
def whoami():
    return {"tenant_id": g.tenant_id}

@app.route("/tenants", methods=["POST"])
def create_tenant():
    db = g.db
    t = Tenant(name=request.json["name"])
    db.add(t); db.commit(); db.refresh(t)
    return jsonify({"tenant_id": t.id}), 201

from sqlalchemy import text

@app.route("/topics/random", methods=["GET"])
def get_random_topics():
    db = g.db
    tenant_id = g.tenant_id
    try:
        # --- Primary (ORM) path ---
        raw_topics = (
            db.query(Topic)
              .filter(Topic.tenant_id == tenant_id)    # explicit, not filter_by
              .order_by(func.random())
              .limit(3)
              .all()
        )
    except Exception:
        # Log the real reason
        app.logger.exception("[topics/random] ORM query failed; falling back to raw SQL")

        # --- Fallback (raw SQL) path ---
        rows = db.execute(
            text("""
                SELECT topic, question
                FROM topics
                WHERE tenant_id = :tid
                ORDER BY RANDOM()
                LIMIT 3
            """),
            {"tid": tenant_id}
        ).fetchall()

        # Normalize result
        results = [{"topic": r[0], "question": (r[1] or "").replace("{topic}", r[0] or "")} for r in rows]
        return jsonify(results)

    # Normal ORM result
    results = []
    for t in raw_topics:
        results.append({
            "topic": t.topic,
            "question": (t.question or "").replace("{topic}", t.topic or "")
        })
    return jsonify(results)


@app.route("/init-session", methods=["POST"])
def init_session():
    db = g.db
    data = request.get_json(silent=True) or {}
    tenant_id = g.tenant_id

    # Use provided token if present; otherwise create one
    session_token = (data.get("session_token") or str(uuid.uuid4())).strip()

    # Avoid cross-tenant clashes: identify user by (tenant_id, username)
    username = (data.get("username") or "Default User").strip()
    email = (data.get("email") or "default@example.com").strip()

    # --- get or create user ---
    user = (
        db.query(User)
          .filter_by(tenant_id=tenant_id, username=username)
          .first()
    )
    if not user:
        user = User(tenant_id=tenant_id, username=username, email=email)
        db.add(user)
        db.commit()
        db.refresh(user)

    # --- get or create session (idempotent by token+tenant) ---
    chat_sess = (
        db.query(ChatSession)
          .filter_by(tenant_id=tenant_id, session_token=session_token)
          .first()
    )
    if not chat_sess:
        chat_sess = ChatSession(
            tenant_id=tenant_id,
            user_id=user.id,
            session_token=session_token,
        )
        db.add(chat_sess)
        db.commit()
        db.refresh(chat_sess)

    # --- get or create case (one case per session) ---
    case = (
        db.query(Case)
          .filter_by(tenant_id=tenant_id, session_id=chat_sess.id)
          .order_by(Case.id.asc())
          .first()
    )
    if not case:
        case = Case(
            tenant_id=tenant_id,
            user_id=user.id,
            session_id=chat_sess.id,
            title=(data.get("case_title") or None)
        )
        db.add(case)
        db.commit()
        db.refresh(case)

    return jsonify({
        "session_id": chat_sess.id,
        "session_token": session_token,
        "case_id": case.id
    })


@app.route("/cases", methods=["POST"])
def create_case():
    db = g.db
    tenant_id = g.tenant_id
    new_case = Case(tenant_id=tenant_id, title=(request.json.get("title") or "")[:255])
    db.add(new_case); db.commit(); db.refresh(new_case)
    return jsonify({"case_id": new_case.id}), 201

@app.route("/cases/<int:case_id>/history", methods=["GET"])
def get_history(case_id):
    db = g.db
    ensure_case(case_id)
    # Join via Case to ensure tenant scoping for Message (which has no tenant_id)
    msgs = (
        db.query(Message)
          .join(Case, Message.case_id == Case.id)
          .filter(Case.id == case_id, Case.tenant_id == g.tenant_id)
          .order_by(Message.timestamp)
          .all()
    )
    return jsonify({
        "messages": [
            {"role": m.role, "content": m.content, "timestamp": m.timestamp.isoformat()}
            for m in msgs
        ]
    })

@app.route("/cases/<int:case_id>/messages", methods=["POST"])
def post_message(case_id):
    mode = request.args.get("mode", "primary")  # "primary" | "rag"
    db = g.db
    ensure_case(case_id)

    timings = {}
    t_all = perf_counter()
    quote_text = ""

    try:
        data = request.get_json(silent=True) or {}
        user_text = (data.get("content") or "").strip()
        button_choice = (data.get("button_choice") or "").strip()
        tenant_id = g.tenant_id

        if not user_text:
            return jsonify({"error": "bad_request", "detail": "Missing 'content'"}), 400

        # ------------------- RAG-only probe mode -------------------
        if mode == "rag":
            with timed("rag_addendum", timings):
                try:
                    rag_res = generate_rag_addendum(user_text, tenant_id=tenant_id, top_k=3)
                    rag_addendum = (rag_res.get("addendum") or "").strip()
                except Exception:
                    rag_addendum = ""
                    timings["rag_error"] = 1
            resp = jsonify({"reply": rag_addendum, "timings": timings})
            total = int((perf_counter() - t_all) * 1000)
            resp.headers["Server-Timing"] = f'rag_addendum;dur={timings.get("rag_addendum",0)}, total;dur={total}'
            return resp, 200

        # ------------------- Normal path -------------------
        # 1) Store user turn
        with timed("db_store_user", timings):
            user_msg = Message(case_id=case_id, role="user", content=user_text, button_choice=button_choice)
            db.add(user_msg); db.commit(); db.refresh(user_msg)

        MATH_RULES = (
            "Format all mathematical expressions as LaTeX. "
            "Use $$...$$ for display equations and \\(...\\) for inline. "
            "Use \\times (not ×), \\cdot, \\frac{a}{b}, exponents as ^{…}, subscripts as _{…}. "
            "Do not output plain-text formulas like 'Pn=Pn-1×(1+r)-M'."
        )    

        # 2) Build conversation payload
        with timed("build_payload", timings):
            messages_payload = build_messages_payload(db, case_id, tenant_id)
            
        # 🔧 prepend our math rule (has to come before user/assistant turns)
        messages_payload.insert(0, {"role": "system", "content": MATH_RULES})     

        # 3) Call OpenAI
        openai_client = get_openai_client()
        with timed("openai", timings):
            resp = openai_client.chat.completions.create(
                model="gpt-4o-mini",
                temperature=1.2,
                messages=messages_payload,
                tools=[{
                    "type": "function",
                    "function": {
                        "name": "extract_followups",
                        "description": "Suggest 6–8 follow-up questions based on the assistant's reply.",
                        "parameters": {
                            "type": "object",
                            "properties": {"questions": {"type": "array", "items": {"type": "string"}}},
                            "required": ["questions"]
                        }
                    }
                }],
                tool_choice="auto"
            )

        choice = resp.choices[0]
        msg_obj = getattr(choice, "message", None)
        finish = getattr(choice, "finish_reason", None)

        if finish == "tool_calls" and msg_obj and getattr(msg_obj, "tool_calls", None):
            tool_call = msg_obj.tool_calls[0]
            args = json.loads(getattr(tool_call.function, "arguments", "{}") or "{}")
            followups = args.get("questions", []) or []
            ai_text = msg_obj.content or "Vielen Dank für Ihre Auswahl. Hier sind mögliche nächste Schritte oder Zusammenfassungen."
        else:
            followups = []
            ai_text = (msg_obj.content if msg_obj else "") or "(no text reply provided)"

        # 4) Extract a few bold **choices** (UI buttons) and trim long lists
        ai_text = truncate_numbered_blocks(ai_text, max_items=3)
        choices = re.findall(r"\*\*(.*?)\*\*", ai_text)
        if len(choices) > 3:
            choices = random.sample(choices, 3)

        # 5) Determine turn number and summary phase
        past_messages = (
            db.query(Message)
              .join(Case, Message.case_id == Case.id)
              .filter(Case.id == case_id, Case.tenant_id == g.tenant_id)
              .order_by(Message.timestamp)
              .all()
        )
        turn = sum(1 for m in past_messages if m.role == "user")
        is_summary = (turn == 3)

        # 6) Off-topic quick gate: no inline **choices** and not summary
        off_topic = (not is_summary) and (len(choices) == 0)
        if off_topic:
            try:
                with timed("db_delete_offtopic_user", timings):
                    db.delete(user_msg)
                    db.commit()
            except Exception:
                db.rollback()
            payload = {
                "reply": ai_text,
                "followups": [],
                "choices": [],
                "is_summary": False,
                "timings": timings
            }
            resp = jsonify(payload)
            total = int((perf_counter() - t_all) * 1000)
            server_timing = ", ".join([f"{k};dur={v}" for k, v in timings.items()])
            resp.headers["Server-Timing"] = f'{server_timing}, total;dur={total}' if server_timing else f'total;dur={total}'
            return resp, 200

        # 7) Summary enrichment (tenant-scoped docs)
        if is_summary:
            with timed("summary_prep", timings):
                # 1) Collect path: original question + button choices + any free-text feedback
                user_turns = [m for m in past_messages if m.role == "user"]
                original_question = user_turns[0].content if user_turns else user_text
                button_choices = [m.button_choice for m in user_turns if m.button_choice]

                # optional: collect short free-text snippets (skip the very first question and empty)
                free_texts = []
                for m in user_turns[1:]:
                    if m.content and m.content.strip() and m.content.strip() != m.button_choice:
                        free_texts.append(m.content.strip())

                # 2) (Optional but helpful) short RAG addendum seeded by path
                try:
                    rag_seed = " ".join([original_question] + button_choices)[:2000]
                    rag_res = generate_rag_addendum(rag_seed, tenant_id=tenant_id, top_k=3)
                    rag_addendum = (rag_res.get("addendum") or "").strip()
                except Exception:
                    rag_addendum = ""
                    timings["rag_error"] = 1

                # 3) Tenant-scoped intro + quote (UI garnish; not sent to model)
                intro_doc = (
                    db.query(Document)
                    .filter_by(tenant_id=tenant_id, title="summary_intro_1")
                    .first()
                )
                quotes = (
                    db.query(Document)
                    .filter_by(tenant_id=tenant_id, title="quote")
                    .all()
                )
                intro_text = intro_doc.content if intro_doc else ""
                quote_text = random.choice(quotes).content if quotes else ""
                intro_filled = (intro_text or "").format(
                    iteration1=(button_choices[0] if len(button_choices) > 0 else ""),
                    iteration2=(button_choices[1] if len(button_choices) > 1 else ""),
                    iteration3=(button_choices[2] if len(button_choices) > 2 else "")
                )

            # 4) Build a dedicated SUMMARY payload (do NOT reuse messages_payload)
            summary_system = (
                "You are writing a concise, session-level executive summary. "
                "Touch each of the selected topics in order, connect them logically, "
                "and end with 3–5 actionable next steps. Use clear markdown with bullets."
            )

            # keep this short and structured—models do better with compact JSON-ish lists
            summary_user = (
                f"ORIGINAL QUESTION:\n{original_question}\n\n"
                f"SELECTED TOPICS (path):\n- " + "\n- ".join([c for c in button_choices if c]) + "\n\n"
                + (("USER FREE-TEXT NOTES:\n- " + "\n- ".join(free_texts[:5]) + "\n\n") if free_texts else "")
                + (("RAG ADDENDUM (optional):\n" + rag_addendum + "\n\n") if rag_addendum else "")
                + "Please produce a single executive summary that references the above path."
            )

            with timed("openai_summary", timings):
                summary_resp = get_openai_client().chat.completions.create(
                    model="gpt-4o-mini",
                    temperature=0.7,
                    messages=[
                        {"role": "system", "content": summary_system},
                        {"role": "user", "content": summary_user},
                    ]
                )
            ai_text = summary_resp.choices[0].message.content.strip() or "(leere Zusammenfassung)"

            # 5) Prepend the tenant intro to the summary text (UI only)
            ai_text = f"{intro_filled}\n\n{ai_text}"
            choices = []  # no choices on summary

        # 8) Store assistant turn
        with timed("db_store_ai", timings):
            ai_msg = Message(case_id=case_id, role="assistant", content=ai_text)
            db.add(ai_msg); db.commit(); db.refresh(ai_msg)

        payload = {
            "reply": ai_text,
            "followups": followups,
            "choices": ([] if is_summary else choices),
            "is_summary": is_summary,
            "footer_quote": (quote_text if is_summary else ""),
            "timings": timings
        }
        resp = jsonify(payload)
        total = int((perf_counter() - t_all) * 1000)
        server_timing = ", ".join([f"{k};dur={v}" for k, v in timings.items()])
        resp.headers["Server-Timing"] = f'{server_timing}, total;dur={total}' if server_timing else f'total;dur={total}'
        return resp

    except Exception as e:
        app.logger.exception("Error in /messages")
        return jsonify({"error": "server_error", "detail": str(e)}), 500

# ------------------------------------------------------------------------------
# Boot
# ------------------------------------------------------------------------------
if __name__ == '__main__':
    app.run(
        host="0.0.0.0",
        port=5004,
        ssl_context=(
            "/var/www/html/decompression/certs/fullchain.pem",
            "/var/www/html/decompression/certs/privkey.pem",
        ),
        debug=True
    )