"""
Rebuild (pre-warm) embeddings for ALL organisations in platform.db.

Run this after any bulk import or whenever you want to pre-compute
embeddings so the first chatbot query is instant.

Usage:
  python rebuild_embeddings.py
  python rebuild_embeddings.py --org-id 2   (single org)
"""
import argparse, json, os, sqlite3, time, re, unicodedata
import torch
from sentence_transformers import SentenceTransformer

BASE_DIR  = os.path.abspath(os.path.dirname(__file__))
DB_PATH   = os.path.join(BASE_DIR, "platform.db")
EMB_DIR   = os.path.join(BASE_DIR, "embeddings")
os.makedirs(EMB_DIR, exist_ok=True)

parser = argparse.ArgumentParser()
parser.add_argument("--org-id", type=int, default=None, help="Rebuild only this org (default: all)")
args = parser.parse_args()

# ── Text helpers (must match app.py) ─────────────────────────────────────────
def normalise(text: str) -> str:
    text = text.lower().strip()
    text = unicodedata.normalize("NFD", text)
    text = "".join(c for c in text if unicodedata.category(c) != "Mn")
    text = re.sub(r"[^\w\s]", " ", text)
    text = re.sub(r"\s+", " ", text).strip()
    return text

def clean_question(q: str) -> str:
    return re.sub(r'\s+\d+\s*$', '', (q or '').strip())

def build_index_text(row) -> str:
    q     = clean_question(row["question"] or "")
    proc  = (row["processus"] or "").strip()
    procd = (row["procedure"] or "").strip()
    parts = [q]
    if proc:
        parts.append(proc)
    if procd:
        parts.append(procd)
    return " | ".join(parts)

# ── Load model ───────────────────────────────────────────────────────────────
print("🔄 Chargement du modèle…")
try:
    model = SentenceTransformer(
        "OrdalieTech/Solon-embeddings-mini-beta-1.1",
        device="cpu", trust_remote_code=True
    )
    print("✓ Modèle principal (Solon) chargé")
except Exception as e:
    print(f"⚠  Modèle principal échoué ({e}), bascule sur secours…")
    model = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2", device="cpu")
    print("✓ Modèle de secours chargé")

# ── DB helpers ────────────────────────────────────────────────────────────────
conn = sqlite3.connect(DB_PATH)
conn.row_factory = sqlite3.Row

if args.org_id:
    orgs = conn.execute(
        "SELECT id, name FROM organisations WHERE id=?", (args.org_id,)
    ).fetchall()
else:
    orgs = conn.execute("SELECT id, name FROM organisations").fetchall()

if not orgs:
    print("Aucune organisation trouvée dans la base de données.")
    raise SystemExit(0)

total_built = 0

for org in orgs:
    oid  = org["id"]
    name = org["name"]

    rows = conn.execute(
        "SELECT id, question, processus, procedure, intent, sub_intent, response "
        "FROM entries WHERE org_id=? ORDER BY id", (oid,)
    ).fetchall()

    if not rows:
        print(f"  [{name}]  0 entrées — ignoré")
        continue

    row_ids = [r["id"]  for r in rows]
    texts   = [build_index_text(r) for r in rows]

    emb_file  = os.path.join(EMB_DIR, f"org_{oid}.pt")
    meta_file = os.path.join(EMB_DIR, f"org_{oid}_meta.json")

    # Remove stale files
    for f in [emb_file, meta_file]:
        if os.path.exists(f):
            os.remove(f)

    print(f"  [{name}]  {len(texts)} questions → calcul embeddings…", end=" ", flush=True)
    t0  = time.time()
    emb = model.encode(texts, convert_to_tensor=True, normalize_embeddings=True,
                       batch_size=64, show_progress_bar=False)
    torch.save(emb, emb_file)
    with open(meta_file, "w") as mf:
        json.dump({"row_ids": row_ids, "org_id": oid, "org_name": name, "v": 4}, mf)

    elapsed = time.time() - t0
    print(f"✓  ({elapsed:.1f}s)  →  org_{oid}.pt")
    total_built += 1

conn.close()
print(f"\n✅ {total_built} organisation(s) reconstruite(s). Redémarrez Flask pour appliquer.")