"""Import training tips from pathways JSON into encoach.training.tip model.

Run inside the Odoo container via:
    odoo shell -d encoach --db_host=db --db_user=odoo --db_password=<pw> \
        --no-http --addons-path=... < /mnt/custom/scripts/import_training_tips.py

This script:
  1. Reads pathways_2_rw.json
  2. Creates encoach.training.tip records (tip_id, category, content)
  3. Computes sentence-transformer embeddings and stores them
"""
import json
import os
import pickle
import sys

os.environ.setdefault("TMPDIR", "/tmp")

JSON_PATH = os.environ.get(
    "TIPS_JSON_PATH",
    "/mnt/custom/scripts/pathways_2_rw.json",
)


def main():
    with open(JSON_PATH, "r", encoding="utf-8") as f:
        data = json.load(f)

    TipModel = env["encoach.training.tip"].sudo()

    tips_data = []
    for unit in data["units"]:
        for page in unit["pages"]:
            for tip in page["tips"]:
                category = tip["category"].lower().replace(" ", "_")
                tips_data.append({
                    "tip_id": tip["id"],
                    "category": category,
                    "content": tip["text"],
                })

    created = 0
    skipped = 0
    for td in tips_data:
        existing = TipModel.search([("tip_id", "=", td["tip_id"])], limit=1)
        if existing:
            skipped += 1
            continue
        TipModel.create(td)
        created += 1

    env.cr.commit()
    print(f"Tips import complete: {created} created, {skipped} skipped (already exist)")

    try:
        from sentence_transformers import SentenceTransformer
        import numpy as np

        print("Computing embeddings with all-MiniLM-L6-v2...")
        model = SentenceTransformer("all-MiniLM-L6-v2")

        tips = TipModel.search([("embedding", "=", False)])
        if not tips:
            print("All tips already have embeddings.")
            return

        for i, tip in enumerate(tips):
            vec = model.encode([tip.content]).astype(np.float32)[0]
            tip.embedding = pickle.dumps(vec)
            if (i + 1) % 10 == 0:
                print(f"  Embedded {i + 1}/{len(tips)} tips...")

        env.cr.commit()
        print(f"Embeddings computed for {len(tips)} tips.")

    except ImportError:
        print("WARNING: sentence-transformers not available. Tips created without embeddings.")
        print("Embeddings can be computed later by re-running this script.")


main()