"""SQLite database for podcast-mindmap: paragraphs, quotes, embeddings."""

import json
import sqlite3
import os
import numpy as np
from typing import Optional

DB_PATH = os.environ.get("DB_PATH", "/data/db.sqlite")


def get_db():
    db = sqlite3.connect(DB_PATH)
    db.row_factory = sqlite3.Row
    db.execute("PRAGMA journal_mode=WAL")
    return db


def init_db():
    db = get_db()
    db.executescript("""
    CREATE TABLE IF NOT EXISTS podcasts (
        id TEXT PRIMARY KEY,
        name TEXT NOT NULL,
        host TEXT,
        description TEXT,
        config_json TEXT
    );

    CREATE TABLE IF NOT EXISTS episodes (
        id TEXT NOT NULL,
        podcast_id TEXT NOT NULL,
        title TEXT NOT NULL,
        guest TEXT,
        staffel INTEGER,
        youtube_id TEXT,
        audio_file TEXT,
        PRIMARY KEY (podcast_id, id),
        FOREIGN KEY (podcast_id) REFERENCES podcasts(id)
    );

    CREATE TABLE IF NOT EXISTS paragraphs (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        podcast_id TEXT NOT NULL,
        episode_id TEXT NOT NULL,
        idx INTEGER NOT NULL,
        start_time REAL,
        end_time REAL,
        text TEXT NOT NULL,
        embedding BLOB,
        UNIQUE(podcast_id, episode_id, idx),
        FOREIGN KEY (podcast_id) REFERENCES podcasts(id)
    );

    CREATE TABLE IF NOT EXISTS topics (
        paragraph_id INTEGER NOT NULL,
        tag TEXT NOT NULL,
        score REAL DEFAULT 1.0,
        PRIMARY KEY (paragraph_id, tag),
        FOREIGN KEY (paragraph_id) REFERENCES paragraphs(id)
    );

    CREATE TABLE IF NOT EXISTS quotes (
        id TEXT NOT NULL,
        podcast_id TEXT NOT NULL,
        episode_id TEXT NOT NULL,
        text TEXT NOT NULL,
        verbatim TEXT,
        speaker TEXT,
        start_time REAL,
        end_time REAL,
        is_top_quote BOOLEAN DEFAULT 0,
        themes_json TEXT DEFAULT '[]',
        PRIMARY KEY (podcast_id, id),
        FOREIGN KEY (podcast_id) REFERENCES podcasts(id)
    );

    CREATE TABLE IF NOT EXISTS staffeln (
        id INTEGER NOT NULL,
        podcast_id TEXT NOT NULL,
        name TEXT NOT NULL,
        color TEXT DEFAULT '#666',
        PRIMARY KEY (podcast_id, id),
        FOREIGN KEY (podcast_id) REFERENCES podcasts(id)
    );

    CREATE TABLE IF NOT EXISTS themes (
        id TEXT NOT NULL,
        podcast_id TEXT NOT NULL,
        label TEXT NOT NULL,
        description TEXT,
        color TEXT DEFAULT '#666',
        episodes_json TEXT DEFAULT '[]',
        PRIMARY KEY (podcast_id, id),
        FOREIGN KEY (podcast_id) REFERENCES podcasts(id)
    );

    CREATE INDEX IF NOT EXISTS idx_paragraphs_podcast ON paragraphs(podcast_id, episode_id);
    CREATE INDEX IF NOT EXISTS idx_quotes_podcast ON quotes(podcast_id, episode_id);
    CREATE INDEX IF NOT EXISTS idx_topics_tag ON topics(tag);
    """)
    db.commit()
    db.close()


def import_podcast(podcast_id: str, mindmap_data: dict, srt_index: dict):
    """Import a podcast's data from mindmap_data.json + srt_index.json into the DB."""
    db = get_db()

    # Podcast
    db.execute(
        "INSERT OR REPLACE INTO podcasts (id, name, host, description) VALUES (?, ?, ?, ?)",
        (podcast_id, mindmap_data.get("name", ""), mindmap_data.get("host", ""),
         mindmap_data.get("description", ""))
    )

    # Staffeln
    for s in mindmap_data.get("staffeln", []):
        db.execute(
            "INSERT OR REPLACE INTO staffeln (id, podcast_id, name, color) VALUES (?, ?, ?, ?)",
            (s["id"], podcast_id, s["name"], s.get("color", "#666"))
        )

    # Themes
    for t in mindmap_data.get("themes", []):
        db.execute(
            "INSERT OR REPLACE INTO themes (id, podcast_id, label, description, color, episodes_json) VALUES (?, ?, ?, ?, ?, ?)",
            (t["id"], podcast_id, t["label"], t.get("description", ""),
             t.get("color", "#666"), json.dumps(t.get("episodes", [])))
        )

    # Episodes
    for ep in mindmap_data.get("episodes", []):
        db.execute(
            "INSERT OR REPLACE INTO episodes (id, podcast_id, title, guest, staffel, audio_file) VALUES (?, ?, ?, ?, ?, ?)",
            (ep["id"], podcast_id, ep["title"], ep.get("guest", ""),
             ep.get("staffel"), ep.get("audioFile"))
        )

    # Quotes
    for q in mindmap_data.get("quotes", []):
        db.execute(
            "INSERT OR REPLACE INTO quotes (id, podcast_id, episode_id, text, verbatim, speaker, start_time, end_time, is_top_quote, themes_json) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
            (q["id"], podcast_id, q["episode"], q["text"], q.get("verbatim"),
             q.get("speaker", ""), q.get("startTime"), q.get("endTime"),
             q.get("isTopQuote", False), json.dumps(q.get("themes", [])))
        )

    # Paragraphs from srt_index
    for ep_key, ep_data in srt_index.items():
        ep_id = ep_key.split("-")[0]  # S1E1-Wachstum → S1E1
        for i, p in enumerate(ep_data.get("paragraphs", [])):
            db.execute(
                "INSERT OR REPLACE INTO paragraphs (podcast_id, episode_id, idx, start_time, end_time, text) VALUES (?, ?, ?, ?, ?, ?)",
                (podcast_id, ep_id, i, p["start"], p["end"], p["text"])
            )

    db.commit()
    db.close()


def get_all_embeddings(podcast_id: Optional[str] = None):
    """Load all embeddings as numpy array + metadata."""
    db = get_db()
    if podcast_id:
        rows = db.execute(
            "SELECT id, podcast_id, episode_id, idx, embedding FROM paragraphs WHERE podcast_id = ? AND embedding IS NOT NULL",
            (podcast_id,)
        ).fetchall()
    else:
        rows = db.execute(
            "SELECT id, podcast_id, episode_id, idx, embedding FROM paragraphs WHERE embedding IS NOT NULL"
        ).fetchall()
    db.close()

    if not rows:
        return None, []

    meta = [{"id": r["id"], "podcast_id": r["podcast_id"],
             "episode_id": r["episode_id"], "idx": r["idx"]} for r in rows]
    vectors = np.array([np.frombuffer(r["embedding"], dtype=np.float32) for r in rows])

    # Normalize for cosine similarity
    norms = np.linalg.norm(vectors, axis=1, keepdims=True)
    norms[norms == 0] = 1
    vectors = vectors / norms

    return vectors, meta


def store_embedding(paragraph_id: int, embedding: list[float]):
    """Store embedding as binary blob."""
    db = get_db()
    blob = np.array(embedding, dtype=np.float32).tobytes()
    db.execute("UPDATE paragraphs SET embedding = ? WHERE id = ?", (blob, paragraph_id))
    db.commit()
    db.close()