TicketTracker/tickettracker/web/queries.py

"""
Requêtes SQL en lecture seule pour le dashboard web.

Toutes les fonctions reçoivent une connexion SQLite ouverte (pattern identique
à repository.py) et retournent des structures Python simples (dict, list).
L'appelant est responsable de l'ouverture et fermeture de la connexion.
"""

import sqlite3


def get_dashboard_stats(conn: sqlite3.Connection) -> dict:
    """Statistiques globales pour la page d'accueil.

    Returns:
        dict avec les clés :
          - total_receipts : int
          - total_spent    : float
          - total_items    : int
          - distinct_products : int
          - receipts_by_store : dict[str, int]
          - spent_by_store    : dict[str, float]
          - date_range        : dict {min, max} ou {min: None, max: None}
    """
    # Statistiques par enseigne
    rows = conn.execute(
        "SELECT store, COUNT(*) AS nb, SUM(total) AS spent FROM receipts GROUP BY store"
    ).fetchall()

    receipts_by_store = {row["store"]: row["nb"] for row in rows}
    spent_by_store = {row["store"]: round(row["spent"], 2) for row in rows}
    total_receipts = sum(receipts_by_store.values())
    total_spent = round(sum(row["spent"] for row in rows), 2) if rows else 0.0

    # Statistiques articles
    item_stats = conn.execute(
        """
        SELECT
            COUNT(*)                        AS total_items,
            COUNT(DISTINCT name_normalized) AS distinct_products
        FROM items
        """
    ).fetchone()

    # Plage de dates
    date_row = conn.execute(
        "SELECT MIN(date) AS d_min, MAX(date) AS d_max FROM receipts"
    ).fetchone()

    return {
        "total_receipts": total_receipts,
        "total_spent": total_spent,
        "total_items": item_stats["total_items"],
        "distinct_products": item_stats["distinct_products"],
        "receipts_by_store": receipts_by_store,
        "spent_by_store": spent_by_store,
        "date_range": {"min": date_row["d_min"], "max": date_row["d_max"]},
    }


def get_monthly_spending(conn: sqlite3.Connection) -> list[dict]:
    """Dépenses mensuelles par enseigne, pour le graphique Chart.js.

    Returns:
        Liste de dicts {month: "2026-01", store: "picnic", total: 45.20},
        triée par mois puis enseigne.
    """
    rows = conn.execute(
        """
        SELECT
            substr(date, 1, 7)  AS month,
            store,
            ROUND(SUM(total), 2) AS total
        FROM receipts
        GROUP BY month, store
        ORDER BY month, store
        """
    ).fetchall()

    return [{"month": r["month"], "store": r["store"], "total": r["total"]} for r in rows]


def get_compare_prices(conn: sqlite3.Connection) -> list[dict]:
    """Comparaison de prix entre Picnic et Leclerc pour les produits communs.

    Combine deux sources :
      - Correspondances exactes (même name_normalized dans les deux enseignes)
      - Correspondances fuzzy validées dans product_matches (status='validated')

    Les doublons éventuels (un produit déjà en exact ET en fuzzy) sont éliminés
    par UNION (qui déduplique) + sélection par nom picnic.

    Returns:
        Liste de dicts {name, price_picnic, price_leclerc, diff, diff_pct, match_type}.
        diff       = price_leclerc - price_picnic  (positif = Leclerc plus cher)
        diff_pct   = diff / MIN(price_picnic, price_leclerc) * 100
        match_type = 'exact' ou 'fuzzy'
    """
    rows = conn.execute(
        """
        WITH avg_by_store AS (
            SELECT
                name_normalized,
                store,
                ROUND(AVG(unit_price), 2) AS avg_price
            FROM price_history
            WHERE name_normalized IS NOT NULL
            GROUP BY name_normalized, store
        ),
        exact_matches AS (
            SELECT
                a.name_normalized                             AS name,
                a.name_normalized                             AS name_display,
                a.avg_price                                   AS price_picnic,
                b.avg_price                                   AS price_leclerc,
                ROUND(b.avg_price - a.avg_price, 2)           AS diff,
                ROUND(
                    (b.avg_price - a.avg_price)
                    / MIN(a.avg_price, b.avg_price) * 100
                , 1)                                          AS diff_pct,
                'exact'                                       AS match_type
            FROM avg_by_store a
            JOIN avg_by_store b
              ON a.name_normalized = b.name_normalized
             AND a.store = 'picnic'
             AND b.store = 'leclerc'
        ),
        fuzzy_matches AS (
            SELECT
                pm.name_picnic                                AS name,
                pm.name_picnic || ' ≈ ' || pm.name_leclerc  AS name_display,
                ap_p.avg_price                                AS price_picnic,
                ap_l.avg_price                                AS price_leclerc,
                ROUND(ap_l.avg_price - ap_p.avg_price, 2)    AS diff,
                ROUND(
                    (ap_l.avg_price - ap_p.avg_price)
                    / MIN(ap_p.avg_price, ap_l.avg_price) * 100
                , 1)                                          AS diff_pct,
                'fuzzy'                                       AS match_type
            FROM product_matches pm
            JOIN avg_by_store ap_p
              ON ap_p.name_normalized = pm.name_picnic  AND ap_p.store = 'picnic'
            JOIN avg_by_store ap_l
              ON ap_l.name_normalized = pm.name_leclerc AND ap_l.store = 'leclerc'
            WHERE pm.status = 'validated'
              -- Exclure si déjà présent en exact match
              AND pm.name_picnic NOT IN (SELECT name FROM exact_matches)
        )
        SELECT name, name_display, price_picnic, price_leclerc, diff, diff_pct, match_type
        FROM (
            SELECT name, name_display, price_picnic, price_leclerc, diff, diff_pct, match_type
            FROM exact_matches
            UNION ALL
            SELECT name, name_display, price_picnic, price_leclerc, diff, diff_pct, match_type
            FROM fuzzy_matches
        )
        ORDER BY ABS(diff) DESC
        """
    ).fetchall()

    return [
        {
            "name": r["name"],
            "name_display": r["name_display"],
            "price_picnic": r["price_picnic"],
            "price_leclerc": r["price_leclerc"],
            "diff": r["diff"],
            "diff_pct": r["diff_pct"],
            "match_type": r["match_type"],
        }
        for r in rows
    ]


def get_product_history(conn: sqlite3.Connection, name: str) -> dict | None:
    """Historique des prix d'un produit normalisé.

    Args:
        conn: Connexion SQLite ouverte.
        name: Valeur de name_normalized à rechercher (sensible à la casse).

    Returns:
        dict {name, min_price, max_price, avg_price, history: list[dict]}
        ou None si le produit est inconnu.
        Chaque entrée de history : {date, store, unit_price, quantity, unit}.
    """
    # Statistiques globales
    stats = conn.execute(
        """
        SELECT
            name_normalized,
            ROUND(MIN(unit_price), 2) AS min_price,
            ROUND(MAX(unit_price), 2) AS max_price,
            ROUND(AVG(unit_price), 2) AS avg_price
        FROM price_history
        WHERE name_normalized = ?
        """,
        (name,),
    ).fetchone()

    if stats is None or stats["name_normalized"] is None:
        return None

    # Historique chronologique
    rows = conn.execute(
        """
        SELECT date, store, unit_price, quantity, unit
        FROM price_history
        WHERE name_normalized = ?
        ORDER BY date
        """,
        (name,),
    ).fetchall()

    return {
        "name": stats["name_normalized"],
        "min_price": stats["min_price"],
        "max_price": stats["max_price"],
        "avg_price": stats["avg_price"],
        "history": [
            {
                "date": r["date"],
                "store": r["store"],
                "unit_price": r["unit_price"],
                "quantity": r["quantity"],
                "unit": r["unit"],
            }
            for r in rows
        ],
    }


def get_all_receipts(conn: sqlite3.Connection) -> list[dict]:
    """Liste tous les tickets avec le nombre d'articles associés.

    Returns:
        Liste de dicts {id, store, date, total, delivery_fee, order_id, nb_items},
        triée par date décroissante (le plus récent en premier).
    """
    rows = conn.execute(
        """
        SELECT
            r.id,
            r.store,
            r.date,
            r.total,
            r.delivery_fee,
            r.order_id,
            COUNT(i.id) AS nb_items
        FROM receipts r
        LEFT JOIN items i ON i.receipt_id = r.id
        GROUP BY r.id
        ORDER BY r.date DESC, r.id DESC
        """
    ).fetchall()

    return [
        {
            "id": r["id"],
            "store": r["store"],
            "date": r["date"],
            "total": r["total"],
            "delivery_fee": r["delivery_fee"],
            "order_id": r["order_id"],
            "nb_items": r["nb_items"],
        }
        for r in rows
    ]


def get_receipt_detail(conn: sqlite3.Connection, receipt_id: int) -> dict | None:
    """Détail complet d'un ticket et de ses articles.

    Args:
        conn:       Connexion SQLite ouverte.
        receipt_id: Id du ticket à récupérer.

    Returns:
        dict avec les champs du ticket + items: list[dict], ou None si introuvable.
    """
    receipt = conn.execute(
        "SELECT id, store, date, total, delivery_fee, order_id FROM receipts WHERE id = ?",
        (receipt_id,),
    ).fetchone()

    if receipt is None:
        return None

    items = conn.execute(
        """
        SELECT id, name_raw, name_normalized, category, quantity, unit, unit_price, total_price
        FROM items
        WHERE receipt_id = ?
        ORDER BY id
        """,
        (receipt_id,),
    ).fetchall()

    return {
        "id": receipt["id"],
        "store": receipt["store"],
        "date": receipt["date"],
        "total": receipt["total"],
        "delivery_fee": receipt["delivery_fee"],
        "order_id": receipt["order_id"],
        "items": [
            {
                "id": i["id"],
                "name_raw": i["name_raw"],
                "name_normalized": i["name_normalized"],
                "category": i["category"],
                "quantity": i["quantity"],
                "unit": i["unit"],
                "unit_price": i["unit_price"],
                "total_price": i["total_price"],
            }
            for i in items
        ],
    }


def get_pending_matches(conn: sqlite3.Connection) -> list[dict]:
    """Paires en attente de validation, avec prix moyens des deux enseignes.

    Returns:
        Liste de dicts {id, name_picnic, price_picnic, name_leclerc, price_leclerc, score}.
        price_picnic / price_leclerc : prix moyen unitaire de ce produit dans la vue
        price_history (None si aucune occurrence pour ce nom normalisé).
    """
    rows = conn.execute(
        """
        SELECT
            pm.id,
            pm.name_picnic,
            pm.name_leclerc,
            pm.score,
            ROUND(AVG(CASE WHEN ph.store='picnic'  THEN ph.unit_price END), 2) AS price_picnic,
            ROUND(AVG(CASE WHEN ph.store='leclerc' THEN ph.unit_price END), 2) AS price_leclerc
        FROM product_matches pm
        LEFT JOIN price_history ph
               ON ph.name_normalized IN (pm.name_picnic, pm.name_leclerc)
        WHERE pm.status = 'pending'
        GROUP BY pm.id
        ORDER BY pm.score DESC
        """
    ).fetchall()

    return [
        {
            "id": r["id"],
            "name_picnic": r["name_picnic"],
            "name_leclerc": r["name_leclerc"],
            "score": r["score"],
            "price_picnic": r["price_picnic"],
            "price_leclerc": r["price_leclerc"],
        }
        for r in rows
    ]


def get_validated_matches(conn: sqlite3.Connection) -> list[dict]:
    """Paires validées pour enrichir get_compare_prices.

    Returns:
        Liste de dicts {name_picnic, price_picnic, name_leclerc, price_leclerc, diff, diff_pct}.
    """
    rows = conn.execute(
        """
        WITH avg_prices AS (
            SELECT name_normalized, store, ROUND(AVG(unit_price), 2) AS avg_price
            FROM price_history
            WHERE name_normalized IS NOT NULL
            GROUP BY name_normalized, store
        )
        SELECT
            pm.id,
            pm.name_picnic,
            pm.name_leclerc,
            ap_p.avg_price                              AS price_picnic,
            ap_l.avg_price                              AS price_leclerc,
            ROUND(ap_l.avg_price - ap_p.avg_price, 2)  AS diff,
            ROUND(
                (ap_l.avg_price - ap_p.avg_price)
                / MIN(ap_p.avg_price, ap_l.avg_price) * 100
            , 1)                                        AS diff_pct
        FROM product_matches pm
        JOIN avg_prices ap_p ON ap_p.name_normalized = pm.name_picnic  AND ap_p.store = 'picnic'
        JOIN avg_prices ap_l ON ap_l.name_normalized = pm.name_leclerc AND ap_l.store = 'leclerc'
        WHERE pm.status = 'validated'
        ORDER BY ABS(ap_l.avg_price - ap_p.avg_price) DESC
        """
    ).fetchall()

    return [
        {
            "name_picnic": r["name_picnic"],
            "name_leclerc": r["name_leclerc"],
            "price_picnic": r["price_picnic"],
            "price_leclerc": r["price_leclerc"],
            "diff": r["diff"],
            "diff_pct": r["diff_pct"],
        }
        for r in rows
    ]


def get_product_list(conn: sqlite3.Connection) -> list[str]:
    """Liste tous les noms normalisés distincts (non NULL) pour le sélecteur.

    Returns:
        Liste de str triée alphabétiquement.
    """
    rows = conn.execute(
        """
        SELECT DISTINCT name_normalized
        FROM items
        WHERE name_normalized IS NOT NULL
        ORDER BY name_normalized
        """
    ).fetchall()

    return [r["name_normalized"] for r in rows]