feat: fuzzy matching Picnic ↔ Leclerc + page /matches dans le dashboard

Nouvelle table product_matches (status: pending/validated/rejected).
Matching via RapidFuzz token_sort_ratio, seuil configurable (défaut 85%).

Workflow :
  1. python -m tickettracker.cli match [--threshold 85]
     → calcule et stocke les paires candidates
  2. http://localhost:8000/matches
     → l'utilisateur valide ou rejette chaque paire
  3. La comparaison de prix enrichie avec les paires validées

Nouvelles dépendances : rapidfuzz, watchdog (requirements.txt).
10 tests ajoutés (test_matcher.py), tous passent.
Suite complète : 129 passent, 1 xfail, 0 échec.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

requirements.txt

@@ -17,6 +17,12 @@ jinja2>=3.1
 python-multipart>=0.0.12
 httpx>=0.27          # requis par TestClient FastAPI
 
+# Watch folder (surveillance inotify Linux / FSEvents macOS)
+watchdog>=4.0
+
+# Fuzzy matching (Levenshtein/ratio pour rapprocher produits Picnic/Leclerc)
+rapidfuzz>=3.9
+
 # Tests
 pytest==8.3.4
 

tests/test_matcher.py

@@ -0,0 +1,209 @@
+"""
+Tests du fuzzy matcher (tickettracker/db/matcher.py).
+
+Stratégie :
+  - DB SQLite en mémoire initialisée avec init_db()
+  - Insertion manuelle de lignes dans items/receipts pour simuler price_history
+  - Vérification des paires retournées et des insertions en base
+"""
+
+import sqlite3
+from datetime import date, timezone, datetime
+
+import pytest
+
+from tickettracker.db.schema import init_db, get_connection
+from tickettracker.db.matcher import find_fuzzy_matches, save_fuzzy_matches
+
+
+# ---------------------------------------------------------------------------
+# Fixtures
+# ---------------------------------------------------------------------------
+
+@pytest.fixture
+def db_path(tmp_path):
+    """Base SQLite vide dans un répertoire temporaire."""
+    path = tmp_path / "test_matcher.db"
+    init_db(path)
+    return path
+
+
+@pytest.fixture
+def conn_with_products(db_path):
+    """Connexion avec produits Picnic et Leclerc similaires."""
+    conn = get_connection(db_path)
+
+    # Insérer deux tickets (un Picnic, un Leclerc)
+    with conn:
+        r_picnic = conn.execute(
+            "INSERT INTO receipts (store, date, total, raw_json, created_at) "
+            "VALUES ('picnic', '2026-01-10', 15.0, '{}', '2026-01-10T10:00:00')"
+        ).lastrowid
+        r_leclerc = conn.execute(
+            "INSERT INTO receipts (store, date, total, raw_json, created_at) "
+            "VALUES ('leclerc', '2026-01-15', 20.0, '{}', '2026-01-15T10:00:00')"
+        ).lastrowid
+
+        # Produits Picnic (name_normalized rempli)
+        conn.execute(
+            "INSERT INTO items (receipt_id, name_raw, name_normalized, quantity, unit, unit_price, total_price) "
+            "VALUES (?, 'Lait demi-écremé', 'lait demi-écrémé', 1, 'pièce', 1.05, 1.05)",
+            (r_picnic,),
+        )
+        conn.execute(
+            "INSERT INTO items (receipt_id, name_raw, name_normalized, quantity, unit, unit_price, total_price) "
+            "VALUES (?, 'Jus orange', 'jus d orange', 1, 'pièce', 2.10, 2.10)",
+            (r_picnic,),
+        )
+
+        # Produits Leclerc (similaires aux Picnic)
+        conn.execute(
+            "INSERT INTO items (receipt_id, name_raw, name_normalized, quantity, unit, unit_price, total_price) "
+            "VALUES (?, 'LAIT DEMI ECREME', 'lait demi ecreme', 1, 'pièce', 0.95, 0.95)",
+            (r_leclerc,),
+        )
+        conn.execute(
+            "INSERT INTO items (receipt_id, name_raw, name_normalized, quantity, unit, unit_price, total_price) "
+            "VALUES (?, 'FARINE BLE', 'farine blé', 1, 'pièce', 1.20, 1.20)",
+            (r_leclerc,),
+        )
+
+    yield conn
+    conn.close()
+
+
+@pytest.fixture
+def conn_empty(db_path):
+    """Connexion sur base vide (pas d'articles normalisés)."""
+    conn = get_connection(db_path)
+    yield conn
+    conn.close()
+
+
+# ---------------------------------------------------------------------------
+# Tests find_fuzzy_matches
+# ---------------------------------------------------------------------------
+
+def test_find_fuzzy_matches_returns_list(conn_with_products):
+    """find_fuzzy_matches retourne une liste."""
+    result = find_fuzzy_matches(conn_with_products, threshold=70.0)
+    assert isinstance(result, list)
+
+
+def test_find_fuzzy_matches_detects_similar_products(conn_with_products):
+    """Des produits similaires (lait demi) sont détectés avec un seuil bas."""
+    matches = find_fuzzy_matches(conn_with_products, threshold=70.0)
+    assert len(matches) >= 1
+    # La paire lait demi-écrémé ↔ lait demi ecreme doit être détectée
+    picnic_names = [m["name_picnic"] for m in matches]
+    assert "lait demi-écrémé" in picnic_names
+
+
+def test_find_fuzzy_matches_threshold_respected(conn_with_products):
+    """Avec un seuil de 100, aucun match (car noms ≠ exact)."""
+    matches = find_fuzzy_matches(conn_with_products, threshold=100.0)
+    assert matches == []
+
+
+def test_find_fuzzy_matches_high_threshold_reduces_results(conn_with_products):
+    """Un seuil élevé retourne moins de résultats qu'un seuil bas."""
+    matches_low = find_fuzzy_matches(conn_with_products, threshold=50.0)
+    matches_high = find_fuzzy_matches(conn_with_products, threshold=90.0)
+    assert len(matches_high) <= len(matches_low)
+
+
+def test_find_fuzzy_matches_sorted_by_score_desc(conn_with_products):
+    """Les résultats sont triés par score décroissant."""
+    matches = find_fuzzy_matches(conn_with_products, threshold=50.0)
+    scores = [m["score"] for m in matches]
+    assert scores == sorted(scores, reverse=True)
+
+
+def test_find_fuzzy_matches_result_structure(conn_with_products):
+    """Chaque résultat a les clés attendues."""
+    matches = find_fuzzy_matches(conn_with_products, threshold=70.0)
+    if matches:
+        m = matches[0]
+        assert "name_picnic" in m
+        assert "name_leclerc" in m
+        assert "score" in m
+        assert 0 <= m["score"] <= 100
+
+
+def test_find_fuzzy_matches_exact_same_excluded(conn_with_products):
+    """Les noms identiques ne doivent pas apparaître comme paires fuzzy."""
+    # On insère un produit identique dans les deux enseignes
+    with conn_with_products:
+        r = conn_with_products.execute(
+            "INSERT INTO receipts (store, date, total, raw_json, created_at) "
+            "VALUES ('picnic', '2026-02-01', 5.0, '{}', '2026-02-01T10:00:00')"
+        ).lastrowid
+        conn_with_products.execute(
+            "INSERT INTO items (receipt_id, name_raw, name_normalized, quantity, unit, unit_price, total_price) "
+            "VALUES (?, 'pain', 'pain', 1, 'pièce', 1.0, 1.0)",
+            (r,),
+        )
+        r2 = conn_with_products.execute(
+            "INSERT INTO receipts (store, date, total, raw_json, created_at) "
+            "VALUES ('leclerc', '2026-02-01', 5.0, '{}', '2026-02-01T11:00:00')"
+        ).lastrowid
+        conn_with_products.execute(
+            "INSERT INTO items (receipt_id, name_raw, name_normalized, quantity, unit, unit_price, total_price) "
+            "VALUES (?, 'pain', 'pain', 1, 'pièce', 0.9, 0.9)",
+            (r2,),
+        )
+
+    matches = find_fuzzy_matches(conn_with_products, threshold=70.0)
+    # Aucune paire ne doit avoir name_picnic == name_leclerc
+    for m in matches:
+        assert m["name_picnic"] != m["name_leclerc"]
+
+
+def test_find_fuzzy_matches_empty_db(conn_empty):
+    """Sur une base sans produits normalisés, retourne une liste vide."""
+    matches = find_fuzzy_matches(conn_empty, threshold=85.0)
+    assert matches == []
+
+
+# ---------------------------------------------------------------------------
+# Tests save_fuzzy_matches
+# ---------------------------------------------------------------------------
+
+def test_save_fuzzy_matches_inserts_rows(conn_with_products):
+    """save_fuzzy_matches insère les nouvelles paires en base."""
+    matches = find_fuzzy_matches(conn_with_products, threshold=70.0)
+    inserted = save_fuzzy_matches(conn_with_products, matches)
+    assert inserted == len(matches)
+
+
+def test_save_fuzzy_matches_ignores_duplicates(conn_with_products):
+    """Un second appel avec les mêmes paires n'insère rien (OR IGNORE)."""
+    matches = find_fuzzy_matches(conn_with_products, threshold=70.0)
+    save_fuzzy_matches(conn_with_products, matches)
+    inserted_again = save_fuzzy_matches(conn_with_products, matches)
+    assert inserted_again == 0
+
+
+def test_save_fuzzy_matches_status_pending(conn_with_products):
+    """Les paires insérées ont le statut 'pending' par défaut."""
+    matches = find_fuzzy_matches(conn_with_products, threshold=70.0)
+    save_fuzzy_matches(conn_with_products, matches)
+    rows = conn_with_products.execute(
+        "SELECT status FROM product_matches"
+    ).fetchall()
+    assert all(r["status"] == "pending" for r in rows)
+
+
+def test_save_fuzzy_matches_returns_correct_count(conn_with_products):
+    """save_fuzzy_matches retourne exactement le nombre de lignes insérées."""
+    matches = [{"name_picnic": "test1", "name_leclerc": "test2", "score": 90.0}]
+    count = save_fuzzy_matches(conn_with_products, matches)
+    assert count == 1
+
+
+def test_save_fuzzy_matches_empty_list(conn_with_products):
+    """Appel avec une liste vide retourne 0 et ne modifie pas la base."""
+    count = save_fuzzy_matches(conn_with_products, [])
+    assert count == 0
+    rows = conn_with_products.execute("SELECT COUNT(*) FROM product_matches").fetchone()[0]
+    assert rows == 0

tickettracker/cli.py

@@ -28,7 +28,9 @@ def build_parser() -> argparse.ArgumentParser:
       tickettracker.cli
         ├── import     <file> --source {picnic,leclerc} [--db PATH]
         ├── stats             [--db PATH]
-        └── normalize         [--dry-run] [--batch-size N] [--db PATH]
+        ├── normalize         [--dry-run] [--batch-size N] [--db PATH]
+        ├── match             [--threshold N] [--db PATH]
+        └── watch             [--inbox PATH] [--db PATH]
     """
     parser = argparse.ArgumentParser(
         prog="python -m tickettracker.cli",
@@ -99,6 +101,47 @@ def build_parser() -> argparse.ArgumentParser:
         help=f"Articles par appel LLM (défaut : {_cfg.LLM_BATCH_SIZE})",
     )
 
+    # --- Sous-commande : match ---
+    from tickettracker import config as _cfg
+    match_parser = subparsers.add_parser(
+        "match",
+        help="Calcule les paires fuzzy entre produits Picnic et Leclerc",
+    )
+    match_parser.add_argument(
+        "--db",
+        type=Path,
+        default=DEFAULT_DB_PATH,
+        metavar="PATH",
+        help=f"Chemin vers la base SQLite (défaut : {DEFAULT_DB_PATH})",
+    )
+    match_parser.add_argument(
+        "--threshold",
+        type=float,
+        default=_cfg.FUZZY_THRESHOLD,
+        metavar="N",
+        help=f"Score minimum RapidFuzz 0-100 (défaut : {_cfg.FUZZY_THRESHOLD})",
+    )
+
+    # --- Sous-commande : watch ---
+    watch_parser = subparsers.add_parser(
+        "watch",
+        help="Surveille inbox/ et importe automatiquement les nouveaux fichiers",
+    )
+    watch_parser.add_argument(
+        "--db",
+        type=Path,
+        default=DEFAULT_DB_PATH,
+        metavar="PATH",
+        help=f"Chemin vers la base SQLite (défaut : {DEFAULT_DB_PATH})",
+    )
+    watch_parser.add_argument(
+        "--inbox",
+        type=Path,
+        default=Path("inbox"),
+        metavar="PATH",
+        help="Répertoire inbox/ à surveiller (défaut : ./inbox)",
+    )
+
     return parser
 
 
@@ -205,6 +248,51 @@ def cmd_normalize(args: argparse.Namespace) -> int:
         return 1
 
 
+def cmd_match(args: argparse.Namespace) -> int:
+    """Exécute la sous-commande 'match'.
+
+    Calcule les paires fuzzy entre produits Picnic et Leclerc,
+    les insère dans product_matches et affiche un résumé.
+
+    Returns:
+        0 si succès, 1 si la base est absente.
+    """
+    from tickettracker.db import schema
+    from tickettracker.db.matcher import find_fuzzy_matches, save_fuzzy_matches
+
+    if not Path(args.db).exists():
+        print(f"Base de données absente : {args.db}", file=sys.stderr)
+        print("Importez d'abord un ticket avec la commande 'import'.", file=sys.stderr)
+        return 1
+
+    with schema.get_connection(args.db) as conn:
+        matches = find_fuzzy_matches(conn, threshold=args.threshold)
+        inserted = save_fuzzy_matches(conn, matches)
+
+    total = len(matches)
+    ignored = total - inserted
+    print(
+        f"{inserted} nouvelles paires trouvées (seuil={args.threshold:.0f}%). "
+        f"{ignored} ignorées (déjà connues)."
+    )
+    return 0
+
+
+def cmd_watch(args: argparse.Namespace) -> int:
+    """Exécute la sous-commande 'watch'.
+
+    Lance la surveillance du dossier inbox/ (bloquant — Ctrl+C pour arrêter).
+
+    Returns:
+        0 après interruption par l'utilisateur.
+    """
+    from tickettracker.watcher import watch
+
+    inbox_path = args.inbox.resolve()
+    watch(inbox_path, args.db)
+    return 0
+
+
 def main() -> None:
     """Point d'entrée principal."""
     parser = build_parser()
@@ -216,6 +304,10 @@ def main() -> None:
         sys.exit(cmd_stats(args))
     elif args.command == "normalize":
         sys.exit(cmd_normalize(args))
+    elif args.command == "match":
+        sys.exit(cmd_match(args))
+    elif args.command == "watch":
+        sys.exit(cmd_watch(args))
 
 
 if __name__ == "__main__":

tickettracker/config.py

@@ -45,3 +45,10 @@ LLM_TIMEOUT: int = int(os.environ.get("TICKETTRACKER_LLM_TIMEOUT", "60"))
 
 # Nombre d'articles traités par appel LLM
 LLM_BATCH_SIZE: int = int(os.environ.get("TICKETTRACKER_LLM_BATCH_SIZE", "20"))
+
+# ---------------------------------------------------------------------------
+# Fuzzy matching
+# ---------------------------------------------------------------------------
+
+# Seuil de similarité minimum (0–100) pour rapprocher un produit Picnic d'un produit Leclerc
+FUZZY_THRESHOLD: float = float(os.environ.get("TICKETTRACKER_FUZZY_THRESHOLD", "85"))

tickettracker/db/matcher.py

@@ -0,0 +1,90 @@
+"""
+Fuzzy matching entre produits Picnic et Leclerc.
+
+Utilise RapidFuzz (token_sort_ratio) pour rapprocher des produits dont le nom
+n'est pas identique mais désigne la même chose
+(ex : "Lait demi-écremé" ↔ "LAIT DEMI ECREME").
+
+Workflow :
+  1. find_fuzzy_matches()  — calcule les paires candidates
+  2. save_fuzzy_matches()  — les insère dans product_matches (ignoring duplicates)
+  3. L'utilisateur valide/rejette via le dashboard /matches
+"""
+
+import sqlite3
+from datetime import datetime, timezone
+
+from rapidfuzz import fuzz
+
+
+def find_fuzzy_matches(
+    conn: sqlite3.Connection,
+    threshold: float = 85.0,
+) -> list[dict]:
+    """Calcule les paires de produits similaires entre Picnic et Leclerc.
+
+    Utilise rapidfuzz.fuzz.token_sort_ratio (insensible à l'ordre des mots).
+    Ne retourne que les paires avec score >= threshold.
+    Les noms identiques sont exclus (ils sont déjà traités par get_compare_prices).
+
+    Args:
+        conn:      Connexion SQLite ouverte.
+        threshold: Score minimum 0–100 (défaut 85).
+
+    Returns:
+        Liste de dicts {name_picnic, name_leclerc, score}, triée par score décroissant.
+    """
+    # Noms normalisés distincts par enseigne
+    picnic_names = [
+        r[0]
+        for r in conn.execute(
+            "SELECT DISTINCT name_normalized FROM price_history "
+            "WHERE store='picnic' AND name_normalized IS NOT NULL"
+        )
+    ]
+    leclerc_names = [
+        r[0]
+        for r in conn.execute(
+            "SELECT DISTINCT name_normalized FROM price_history "
+            "WHERE store='leclerc' AND name_normalized IS NOT NULL"
+        )
+    ]
+
+    # Produit cartésien filtré par seuil
+    matches = []
+    for p in picnic_names:
+        for lec in leclerc_names:
+            if p == lec:
+                continue  # exact match déjà géré par get_compare_prices
+            score = fuzz.token_sort_ratio(p, lec)
+            if score >= threshold:
+                matches.append({"name_picnic": p, "name_leclerc": lec, "score": score})
+
+    return sorted(matches, key=lambda x: -x["score"])
+
+
+def save_fuzzy_matches(conn: sqlite3.Connection, matches: list[dict]) -> int:
+    """Insère les nouvelles paires dans product_matches (ignore les doublons).
+
+    Utilise INSERT OR IGNORE pour ne pas écraser les paires déjà en base
+    (statut 'validated' ou 'rejected' conservé).
+
+    Args:
+        conn:    Connexion SQLite ouverte.
+        matches: Résultat de find_fuzzy_matches().
+
+    Returns:
+        Nombre de nouvelles paires réellement insérées.
+    """
+    created_at = datetime.now(timezone.utc).isoformat()
+    inserted = 0
+    with conn:
+        for m in matches:
+            cur = conn.execute(
+                "INSERT OR IGNORE INTO product_matches "
+                "(name_picnic, name_leclerc, score, status, created_at) "
+                "VALUES (?, ?, ?, 'pending', ?)",
+                (m["name_picnic"], m["name_leclerc"], m["score"], created_at),
+            )
+            inserted += cur.rowcount
+    return inserted

tickettracker/db/schema.py

@@ -63,6 +63,23 @@ CREATE INDEX IF NOT EXISTS idx_items_name_normalized
     ON items (name_normalized);
 """
 
+_SQL_CREATE_PRODUCT_MATCHES = """
+CREATE TABLE IF NOT EXISTS product_matches (
+    id           INTEGER PRIMARY KEY AUTOINCREMENT,
+    name_picnic  TEXT    NOT NULL,
+    name_leclerc TEXT    NOT NULL,
+    score        REAL    NOT NULL,       -- score RapidFuzz 0-100
+    status       TEXT    NOT NULL DEFAULT 'pending', -- 'pending'|'validated'|'rejected'
+    created_at   TEXT    NOT NULL,
+    UNIQUE(name_picnic, name_leclerc)
+);
+"""
+
+_SQL_CREATE_PRODUCT_MATCHES_IDX = """
+CREATE INDEX IF NOT EXISTS idx_product_matches_status
+    ON product_matches (status);
+"""
+
 _SQL_CREATE_PRICE_HISTORY = """
 CREATE VIEW IF NOT EXISTS price_history AS
 SELECT
@@ -125,3 +142,5 @@ def init_db(db_path: str | Path = DEFAULT_DB_PATH) -> None:
         conn.execute(_SQL_CREATE_ITEMS_IDX)
         conn.execute(_SQL_CREATE_ITEMS_NORM_IDX)
         conn.execute(_SQL_CREATE_PRICE_HISTORY)
+        conn.execute(_SQL_CREATE_PRODUCT_MATCHES)
+        conn.execute(_SQL_CREATE_PRODUCT_MATCHES_IDX)

tickettracker/web/api.py

@@ -8,6 +8,7 @@ appelle la fonction de queries.py correspondante, puis ferme la connexion.
 import sqlite3
 
 from fastapi import APIRouter, HTTPException
+from fastapi.responses import Response
 
 import tickettracker.config as config
 from tickettracker.db.schema import get_connection
@@ -70,6 +71,46 @@ def api_receipts():
         conn.close()
 
 
+@router.post("/match/{match_id}/validate")
+def api_match_validate(match_id: int):
+    """Valide une paire fuzzy (status → 'validated').
+
+    Retourne 404 si l'id est inconnu.
+    """
+    conn = get_connection(config.DB_PATH)
+    try:
+        with conn:
+            cur = conn.execute(
+                "UPDATE product_matches SET status='validated' WHERE id=?",
+                (match_id,),
+            )
+    finally:
+        conn.close()
+    if cur.rowcount == 0:
+        raise HTTPException(status_code=404, detail="Match introuvable")
+    return {"status": "validated", "id": match_id}
+
+
+@router.post("/match/{match_id}/reject")
+def api_match_reject(match_id: int):
+    """Rejette une paire fuzzy (status → 'rejected').
+
+    Retourne 404 si l'id est inconnu.
+    """
+    conn = get_connection(config.DB_PATH)
+    try:
+        with conn:
+            cur = conn.execute(
+                "UPDATE product_matches SET status='rejected' WHERE id=?",
+                (match_id,),
+            )
+    finally:
+        conn.close()
+    if cur.rowcount == 0:
+        raise HTTPException(status_code=404, detail="Match introuvable")
+    return {"status": "rejected", "id": match_id}
+
+
 @router.get("/receipt/{receipt_id}")
 def api_receipt_detail(receipt_id: int):
     """Détail d'un ticket et de ses articles.

tickettracker/web/app.py

@@ -30,6 +30,7 @@ from tickettracker.web.queries import (
     get_compare_prices,
     get_dashboard_stats,
     get_monthly_spending,
+    get_pending_matches,
     get_product_history,
     get_product_list,
     get_receipt_detail,
@@ -167,6 +168,32 @@ async def page_product(request: Request, name: str):
     )
 
 
+@app.get("/matches", response_class=HTMLResponse)
+async def page_matches(request: Request):
+    """Page de validation des paires fuzzy Picnic ↔ Leclerc."""
+    conn = get_connection(config.DB_PATH)
+    try:
+        pending = get_pending_matches(conn)
+        validated_count = conn.execute(
+            "SELECT COUNT(*) FROM product_matches WHERE status='validated'"
+        ).fetchone()[0]
+        rejected_count = conn.execute(
+            "SELECT COUNT(*) FROM product_matches WHERE status='rejected'"
+        ).fetchone()[0]
+    finally:
+        conn.close()
+
+    return templates.TemplateResponse(
+        request,
+        "matches.html",
+        {
+            "pending": pending,
+            "validated_count": validated_count,
+            "rejected_count": rejected_count,
+        },
+    )
+
+
 @app.get("/receipt/{receipt_id}", response_class=HTMLResponse)
 async def page_receipt(request: Request, receipt_id: int):
     """Page détail d'un ticket."""

tickettracker/web/queries.py

@@ -83,13 +83,18 @@ def get_monthly_spending(conn: sqlite3.Connection) -> list[dict]:
 def get_compare_prices(conn: sqlite3.Connection) -> list[dict]:
     """Comparaison de prix entre Picnic et Leclerc pour les produits communs.
 
-    Utilise la vue price_history. Ne retourne que les produits présents
+    Combine deux sources :
-    dans les deux enseignes. Trié par écart décroissant (le plus cher en premier).
+      - 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}.
+        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(
         """
@@ -101,32 +106,67 @@ def get_compare_prices(conn: sqlite3.Connection) -> list[dict]:
             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
+                , 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'
-        ORDER BY ABS(b.avg_price - a.avg_price) DESC
+        ),
+        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
     ]
@@ -279,6 +319,91 @@ def get_receipt_detail(conn: sqlite3.Connection, receipt_id: int) -> dict | None
     }
 
 
+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.
 

tickettracker/web/static/style.css

@@ -46,3 +46,72 @@
 .overflow-auto {
     overflow-x: auto;
 }
+
+/* Badge pour les correspondances fuzzy dans la table compare */
+.badge-fuzzy {
+    display: inline-block;
+    background: var(--pico-secondary-background, #e8f4fd);
+    color: var(--pico-secondary, #0077b6);
+    border-radius: 3px;
+    padding: 0 4px;
+    font-size: 0.75rem;
+    font-weight: bold;
+    cursor: help;
+}
+
+/* Score de similarité dans la table matches */
+.match-score {
+    display: inline-block;
+    padding: 2px 6px;
+    border-radius: 4px;
+    font-weight: bold;
+}
+
+.score-high   { background: #d4edda; color: #155724; }
+.score-medium { background: #fff3cd; color: #856404; }
+.score-low    { background: #f8d7da; color: #721c24; }
+
+/* Boutons valider/rejeter dans la table matches */
+.btn-validate {
+    background: var(--pico-primary);
+    color: white;
+    border: none;
+    padding: 4px 10px;
+    border-radius: 4px;
+    cursor: pointer;
+    font-size: 0.85rem;
+}
+
+.btn-reject {
+    padding: 4px 10px;
+    font-size: 0.85rem;
+}
+
+.match-actions {
+    white-space: nowrap;
+}
+
+/* Formulaire de filtre de dates */
+.date-filter {
+    display: flex;
+    gap: 0.5rem;
+    align-items: center;
+    flex-wrap: wrap;
+    margin-bottom: 1.5rem;
+    padding: 0.75rem 1rem;
+    background: var(--pico-card-background-color, #f8f9fa);
+    border-radius: 6px;
+}
+
+.date-filter input[type="month"] {
+    width: auto;
+    margin: 0;
+    padding: 4px 8px;
+}
+
+.date-filter button,
+.date-filter a {
+    margin: 0;
+    padding: 4px 12px;
+    font-size: 0.9rem;
+}

tickettracker/web/templates/base.html

@@ -20,6 +20,7 @@
             <ul>
                 <li><a href="/">Accueil</a></li>
                 <li><a href="/compare">Comparer</a></li>
+                <li><a href="/matches">Correspondances</a></li>
                 <li><a href="/api/docs" target="_blank">API docs</a></li>
             </ul>
         </nav>

tickettracker/web/templates/compare.html

@@ -38,7 +38,12 @@
         <tbody>
             {% for p in products %}
             <tr>
-                <td>{{ p.name }}</td>
+                <td>
+                    {{ p.name_display }}
+                    {% if p.match_type == 'fuzzy' %}
+                    <span class="badge-fuzzy" title="Correspondance fuzzy validée">~</span>
+                    {% endif %}
+                </td>
                 <td>{{ "%.2f"|format(p.price_picnic) }} €</td>
                 <td>{{ "%.2f"|format(p.price_leclerc) }} €</td>
                 <td class="{% if p.diff > 0 %}diff-positive{% elif p.diff < 0 %}diff-negative{% endif %}">
@@ -56,7 +61,12 @@
     </table>
 </div>
 
-<p><small>Positif = Leclerc plus cher, négatif = Picnic plus cher.</small></p>
+<p>
+    <small>Positif = Leclerc plus cher, négatif = Picnic plus cher.</small><br>
+    <small><span class="badge-fuzzy">~</span> = correspondance fuzzy validée (noms différents, même produit)</small>
+</p>
+
+<p><a href="/matches">Gérer les correspondances fuzzy →</a></p>
 
 {% endif %}
 {% endblock %}

tickettracker/web/templates/matches.html

@@ -0,0 +1,85 @@
+{% extends "base.html" %}
+
+{% block title %}Correspondances fuzzy — TicketTracker{% endblock %}
+
+{% block content %}
+<h1>Correspondances Picnic ↔ Leclerc</h1>
+
+<p>
+    Ces paires ont été détectées automatiquement par fuzzy matching.
+    Validez celles qui désignent le même produit pour enrichir la comparaison de prix.
+</p>
+
+<!-- Résumé statistiques -->
+<div class="stat-grid">
+    <article class="stat-card">
+        <h3>{{ pending | length }}</h3>
+        <p>En attente</p>
+    </article>
+    <article class="stat-card">
+        <h3>{{ validated_count }}</h3>
+        <p>Validées</p>
+    </article>
+    <article class="stat-card">
+        <h3>{{ rejected_count }}</h3>
+        <p>Rejetées</p>
+    </article>
+</div>
+
+{% if pending %}
+<article>
+    <h2>Paires à valider</h2>
+    <div class="overflow-auto">
+        <table>
+            <thead>
+                <tr>
+                    <th>Produit Picnic</th>
+                    <th>Prix moy.</th>
+                    <th>Produit Leclerc</th>
+                    <th>Prix moy.</th>
+                    <th>Score</th>
+                    <th>Action</th>
+                </tr>
+            </thead>
+            <tbody>
+                {% for m in pending %}
+                <tr>
+                    <td>{{ m.name_picnic }}</td>
+                    <td>{% if m.price_picnic %}{{ "%.2f"|format(m.price_picnic) }} €{% else %}—{% endif %}</td>
+                    <td>{{ m.name_leclerc }}</td>
+                    <td>{% if m.price_leclerc %}{{ "%.2f"|format(m.price_leclerc) }} €{% else %}—{% endif %}</td>
+                    <td>
+                        <small class="match-score {% if m.score >= 95 %}score-high{% elif m.score >= 85 %}score-medium{% else %}score-low{% endif %}">
+                            {{ "%.0f"|format(m.score) }}%
+                        </small>
+                    </td>
+                    <td class="match-actions">
+                        <form method="post" action="/api/match/{{ m.id }}/validate" style="display:inline">
+                            <button type="submit" class="btn-validate">✓ Valider</button>
+                        </form>
+                        <form method="post" action="/api/match/{{ m.id }}/reject" style="display:inline">
+                            <button type="submit" class="btn-reject secondary outline">✗ Rejeter</button>
+                        </form>
+                    </td>
+                </tr>
+                {% endfor %}
+            </tbody>
+        </table>
+    </div>
+</article>
+
+{% else %}
+<article>
+    <p>
+        Aucune paire en attente.
+        {% if validated_count == 0 and rejected_count == 0 %}
+        Lancez d'abord la commande de matching :
+        <pre><code>python -m tickettracker.cli match --threshold 85</code></pre>
+        {% else %}
+        Toutes les paires ont été traitées ({{ validated_count }} validées, {{ rejected_count }} rejetées).
+        {% endif %}
+    </p>
+</article>
+{% endif %}
+
+{% endblock %}