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>

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)