Integrate OpenFlights API for free, no-auth flight data generation

- Added openFlightsService.js to fetch and cache OpenFlights airport/airline/routes data
- Validates airport codes exist in OpenFlights database (6072+ airports)
- Generates realistic flights using major international airlines
- Creates varied routing options: direct, 1-stop, 2-stop flights
- Updated flightService.js to use OpenFlights as primary source before Amadeus
- OpenFlights as fallback if Amadeus unavailable or returns no results
- No API keys or authentication required
- Cached locally to avoid repeated network requests
- Realistic pricing, times, and stop locations

Docker container rebuilt with OpenFlights integration.

tests/test_scraper_coverage.py

@@ -0,0 +1,95 @@
+import json
+import os
+import sqlite3
+from pathlib import Path
+
+import pytest
+
+from gov_travel.scrapers import _extract_country_from_title, _normalize_header
+
+DEFAULT_DB = Path(__file__).resolve().parent.parent / "data" / "travel_rates_scraped.sqlite3"
+DB_PATH = Path(os.environ.get("GOV_TRAVEL_DB", DEFAULT_DB))
+
+
+@pytest.fixture(scope="module")
+def conn():
+    if not DB_PATH.exists():
+        pytest.skip(
+            f"Scraped DB not found at {DB_PATH}. Run `python -m gov_travel.main --db {DB_PATH}` first."
+        )
+    connection = sqlite3.connect(DB_PATH)
+    connection.row_factory = sqlite3.Row
+    yield connection
+    connection.close()
+
+
+def _norm(value):
+    return value.strip().lower() if isinstance(value, str) else value
+
+
+@pytest.mark.accommodations
+def test_accommodations_cover_all_raw_cities(conn):
+    cursor = conn.execute("SELECT data_json FROM raw_tables WHERE source = 'accommodations'")
+    expected_cities = set()
+    for (data_json,) in cursor.fetchall():
+        rows = json.loads(data_json)
+        for row in rows:
+            normalized = {_normalize_header(k): v for k, v in row.items()}
+            city = normalized.get("city") or normalized.get("location")
+            if city:
+                expected_cities.add(_norm(city))
+
+    actual_cities = {
+        _norm(city)
+        for (city,) in conn.execute(
+            "SELECT DISTINCT city FROM accommodations WHERE city IS NOT NULL"
+        )
+    }
+
+    missing = sorted(expected_cities - actual_cities)
+    assert not missing, f"Missing accommodations entries for {len(missing)} cities (e.g., {missing[:5]})"
+
+
+@pytest.mark.per_diem
+def test_per_diem_covers_all_countries(conn):
+    cursor = conn.execute(
+        "SELECT title FROM raw_tables WHERE source = 'international' AND title IS NOT NULL"
+    )
+    expected_countries = set()
+    for (title,) in cursor.fetchall():
+        country = _extract_country_from_title(title)
+        if country:
+            expected_countries.add(_norm(country))
+
+    actual_countries = {
+        _norm(country)
+        for (country,) in conn.execute(
+            "SELECT DISTINCT country FROM rate_entries WHERE source = 'international' AND country IS NOT NULL"
+        )
+    }
+
+    missing = sorted(expected_countries - actual_countries)
+    assert not missing, f"Countries missing per-diem entries: {missing}"
+
+
+@pytest.mark.per_diem
+def test_per_diem_has_meal_types(conn):
+    meal_types = {"breakfast", "lunch", "dinner", "incidental amount"}
+    cursor = conn.execute(
+        "SELECT DISTINCT country FROM rate_entries WHERE source = 'international' AND country IS NOT NULL"
+    )
+
+    missing_countries = []
+    for (country,) in cursor.fetchall():
+        rate_types = {
+            _norm(rate_type)
+            for (rate_type,) in conn.execute(
+                "SELECT DISTINCT rate_type FROM rate_entries WHERE source = 'international' AND country = ?",
+                (country,),
+            )
+            if rate_type
+        }
+        if not rate_types.intersection(meal_types):
+            missing_countries.append(country)
+
+    assert not missing_countries, f"Countries without meal-type entries: {missing_countries}"