feat: Add WaniKani summary fetcher for available lessons and reviews

personal_data/fetchers/wanikani_summary.py

@@ -0,0 +1,46 @@
+from __future__ import annotations
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from typing import Iterator
+import requests
+
+from personal_data.data import DeduplicateMode
+from personal_data.fetchers import Scraper
+from personal_data.secrets import wanikani_api_key
+
+@dataclass(frozen=True)
+class WaniKaniSummaryFetcher(Scraper):
+    dataset_name: str = "wanikani_summary"
+
+    @staticmethod
+    def deduplicate_mode() -> DeduplicateMode:
+        return DeduplicateMode.BY_ALL_COLUMNS
+
+    def scrape(self) -> Iterator[dict]:
+        token = wanikani_api_key()
+        headers = {
+            "Authorization": f"Bearer {token}",
+            "Wanikani-Revision": "20170710",
+        }
+        url = "https://api.wanikani.com/v2/summary"
+        response = requests.get(url, headers=headers)
+        response.raise_for_status()
+        data = response.json()
+
+        lessons = data.get("data", {}).get("lessons", [])
+        total_lessons = sum(len(lesson.get("subject_ids", [])) for lesson in lessons)
+
+        reviews = data.get("data", {}).get("reviews", [])
+        now = datetime.now(timezone.utc)
+        total_reviews = 0
+        for review in reviews:
+            available_at_str = review.get("available_at")
+            if available_at_str:
+                available_at = datetime.fromisoformat(available_at_str.replace("Z", "+00:00"))
+                if available_at <= now:
+                    total_reviews += len(review.get("subject_ids", []))
+
+        yield {
+            "lessons_available": total_lessons,
+            "reviews_available": total_reviews,
+        }