Ship native iOS app, Wiki/TTS backend, and skeleton loading UX.

Replace the WebView shell with SwiftUI screens, add account-scoped Wiki and TTS APIs with adaptive review and photo scan support, and keep web/iOS pages usable while data loads asynchronously. Co-authored-by: Cursor <cursoragent@cursor.com>
2026-06-08 15:13:59 +08:00
parent db5465c646
commit 05e173b293
115 changed files with 12709 additions and 1212 deletions
@@ -0,0 +1,143 @@
+from __future__ import annotations
+
+import json
+import urllib.error
+import urllib.request
+
+from config import get_settings
+
+
+class LlmServiceError(Exception):
+    pass
+
+
+class LlmService:
+    def is_configured(self) -> bool:
+        return bool(get_settings().deepseek_api_key.strip())
+
+    def model_name(self) -> str:
+        return get_settings().deepseek_model
+
+    def _chat_completion(
+        self,
+        *,
+        messages: list[dict],
+        temperature: float = 0.2,
+        max_tokens: int = 1800,
+        response_format: dict | None = None,
+        timeout: int = 15,
+    ) -> str:
+        settings = get_settings()
+        api_key = settings.deepseek_api_key.strip()
+        if not api_key:
+            raise LlmServiceError("DeepSeek API key is not configured")
+
+        payload: dict = {
+            "model": settings.deepseek_model,
+            "messages": messages,
+            "temperature": temperature,
+            "max_tokens": max_tokens,
+        }
+        if response_format:
+            payload["response_format"] = response_format
+
+        request = urllib.request.Request(
+            f"{settings.deepseek_base_url.rstrip('/')}/v1/chat/completions",
+            data=json.dumps(payload).encode("utf-8"),
+            headers={
+                "Authorization": f"Bearer {api_key}",
+                "Content-Type": "application/json",
+            },
+            method="POST",
+        )
+        try:
+            with urllib.request.urlopen(request, timeout=timeout) as response:
+                body = json.loads(response.read().decode("utf-8"))
+        except urllib.error.HTTPError as exc:
+            detail = exc.read().decode("utf-8", errors="replace")
+            raise LlmServiceError(f"DeepSeek HTTP {exc.code}: {detail}") from exc
+        except urllib.error.URLError as exc:
+            raise LlmServiceError(f"DeepSeek request failed: {exc.reason}") from exc
+
+        try:
+            content = body["choices"][0]["message"]["content"]
+        except (KeyError, IndexError, TypeError) as exc:
+            raise LlmServiceError("DeepSeek response format is invalid") from exc
+        return str(content).strip()
+
+    @staticmethod
+    def _strip_code_fence(text: str) -> str:
+        cleaned = text.strip()
+        if not cleaned.startswith("```"):
+            return cleaned
+        cleaned = cleaned.strip("`").strip()
+        if cleaned.lower().startswith("json"):
+            cleaned = cleaned[4:].lstrip()
+        elif cleaned.lower().startswith("markdown"):
+            cleaned = cleaned[8:].lstrip()
+        return cleaned.strip()
+
+    def enhance_wiki_page(self, *, title: str, draft_markdown: str) -> str:
+        prompt = (
+            "你是 WordLoop 个人学习 Wiki 的编译器，遵循 Karpathy Wiki 思路："
+            "把原始学习事件整理成简洁、可检索的 Markdown 知识页。\n"
+            "要求：\n"
+            "1. 保留草稿中的事实数据（释义、掌握度、练习记录等），不要编造。\n"
+            "2. 优化结构、摘要与关联说明，便于日后复习。\n"
+            "3. 只输出 Markdown 正文，不要代码块包裹，不要额外解释。\n"
+            f"\n页面标题：{title}\n\n草稿：\n{draft_markdown}"
+        )
+        content = self._chat_completion(
+            messages=[
+                {"role": "system", "content": "你是严谨的学习笔记整理助手。"},
+                {"role": "user", "content": prompt},
+            ],
+            temperature=0.2,
+            max_tokens=1800,
+        )
+        cleaned = self._strip_code_fence(content)
+        return cleaned or draft_markdown
+
+    def parse_word_pairs_from_text(self, text: str) -> list[dict[str, str]]:
+        prompt = (
+            "你是英语学习词表整理助手。从 OCR 识别文本中提取英汉单词对。\n"
+            "要求：\n"
+            "1. 只提取明确的单词/短语与其中文释义，不要编造。\n"
+            "2. 忽略页码、标题、序号、噪声行。\n"
+            "3. 输出 JSON 对象，格式为 {\"items\":[{\"en\":\"...\",\"zh\":\"...\"}]}。\n"
+            "4. en 为英文，zh 为中文；若原文是中文在前英文在后，也要正确归位。\n"
+            "5. 只输出 JSON，不要解释。\n"
+            f"\nOCR 文本：\n{text.strip()}"
+        )
+        content = self._chat_completion(
+            messages=[
+                {"role": "system", "content": "你只输出合法 JSON。"},
+                {"role": "user", "content": prompt},
+            ],
+            temperature=0.1,
+            max_tokens=4000,
+            response_format={"type": "json_object"},
+            timeout=30,
+        )
+        cleaned = self._strip_code_fence(content)
+        try:
+            payload = json.loads(cleaned)
+        except json.JSONDecodeError as exc:
+            raise LlmServiceError("词表解析结果不是合法 JSON") from exc
+
+        raw_items = payload.get("items") if isinstance(payload, dict) else payload
+        if not isinstance(raw_items, list):
+            raise LlmServiceError("词表解析结果缺少 items 数组")
+
+        pairs: list[dict[str, str]] = []
+        for item in raw_items:
+            if not isinstance(item, dict):
+                continue
+            en = str(item.get("en") or item.get("source_text") or "").strip()
+            zh = str(item.get("zh") or item.get("target_text") or "").strip()
+            if en and zh:
+                pairs.append({"en": en, "zh": zh})
+        return pairs
+
+
+llm_service = LlmService()