feat: 初始化孕育无忧全栈项目基础代码结构

完成项目初始搭建，包含后端FastAPI+MongoDB服务、前端React+Vite项目的完整基础结构，添加各类配置文件与依赖清单，涵盖核心业务初始框架。
2026-05-21 00:46:48 +08:00
commit 409dabafb5
142 changed files with 31010 additions and 0 deletions
--- a/backend/app/services/ocr.py
+++ b/backend/app/services/ocr.py
@@ -0,0 +1,142 @@
+"""
+OCR service: Use vision model to extract medical report data from images.
+Supports OpenAI-compatible APIs (e.g. SiliconFlow).
+"""
+
+import base64
+import json
+import logging
+from pathlib import Path
+
+import aiofiles
+from openai import AsyncOpenAI
+
+from app.config import settings
+
+logger = logging.getLogger(__name__)
+
+OCR_PROMPT = """请分析这张图片，首先判断它是否是孕期/产检相关的医疗检查报告。
+
+**孕期相关报告包括但不限于：**
+- 产检报告（B超/彩超、NT检查、四维彩超、大排畸等）
+- 血液检查（血常规、血糖、血型、唐氏筛查、TORCH、甲功等）
+- 尿液检查（尿常规等）
+- 肝肾功能检查
+- 凝血功能检查
+- 心电图报告
+- 白带常规
+- 胎心监护报告
+- 其他与孕期产检相关的检查报告
+
+**不属于孕期报告的情况：**
+- 非医疗报告（发票、收据、菜单、截图、照片等）
+- 与孕期无关的医疗报告（如骨科、眼科、口腔科等非产检相关）
+- 无法识别内容的模糊图片
+
+**请严格按以下JSON格式返回（不要输出其他内容）：**
+
+如果**不是**孕期相关报告，返回：
+{
+  "is_pregnancy_report": false,
+  "reject_reason": "简短说明为什么不是孕期报告（如：这是一张超市购物小票）"
+}
+
+如果**是**孕期相关报告，提取所有检查指标并返回：
+{
+  "is_pregnancy_report": true,
+  "patient_name": "患者姓名（如有）",
+  "report_type": "报告类型（如血常规、尿常规、B超等）",
+  "report_date": "报告日期（如有，格式 YYYY-MM-DD）",
+  "data_items": [
+    {
+      "name": "测定项目",
+      "value": "测定结果（只提取数字，不要单位）",
+      "unit": "单位",
+      "reference_range": "参考范围（如有）"
+    }
+  ]
+}
+
+**重要提示：**
+1. 必须先判断是否为孕期报告，is_pregnancy_report 字段必填
+2. value字段只填写纯数字，不要包含单位（如"23.5"而不是"23.5mm"）
+3. 单位单独放在unit字段中
+4. 如果是B超报告，务必确保双顶径(BPD)、腹围(AC)、股骨长(FL)这三个指标被正确提取"""
+
+
+def _get_client() -> AsyncOpenAI:
+    return AsyncOpenAI(api_key=settings.ai_api_key, base_url=settings.ai_base_url)
+
+
+async def ocr_recognize(image_path: str) -> dict:
+    """
+    Use vision model to extract medical report data from an image.
+    Returns structured OCR results.
+    """
+    # Resolve path relative to upload_dir
+    if image_path.startswith("/uploads/"):
+        relative = image_path[len("/uploads/"):]
+        full_path = Path(settings.upload_dir) / relative
+    else:
+        full_path = Path(settings.upload_dir) / image_path.lstrip("/")
+
+    if not full_path.exists():
+        logger.error("OCR 文件不存在: %s", full_path)
+        raise FileNotFoundError(f"报告图片不存在: {image_path}")
+
+    # Async file read
+    async with aiofiles.open(full_path, "rb") as f:
+        raw = await f.read()
+    image_data = base64.standard_b64encode(raw).decode("utf-8")
+
+    # Determine media type
+    suffix = full_path.suffix.lower()
+    media_type_map = {
+        ".jpg": "image/jpeg",
+        ".jpeg": "image/jpeg",
+        ".png": "image/png",
+        ".webp": "image/webp",
+    }
+    media_type = media_type_map.get(suffix, "image/jpeg")
+
+    client = _get_client()
+    try:
+        response = await client.chat.completions.create(
+            model=settings.ai_vision_model,
+            max_tokens=2048,
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:{media_type};base64,{image_data}",
+                            },
+                        },
+                        {
+                            "type": "text",
+                            "text": OCR_PROMPT,
+                        },
+                    ],
+                }
+            ],
+        )
+    except Exception as e:
+        err_msg = str(e)
+        logger.error("AI OCR 调用失败: %s: %s", type(e).__name__, err_msg[:200])
+        if "auth" in err_msg.lower() or "api key" in err_msg.lower():
+            raise RuntimeError("AI 服务认证失败，请检查 API Key 是否有效") from e
+        if "<html" in err_msg.lower() or "cloudflare" in err_msg.lower():
+            raise RuntimeError("AI 服务请求被拦截，请检查 API 配置") from e
+        raise RuntimeError("AI 识别服务暂时不可用，请稍后重试") from e
+
+    response_text = response.choices[0].message.content
+    try:
+        if "```json" in response_text:
+            response_text = response_text.split("```json")[1].split("```")[0]
+        elif "```" in response_text:
+            response_text = response_text.split("```")[1].split("```")[0]
+        return json.loads(response_text.strip())
+    except json.JSONDecodeError:
+        return {"raw_text": response_text, "data_items": []}