chore: 初始化项目结构并添加基础配置文件
添加后端API和前端项目的初始目录结构，包括应用层模块、工具模块和模型定义 配置前后端的.dockerignore文件以排除开发环境文件 添加前端样式文件，包括字体、Tailwind CSS和主题配置 初始化Python虚拟环境中的类型标记文件
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后端-孕期报告解读-api/app/services/__init__.py
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后端-孕期报告解读-api/app/services/__init__.py
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后端-孕期报告解读-api/app/services/ai_service.py
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后端-孕期报告解读-api/app/services/ai_service.py
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"""
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AI service: Report analysis, chat, doctor questions.
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Uses OpenAI-compatible API (e.g. SiliconFlow).
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"""
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import json
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import logging
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from typing import Optional
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from openai import AsyncOpenAI
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from app.config import settings
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from app.models.report import Report
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from app.models.user import User
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logger = logging.getLogger(__name__)
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def _get_client() -> AsyncOpenAI:
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return AsyncOpenAI(api_key=settings.ai_api_key, base_url=settings.ai_base_url)
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def _build_report_context(report: Report, user: User) -> str:
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"""构建报告上下文给 AI"""
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lines = [
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f"孕妈信息: 年龄{user.age}岁",
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]
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if report.pregnancy_week:
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lines.append(f"检查时孕周: 第{report.pregnancy_week}周")
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if report.report_date:
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lines.append(f"报告日期: {report.report_date}")
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if user.last_period_date:
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lines.append(f"末次月经: {user.last_period_date}")
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if user.height:
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lines.append(f"身高: {user.height}cm")
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if user.current_weight:
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lines.append(f"当前体重: {user.current_weight}kg")
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if user.blood_type:
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lines.append(f"血型: {user.blood_type}")
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if user.medical_history:
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lines.append(f"病史: {', '.join(user.medical_history)}")
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lines.append(f"\n报告类型: {report.report_type}")
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lines.append("\n检查指标:")
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for item in report.data_items:
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ref = f", 参考范围: {item.reference_range}" if item.reference_range else ""
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lines.append(f" - {item.name}: {item.value} {item.unit}{ref}")
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if report.symptoms:
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lines.append(f"\n自觉症状: {', '.join(report.symptoms)}")
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return "\n".join(lines)
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def _parse_json_response(text: str) -> dict:
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"""从 AI 回复中提取 JSON"""
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if "```json" in text:
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text = text.split("```json")[1].split("```")[0]
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elif "```" in text:
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text = text.split("```")[1].split("```")[0]
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return json.loads(text.strip())
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async def _chat_complete(system: str, user_content: str, messages: Optional[list] = None, max_tokens: int = 4096) -> str:
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"""统一调用 AI 文本模型"""
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client = _get_client()
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msgs = [{"role": "system", "content": system}]
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if messages:
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msgs.extend(messages)
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else:
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msgs.append({"role": "user", "content": user_content})
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try:
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response = await client.chat.completions.create(
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model=settings.ai_text_model,
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max_tokens=max_tokens,
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temperature=0.7, # 降低温度提高生成速度
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messages=msgs,
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)
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except Exception as e:
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err_msg = str(e)
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logger.error("AI 调用失败: %s: %s", type(e).__name__, err_msg[:200])
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if "auth" in err_msg.lower() or "api key" in err_msg.lower():
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raise RuntimeError("AI 服务认证失败，请检查 API Key") from e
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raise RuntimeError("AI 服务暂时不可用，请稍后重试") from e
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return response.choices[0].message.content
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# ─── Report Analysis ───
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ANALYSIS_SYSTEM_PROMPT = """你是一位专业、温暖的孕期健康顾问AI。你的任务是解读孕期检查报告，用通俗易懂的语言帮助孕妈理解检查结果。
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核心要求：
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1. 必须结合孕周来判断指标是否正常（孕期不同阶段的正常范围不同，如血红蛋白在孕中晚期会生理性下降）
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2. 将每个指标的测定结果与报告中的参考范围对比，准确判断偏高/偏低/正常
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3. 如果参考范围缺失，根据你的医学知识和该孕周的标准范围来判断
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4. 用温暖、关怀的语气，避免医学术语恐吓
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5. 对每个指标给出白话化的昵称（如血红蛋白→"氧气快递员"，双顶径→"宝宝头围"，腹围→"宝宝肚围"，股骨长→"宝宝腿长"）
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6. 解释每个指标的含义、对宝宝的影响、行动建议
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7. 给出整体评估和风险等级
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8. 行动建议按优先级排序
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**特别注意：**
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- 如果是B超报告，重点关注胎儿生长指标（双顶径、腹围、股骨长），即使只有这3个指标也要给出完整的解读
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- B超报告的解读要重点说明宝宝的生长发育情况，是否符合孕周标准
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- 即使指标数量很少（如只有3个），也要给出详细的解读和建议
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请严格按以下JSON格式返回：
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{
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"overall_status": "normal/attention/warning",
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"risk_level": "low/medium/high",
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"overall_summary": "整体评估文案",
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"emotional_summary": "情感化总结，如'宝宝发育良好，继续保持'",
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"categories": [
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{
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"category": "分类名称",
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"status": "normal/attention/warning",
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"indicators": [
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{
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"name": "指标名",
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"nickname": "白话昵称",
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"value": "值",
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"status": "normal/high/low",
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"what_is_it": "通俗解释",
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"impact": "对宝宝的影响",
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"suggestion": "行动建议",
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"recheck_reminder": "复查提醒（可选）"
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}
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]
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}
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],
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"action_items": [
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{
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"content": "具体建议",
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"priority": "high/medium/low",
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"category": "饮食/运动/就医/复查"
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}
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],
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"tips": ["温馨提示1", "温馨提示2"]
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}"""
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async def analyze_report(report: Report, user: User) -> dict:
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"""调用 AI 解读报告"""
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logger.info("开始AI解读报告 report_id=%s", report.id)
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context = _build_report_context(report, user)
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response_text = await _chat_complete(
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system=ANALYSIS_SYSTEM_PROMPT,
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user_content=f"请解读以下孕期检查报告:\n\n{context}",
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)
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try:
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return _parse_json_response(response_text)
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except (json.JSONDecodeError, IndexError):
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return {
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"overall_status": "normal",
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"risk_level": "low",
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"overall_summary": response_text,
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"emotional_summary": "报告解读完成",
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"categories": [],
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"action_items": [],
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"tips": [],
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}
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# ─── Chat ───
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CHAT_SYSTEM_PROMPT = """你是一位专业、温暖的孕期健康顾问AI。孕妈正在就她的检查报告向你提问。
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要求：
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1. 用通俗易懂、温暖关怀的语气回答
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2. 基于报告数据给出专业建议
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3. 遇到严重问题建议及时就医
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4. 回答简洁明了，避免过长
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以下是孕妈的检查报告信息：
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{report_context}"""
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async def chat_with_ai(
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report: Report, user: User, messages: list[dict], user_message: str
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) -> str:
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"""AI 对话追问"""
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context = _build_report_context(report, user)
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system = CHAT_SYSTEM_PROMPT.format(report_context=context)
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conversation = [{"role": "system", "content": system}]
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for msg in messages[-10:]:
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conversation.append({"role": msg["role"], "content": msg["content"]})
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conversation.append({"role": "user", "content": user_message})
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client = _get_client()
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try:
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response = await client.chat.completions.create(
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model=settings.ai_text_model,
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max_tokens=1024,
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messages=conversation,
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)
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except Exception as e:
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logger.error("AI 对话失败: %s", e)
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raise RuntimeError("AI 对话服务暂时不可用") from e
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return response.choices[0].message.content
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# ─── Doctor Questions ───
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DOCTOR_QUESTIONS_SYSTEM_PROMPT = """你是一位孕期健康顾问AI。根据检查报告中的异常项，帮孕妈生成3-5条看医生时可以问的问题。
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要求：
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1. 问题要专业但容易理解
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2. 按重要性排序
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3. 给出为什么要问这个问题的原因
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请严格按以下JSON格式返回：
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{
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"questions": [
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{
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"category": "分类",
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"question": "问医生的话术",
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"reason": "为什么要问这个",
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"priority": "high/medium/low"
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}
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]
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}"""
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async def generate_doctor_questions(report: Report, user: User) -> dict:
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"""生成问诊清单"""
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context = _build_report_context(report, user)
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response_text = await _chat_complete(
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system=DOCTOR_QUESTIONS_SYSTEM_PROMPT,
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user_content=f"请根据以下报告生成问诊清单:\n\n{context}",
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max_tokens=1024,
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)
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try:
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return _parse_json_response(response_text)
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except (json.JSONDecodeError, IndexError):
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return {"questions": []}
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142
后端-孕期报告解读-api/app/services/ocr.py
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后端-孕期报告解读-api/app/services/ocr.py
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"""
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OCR service: Use vision model to extract medical report data from images.
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Supports OpenAI-compatible APIs (e.g. SiliconFlow).
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"""
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import base64
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import json
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import logging
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from pathlib import Path
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import aiofiles
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from openai import AsyncOpenAI
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from app.config import settings
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logger = logging.getLogger(__name__)
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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。
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**孕期相关报告包括但不限于：**
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- 产检报告（B超/彩超、NT检查、四维彩超、大排畸等）
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- 血液检查（血常规、血糖、血型、唐氏筛查、TORCH、甲功等）
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- 尿液检查（尿常规等）
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- 肝肾功能检查
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- 凝血功能检查
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- 心电图报告
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- 白带常规
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- 胎心监护报告
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- 其他与孕期产检相关的检查报告
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**不属于孕期报告的情况：**
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- 非医疗报告（发票、收据、菜单、截图、照片等）
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- 与孕期无关的医疗报告（如骨科、眼科、口腔科等非产检相关）
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- 无法识别内容的模糊图片
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**请严格按以下JSON格式返回（不要输出其他内容）：**
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如果**不是**孕期相关报告，返回：
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{
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"is_pregnancy_report": false,
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"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）"
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}
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如果**是**孕期相关报告，提取所有检查指标并返回：
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{
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"is_pregnancy_report": true,
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"patient_name": "患者姓名（如有）",
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"report_type": "报告类型（如血常规、尿常规、B超等）",
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"report_date": "报告日期（如有，格式 YYYY-MM-DD）",
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"data_items": [
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{
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"name": "测定项目",
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"value": "测定结果（只提取数字，不要单位）",
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"unit": "单位",
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"reference_range": "参考范围（如有）"
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}
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]
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}
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**重要提示：**
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1. 必须先判断是否为孕期报告，is_pregnancy_report 字段必填
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2. value字段只填写纯数字，不要包含单位（如"23.5"而不是"23.5mm"）
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3. 单位单独放在unit字段中
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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"""
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def _get_client() -> AsyncOpenAI:
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return AsyncOpenAI(api_key=settings.ai_api_key, base_url=settings.ai_base_url)
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async def ocr_recognize(image_path: str) -> dict:
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"""
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Use vision model to extract medical report data from an image.
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Returns structured OCR results.
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"""
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# Resolve path relative to upload_dir
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if image_path.startswith("/uploads/"):
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relative = image_path[len("/uploads/"):]
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full_path = Path(settings.upload_dir) / relative
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else:
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full_path = Path(settings.upload_dir) / image_path.lstrip("/")
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if not full_path.exists():
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logger.error("OCR 文件不存在: %s", full_path)
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raise FileNotFoundError(f"报告图片不存在: {image_path}")
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# Async file read
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async with aiofiles.open(full_path, "rb") as f:
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raw = await f.read()
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image_data = base64.standard_b64encode(raw).decode("utf-8")
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# Determine media type
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suffix = full_path.suffix.lower()
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media_type_map = {
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".jpg": "image/jpeg",
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".jpeg": "image/jpeg",
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".png": "image/png",
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".webp": "image/webp",
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}
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media_type = media_type_map.get(suffix, "image/jpeg")
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client = _get_client()
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try:
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response = await client.chat.completions.create(
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model=settings.ai_vision_model,
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max_tokens=2048,
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:{media_type};base64,{image_data}",
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},
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},
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{
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"type": "text",
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"text": OCR_PROMPT,
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},
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],
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}
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],
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)
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except Exception as e:
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err_msg = str(e)
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logger.error("AI OCR 调用失败: %s: %s", type(e).__name__, err_msg[:200])
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if "auth" in err_msg.lower() or "api key" in err_msg.lower():
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raise RuntimeError("AI 服务认证失败，请检查 API Key 是否有效") from e
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if "<html" in err_msg.lower() or "cloudflare" in err_msg.lower():
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raise RuntimeError("AI 服务请求被拦截，请检查 API 配置") from e
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raise RuntimeError("AI 识别服务暂时不可用，请稍后重试") from e
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response_text = response.choices[0].message.content
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try:
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if "```json" in response_text:
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response_text = response_text.split("```json")[1].split("```")[0]
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||||
elif "```" in response_text:
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||||
response_text = response_text.split("```")[1].split("```")[0]
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||||
return json.loads(response_text.strip())
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||||
except json.JSONDecodeError:
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||||
return {"raw_text": response_text, "data_items": []}
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24
后端-孕期报告解读-api/app/services/storage.py
Normal file
24
后端-孕期报告解读-api/app/services/storage.py
Normal file
@@ -0,0 +1,24 @@
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||||
import os
|
||||
import uuid
|
||||
from pathlib import Path
|
||||
|
||||
import aiofiles
|
||||
from fastapi import UploadFile
|
||||
|
||||
from app.config import settings
|
||||
|
||||
|
||||
async def save_upload_file(file: UploadFile, sub_dir: str = "reports") -> str:
|
||||
"""Save uploaded file and return the relative URL path"""
|
||||
upload_path = Path(settings.upload_dir) / sub_dir
|
||||
upload_path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
ext = Path(file.filename).suffix if file.filename else ".jpg"
|
||||
filename = f"{uuid.uuid4().hex}{ext}"
|
||||
file_path = upload_path / filename
|
||||
|
||||
content = await file.read()
|
||||
async with aiofiles.open(file_path, "wb") as f:
|
||||
await f.write(content)
|
||||
|
||||
return f"/uploads/{sub_dir}/{filename}"
|
||||
26
后端-孕期报告解读-api/app/services/wechat.py
Normal file
26
后端-孕期报告解读-api/app/services/wechat.py
Normal file
@@ -0,0 +1,26 @@
|
||||
import httpx
|
||||
|
||||
from app.config import settings
|
||||
|
||||
|
||||
async def code_to_session(code: str) -> dict:
|
||||
"""用小程序 code 换取 openid 和 session_key"""
|
||||
url = "https://api.weixin.qq.com/sns/jscode2session"
|
||||
params = {
|
||||
"appid": settings.wechat_app_id,
|
||||
"secret": settings.wechat_app_secret,
|
||||
"js_code": code,
|
||||
"grant_type": "authorization_code",
|
||||
}
|
||||
async with httpx.AsyncClient() as client:
|
||||
resp = await client.get(url, params=params)
|
||||
data = resp.json()
|
||||
|
||||
if "errcode" in data and data["errcode"] != 0:
|
||||
raise Exception(f"微信登录失败: {data.get('errmsg', '未知错误')}")
|
||||
|
||||
return {
|
||||
"openid": data["openid"],
|
||||
"session_key": data["session_key"],
|
||||
"unionid": data.get("unionid"),
|
||||
}
|
||||
Reference in New Issue
Block a user