cfc/cfc-langgraph/app/api/meal.py

import uuid
import json
import logging
import re
from fastapi import APIRouter
from app.models.meal import (
    FoodRecognizeRequest,
    FoodRecognizeResponse,
    FoodsItem,
    MenuGenerateRequest,
    MenuGenerateResponse,
)
from app.config import settings

logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api/v1", tags=["meal"])


@router.post("/food/recognize", response_model=FoodRecognizeResponse)
async def recognize_food(req: FoodRecognizeRequest):
    """食材识别: 上传图片 URL, 返回识别食材列表"""
    trace_id = str(uuid.uuid4())
    try:
        from langchain_openai import ChatOpenAI
        from langchain_core.messages import HumanMessage, SystemMessage

        llm = ChatOpenAI(
            model=settings.llm_model,
            api_key=settings.llm_api_key,
            base_url=settings.llm_base_url,
            temperature=0,
        )

        prompt = f"""请识别以下图片中的主要食材（3-8种），按置信度排序。
返回 JSON 数组格式，不要包含其他内容：
[{{"name": "食材名称", "confidence": 0.95, "category": "蔬菜/水果/肉禽/水产/蛋奶/谷物/调味/其他"}}]

图片链接: {req.image_url}
要求: 只返回 JSON 数组，不要有任何其他文字。"""

        response = llm.invoke([
            SystemMessage(content="你是一个专业的食材识别助手。"),
            HumanMessage(content=prompt),
        ])

        text = response.content.strip()
        try:
            foods = json.loads(text)
        except json.JSONDecodeError:
            match = re.search(r'\[[\s\S]*\]', text)
            foods = json.loads(match.group()) if match else []

        foods_list = []
        for item in foods:
            if isinstance(item, dict):
                foods_list.append(FoodsItem(
                    name=item.get("name", "未知"),
                    confidence=float(item.get("confidence", 0.5)),
                    category=item.get("category", "other"),
                ))

        return FoodRecognizeResponse(foods=foods_list, raw_response=text, trace_id=trace_id)
    except Exception as e:
        logger.error("食材识别失败: %s", e, exc_info=True)
        return FoodRecognizeResponse(foods=[], trace_id=trace_id)


@router.post("/menu/generate", response_model=MenuGenerateResponse)
async def generate_menu(req: MenuGenerateRequest):
    """菜单生成: 根据食材和用餐人数生成一日三餐菜单"""
    trace_id = str(uuid.uuid4())
    try:
        from langchain_openai import ChatOpenAI
        from langchain_core.messages import HumanMessage, SystemMessage

        try:
            selected_foods = json.loads(req.selected_foods) if req.selected_foods else []
        except json.JSONDecodeError:
            selected_foods = []

        try:
            participants = json.loads(req.participants) if req.participants else []
        except json.JSONDecodeError:
            participants = []

        participant_count = len(participants) if participants else 1
        foods_text = ", ".join([f.get("name", "") for f in selected_foods]) if selected_foods else "根据可用食材"

        constraints = []
        if req.allergies:
            constraints.append(f"禁忌: {req.allergies}")
        if req.health_goals:
            constraints.append(f"健康目标: {req.health_goals}")
        if req.cuisine_pref:
            constraints.append(f"菜系偏好: {req.cuisine_pref}")
        if req.spice_level is not None:
            constraints.append(f"辣度: {req.spice_level}/5")
        constraints_text = "\n".join(constraints) if constraints else "无特殊限制"

        prompt = f"""请为{participant_count}人生成{req.date}的一日三餐菜单。

可用食材: {foods_text}
用餐人数: {participant_count}人
{constraints_text}

请返回 JSON 格式：
{{"meals": [{{"type": "breakfast", "name": "早餐", "dishes": [{{"name": "菜品名", "ingredients": [{{"name": "食材", "grams": 100}}], "cooking_method": "烹饪方法", "nutrition": {{"calories": 200, "protein": 10, "carbs": 30, "fat": 5}}, "notes": "备注"}}]}}]}}

要求:
1. 早/午/晚各至少1-2道菜
2. 食材用量按{participant_count}人份计算
3. 营养均衡，考虑健康目标
4. 只用提供的食材
5. 只返回 JSON，不要其他文字"""

        llm = ChatOpenAI(
            model=settings.llm_model,
            api_key=settings.llm_api_key,
            base_url=settings.llm_base_url,
            temperature=0.7,
        )

        response = llm.invoke([
            SystemMessage(content="你是一个专业营养师和厨师，擅长根据食材和健康目标设计食谱。"),
            HumanMessage(content=prompt),
        ])

        text = response.content.strip()
        match = re.search(r'\{[\s\S]*\}', text)
        menu_json = match.group() if match else '{"meals": []}'

        return MenuGenerateResponse(menu_json=menu_json, trace_id=trace_id)
    except Exception as e:
        logger.error("菜单生成失败: %s", e, exc_info=True)
        return MenuGenerateResponse(menu_json='{"meals": []}', trace_id=trace_id)