PMFlow/backend/app/services/recipe/service.py

"""食谱服务 - 包含热量识别与营养分析"""

import base64
import io
from typing import Optional
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession

from ...models import Recipe
from ...schemas import RecipeCreate, RecipeUpdate, RecipeResponse
from ..ai_service_core import AIService
from ...config.ai_prompts import PROMPT_ANALYZE_FOOD_CALORIES


class RecipeService:
    """食谱服务"""

    def __init__(self, ai_service: AIService):
        self.ai_service = ai_service

    async def create_recipe(
        self,
        db: AsyncSession,
        recipe_data: RecipeCreate,
        tenant_id: int,
    ) -> Recipe:
        """创建食谱"""
        recipe = Recipe(
            **recipe_data.model_dump(),
            tenant_id=tenant_id,
            status="draft",
        )
        db.add(recipe)
        await db.commit()
        await db.refresh(recipe)
        return recipe

    async def get_recipe(
        self,
        db: AsyncSession,
        recipe_id: int,
        tenant_id: int,
    ) -> Optional[Recipe]:
        """获取单个食谱"""
        result = await db.execute(
            select(Recipe).where(
                Recipe.id == recipe_id,
                Recipe.tenant_id == tenant_id,
            )
        )
        return result.scalar_one_or_none()

    async def list_recipes(
        self,
        db: AsyncSession,
        tenant_id: int,
        skip: int = 0,
        limit: int = 20,
        category: Optional[str] = None,
        cuisine: Optional[str] = None,
        status: Optional[str] = None,
        is_favorite: Optional[bool] = None,
    ) -> list[Recipe]:
        """获取食谱列表"""
        query = select(Recipe).where(Recipe.tenant_id == tenant_id)

        if category:
            query = query.where(Recipe.category == category)
        if cuisine:
            query = query.where(Recipe.cuisine == cuisine)
        if status:
            query = query.where(Recipe.status == status)
        if is_favorite is not None:
            query = query.where(Recipe.is_favorite == is_favorite)

        query = query.order_by(Recipe.created_at.desc()).offset(skip).limit(limit)
        result = await db.execute(query)
        return list(result.scalars().all())

    async def update_recipe(
        self,
        db: AsyncSession,
        recipe_id: int,
        recipe_data: RecipeUpdate,
        tenant_id: int,
    ) -> Optional[Recipe]:
        """更新食谱"""
        recipe = await self.get_recipe(db, recipe_id, tenant_id)
        if not recipe:
            return None

        update_data = recipe_data.model_dump(exclude_unset=True)
        for field, value in update_data.items():
            setattr(recipe, field, value)

        await db.commit()
        await db.refresh(recipe)
        return recipe

    async def delete_recipe(
        self,
        db: AsyncSession,
        recipe_id: int,
        tenant_id: int,
    ) -> bool:
        """删除食谱"""
        recipe = await self.get_recipe(db, recipe_id, tenant_id)
        if not recipe:
            return False

        await db.delete(recipe)
        await db.commit()
        return True

    async def analyze_food_from_description(
        self,
        db: AsyncSession,
        description: str,
        tenant_id: int,
    ) -> dict:
        """
        基于食物描述分析热量和营养成分

        注意：此方法使用纯文本分析。对于图片识别，
        建议先使用图像识别服务将图片转为描述文本，再调用此方法。
        """
        tid = str(tenant_id)
        prompt = PROMPT_ANALYZE_FOOD_CALORIES.format(food_description=description)

        try:
            response_text = await self.ai_service._invoke_with_retry(
                prompt,
                db=db,
                tenant_id=tid,
                function_name="analyze_food_calories",
                use_cache=True,
            )
            result = self.ai_service._parse_json(response_text)
            return {
                "success": True,
                "data": result,
                "message": "分析成功",
            }
        except Exception as e:
            return {
                "success": False,
                "data": None,
                "message": f"分析失败: {str(e)}",
            }

    async def create_recipe_with_ai_analysis(
        self,
        db: AsyncSession,
        recipe_data: RecipeCreate,
        food_description: str,
        tenant_id: int,
    ) -> tuple[Recipe, dict]:
        """
        创建食谱并进行 AI 热量分析

        Args:
            db: 数据库会话
            recipe_data: 食谱基础数据
            food_description: 食物描述（可以是图片识别后的描述）
            tenant_id: 租户ID

        Returns:
            (Recipe, analysis_result): 创建的食谱对象和分析结果
        """
        # 先创建食谱
        recipe = await self.create_recipe(db, recipe_data, tenant_id)

        # 进行 AI 分析
        analysis = await self.analyze_food_from_description(
            db, food_description, tenant_id
        )

        if analysis["success"] and analysis["data"]:
            # 更新食谱的营养信息
            data = analysis["data"]
            recipe.calories = data.get("calories")
            recipe.protein = data.get("protein")
            recipe.fat = data.get("fat")
            recipe.carbs = data.get("carbs")
            recipe.fiber = data.get("fiber")
            recipe.ai_recognition_confidence = data.get("confidence")
            recipe.ai_analysis_result = data
            recipe.category = data.get("category") or recipe_data.category
            recipe.cuisine = data.get("cuisine") or recipe_data.cuisine

            await db.commit()
            await db.refresh(recipe)

        return recipe, analysis

    def decode_base64_image(self, image_base64: str) -> tuple[bytes, str]:
        """
        解码 Base64 图片数据

        Args:
            image_base64: Base64 编码的图片数据（可能包含 data:image/xxx;base64, 前缀）

        Returns:
            (image_bytes, mime_type): 图片字节数据和 MIME 类型
        """
        # 移除可能的数据 URI 前缀
        if "," in image_base64:
            header, image_base64 = image_base64.split(",", 1)
            # 提取 MIME 类型
            mime_type = header.split(";")[0].split(":")[-1] if ":" in header else "image/jpeg"
        else:
            mime_type = "image/jpeg"

        # 解码 Base64
        image_bytes = base64.b64decode(image_base64)
        return image_bytes, mime_type

    async def upload_image(
        self,
        image_base64: str,
        filename: Optional[str] = None,
    ) -> str:
        """
        上传图片并返回 URL

        注意：这是一个示例实现，实际项目中应该集成：
        - AWS S3
        - 阿里云 OSS
        - 腾讯云 COS
        - MinIO
        等对象存储服务

        Args:
            image_base64: Base64 编码的图片数据
            filename: 原始文件名

        Returns:
            图片 URL
        """
        # TODO: 集成实际的文件存储服务
        # 这里只是示例，实际应该上传到对象存储
        image_bytes, mime_type = self.decode_base64_image(image_base64)

        # 示例：生成一个模拟 URL
        # 实际应该上传后返回真实的 URL
        import hashlib
        import time

        file_hash = hashlib.md5(image_bytes).hexdigest()
        timestamp = int(time.time())
        mock_url = f"https://example.com/recipe-images/{file_hash}_{timestamp}.jpg"

        return mock_url


# 单例
_recipe_service: Optional[RecipeService] = None


def get_recipe_service() -> RecipeService:
    """获取食谱服务单例"""
    global _recipe_service
    if _recipe_service is None:
        from ..ai_service_core import get_ai_service
        _recipe_service = RecipeService(ai_service=get_ai_service())
    return _recipe_service