# 檔案路徑: app/routers/ai_router.py

from fastapi import APIRouter, File, UploadFile, HTTPException
from pydantic import BaseModel
from typing import Dict, Any, List, Optional
import logging

# 導入新的整合服務
from ..services.integrated_food_analysis_service import analyze_food_image_integrated

# 設置日誌
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

router = APIRouter(
    prefix="/ai",
    tags=["AI"],
)

# 新增 Pydantic 模型定義
class IntegratedAnalysisResponse(BaseModel):
    success: bool
    analysis_time: float
    food_analysis: Dict[str, Any]
    reference_analysis: Dict[str, Any]
    weight_analysis: Dict[str, Any]
    nutrition_analysis: Dict[str, Any]
    summary: Dict[str, Any]
    architecture: Dict[str, str]

class WeightEstimationResponse(BaseModel):
    food_type: str
    estimated_weight: float
    weight_confidence: float
    weight_error_range: List[float]
    nutrition: Dict[str, Any]
    reference_object: Optional[str] = None
    note: str

class FoodAnalysisResponse(BaseModel):
    food_name: str
    nutrition_info: Dict[str, Any]

@router.post("/analyze-food-image/")
async def analyze_food_image_endpoint(file: UploadFile = File(...)):
    """
    這個端點接收使用者上傳的食物圖片，使用 AI 模型進行辨識，
    並返回辨識出的食物名稱。
    """
    # 檢查上傳的檔案是否為圖片格式
    if not file.content_type or not file.content_type.startswith("image/"):
        raise HTTPException(status_code=400, detail="上傳的檔案不是圖片格式。")

    try:
        # 讀取圖片數據
        image_bytes = await file.read()
        
        # 使用新的整合服務進行分析
        result = analyze_food_image_integrated(image_bytes, debug=False)
        
        if not result.get("success", False):
            raise HTTPException(status_code=500, detail=result.get("error_message", "分析失敗"))
        
        # 返回簡化的結果
        return {
            "food_name": result["food_analysis"]["food_name"],
            "nutrition_info": result["nutrition_analysis"]["adjusted_nutrition"]
        }
        
    except Exception as e:
        logger.error(f"食物分析失敗: {str(e)}")
        raise HTTPException(status_code=500, detail=f"分析失敗: {str(e)}")

@router.post("/analyze-food-image-with-weight/", response_model=WeightEstimationResponse)
async def analyze_food_image_with_weight_endpoint(file: UploadFile = File(...)):
    """
    整合食物辨識、重量估算與營養分析的端點。
    使用新的架構：FOOD101 → YOLO(參考物) → SAM+DPT → USDA API
    """
    # 檢查上傳的檔案是否為圖片格式
    if not file.content_type or not file.content_type.startswith("image/"):
        raise HTTPException(status_code=400, detail="上傳的檔案不是圖片格式。")

    try:
        # 讀取圖片數據
        image_bytes = await file.read()
        
        # 使用新的整合服務進行分析
        result = analyze_food_image_integrated(image_bytes, debug=False)
        
        if not result.get("success", False):
            raise HTTPException(status_code=500, detail=result.get("error_message", "分析失敗"))
        
        # 轉換為舊格式以保持向後兼容
        weight_analysis = result["weight_analysis"]
        nutrition_analysis = result["nutrition_analysis"]
        
        return WeightEstimationResponse(
            food_type=result["food_analysis"]["food_name"],
            estimated_weight=weight_analysis["estimated_weight"],
            weight_confidence=weight_analysis["weight_confidence"],
            weight_error_range=weight_analysis["weight_error_range"],
            nutrition=nutrition_analysis["adjusted_nutrition"],
            reference_object=weight_analysis["reference_object"],
            note=f"使用新架構分析，耗時 {result['analysis_time']} 秒"
        )
        
    except Exception as e:
        logger.error(f"重量估算分析失敗: {str(e)}")
        raise HTTPException(status_code=500, detail=f"分析失敗: {str(e)}")

@router.post("/analyze-food-image-integrated/")
async def analyze_food_image_integrated_endpoint(file: UploadFile = File(...)):
    """
    新的整合分析端點，返回完整的分析結果
    架構：FOOD101 → YOLO(參考物) → SAM+DPT → USDA API
    """
    # 檢查上傳的檔案是否為圖片格式
    if not file.content_type or not file.content_type.startswith("image/"):
        raise HTTPException(status_code=400, detail="上傳的檔案不是圖片格式。")

    try:
        # 讀取圖片數據
        image_bytes = await file.read()
        
        # 使用新的整合服務進行分析
        result = analyze_food_image_integrated(image_bytes, debug=False)
        
        return result
        
    except Exception as e:
        logger.error(f"整合分析失敗: {str(e)}")
        raise HTTPException(status_code=500, detail=f"分析失敗: {str(e)}")

@router.get("/health")
async def health_check():
    """
    健康檢查端點，確認 AI 服務是否正常運作
    """
    return {
        "status": "healthy",
        "services": {
            "food_classification": "available (FOOD101)",
            "reference_detection": "available (YOLO)",
            "weight_estimation": "available (SAM+DPT)",
            "nutrition_api": "available (USDA)",
            "integrated_analysis": "available"
        },
        "architecture": {
            "layer_1": "FOOD101 (食物識別)",
            "layer_2": "YOLO (參考物偵測)",
            "layer_3": "SAM+DPT (重量計算)",
            "layer_4": "USDA API (營養查詢)",
            "layer_5": "重量調整 (營養計算)"
        }
    }