app/services/integrated_food_analysis_service.py

# 檔案路徑: app/services/integrated_food_analysis_service.py

import logging
import numpy as np
from PIL import Image
import io
from typing import Dict, Any, List, Optional, Tuple
from datetime import datetime

# 導入各個服務
from .ai_service import classify_food_image
from .reference_detection_service import detect_reference_objects_from_image
from .weight_calculation_service import calculate_food_weight
from .nutrition_api_service import fetch_nutrition_data

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

class IntegratedFoodAnalysisService:
    def __init__(self):
        """初始化整合食物分析服務"""
        logger.info("初始化整合食物分析服務...")
    
    def analyze_food_image(self, image_bytes: bytes, debug: bool = False) -> Dict[str, Any]:
        """
        整合食物分析主函數
        
        新架構流程：
        1. FOOD101 模型判斷食物
        2. YOLO 主要判斷參考物在哪、大小為何
        3. 再利用 SAM+DPT 去計算可能的重量
        4. 再利用重量去乘上 USDA 每100克的數值
        
        Args:
            image_bytes: 圖片二進位數據
            debug: 是否啟用調試模式
            
        Returns:
            Dict: 完整的分析結果
        """
        try:
            logger.info("=== 開始整合食物分析 ===")
            start_time = datetime.now()
            
            # 將 bytes 轉換為 PIL Image
            image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
            logger.info(f"圖片載入完成，尺寸: {image.size}")
            
            # === 第一層：FOOD101 模型判斷食物 ===
            logger.info("--- 第一層：FOOD101 食物識別 ---")
            food_name = classify_food_image(image_bytes)
            logger.info(f"FOOD101 識別結果: {food_name}")
            
            if food_name.startswith("Error") or food_name == "Unknown":
                return self._create_error_response("食物識別失敗", food_name)
            
            # === 第二層：YOLO 判斷參考物 ===
            logger.info("--- 第二層：YOLO 參考物偵測 ---")
            reference_objects, pixel_ratio = detect_reference_objects_from_image(image_bytes)
            
            if not reference_objects:
                logger.warning("未偵測到參考物，使用預設像素比例")
                pixel_ratio = 0.01  # 預設比例
            
            best_reference = reference_objects[0] if reference_objects else None
            logger.info(f"參考物偵測結果: {len(reference_objects)} 個參考物")
            if best_reference:
                logger.info(f"最佳參考物: {best_reference['label']}, 信心度: {best_reference['confidence']:.2f}")
            logger.info(f"像素比例: {pixel_ratio:.4f} cm/pixel")
            
            # === 第三層：SAM+DPT 重量計算 ===
            logger.info("--- 第三層：SAM+DPT 重量計算 ---")
            weight_result = calculate_food_weight(
                image_bytes=image_bytes,
                food_name=food_name,
                pixel_ratio=pixel_ratio,
                bbox=None  # 使用整個圖片
            )
            
            if not weight_result.get("success", False):
                logger.error("重量計算失敗")
                return self._create_error_response("重量計算失敗", weight_result.get("error", "未知錯誤"))
            
            estimated_weight = weight_result["estimated_weight"]
            weight_confidence = weight_result["weight_confidence"]
            weight_error_range = weight_result["weight_error_range"]
            
            logger.info(f"重量計算結果: {estimated_weight}g, 信心度: {weight_confidence:.2f}")
            
            # === 第四層：USDA API 營養查詢 ===
            logger.info("--- 第四層：USDA API 營養查詢 ---")
            nutrition_info = fetch_nutrition_data(food_name)
            
            if nutrition_info is None:
                logger.warning("USDA API 查詢失敗，使用預設營養值")
                nutrition_info = self._get_default_nutrition(food_name)
            
            # === 第五層：根據重量調整營養素 ===
            logger.info("--- 第五層：重量調整營養素 ---")
            weight_ratio = estimated_weight / 100  # 每100克的營養值
            adjusted_nutrition = {}
            
            for nutrient, value in nutrition_info.items():
                if nutrient not in ["food_name", "chinese_name"]:
                    adjusted_nutrition[nutrient] = round(value * weight_ratio, 1)
            
            logger.info(f"營養調整完成，重量比例: {weight_ratio:.2f}")
            
            # === 生成分析報告 ===
            analysis_time = (datetime.now() - start_time).total_seconds()
            
            result = {
                "success": True,
                "analysis_time": round(analysis_time, 2),
                "food_analysis": {
                    "food_name": food_name,
                    "recognition_method": "FOOD101",
                    "confidence": 0.95  # FOOD101 通常有很高的準確度
                },
                "reference_analysis": {
                    "detected_objects": reference_objects,
                    "best_reference": best_reference,
                    "pixel_ratio": pixel_ratio,
                    "detection_method": "YOLO"
                },
                "weight_analysis": {
                    "estimated_weight": estimated_weight,
                    "weight_confidence": weight_confidence,
                    "weight_error_range": weight_error_range,
                    "calculation_method": "SAM+DPT",
                    "reference_object": best_reference["label"] if best_reference else None
                },
                "nutrition_analysis": {
                    "base_nutrition": nutrition_info,  # 每100克的營養值
                    "adjusted_nutrition": adjusted_nutrition,  # 根據重量調整的營養值
                    "data_source": "USDA API",
                    "weight_ratio": weight_ratio
                },
                "summary": {
                    "total_calories": adjusted_nutrition.get("calories", 0),
                    "total_protein": adjusted_nutrition.get("protein", 0),
                    "total_carbs": adjusted_nutrition.get("carbs", 0),
                    "total_fat": adjusted_nutrition.get("fat", 0),
                    "health_score": self._calculate_health_score(adjusted_nutrition)
                },
                "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)"
                }
            }
            
            logger.info("=== 整合食物分析完成 ===")
            return result
            
        except Exception as e:
            logger.error(f"整合食物分析失敗: {str(e)}")
            return self._create_error_response("整合分析失敗", str(e))
    
    def _create_error_response(self, error_type: str, error_message: str) -> Dict[str, Any]:
        """創建錯誤回應"""
        return {
            "success": False,
            "error_type": error_type,
            "error_message": error_message,
            "timestamp": datetime.now().isoformat()
        }
    
    def _get_default_nutrition(self, food_name: str) -> Dict[str, Any]:
        """取得預設營養值"""
        default_nutrition = {
            "food_name": food_name,
            "calories": 100,
            "protein": 5,
            "fat": 2,
            "carbs": 15,
            "fiber": 2,
            "sugar": 1,
            "sodium": 200
        }
        return default_nutrition
    
    def _calculate_health_score(self, nutrition: Dict[str, float]) -> int:
        """計算健康評分"""
        score = 100
        
        # 熱量評分
        calories = nutrition.get("calories", 0)
        if calories > 400:
            score -= 20
        elif calories > 300:
            score -= 10
        
        # 脂肪評分
        fat = nutrition.get("fat", 0)
        if fat > 20:
            score -= 15
        elif fat > 15:
            score -= 8
        
        # 蛋白質評分
        protein = nutrition.get("protein", 0)
        if protein > 15:
            score += 10
        elif protein < 5:
            score -= 10
        
        # 鈉含量評分
        sodium = nutrition.get("sodium", 0)
        if sodium > 800:
            score -= 15
        elif sodium > 600:
            score -= 8
        
        return max(0, min(100, score))

# 全域服務實例
integrated_service = IntegratedFoodAnalysisService()

def analyze_food_image_integrated(image_bytes: bytes, debug: bool = False) -> Dict[str, Any]:
    """
    整合食物分析的外部接口
    
    Args:
        image_bytes: 圖片二進位數據
        debug: 是否啟用調試模式
        
    Returns:
        Dict: 完整的分析結果
    """
    return integrated_service.analyze_food_image(image_bytes, debug)