deploy: 推送到 Hugging Face Spaces

food_analyzer.py

@@ -3,10 +3,10 @@ from fastapi.middleware.cors import CORSMiddleware
 from PIL import Image
 import io
 import base64
-from transformers import pipeline
+from transformers.pipelines import pipeline  # 修正匯入
 import requests
 import json
-from typing import Dict, Any
+from typing import Dict, Any, List, Optional
 from pydantic import BaseModel
 import uvicorn
 
@@ -245,33 +245,26 @@ async def analyze_food(file: UploadFile = File(...)):
         # 檢查模型是否載入成功
         if not food_classifier:
             raise HTTPException(status_code=500, detail="AI模型尚未載入，請稍後再試")
-        
         # 檢查文件類型
-        if not file.content_type.startswith("image/"):
+        if not file.content_type or not file.content_type.startswith("image/"):
             raise HTTPException(status_code=400, detail="請上傳圖片文件")
-        
         # 讀取圖片
         image_data = await file.read()
         image = Image.open(io.BytesIO(image_data))
-        
         # 確保圖片是RGB格式
         if image.mode != "RGB":
             image = image.convert("RGB")
-        
         # 使用AI模型進行食物辨識
         results = food_classifier(image)
-        
-        # 獲取最高信心度的結果
+        if not isinstance(results, list) or not results:
+            raise HTTPException(status_code=500, detail="AI模型辨識失敗")
         top_result = results[0]
-        food_name = top_result["label"]
-        confidence = top_result["score"]
-        
+        food_name = str(top_result.get("label", "Unknown"))
+        confidence = float(top_result.get("score", 0.0))
         # 獲取營養資訊
         nutrition_info = get_nutrition_info(food_name)
-        
         # 生成AI建議
         ai_suggestions = generate_ai_suggestions(food_name, nutrition_info)
-        
         return FoodAnalysisResponse(
             success=True,
             food_name=food_name,
@@ -280,7 +273,6 @@ async def analyze_food(file: UploadFile = File(...)):
             ai_suggestions=ai_suggestions,
             message="食物分析完成"
         )
-        
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"分析失敗: {str(e)}")
 
@@ -291,38 +283,30 @@ async def analyze_food_base64(image_data: dict):
         # 檢查模型是否載入成功
         if not food_classifier:
             raise HTTPException(status_code=500, detail="AI模型尚未載入，請稍後再試")
-        
         # 解碼base64圖片
         base64_string = image_data.get("image", "")
         if not base64_string:
             raise HTTPException(status_code=400, detail="缺少圖片資料")
-        
         # 移除base64前綴（如果有的話）
         if "," in base64_string:
-            base64_string = base64_string.split(",")[1]
-        
+            base64_string = base64_string.split(",", 1)[1]
         # 解碼圖片
         image_bytes = base64.b64decode(base64_string)
         image = Image.open(io.BytesIO(image_bytes))
-        
         # 確保圖片是RGB格式
         if image.mode != "RGB":
             image = image.convert("RGB")
-        
         # 使用AI模型進行食物辨識
         results = food_classifier(image)
-        
-        # 獲取最高信心度的結果
+        if not isinstance(results, list) or not results:
+            raise HTTPException(status_code=500, detail="AI模型辨識失敗")
         top_result = results[0]
-        food_name = top_result["label"]
-        confidence = top_result["score"]
-        
+        food_name = str(top_result.get("label", "Unknown"))
+        confidence = float(top_result.get("score", 0.0))
         # 獲取營養資訊
         nutrition_info = get_nutrition_info(food_name)
-        
         # 生成AI建議
         ai_suggestions = generate_ai_suggestions(food_name, nutrition_info)
-        
         return FoodAnalysisResponse(
             success=True,
             food_name=food_name,
@@ -331,7 +315,6 @@ async def analyze_food_base64(image_data: dict):
             ai_suggestions=ai_suggestions,
             message="食物分析完成"
         )
-        
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"分析失敗: {str(e)}")
 

services/meal_service.py

@@ -22,17 +22,16 @@ class MealService:
             food_name=food_name,
             meal_type=meal_type,
             portion_size=portion_size,
-            calories=nutrition.get('calories', 0),
-            protein=nutrition.get('protein', 0),
-            carbs=nutrition.get('carbs', 0),
-            fat=nutrition.get('fat', 0),
-            fiber=nutrition.get('fiber', 0),
+            calories=float(nutrition.get('calories', 0)),
+            protein=float(nutrition.get('protein', 0)),
+            carbs=float(nutrition.get('carbs', 0)),
+            fat=float(nutrition.get('fat', 0)),
+            fiber=float(nutrition.get('fiber', 0)),
             meal_date=meal_date,
             image_url=image_url,
             ai_analysis=ai_analysis,
             created_at=datetime.utcnow()
         )
-        
         self.db.add(meal_log)
         self.db.commit()
         self.db.refresh(meal_log)
@@ -46,14 +45,12 @@ class MealService:
     ) -> List[MealLog]:
         """獲取用餐記錄"""
         query = self.db.query(MealLog)
-        
         if start_date:
             query = query.filter(MealLog.meal_date >= start_date)
         if end_date:
             query = query.filter(MealLog.meal_date <= end_date)
         if meal_type:
             query = query.filter(MealLog.meal_type == meal_type)
-            
         return query.order_by(MealLog.meal_date.desc()).all()
 
     def get_nutrition_summary(
@@ -63,27 +60,28 @@ class MealService:
     ) -> Dict[str, float]:
         """獲取指定時間範圍內的營養攝入總結"""
         meals = self.get_meal_logs(start_date, end_date)
-        
-        summary = {
-            'total_calories': 0,
-            'total_protein': 0,
-            'total_carbs': 0,
-            'total_fat': 0,
-            'total_fiber': 0
+        summary: Dict[str, float] = {
+            'total_calories': 0.0,
+            'total_protein': 0.0,
+            'total_carbs': 0.0,
+            'total_fat': 0.0,
+            'total_fiber': 0.0
         }
-        
         for meal in meals:
-            # 根據份量大小調整營養值
+            portion_size = getattr(meal, 'portion_size', 'medium')
+            calories = getattr(meal, 'calories', 0.0)
+            protein = getattr(meal, 'protein', 0.0)
+            carbs = getattr(meal, 'carbs', 0.0)
+            fat = getattr(meal, 'fat', 0.0)
+            fiber = getattr(meal, 'fiber', 0.0)
             multiplier = {
                 'small': 0.7,
                 'medium': 1.0,
                 'large': 1.3
-            }.get(meal.portion_size, 1.0)
-            
-            summary['total_calories'] += meal.calories * multiplier
-            summary['total_protein'] += meal.protein * multiplier
-            summary['total_carbs'] += meal.carbs * multiplier
-            summary['total_fat'] += meal.fat * multiplier
-            summary['total_fiber'] += meal.fiber * multiplier
-            
+            }.get(portion_size, 1.0)
+            summary['total_calories'] += float(calories) * multiplier
+            summary['total_protein'] += float(protein) * multiplier
+            summary['total_carbs'] += float(carbs) * multiplier
+            summary['total_fat'] += float(fat) * multiplier
+            summary['total_fiber'] += float(fiber) * multiplier
         return summary