diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 0000000000000000000000000000000000000000..d5a46ac3bb27dc979fbfbcc01b12c553a9d54332 --- /dev/null +++ b/.coveragerc @@ -0,0 +1,38 @@ +[run] +source = . +omit = + */tests/* + */venv/* + */.venv/* + */env/* + */.env/* + */site-packages/* + */__pycache__/* + +[report] +# Lines to exclude from coverage consideration +exclude_lines = + # Don't complain about debug-only code + pragma: no cover + def __repr__ + if self\.debug + + # Don't complain if tests don't hit defensive assertion code + raise AssertionError + raise NotImplementedError + + # Don't complain if non-runnable code isn't run: + if 0: + if __name__ == .__main__. + pass +precision = 2 +show_missing = True + +[html] +directory = htmlcov + +[paths] +source = + . + */backend/* + */app/* \ No newline at end of file diff --git a/.coveragerc.new b/.coveragerc.new new file mode 100644 index 0000000000000000000000000000000000000000..c624114db74317ff5127d25f81440ac813888b19 --- /dev/null +++ b/.coveragerc.new @@ -0,0 +1,23 @@ +[run] +source = . +omit = + */tests/* + */venv/* + */.venv/* + */env/* + */.env/* + */site-packages/* + +[report] +exclude_lines = + pragma: no cover + def __repr__ + if self\.debug + raise AssertionError + raise NotImplementedError + if 0: + if __name__ == ['"].*?['"] + @(abc\\.)?abstractmethod + +[html] +directory = htmlcov diff --git a/.github/workflows/python-ci.yml b/.github/workflows/python-ci.yml new file mode 100644 index 0000000000000000000000000000000000000000..64f6a525727854151573c85c8148e5208c708d70 --- /dev/null +++ b/.github/workflows/python-ci.yml @@ -0,0 +1,49 @@ +name: Python CI + +on: + push: + branches: [ main, master ] + pull_request: + branches: [ main, master ] + +jobs: + test: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ['3.9', '3.10', '3.11'] + + steps: + - uses: actions/checkout@v3 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + pip install pytest pytest-cov + + - name: Run tests with coverage + run: | + python -m pytest tests/ --cov=backend --cov-report=xml + env: + CLAUDE_API_KEY: ${{ secrets.CLAUDE_API_KEY }} + + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v3 + with: + token: ${{ secrets.CODECOV_TOKEN }} + file: ./coverage.xml + fail_ci_if_error: false + + - name: Upload coverage report + uses: actions/upload-artifact@v3 + with: + name: coverage-report + path: htmlcov/ + if-no-files-found: error + retention-days: 5 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..04a3266886b16b05e1975a3105a421c3a8021577 --- /dev/null +++ b/.gitignore @@ -0,0 +1,91 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# Virtual Environment +venv/ +env/ +ENV/ +.env +.venv + +# IDE +.idea/ +.vscode/ +*.swp +*.swo +*~ + +# Testing +.coverage +htmlcov/ +.pytest_cache/ + +# Logs +*.log + +# Environment variables +.env + +# Local development +*.db +*.sqlite3 + +# Frontend +node_modules/ +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +# Build +dist/ +build/ + +# macOS +.DS_Store + +# Windows +Thumbs.db +ehthumbs.db +Desktop.ini + +# Project specific +backend/.env +frontend/.env + +# Coverage reports +coverage/ +.coverage.* + +# Jupyter Notebook +.ipynb_checkpoints + +# VS Code +.vscode/* +!.vscode/settings.json +!.vscode/tasks.json +!.vscode/launch.json +!.vscode/extensions.json + +# MyPy +.mypy_cache/ +.dmypy.json +dmypy.json diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000000000000000000000000000000000000..52b3fbbcfca2eb4cdec108e5bd035126f217b293 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,8 @@ +include README.md +include requirements.txt +include .coveragerc +include pytest.ini +recursive-include backend/app/templates * +recursive-include tests * +recursive-exclude * __pycache__ +recursive-exclude * *.py[co] diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..7d102024b5c3c2bcb666ae0f713592f4f67b3b84 --- /dev/null +++ b/README.md @@ -0,0 +1,148 @@ +# Health Assistant AI + +一個整合飲食追蹤、運動記錄和AI圖像辨識的健康生活助手應用。 + +## 主要功能 + +- 🍽️ 飲食記錄（支援AI圖像辨識） +- 💧 飲水追蹤 +- 🏃‍♂️ 運動記錄 +- 📊 營養分析儀表板 +- 🤖 AI驅動的個人化建議 + +## 技術堆疊 + +### 前端 +- React +- TailwindCSS +- Chart.js + +### 後端 +- Python FastAPI +- SQLAlchemy +- PostgreSQL +- TensorFlow/PyTorch +- Pydantic +- HuggingFace Transformers +- Anthropic Claude API + +## 安裝說明 + +1. 克隆專案 +```bash +git clone https://github.com/yourusername/health_assistant.git +cd health_assistant +``` + +2. 設置 Python 虛擬環境並安裝依賴： +```bash +python -m venv venv +source venv/bin/activate # Linux/Mac +# 或 +.\venv\Scripts\activate # Windows + +pip install -e . # 以開發模式安裝 +``` + +3. 安裝前端依賴： +```bash +cd frontend +npm install +``` + +## 開發說明 + +### 後端開發 +```bash +# 啟動後端開發服務器 +uvicorn backend.main:app --reload +``` + +### 前端開發 +```bash +cd frontend +npm run dev +``` + +## 測試 + +### 運行測試 + +運行所有測試： +```bash +pytest +``` + +運行特定測試文件： +```bash +pytest tests/test_api/test_main.py # 運行 API 測試 +pytest tests/test_services/ # 運行服務層測試 +pytest -k "test_function_name" # 運行特定測試函數 +``` + +### 測試覆蓋率報告 + +生成測試覆蓋率報告： +```bash +pytest --cov=backend --cov-report=html +``` + +這將在 `htmlcov` 目錄下生成 HTML 格式的覆蓋率報告。 + +### 代碼風格檢查 + +使用 black 和 isort 進行代碼格式化： +```bash +black . +isort . +``` + +### 類型檢查 + +運行 mypy 進行靜態類型檢查： +```bash +mypy . +``` + +## 持續整合 (CI) + +項目使用 GitHub Actions 進行持續整合。每次推送代碼或創建 Pull Request 時，會自動運行以下檢查： + +- 在 Python 3.9, 3.10, 3.11 上運行測試 +- 生成測試覆蓋率報告 +- 上傳覆蓋率到 Codecov + +### 本地運行 CI 檢查 + +在提交代碼前，可以本地運行 CI 檢查： +```bash +# 運行測試和覆蓋率 +pytest --cov=backend + +# 檢查代碼風格 +black --check . +isort --check-only . + +# 運行類型檢查 +mypy . +``` + +## 測試覆蓋率要求 + +- 所有新代碼應該有對應的測試 +- 目標是達到至少 80% 的代碼覆蓋率 +- 關鍵業務邏輯應該有完整的測試覆蓋 +- 測試應該包含成功和失敗案例 +- 使用 `# pragma: no cover` 時需提供正當理由 + +## 貢獻指南 + +1. Fork 項目 +2. 創建特性分支 (`git checkout -b feature/AmazingFeature`) +3. 提交更改 (`git commit -m 'Add some AmazingFeature'`) +4. 推送到分支 (`git push origin feature/AmazingFeature`) +5. 開啟 Pull Request + +## 許可證 + +MIT License - 詳見 [LICENSE](LICENSE) 文件 diff --git a/ai_food_analyzer.html b/ai_food_analyzer.html new file mode 100644 index 0000000000000000000000000000000000000000..03808799f09fb38f30b901ea11bc56aacf3e23aa --- /dev/null +++ b/ai_food_analyzer.html @@ -0,0 +1,1679 @@ + + + + + + AI食物營養分析器 - 個人化版本 + + + + +
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🍎 AI食物營養分析器

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智能分析您的飲食營養

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識別的食物

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基本營養素

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今日目標進度

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今日食物記錄

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本週熱量趨勢

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+ + + \ No newline at end of file diff --git a/backend/.env b/backend/.env new file mode 100644 index 0000000000000000000000000000000000000000..b3fc3d543aa48ab7c7601f0eb1b7f24dedf76802 --- /dev/null +++ b/backend/.env @@ -0,0 +1,13 @@ +# 資料庫設定 +DATABASE_URL=sqlite:///./data/health_assistant.db + +# Redis 設定 +REDIS_HOST=localhost +REDIS_PORT=6379 +REDIS_DB=0 + +# API 設定 +API_HOST=localhost +API_PORT=8000 + +USDA_API_KEY="0bZCszzPSbc5r6RXfm0aHKtWGX2V0SX1hLiMmXwi" diff --git a/backend/.env.example b/backend/.env.example new file mode 100644 index 0000000000000000000000000000000000000000..be195d5756cded81c857953750dcfb3cad60ef00 --- /dev/null +++ b/backend/.env.example @@ -0,0 +1,2 @@ +COMPUTER_VISION_ENDPOINT=your_azure_endpoint +COMPUTER_VISION_KEY=your_azure_key diff --git a/backend/__pycache__/app.cpython-313.pyc b/backend/__pycache__/app.cpython-313.pyc new file mode 100644 index 0000000000000000000000000000000000000000..231535dd5b045fff462b480376b45f1ba6f31849 Binary files /dev/null and b/backend/__pycache__/app.cpython-313.pyc differ diff --git a/backend/__pycache__/main.cpython-313.pyc b/backend/__pycache__/main.cpython-313.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5b7de3f97bdee2e5cf6da3ea552cf0f96d0378f7 Binary files /dev/null and b/backend/__pycache__/main.cpython-313.pyc differ diff --git a/backend/app.py b/backend/app.py new file mode 100644 index 0000000000000000000000000000000000000000..8e56fd781edb5d62a070b46ad49b8927516d90f0 --- /dev/null +++ b/backend/app.py @@ -0,0 +1,65 @@ +from fastapi import FastAPI, File, UploadFile +from fastapi.middleware.cors import CORSMiddleware +import uvicorn +from datetime import datetime +from PIL import Image +import io +import random + +app = FastAPI() + +# 允許跨域請求 +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# 模擬食物列表 +FOOD_ITEMS = [ + "牛肉麵", + "滷肉飯", + "炒飯", + "水餃", + "炸雞", + "三明治", + "沙拉", + "義大利麵", + "披薩", + "漢堡" +] + +@app.post("/api/ai/analyze-food") +async def analyze_food(file: UploadFile = File(...)): + # 讀取圖片（僅作為示範，不進行實際分析） + contents = await file.read() + image = Image.open(io.BytesIO(contents)) + + # 隨機選擇一個食物和信心度 + food = random.choice(FOOD_ITEMS) + confidence = random.uniform(85.0, 99.9) + + # 模擬營養資訊 + nutrition = { + "calories": random.randint(200, 800), + "protein": random.randint(10, 30), + "carbs": random.randint(20, 60), + "fat": random.randint(5, 25) + } + + # 返回結果 + return { + "success": True, + "analysis_time": datetime.now().isoformat(), + "top_prediction": { + "label": food, + "confidence": confidence, + "nutrition": nutrition, + "description": f"這是一道美味的{food}，營養豐富且美味可口。" + } + } + +if __name__ == "__main__": + uvicorn.run(app, host="127.0.0.1", port=8000) diff --git a/backend/app/__init__.py b/backend/app/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/backend/app/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/app/__pycache__/__init__.cpython-313.pyc b/backend/app/__pycache__/__init__.cpython-313.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d13269c5e5493d3bf702c500e9b4d7db6328773a Binary files /dev/null and b/backend/app/__pycache__/__init__.cpython-313.pyc differ diff --git a/backend/app/__pycache__/main.cpython-313.pyc b/backend/app/__pycache__/main.cpython-313.pyc new file mode 100644 index 0000000000000000000000000000000000000000..65c80d58a5a9947fe14d048fee568562bc6290cc Binary files /dev/null and b/backend/app/__pycache__/main.cpython-313.pyc differ diff --git a/backend/app/database.py b/backend/app/database.py new file mode 100644 index 0000000000000000000000000000000000000000..111aa4c624e6bc14d47ff41862aba34010af0c62 --- /dev/null +++ b/backend/app/database.py @@ -0,0 +1,31 @@ +from sqlalchemy import create_engine +from sqlalchemy.ext.declarative import declarative_base +from sqlalchemy.orm import sessionmaker +import os + +# 確保資料庫目錄存在 +DB_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "data") +os.makedirs(DB_DIR, exist_ok=True) + +# 資料庫 URL +SQLALCHEMY_DATABASE_URL = f"sqlite:///{os.path.join(DB_DIR, 'health_assistant.db')}" + +# 創建資料庫引擎 +engine = create_engine( + SQLALCHEMY_DATABASE_URL, + connect_args={"check_same_thread": False} # SQLite 特定配置 +) + +# 創建 SessionLocal 類 +SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine) + +# 創建 Base 類 +Base = declarative_base() + +# 獲取資料庫會話的依賴項 +def get_db(): + db = SessionLocal() + try: + yield db + finally: + db.close() diff --git a/backend/app/init_db.py b/backend/app/init_db.py new file mode 100644 index 0000000000000000000000000000000000000000..4f72b9e2bdc61bedfefbf0b4521d6fe97597d7b3 --- /dev/null +++ b/backend/app/init_db.py @@ -0,0 +1,70 @@ +from .database import Base, engine, SessionLocal +from .models.meal_log import MealLog +from .models.nutrition import Nutrition +import json + +def init_db(): + """初始化資料庫並填入初始營養數據""" + print("Creating database tables...") + # 根據模型建立所有表格 + Base.metadata.create_all(bind=engine) + print("Database tables created successfully!") + + # 檢查是否已有資料，避免重複新增 + db = SessionLocal() + if db.query(Nutrition).count() == 0: + print("Populating nutrition table with initial data...") + + # 從 main.py 移植過來的模擬資料 + mock_nutrition_data = [ + { + "food_name": "hamburger", "chinese_name": "漢堡", "calories": 540, "protein": 25, "fat": 31, "carbs": 40, + "fiber": 3, "sugar": 6, "sodium": 1040, "health_score": 45, + "recommendations": ["脂肪和鈉含量過高，建議減少食用頻率。"], + "warnings": ["高熱量", "高脂肪", "高鈉"] + }, + { + "food_name": "pizza", "chinese_name": "披薩", "calories": 266, "protein": 11, "fat": 10, "carbs": 33, + "fiber": 2, "sugar": 4, "sodium": 598, "health_score": 65, + "recommendations": ["可搭配沙拉以增加纖維攝取。"], + "warnings": ["高鈉"] + }, + { + "food_name": "sushi", "chinese_name": "壽司", "calories": 200, "protein": 12, "fat": 8, "carbs": 20, + "fiber": 1, "sugar": 2, "sodium": 380, "health_score": 85, + "recommendations": ["優質的蛋白質和碳水化合物來源。"], + "warnings": [] + }, + { + "food_name": "fried rice", "chinese_name": "炒飯", "calories": 238, "protein": 8, "fat": 12, "carbs": 26, + "fiber": 2, "sugar": 3, "sodium": 680, "health_score": 60, + "recommendations": ["注意油脂和鈉含量。"], + "warnings": ["高鈉"] + }, + { + "food_name": "chicken wings", "chinese_name": "雞翅", "calories": 203, "protein": 18, "fat": 14, "carbs": 0, + "fiber": 0, "sugar": 0, "sodium": 380, "health_score": 70, + "recommendations": ["蛋白質的良好來源。"], + "warnings": [] + }, + { + "food_name": "salad", "chinese_name": "沙拉", "calories": 33, "protein": 3, "fat": 0.2, "carbs": 6, + "fiber": 3, "sugar": 3, "sodium": 65, "health_score": 95, + "recommendations": ["低熱量高纖維，是健康的選擇。"], + "warnings": [] + } + ] + + for food_data in mock_nutrition_data: + db_item = Nutrition(**food_data) + db.add(db_item) + + db.commit() + print(f"{len(mock_nutrition_data)} items populated.") + else: + print("Nutrition table already contains data. Skipping population.") + + db.close() + +if __name__ == "__main__": + init_db() diff --git a/backend/app/main.py b/backend/app/main.py new file mode 100644 index 0000000000000000000000000000000000000000..efd542600869e1322fb58086f9a6e0959136119d --- /dev/null +++ b/backend/app/main.py @@ -0,0 +1,26 @@ +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from .routers import ai_router, meal_router +from .database import engine, Base + +# 創建資料庫表 +Base.metadata.create_all(bind=engine) + +app = FastAPI(title="Health Assistant API") + +# 配置 CORS +app.add_middleware( + CORSMiddleware, + allow_origins=["http://localhost:5173"], # React 開發伺服器的位址 + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# 註冊路由 +app.include_router(ai_router.router) +app.include_router(meal_router.router) + +@app.get("/") +async def root(): + return {"message": "Health Assistant API is running"} diff --git a/backend/app/models/meal_log.py b/backend/app/models/meal_log.py new file mode 100644 index 0000000000000000000000000000000000000000..1d5910688a8e03c51348bf0376168ec584804f16 --- /dev/null +++ b/backend/app/models/meal_log.py @@ -0,0 +1,19 @@ +from sqlalchemy import Column, Integer, String, Float, DateTime, JSON +from ..database import Base + +class MealLog(Base): + __tablename__ = "meal_logs" + + id = Column(Integer, primary_key=True, index=True) + food_name = Column(String, index=True) + meal_type = Column(String) # breakfast, lunch, dinner, snack + portion_size = Column(String) # small, medium, large + calories = Column(Float) + protein = Column(Float) + carbs = Column(Float) + fat = Column(Float) + fiber = Column(Float) + meal_date = Column(DateTime, index=True) + image_url = Column(String) + ai_analysis = Column(JSON) # 儲存完整的 AI 分析結果 + created_at = Column(DateTime) diff --git a/backend/app/models/nutrition.py b/backend/app/models/nutrition.py new file mode 100644 index 0000000000000000000000000000000000000000..fd118c5ddfd95c474724c16a4414f0be91842fd5 --- /dev/null +++ b/backend/app/models/nutrition.py @@ -0,0 +1,22 @@ +# backend/app/models/nutrition.py +from sqlalchemy import Column, Integer, String, Float, JSON +from ..database import Base + +class Nutrition(Base): + __tablename__ = "nutrition" + + id = Column(Integer, primary_key=True, index=True) + food_name = Column(String, unique=True, index=True, nullable=False) + chinese_name = Column(String) + calories = Column(Float) + protein = Column(Float) + fat = Column(Float) + carbs = Column(Float) + fiber = Column(Float) + sugar = Column(Float) + sodium = Column(Float) + # For more complex data like vitamins, minerals, etc. + details = Column(JSON) + health_score = Column(Integer) + recommendations = Column(JSON) + warnings = Column(JSON) \ No newline at end of file diff --git a/backend/app/routers/__pycache__/ai_router.cpython-313.pyc b/backend/app/routers/__pycache__/ai_router.cpython-313.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0b714e70057d89fbec936f5dc0d0561a19806ee5 Binary files /dev/null and b/backend/app/routers/__pycache__/ai_router.cpython-313.pyc differ diff --git a/backend/app/routers/ai_router.py b/backend/app/routers/ai_router.py new file mode 100644 index 0000000000000000000000000000000000000000..3933790422869312d3e9ff2c8cd76fee9395fc30 --- /dev/null +++ b/backend/app/routers/ai_router.py @@ -0,0 +1,34 @@ +# 檔案路徑: backend/app/routers/ai_router.py + +from fastapi import APIRouter, File, UploadFile, HTTPException +from ..services import ai_service # 引入我們的 AI 服務 + +router = APIRouter( + prefix="/ai", + tags=["AI"], +) + +@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。") + + # 讀取圖片的二進位制內容 + image_bytes = await file.read() + + # 呼叫 AI 服務中的分類函式 + food_name = ai_service.classify_food_image(image_bytes) + + # 處理辨識失敗或錯誤的情況 + if "Error" in food_name or food_name == "Unknown": + raise HTTPException(status_code=500, detail=f"無法辨識圖片中的食物。模型回傳: {food_name}") + + # TODO: 在下一階段，我們會在這裡加入從資料庫查詢營養資訊的邏輯 + # 目前，我們先直接回傳辨識出的食物名稱 + + return {"food_name": food_name} \ No newline at end of file diff --git a/backend/app/routers/meal_router.py b/backend/app/routers/meal_router.py new file mode 100644 index 0000000000000000000000000000000000000000..15c4055aaaebd74f8aeb2682016bcd45ece24da0 --- /dev/null +++ b/backend/app/routers/meal_router.py @@ -0,0 +1,103 @@ +from fastapi import APIRouter, Depends, HTTPException +from sqlalchemy.orm import Session +from typing import List, Dict, Any, Optional +from datetime import datetime +from ..services.meal_service import MealService +from ..database import get_db +from pydantic import BaseModel + +router = APIRouter(prefix="/api/meals", tags=["Meals"]) + +class MealCreate(BaseModel): + food_name: str + meal_type: str + portion_size: str + meal_date: datetime + nutrition: Dict[str, float] + image_url: Optional[str] = None + ai_analysis: Optional[Dict[str, Any]] = None + +class DateRange(BaseModel): + start_date: datetime + end_date: datetime + meal_type: Optional[str] = None + +@router.post("/log") +async def create_meal_log( + meal: MealCreate, + db: Session = Depends(get_db) +) -> Dict[str, Any]: + """創建新的用餐記錄""" + meal_service = MealService(db) + try: + meal_log = meal_service.create_meal_log( + food_name=meal.food_name, + meal_type=meal.meal_type, + portion_size=meal.portion_size, + nutrition=meal.nutrition, + meal_date=meal.meal_date, + image_url=meal.image_url, + ai_analysis=meal.ai_analysis + ) + return { + "success": True, + "message": "用餐記錄已創建", + "data": { + "id": meal_log.id, + "food_name": meal_log.food_name, + "meal_type": meal_log.meal_type, + "meal_date": meal_log.meal_date + } + } + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + +@router.post("/list") +async def get_meal_logs( + date_range: DateRange, + db: Session = Depends(get_db) +) -> Dict[str, Any]: + """獲取用餐記錄列表""" + meal_service = MealService(db) + try: + logs = meal_service.get_meal_logs( + start_date=date_range.start_date, + end_date=date_range.end_date, + meal_type=date_range.meal_type + ) + return { + "success": True, + "data": [{ + "id": log.id, + "food_name": log.food_name, + "meal_type": log.meal_type, + "portion_size": log.portion_size, + "calories": log.calories, + "protein": log.protein, + "carbs": log.carbs, + "fat": log.fat, + "meal_date": log.meal_date, + "image_url": log.image_url + } for log in logs] + } + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + +@router.post("/nutrition-summary") +async def get_nutrition_summary( + date_range: DateRange, + db: Session = Depends(get_db) +) -> Dict[str, Any]: + """獲取營養攝入總結""" + meal_service = MealService(db) + try: + summary = meal_service.get_nutrition_summary( + start_date=date_range.start_date, + end_date=date_range.end_date + ) + return { + "success": True, + "data": summary + } + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) diff --git a/backend/app/services/__init__.py b/backend/app/services/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0f39f8b94933990e7d7ba2c458cfbab7333218ef --- /dev/null +++ b/backend/app/services/__init__.py @@ -0,0 +1,5 @@ +# This file makes the services directory a Python package +# Import the HybridFoodAnalyzer class to make it available when importing from app.services +from .food_analyzer_service import HybridFoodAnalyzer + +__all__ = ['HybridFoodAnalyzer'] diff --git a/backend/app/services/__pycache__/ai_service.cpython-313.pyc b/backend/app/services/__pycache__/ai_service.cpython-313.pyc new file mode 100644 index 0000000000000000000000000000000000000000..74f915a536a9631bd5c02bff0a627dcc7f3e8f1f Binary files /dev/null and b/backend/app/services/__pycache__/ai_service.cpython-313.pyc differ diff --git a/backend/app/services/ai_service.py b/backend/app/services/ai_service.py new file mode 100644 index 0000000000000000000000000000000000000000..fc2e089f34945c242d352d585311c11fa350328a --- /dev/null +++ b/backend/app/services/ai_service.py @@ -0,0 +1,96 @@ +# 檔案路徑: backend/app/services/ai_service.py + +from transformers.pipelines import pipeline +from PIL import Image +import io +import logging + +# 設置日誌 +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +# 全局變量 +image_classifier = None + +def load_model(): + """載入模型的函數""" + global image_classifier + + try: + logger.info("正在載入食物辨識模型...") + + # 載入模型 - 移除不支持的參數 + image_classifier = pipeline( + "image-classification", + model="juliensimon/autotrain-food101-1471154053", + device=-1 # 使用CPU + ) + + logger.info("模型載入成功！") + return True + + except Exception as e: + logger.error(f"模型載入失敗: {str(e)}") + image_classifier = None + return False + +def classify_food_image(image_bytes: bytes) -> str: + """ + 接收圖片的二進位制數據，進行分類並返回可能性最高的食物名稱。 + """ + global image_classifier + + # 如果模型未載入，嘗試重新載入 + if image_classifier is None: + logger.warning("模型未載入，嘗試重新載入...") + if not load_model(): + return "Error: Model not loaded" + + if image_classifier is None: + return "Error: Model could not be loaded" + + try: + # 驗證圖片數據 + if not image_bytes: + return "Error: Empty image data" + + # 從記憶體中的 bytes 打開圖片 + image = Image.open(io.BytesIO(image_bytes)) + + # 確保圖片是RGB格式 + if image.mode != 'RGB': + image = image.convert('RGB') + + logger.info(f"處理圖片，尺寸: {image.size}") + + # 使用模型管線進行分類 + pipeline_output = image_classifier(image) + + logger.info(f"模型輸出: {pipeline_output}") + + # 處理輸出結果 + if not pipeline_output: + return "Unknown" + + # pipeline_output 通常是一個列表 + if isinstance(pipeline_output, list) and len(pipeline_output) > 0: + result = pipeline_output[0] + if isinstance(result, dict) and 'label' in result: + label = result['label'] + confidence = result.get('score', 0) + + logger.info(f"辨識結果: {label}, 信心度: {confidence:.2f}") + + # 標籤可能包含底線，我們將其替換為空格，並讓首字母大寫 + formatted_label = str(label).replace('_', ' ').title() + return formatted_label + + return "Unknown" + + except Exception as e: + logger.error(f"圖片分類過程中發生錯誤: {str(e)}") + return f"Error: {str(e)}" + +# 在模塊載入時嘗試載入模型 +logger.info("初始化 AI 服務...") +load_model() \ No newline at end of file diff --git a/backend/app/services/food_analyzer_service.py b/backend/app/services/food_analyzer_service.py new file mode 100644 index 0000000000000000000000000000000000000000..b21f867840fcc2e52c8f8f90d323053616fd1d2c --- /dev/null +++ b/backend/app/services/food_analyzer_service.py @@ -0,0 +1,256 @@ +import torch +import json +from typing import Dict, Any +from PIL import Image +from io import BytesIO +import base64 +import os +from dotenv import load_dotenv + +# Load environment variables +load_dotenv() + +class HybridFoodAnalyzer: + def __init__(self, claude_api_key: str = None): + """ + Initialize the HybridFoodAnalyzer with HuggingFace model and Claude API. + + Args: + claude_api_key: Optional API key for Claude. If not provided, will try to get from environment variable CLAUDE_API_KEY. + """ + # Initialize HuggingFace model + from transformers import AutoImageProcessor, AutoModelForImageClassification + + print("Loading HuggingFace food recognition model...") + self.processor = AutoImageProcessor.from_pretrained("nateraw/food") + self.model = AutoModelForImageClassification.from_pretrained("nateraw/food") + self.model.eval() # Set model to evaluation mode + + # Initialize Claude API + print("Initializing Claude API...") + import anthropic + self.claude_api_key = claude_api_key or os.getenv('CLAUDE_API_KEY') + if not self.claude_api_key: + raise ValueError("Claude API key is required. Please set CLAUDE_API_KEY environment variable or pass it as an argument.") + + self.claude_client = anthropic.Anthropic(api_key=self.claude_api_key) + + def recognize_food(self, image: Image.Image) -> Dict[str, Any]: + """ + Recognize food from an image using HuggingFace model. + + Args: + image: PIL Image object containing the food image + + Returns: + Dictionary containing food name and confidence score + """ + try: + print("Processing image for food recognition...") + inputs = self.processor(images=image, return_tensors="pt") + + with torch.no_grad(): + outputs = self.model(**inputs) + predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) + + predicted_class_id = predictions.argmax().item() + confidence = predictions[0][predicted_class_id].item() + food_name = self.model.config.id2label[predicted_class_id] + + # Map common food names to Chinese + food_name_mapping = { + "hamburger": "漢堡", + "pizza": "披薩", + "sushi": "壽司", + "fried rice": "炒飯", + "chicken wings": "雞翅", + "salad": "沙拉", + "apple": "蘋果", + "banana": "香蕉", + "orange": "橙子", + "noodles": "麵條" + } + + chinese_name = food_name_mapping.get(food_name.lower(), food_name) + + return { + "food_name": food_name, + "chinese_name": chinese_name, + "confidence": confidence, + "success": True + } + + except Exception as e: + print(f"Error in food recognition: {str(e)}") + return { + "success": False, + "error": str(e) + } + + def analyze_nutrition(self, food_name: str) -> Dict[str, Any]: + """ + Analyze nutrition information for a given food using Claude API. + + Args: + food_name: Name of the food to analyze + + Returns: + Dictionary containing nutrition information + """ + try: + print(f"Analyzing nutrition for {food_name}...") + prompt = f""" + 請分析 {food_name} 的營養成分（每100g），並以JSON格式回覆： + {{ + "calories": 數值, + "protein": 數值, + "fat": 數值, + "carbs": 數值, + "fiber": 數值, + "sugar": 數值, + "sodium": 數值 + }} + """ + + message = self.claude_client.messages.create( + model="claude-3-sonnet-20240229", + max_tokens=500, + messages=[{"role": "user", "content": prompt}] + ) + + # Extract and parse the JSON response + response_text = message.content[0].text + try: + nutrition_data = json.loads(response_text.strip()) + return { + "success": True, + "nutrition": nutrition_data + } + except json.JSONDecodeError as e: + print(f"Error parsing Claude response: {e}") + return { + "success": False, + "error": f"Failed to parse nutrition data: {e}", + "raw_response": response_text + } + + except Exception as e: + print(f"Error in nutrition analysis: {str(e)}") + return { + "success": False, + "error": str(e) + } + + def process_image(self, image_data: bytes) -> Dict[str, Any]: + """ + Process an image to recognize food and analyze its nutrition. + + Args: + image_data: Binary image data + + Returns: + Dictionary containing recognition and analysis results + """ + try: + # Convert bytes to PIL Image + image = Image.open(BytesIO(image_data)) + + # Step 1: Recognize food + recognition_result = self.recognize_food(image) + if not recognition_result.get("success"): + return recognition_result + + # Step 2: Analyze nutrition + nutrition_result = self.analyze_nutrition(recognition_result["food_name"]) + if not nutrition_result.get("success"): + return nutrition_result + + # Calculate health score + nutrition = nutrition_result["nutrition"] + health_score = self.calculate_health_score(nutrition) + + # Generate recommendations and warnings + recommendations = self.generate_recommendations(nutrition) + warnings = self.generate_warnings(nutrition) + + return { + "success": True, + "food_name": recognition_result["food_name"], + "chinese_name": recognition_result["chinese_name"], + "confidence": recognition_result["confidence"], + "nutrition": nutrition, + "analysis": { + "healthScore": health_score, + "recommendations": recommendations, + "warnings": warnings + } + } + + except Exception as e: + return { + "success": False, + "error": f"Failed to process image: {str(e)}" + } + + def calculate_health_score(self, nutrition: Dict[str, float]) -> int: + """Calculate a health score based on nutritional values""" + score = 100 + + # 熱量評分 + if nutrition["calories"] > 400: + score -= 20 + elif nutrition["calories"] > 300: + score -= 10 + + # 脂肪評分 + if nutrition["fat"] > 20: + score -= 15 + elif nutrition["fat"] > 15: + score -= 8 + + # 蛋白質評分 + if nutrition["protein"] > 15: + score += 10 + elif nutrition["protein"] < 5: + score -= 10 + + # 鈉含量評分 + if "sodium" in nutrition and nutrition["sodium"] > 800: + score -= 15 + elif "sodium" in nutrition and nutrition["sodium"] > 600: + score -= 8 + + return max(0, min(100, score)) + + def generate_recommendations(self, nutrition: Dict[str, float]) -> list: + """Generate dietary recommendations based on nutrition data""" + recommendations = [] + + if nutrition["protein"] < 10: + recommendations.append("建議增加蛋白質攝取，可搭配雞蛋或豆腐") + + if nutrition["fat"] > 20: + recommendations.append("脂肪含量較高，建議適量食用") + + if "fiber" in nutrition and nutrition["fiber"] < 3: + recommendations.append("纖維含量不足，建議搭配蔬菜沙拉") + + if "sodium" in nutrition and nutrition["sodium"] > 600: + recommendations.append("鈉含量偏高，建議多喝水並減少其他鹽分攝取") + + return recommendations + + def generate_warnings(self, nutrition: Dict[str, float]) -> list: + """Generate dietary warnings based on nutrition data""" + warnings = [] + + if nutrition["calories"] > 500: + warnings.append("高熱量食物") + + if nutrition["fat"] > 25: + warnings.append("高脂肪食物") + + if "sodium" in nutrition and nutrition["sodium"] > 1000: + warnings.append("高鈉食物") + + return warnings diff --git a/backend/app/services/meal_service.py b/backend/app/services/meal_service.py new file mode 100644 index 0000000000000000000000000000000000000000..38d39dcf7d4f60b1322f2c6bf7d1a28317617ca0 --- /dev/null +++ b/backend/app/services/meal_service.py @@ -0,0 +1,89 @@ +from datetime import datetime +from typing import List, Dict, Any, Optional +from sqlalchemy.orm import Session +from ..models.meal_log import MealLog + +class MealService: + def __init__(self, db: Session): + self.db = db + + def create_meal_log( + self, + food_name: str, + meal_type: str, + portion_size: str, + nutrition: Dict[str, float], + meal_date: datetime, + image_url: Optional[str] = None, + ai_analysis: Optional[Dict[str, Any]] = None + ) -> MealLog: + """創建新的用餐記錄""" + meal_log = MealLog( + 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), + 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) + return meal_log + + def get_meal_logs( + self, + start_date: Optional[datetime] = None, + end_date: Optional[datetime] = None, + meal_type: Optional[str] = None + ) -> 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( + self, + start_date: datetime, + end_date: datetime + ) -> 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 + } + + for meal in meals: + # 根據份量大小調整營養值 + 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 + + return summary diff --git a/backend/app/services/nutrition_api_service.py b/backend/app/services/nutrition_api_service.py new file mode 100644 index 0000000000000000000000000000000000000000..23a2e0dcc207c069ee1c840c5942c9ce71af908b --- /dev/null +++ b/backend/app/services/nutrition_api_service.py @@ -0,0 +1,101 @@ +# backend/app/services/nutrition_api_service.py +import os +import requests +from dotenv import load_dotenv +import logging + +# 設置日誌 +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +# 載入環境變數 +load_dotenv() + +# 從環境變數中獲取 API 金鑰 +USDA_API_KEY = os.getenv("USDA_API_KEY", "DEMO_KEY") +USDA_API_URL = "https://api.nal.usda.gov/fdc/v1/foods/search" + +# 我們關心的主要營養素及其在 USDA API 中的名稱或編號 +# 我們可以透過 nutrient.nutrientNumber 或 nutrient.name 來匹配 +NUTRIENT_MAP = { + 'calories': 'Energy', + 'protein': 'Protein', + 'fat': 'Total lipid (fat)', + 'carbs': 'Carbohydrate, by difference', + 'fiber': 'Fiber, total dietary', + 'sugar': 'Total sugars', + 'sodium': 'Sodium, Na' +} + +def fetch_nutrition_data(food_name: str): + """ + 從 USDA FoodData Central API 獲取食物的營養資訊。 + + :param food_name: 要查詢的食物名稱 (例如 "Donuts")。 + :return: 包含營養資訊的字典，如果找不到則返回 None。 + """ + if not USDA_API_KEY: + logger.error("USDA_API_KEY 未設定，無法查詢營養資訊。") + return None + + params = { + 'query': food_name, + 'api_key': USDA_API_KEY, + 'dataType': 'Branded', # 優先搜尋品牌食品，結果通常更符合預期 + 'pageSize': 1 # 我們只需要最相關的一筆結果 + } + + try: + logger.info(f"正在向 USDA API 查詢食物：{food_name}") + response = requests.get(USDA_API_URL, params=params) + response.raise_for_status() # 如果請求失敗 (例如 4xx 或 5xx)，則會拋出異常 + + data = response.json() + + # 檢查是否有找到食物 + if data.get('foods') and len(data['foods']) > 0: + food_data = data['foods'][0] # 取第一個最相關的結果 + logger.info(f"從 API 成功獲取到食物 '{food_data.get('description')}' 的資料") + + nutrition_info = { + "food_name": food_data.get('description', food_name).capitalize(), + "chinese_name": None, # USDA API 不提供中文名 + } + + # 遍歷我們需要的營養素 + extracted_nutrients = {key: 0.0 for key in NUTRIENT_MAP.keys()} # 初始化 + + for nutrient in food_data.get('foodNutrients', []): + for key, name in NUTRIENT_MAP.items(): + if nutrient.get('nutrientName').strip().lower() == name.strip().lower(): + # 將值存入我們的格式 + extracted_nutrients[key] = float(nutrient.get('value', 0.0)) + break # 找到後就跳出內層迴圈 + + nutrition_info.update(extracted_nutrients) + + # 由於 USDA 不直接提供健康建議，我們先回傳原始數據 + # 後續可以在 main.py 中根據這些數據生成我們自己的建議 + return nutrition_info + + else: + logger.warning(f"在 USDA API 中找不到食物：{food_name}") + return None + + except requests.exceptions.RequestException as e: + logger.error(f"請求 USDA API 時發生錯誤: {e}") + return None + except Exception as e: + logger.error(f"處理 API 回應時發生未知錯誤: {e}") + return None + +if __name__ == '__main__': + # 測試此模組的功能 + test_food = "donuts" + nutrition = fetch_nutrition_data(test_food) + if nutrition: + import json + print(f"成功獲取 '{test_food}' 的營養資訊:") + print(json.dumps(nutrition, indent=2)) + else: + print(f"無法獲取 '{test_food}' 的營養資訊。") \ No newline at end of file diff --git a/backend/food_analyzer.py b/backend/food_analyzer.py new file mode 100644 index 0000000000000000000000000000000000000000..143dd33eab4bef93634d47a71ae49a09a758b3e5 --- /dev/null +++ b/backend/food_analyzer.py @@ -0,0 +1,339 @@ +from fastapi import FastAPI, UploadFile, File, HTTPException +from fastapi.middleware.cors import CORSMiddleware +from PIL import Image +import io +import base64 +from transformers import pipeline +import requests +import json +from typing import Dict, Any +from pydantic import BaseModel +import uvicorn + +app = FastAPI(title="Health Assistant AI - Food Recognition API") + +# CORS設定 +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], # 生產環境請設定具體的域名 + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# 初始化Hugging Face模型 +try: + # 使用nateraw/food專門的食物分類模型 + food_classifier = pipeline( + "image-classification", + model="nateraw/food", + device=-1 # 使用CPU，如果有GPU可以設為0 + ) + print("nateraw/food 食物辨識模型載入成功") +except Exception as e: + print(f"模型載入失敗: {e}") + food_classifier = None + +# 食物營養資料庫（擴展版，涵蓋nateraw/food模型常見的食物類型） +NUTRITION_DATABASE = { + # 水果類 + "apple": {"name": "蘋果", "calories_per_100g": 52, "protein": 0.3, "carbs": 14, "fat": 0.2, "fiber": 2.4, "sugar": 10.4, "vitamin_c": 4.6}, + "banana": {"name": "香蕉", "calories_per_100g": 89, "protein": 1.1, "carbs": 23, "fat": 0.3, "fiber": 2.6, "sugar": 12.2, "potassium": 358}, + "orange": {"name": "橘子", "calories_per_100g": 47, "protein": 0.9, "carbs": 12, "fat": 0.1, "fiber": 2.4, "sugar": 9.4, "vitamin_c": 53.2}, + "strawberry": {"name": "草莓", "calories_per_100g": 32, "protein": 0.7, "carbs": 7.7, "fat": 0.3, "fiber": 2, "sugar": 4.9, "vitamin_c": 58.8}, + "grape": {"name": "葡萄", "calories_per_100g": 62, "protein": 0.6, "carbs": 16.8, "fat": 0.2, "fiber": 0.9, "sugar": 16.1}, + + # 主食類 + "bread": {"name": "麵包", "calories_per_100g": 265, "protein": 9, "carbs": 49, "fat": 3.2, "fiber": 2.7, "sodium": 491}, + "rice": {"name": "米飯", "calories_per_100g": 130, "protein": 2.7, "carbs": 28, "fat": 0.3, "fiber": 0.4}, + "pasta": {"name": "義大利麵", "calories_per_100g": 131, "protein": 5, "carbs": 25, "fat": 1.1, "fiber": 1.8}, + "noodles": {"name": "麵條", "calories_per_100g": 138, "protein": 4.5, "carbs": 25, "fat": 2.2, "fiber": 1.2}, + "pizza": {"name": "披薩", "calories_per_100g": 266, "protein": 11, "carbs": 33, "fat": 10, "sodium": 598}, + + # 肉類 + "chicken": {"name": "雞肉", "calories_per_100g": 165, "protein": 31, "carbs": 0, "fat": 3.6, "iron": 0.9}, + "beef": {"name": "牛肉", "calories_per_100g": 250, "protein": 26, "carbs": 0, "fat": 15, "iron": 2.6, "zinc": 4.8}, + "pork": {"name": "豬肉", "calories_per_100g": 242, "protein": 27, "carbs": 0, "fat": 14, "thiamine": 0.7}, + "fish": {"name": "魚肉", "calories_per_100g": 206, "protein": 22, "carbs": 0, "fat": 12, "omega_3": "豐富"}, + + # 蔬菜類 + "broccoli": {"name": "花椰菜", "calories_per_100g": 34, "protein": 2.8, "carbs": 7, "fat": 0.4, "fiber": 2.6, "vitamin_c": 89.2}, + "carrot": {"name": "胡蘿蔔", "calories_per_100g": 41, "protein": 0.9, "carbs": 10, "fat": 0.2, "fiber": 2.8, "vitamin_a": 835}, + "tomato": {"name": "番茄", "calories_per_100g": 18, "protein": 0.9, "carbs": 3.9, "fat": 0.2, "fiber": 1.2, "vitamin_c": 13.7}, + "lettuce": {"name": "萵苣", "calories_per_100g": 15, "protein": 1.4, "carbs": 2.9, "fat": 0.2, "fiber": 1.3, "folate": 38}, + + # 飲品類 + "coffee": {"name": "咖啡", "calories_per_100g": 2, "protein": 0.3, "carbs": 0, "fat": 0, "caffeine": 95}, + "tea": {"name": "茶", "calories_per_100g": 1, "protein": 0, "carbs": 0.3, "fat": 0, "antioxidants": "豐富"}, + "milk": {"name": "牛奶", "calories_per_100g": 42, "protein": 3.4, "carbs": 5, "fat": 1, "calcium": 113}, + "juice": {"name": "果汁", "calories_per_100g": 45, "protein": 0.7, "carbs": 11, "fat": 0.2, "vitamin_c": "因果汁種類而異"}, + + # 甜點類 + "cake": {"name": "蛋糕", "calories_per_100g": 257, "protein": 4, "carbs": 46, "fat": 6, "sugar": 35}, + "cookie": {"name": "餅乾", "calories_per_100g": 502, "protein": 5.9, "carbs": 64, "fat": 25, "sugar": 39}, + "ice_cream": {"name": "冰淇淋", "calories_per_100g": 207, "protein": 3.5, "carbs": 24, "fat": 11, "sugar": 21}, + "chocolate": {"name": "巧克力", "calories_per_100g": 546, "protein": 4.9, "carbs": 61, "fat": 31, "sugar": 48}, + + # 其他常見食物 + "egg": {"name": "雞蛋", "calories_per_100g": 155, "protein": 13, "carbs": 1.1, "fat": 11, "choline": 294}, + "cheese": {"name": "起司", "calories_per_100g": 113, "protein": 7, "carbs": 1, "fat": 9, "calcium": 200}, + "yogurt": {"name": "優格", "calories_per_100g": 59, "protein": 10, "carbs": 3.6, "fat": 0.4, "probiotics": "豐富"}, + "nuts": {"name": "堅果", "calories_per_100g": 607, "protein": 15, "carbs": 7, "fat": 54, "vitamin_e": 26}, + "salad": {"name": "沙拉", "calories_per_100g": 20, "protein": 1.5, "carbs": 4, "fat": 0.2, "fiber": 2, "vitamins": "多種維生素"} +} + +# 回應模型 +class FoodAnalysisResponse(BaseModel): + success: bool + food_name: str + confidence: float + nutrition_info: Dict[str, Any] + ai_suggestions: list + message: str + +class HealthResponse(BaseModel): + status: str + message: str + +def get_nutrition_info(food_name: str) -> Dict[str, Any]: + """根據食物名稱獲取營養資訊""" + # 將食物名稱轉為小寫並清理 + food_key = food_name.lower().strip() + + # 移除常見的修飾詞和格式化字符 + food_key = food_key.replace("_", " ").replace("-", " ") + + # 直接匹配 + if food_key in NUTRITION_DATABASE: + return NUTRITION_DATABASE[food_key] + + # 模糊匹配 - 檢查是否包含關鍵字 + for key, value in NUTRITION_DATABASE.items(): + if key in food_key or food_key in key: + return value + # 也檢查中文名稱 + if value["name"] in food_name: + return value + + # 更智能的匹配 - 處理複合詞 + food_words = food_key.split() + for word in food_words: + for key, value in NUTRITION_DATABASE.items(): + if word == key or word in key: + return value + + # 特殊情況處理 + special_mappings = { + "french fries": "potato", + "hamburger": "beef", + "sandwich": "bread", + "soda": "juice", + "water": {"name": "水", "calories_per_100g": 0, "protein": 0, "carbs": 0, "fat": 0}, + "soup": {"name": "湯", "calories_per_100g": 50, "protein": 2, "carbs": 8, "fat": 1, "sodium": 400} + } + + for special_key, mapping in special_mappings.items(): + if special_key in food_key: + if isinstance(mapping, str): + return NUTRITION_DATABASE.get(mapping, {"name": food_name, "message": "營養資料不完整"}) + else: + return mapping + + # 如果沒有找到，返回預設值 + return { + "name": food_name, + "calories_per_100g": "未知", + "protein": "未知", + "carbs": "未知", + "fat": "未知", + "message": f"抱歉，暫時沒有「{food_name}」的詳細營養資料，建議查詢專業營養資料庫" + } + +def generate_ai_suggestions(food_name: str, nutrition_info: Dict) -> list: + """根據食物和營養資訊生成AI建議""" + suggestions = [] + food_name_lower = food_name.lower() + + # 檢查是否有完整的營養資訊 + if isinstance(nutrition_info.get("calories_per_100g"), (int, float)): + calories = nutrition_info["calories_per_100g"] + + # 熱量相關建議 + if calories > 400: + suggestions.append("⚠️ 這是高熱量食物，建議控制份量，搭配運動") + elif calories > 200: + suggestions.append("🍽️ 中等熱量食物，適量食用，建議搭配蔬菜") + elif calories < 50: + suggestions.append("✅ 低熱量食物，適合減重期間食用") + + # 營養素相關建議 + protein = nutrition_info.get("protein", 0) + if isinstance(protein, (int, float)) and protein > 20: + suggestions.append("💪 高蛋白食物，有助於肌肉發展和修復") + + fiber = nutrition_info.get("fiber", 0) + if isinstance(fiber, (int, float)) and fiber > 3: + suggestions.append("🌿 富含纖維，有助於消化健康和增加飽足感") + + sugar = nutrition_info.get("sugar", 0) + if isinstance(sugar, (int, float)) and sugar > 20: + suggestions.append("🍯 含糖量較高，建議適量食用，避免血糖快速上升") + + # 特殊營養素 + if nutrition_info.get("vitamin_c", 0) > 30: + suggestions.append("🍊 富含維生素C，有助於增強免疫力和抗氧化") + + if nutrition_info.get("calcium", 0) > 100: + suggestions.append("🦴 富含鈣質，有助於骨骼和牙齒健康") + + if nutrition_info.get("omega_3"): + suggestions.append("🐟 含有Omega-3脂肪酸，對心血管健康有益") + + # 根據食物類型給出特定建議 + if any(fruit in food_name_lower for fruit in ["apple", "banana", "orange", "strawberry", "grape"]): + suggestions.append("🍎 建議在餐前或運動前食用，提供天然糖分和維生素") + + elif any(meat in food_name_lower for meat in ["chicken", "beef", "pork", "fish"]): + suggestions.append("🥩 建議搭配蔬菜食用，選擇健康的烹調方式（烤、蒸、煮）") + + elif any(sweet in food_name_lower for sweet in ["cake", "cookie", "ice_cream", "chocolate"]): + suggestions.append("🍰 甜點建議偶爾享用，可在運動後適量食用") + suggestions.append("💡 可以考慮與朋友分享，減少單次攝取量") + + elif any(drink in food_name_lower for drink in ["coffee", "tea"]): + suggestions.append("☕ 建議控制咖啡因攝取量，避免影響睡眠") + + elif "salad" in food_name_lower: + suggestions.append("🥗 很棒的選擇！可以添加堅果或橄欖油增加健康脂肪") + + # 通用健康建議 + if not suggestions: + suggestions.extend([ + "🍽️ 建議均衡飲食，搭配多樣化的食物", + "💧 記得多喝水，保持身體水分充足", + "🏃‍♂️ 搭配適量運動，維持健康生活型態" + ]) + else: + # 添加一些通用的健康提醒 + suggestions.append("💧 記得多喝水，幫助營養吸收") + if len(suggestions) < 4: + suggestions.append("⚖️ 注意食物份量，適量攝取是健康飲食的關鍵") + + return suggestions[:5] # 限制建議數量，避免過多 + +@app.get("/", response_model=HealthResponse) +async def root(): + """API根路径""" + return HealthResponse( + status="success", + message="Health Assistant AI - Food Recognition API is running!" + ) + +@app.get("/health", response_model=HealthResponse) +async def health_check(): + """健康檢查端點""" + model_status = "正常" if food_classifier else "模型載入失敗" + return HealthResponse( + status="success", + message=f"API運行正常，模型狀態: {model_status}" + ) + +@app.post("/analyze-food", response_model=FoodAnalysisResponse) +async def analyze_food(file: UploadFile = File(...)): + """分析上傳的食物圖片""" + try: + # 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/"): + 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 + top_result = results[0] + food_name = top_result["label"] + confidence = top_result["score"] + + # 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, + confidence=round(confidence * 100, 2), + nutrition_info=nutrition_info, + 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)}") + +@app.post("/analyze-food-base64", response_model=FoodAnalysisResponse) +async def analyze_food_base64(image_data: dict): + """分析base64編碼的食物圖片""" + try: + # 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] + + # 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 + top_result = results[0] + food_name = top_result["label"] + confidence = top_result["score"] + + # 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, + confidence=round(confidence * 100, 2), + nutrition_info=nutrition_info, + 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)}") + +if __name__ == "__main__": + uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/backend/main.py b/backend/main.py new file mode 100644 index 0000000000000000000000000000000000000000..4997a7bc3cf58f4fdb55bf9233e8b0a8e1c7299c --- /dev/null +++ b/backend/main.py @@ -0,0 +1,209 @@ +from fastapi import FastAPI, UploadFile, File, HTTPException, Depends +from fastapi.middleware.cors import CORSMiddleware +from pydantic import BaseModel +from typing import Dict, Any, List, Optional +import uvicorn +import base64 +from io import BytesIO +from PIL import Image +import json +import os +from dotenv import load_dotenv + +# Database and services +from .app.database import get_db +from sqlalchemy.orm import Session +from .app.models.nutrition import Nutrition +from .app.services import nutrition_api_service + +# Routers +from .app.routers import ai_router, meal_router + +app = FastAPI(title="Health Assistant API") +app.include_router(ai_router.router) +app.include_router(meal_router.router) + +# Load environment variables +load_dotenv() + +# CORS middleware to allow frontend access +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +class FoodRecognitionResponse(BaseModel): + food_name: str + chinese_name: str + confidence: float + success: bool + +class NutritionAnalysisResponse(BaseModel): + success: bool + food_name: str + chinese_name: Optional[str] = None + nutrition: Dict[str, Any] + analysis: Dict[str, Any] + +class ErrorResponse(BaseModel): + detail: str + +@app.get("/") +async def root(): + return {"message": "Health Assistant API is running"} + +@app.get("/api/analyze-nutrition/{food_name}", response_model=NutritionAnalysisResponse, responses={404: {"model": ErrorResponse}}) +async def analyze_nutrition(food_name: str, db: Session = Depends(get_db)): + """ + Analyze nutrition for a recognized food item by querying the database. + If not found locally, it queries an external API and saves the new data. + """ + try: + # Query the database for the food item (case-insensitive search) + food_item = db.query(Nutrition).filter(Nutrition.food_name.ilike(f"%{food_name.strip()}%")).first() + + if not food_item: + # If not found, query the external API + print(f"Food '{food_name}' not in local DB. Querying external API...") + api_data = nutrition_api_service.fetch_nutrition_data(food_name) + + if not api_data: + # If external API also doesn't find it, raise 404 + raise HTTPException(status_code=404, detail=f"Food '{food_name}' not found in local database or external API.") + + # Create a new Nutrition object from the API data + recommendations = generate_recommendations(api_data) + warnings = generate_warnings(api_data) + health_score = calculate_health_score(api_data) + + new_food_item = Nutrition( + food_name=api_data.get('food_name', food_name), + chinese_name=api_data.get('chinese_name'), + calories=api_data.get('calories', 0), + protein=api_data.get('protein', 0), + fat=api_data.get('fat', 0), + carbs=api_data.get('carbs', 0), + fiber=api_data.get('fiber', 0), + sugar=api_data.get('sugar', 0), + sodium=api_data.get('sodium', 0), + health_score=health_score, + recommendations=recommendations, + warnings=warnings, + details={} # API doesn't provide extra details in our format + ) + + # Add to DB and commit + db.add(new_food_item) + db.commit() + db.refresh(new_food_item) + print(f"Saved new food '{new_food_item.food_name}' to the database.") + food_item = new_food_item # Use the new item for the response + + if not food_item: + raise HTTPException(status_code=404, detail=f"Food '{food_name}' not found in the database.") + + # Structure the response + nutrition_details = { + "calories": food_item.calories, + "protein": food_item.protein, + "fat": food_item.fat, + "carbs": food_item.carbs, + "fiber": food_item.fiber, + "sugar": food_item.sugar, + "sodium": food_item.sodium, + } + + # Safely add details if they exist and are a dictionary + if isinstance(food_item.details, dict): + nutrition_details.update(food_item.details) + + analysis_details = { + "healthScore": food_item.health_score, + "recommendations": food_item.recommendations, + "warnings": food_item.warnings + } + + return { + "success": True, + "food_name": food_item.food_name, + "chinese_name": food_item.chinese_name, + "nutrition": nutrition_details, + "analysis": analysis_details + } + + except HTTPException: + raise + except Exception as e: + # Log the error for debugging + print(f"Error in analyze_nutrition: {str(e)}") + raise HTTPException(status_code=500, detail=f"Internal server error while processing '{food_name}'.") + +def calculate_health_score(nutrition: Dict[str, Any]) -> int: + """Calculate a health score based on nutritional values""" + score = 100 + + # 熱量評分 + if nutrition.get("calories", 0) > 400: + score -= 20 + elif nutrition.get("calories", 0) > 300: + score -= 10 + + # 脂肪評分 + if nutrition.get("fat", 0) > 20: + score -= 15 + elif nutrition.get("fat", 0) > 15: + score -= 8 + + # 蛋白質評分 + if nutrition.get("protein", 0) > 15: + score += 10 + elif nutrition.get("protein", 0) < 5: + score -= 10 + + # 鈉含量評分 + if nutrition.get("sodium", 0) > 800: + score -= 15 + elif nutrition.get("sodium", 0) > 600: + score -= 8 + + return max(0, min(100, score)) + +def generate_recommendations(nutrition: Dict[str, Any]) -> List[str]: + """Generate dietary recommendations based on nutrition data""" + recommendations = [] + + if nutrition.get("protein", 0) < 10: + recommendations.append("建議增加蛋白質攝取，可搭配雞蛋或豆腐") + + if nutrition.get("fat", 0) > 20: + recommendations.append("脂肪含量較高，建議適量食用") + + if nutrition.get("fiber", 0) < 3: + recommendations.append("纖維含量不足，建議搭配蔬菜沙拉") + + if nutrition.get("sodium", 0) > 600: + recommendations.append("鈉含量偏高，建議多喝水並減少其他鹽分攝取") + + return recommendations + +def generate_warnings(nutrition: Dict[str, Any]) -> List[str]: + """Generate dietary warnings based on nutrition data""" + warnings = [] + + if nutrition.get("calories", 0) > 500: + warnings.append("高熱量食物") + + if nutrition.get("fat", 0) > 25: + warnings.append("高脂肪食物") + + if nutrition.get("sodium", 0) > 1000: + warnings.append("高鈉食物") + + return warnings + +if __name__ == "__main__": + # It's better to run with `uvicorn backend.main:app --reload` from the project root. + uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True) \ No newline at end of file diff --git a/backend/requirements.txt b/backend/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..e1526dd80b7f43ebc90f85a89bf0f3151c38b6c3 Binary files /dev/null and b/backend/requirements.txt differ diff --git a/backend/setup.py b/backend/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..8053af6eed72a75288a8ee5bd5f6b835780d9dd6 --- /dev/null +++ b/backend/setup.py @@ -0,0 +1,47 @@ +import os +import sys +from pathlib import Path + +def setup_project(): + """設置專案目錄結構和必要檔案""" + # 獲取專案根目錄 + project_root = Path(__file__).parent + + # 創建必要的目錄 + directories = [ + 'data', # 資料庫目錄 + 'logs', # 日誌目錄 + 'uploads' # 上傳檔案目錄 + ] + + for directory in directories: + dir_path = project_root / directory + dir_path.mkdir(exist_ok=True) + print(f"Created directory: {dir_path}") + + # 創建 .env 檔案（如果不存在） + env_file = project_root / '.env' + if not env_file.exists(): + with open(env_file, 'w', encoding='utf-8') as f: + f.write("""# 資料庫設定 +DATABASE_URL=sqlite:///./data/health_assistant.db + +# Redis 設定 +REDIS_HOST=localhost +REDIS_PORT=6379 +REDIS_DB=0 + +# API 設定 +API_HOST=localhost +API_PORT=8000 +""") + print(f"Created .env file: {env_file}") + + print("\nProject setup completed successfully!") + print("\nNext steps:") + print("1. Install required packages: pip install -r requirements.txt") + print("2. Start the Redis server") + print("3. Run the application: uvicorn app.main:app --reload") + +if __name__ == "__main__": + setup_project() diff --git a/conftest.py b/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..aa226358c7dabd3f318f77fa77eff852f57de9df --- /dev/null +++ b/conftest.py @@ -0,0 +1,7 @@ +import os +import sys +from pathlib import Path + +# Add the backend directory to the Python path +sys.path.insert(0, str(Path(__file__).parent / "backend")) +sys.path.insert(0, str(Path(__file__).parent)) diff --git a/coverage.ini b/coverage.ini new file mode 100644 index 0000000000000000000000000000000000000000..119a0d4950b1f5962a7006e198262724bd9e66e2 --- /dev/null +++ b/coverage.ini @@ -0,0 +1,22 @@ +[run] +source = . +omit = + */tests/* + */venv/* + */.venv/* + */env/* + */.env/* + */site-packages/* + +[report] +exclude_lines = + pragma: no cover + def __repr__ + if self\.debug + raise AssertionError + raise NotImplementedError + if 0: + if __name__ == .__main__. + +[html] +directory = htmlcov diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000000000000000000000000000000000000..0623df6dc14930ba1b8936573924d1ad472af525 --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,13 @@ + + + + + + + 健康助手 + + +
+ + + diff --git a/frontend/package-lock.json b/frontend/package-lock.json new file mode 100644 index 0000000000000000000000000000000000000000..d2778f2a2cd6ea978e9ef2d9f1a9ec84506f6e81 --- /dev/null +++ b/frontend/package-lock.json @@ -0,0 +1,6406 @@ +{ + "name": "health-assistant", + "version": "0.1.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "health-assistant", + "version": "0.1.0", + "dependencies": { + "@headlessui/react": "^1.7.17", + "@heroicons/react": "^2.0.18", + "@tensorflow-models/mobilenet": "^2.1.1", + "@tensorflow/tfjs": "^4.11.0", + "axios": "^1.6.2", + "chart.js": "^4.4.0", + "react": "^18.2.0", + "react-chartjs-2": "^5.2.0", + "react-dom": "^18.2.0", + "react-router-dom": "^6.20.0", + "react-toastify": "^9.1.3" + }, + "devDependencies": { + "@types/react": "^18.2.37", + "@types/react-dom": "^18.2.15", + "@vitejs/plugin-react": "^4.2.0", + "autoprefixer": "^10.4.16", + "eslint": "^8.53.0", + "eslint-plugin-react": "^7.33.2", + 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+{ + "name": "health-assistant", + "private": true, + "version": "0.1.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "vite build", + "lint": "eslint . --ext js,jsx --report-unused-disable-directives --max-warnings 0", + "preview": "vite preview" + }, + "dependencies": { + "@headlessui/react": "^1.7.17", + "@heroicons/react": "^2.0.18", + "@tensorflow/tfjs": "^4.11.0", + "@tensorflow-models/mobilenet": "^2.1.1", + "axios": "^1.6.2", + "chart.js": "^4.4.0", + "react": "^18.2.0", + "react-chartjs-2": "^5.2.0", + "react-dom": "^18.2.0", + "react-router-dom": "^6.20.0", + "react-toastify": "^9.1.3" + }, + "devDependencies": { + "@types/react": "^18.2.37", + "@types/react-dom": "^18.2.15", + "@vitejs/plugin-react": "^4.2.0", + "autoprefixer": "^10.4.16", + "eslint": "^8.53.0", + "eslint-plugin-react": "^7.33.2", + "eslint-plugin-react-hooks": "^4.6.0", + "eslint-plugin-react-refresh": "^0.4.4", + "postcss": "^8.4.31", + "tailwindcss": "^3.3.5", + "vite": "^5.0.0" + } +} diff --git a/frontend/src/App.jsx b/frontend/src/App.jsx new file mode 100644 index 0000000000000000000000000000000000000000..e480bf88e82df61999988fcdb4ab89c9f2e99e56 --- /dev/null +++ b/frontend/src/App.jsx @@ -0,0 +1,38 @@ +import React from 'react'; +import { BrowserRouter as Router, Routes, Route } from 'react-router-dom'; +import Navbar from './components/Navbar'; +import Dashboard from './pages/Dashboard'; +import FoodTracker from './pages/FoodTracker'; +import AIFoodAnalyzer from './pages/AIFoodAnalyzer'; +import WaterTracker from './pages/WaterTracker'; +import ExerciseTracker from './pages/ExerciseTracker'; +import Profile from './pages/Profile'; +import Login from './pages/Login'; +import Register from './pages/Register'; +import { ToastContainer } from 'react-toastify'; +import 'react-toastify/dist/ReactToastify.css'; + +function App() { + return ( + +
+ +
+ + } /> + } /> + } /> + } /> + } /> + } /> + } /> + } /> + +
+ +
+ + ); +} + +export default App; diff --git a/frontend/src/components/Navbar.jsx b/frontend/src/components/Navbar.jsx new file mode 100644 index 0000000000000000000000000000000000000000..e3971e63613b978c24f02820c53289d0f7a5957f --- /dev/null +++ b/frontend/src/components/Navbar.jsx @@ -0,0 +1,130 @@ +import React from 'react'; +import { Fragment } from 'react'; +import { Link } from 'react-router-dom'; +import { Disclosure, Menu, Transition } from '@headlessui/react'; +import { Bars3Icon, XMarkIcon, UserCircleIcon } from '@heroicons/react/24/outline'; + +const navigation = [ + { name: '儀表板', href: '/', current: true }, + { name: '飲食追蹤', href: '/food', current: false }, + { name: 'AI食物分析', href: '/food-ai', current: false }, + { name: '飲水紀錄', href: '/water', current: false }, + { name: '運動紀錄', href: '/exercise', current: false }, +]; + +function classNames(...classes) { + return classes.filter(Boolean).join(' '); +} + +export default function Navbar() { + return ( + + {({ open }) => ( + <> +
+
+
+
+ 健康助手 +
+
+ {navigation.map((item) => ( + + {item.name} + + ))} +
+
+ +
+ +
+ + +
+ + + + {({ active }) => ( + + 個人資料 + + )} + + + {({ active }) => ( + + 登出 + + )} + + + + +
+ +
+ + {open ? ( + +
+
+
+ + +
+ {navigation.map((item) => ( + + {item.name} + + ))} +
+ + + )} + + ); +} diff --git a/frontend/src/index.css b/frontend/src/index.css new file mode 100644 index 0000000000000000000000000000000000000000..2f2ebcdcf597927de99d43a5aabb93bb3822b9d2 --- /dev/null +++ b/frontend/src/index.css @@ -0,0 +1,14 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +:root { + font-family: Inter, system-ui, Avenir, Helvetica, Arial, sans-serif; + line-height: 1.5; + font-weight: 400; + + font-synthesis: none; + text-rendering: optimizeLegibility; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} diff --git a/frontend/src/main.jsx b/frontend/src/main.jsx new file mode 100644 index 0000000000000000000000000000000000000000..54b39dd1d900e866bb91ee441d372a8924b9d87a --- /dev/null +++ b/frontend/src/main.jsx @@ -0,0 +1,10 @@ +import React from 'react' +import ReactDOM from 'react-dom/client' +import App from './App.jsx' +import './index.css' + +ReactDOM.createRoot(document.getElementById('root')).render( + + + , +) diff --git a/frontend/src/pages/AIFoodAnalyzer.css b/frontend/src/pages/AIFoodAnalyzer.css new file mode 100644 index 0000000000000000000000000000000000000000..d540ae08688350233b9b58430fa9b4aad6ff598c --- /dev/null +++ b/frontend/src/pages/AIFoodAnalyzer.css @@ -0,0 +1,348 @@ +/* AI Food Analyzer Styles */ +.app-container { + background: rgba(255, 255, 255, 0.95); + border-radius: 20px; + padding: 2rem; + box-shadow: 0 15px 35px rgba(0, 0, 0, 0.1); + max-width: 500px; + width: 100%; + max-height: 90vh; + overflow-y: auto; +} + +.header { + text-align: center; + margin-bottom: 2rem; +} + +.title { + color: #333; + font-size: 1.8rem; + font-weight: bold; + margin-bottom: 0.5rem; +} + +.subtitle { + color: #666; + font-size: 0.9rem; +} + +.tab-nav { + display: flex; + background: rgba(102, 126, 234, 0.1); + border-radius: 15px; + margin-bottom: 2rem; + padding: 0.3rem; +} + +.tab-btn { + flex: 1; + padding: 0.8rem; + border: none; + background: transparent; + border-radius: 12px; + font-weight: bold; + cursor: pointer; + transition: all 0.3s; + color: #666; +} + +.tab-btn.active { + background: linear-gradient(45deg, #667eea, #764ba2); + color: white; + box-shadow: 0 4px 15px rgba(102, 126, 234, 0.3); +} + +.tab-content { + display: none; +} + +.tab-content.active { + display: block; +} + +/* 個人資料表單 */ +.profile-form { + display: grid; + gap: 1.5rem; +} + +.input-group { + display: flex; + flex-direction: column; +} + +.input-group label { + font-weight: bold; + color: #333; + margin-bottom: 0.5rem; + font-size: 0.9rem; +} + +.input-group input, .input-group select { + padding: 12px; + border: 2px solid #e0e0e0; + border-radius: 10px; + font-size: 1rem; + transition: border-color 0.3s; +} + +.input-group input:focus, .input-group select:focus { + outline: none; + border-color: #667eea; +} + +.input-row { + display: grid; + grid-template-columns: 1fr 1fr; + gap: 1rem; +} + +.save-profile-btn { + background: linear-gradient(45deg, #43cea2, #185a9d); + color: white; + border: none; + padding: 15px; + border-radius: 15px; + font-weight: bold; + cursor: pointer; + font-size: 1rem; + transition: transform 0.2s; +} + +.save-profile-btn:hover { + transform: translateY(-2px); +} + +/* 食物分析區域 */ +.camera-container { + position: relative; + margin-bottom: 1.5rem; + text-align: center; +} + +#video { + width: 100%; + max-width: 300px; + border-radius: 15px; + box-shadow: 0 5px 15px rgba(0, 0, 0, 0.2); +} + +#canvas { + display: none; +} + +.controls { + display: flex; + gap: 1rem; + justify-content: center; + margin: 1.5rem 0; +} + +button { + background: linear-gradient(45deg, #667eea, #764ba2); + color: white; + border: none; + padding: 12px 20px; + border-radius: 25px; + cursor: pointer; + font-weight: bold; + transition: transform 0.2s; + font-size: 0.9rem; +} + +button:hover { + transform: translateY(-2px); +} + +button:disabled { + opacity: 0.6; + cursor: not-allowed; +} + +.upload-btn { + background: linear-gradient(45deg, #43cea2, #185a9d); +} + +.file-input { + display: none; +} + +.loading { + display: none; + margin: 1rem 0; + text-align: center; +} + +.spinner { + border: 3px solid #f3f3f3; + border-top: 3px solid #667eea; + border-radius: 50%; + width: 30px; + height: 30px; + animation: spin 1s linear infinite; + margin: 0 auto 1rem; +} + +@keyframes spin { + 0% { transform: rotate(0deg); } + 100% { transform: rotate(360deg); } +} + +.result { + margin-top: 2rem; + padding: 1.5rem; + background: rgba(102, 126, 234, 0.1); + border-radius: 15px; +} + +.food-info { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 1.5rem; +} + +.food-name { + font-size: 1.3rem; + font-weight: bold; + color: #333; +} + +.add-to-diary { + background: linear-gradient(45deg, #ff6b6b, #ee5a24); + padding: 8px 16px; + border-radius: 20px; + font-size: 0.8rem; +} + +.nutrition-grid { + display: grid; + grid-template-columns: 1fr 1fr; + gap: 0.8rem; + margin-bottom: 1.5rem; +} + +.nutrition-item { + background: white; + padding: 0.8rem; + border-radius: 10px; + box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1); + text-align: center; +} + +.nutrition-label { + font-size: 0.9rem; + color: #666; + margin-bottom: 0.3rem; +} + +.nutrition-value { + font-size: 1.1rem; + font-weight: bold; + color: #333; +} + +.daily-recommendation { + background: linear-gradient(45deg, rgba(255, 107, 107, 0.1), rgba(238, 90, 36, 0.1)); + padding: 1rem; + border-radius: 10px; + border-left: 4px solid #ff6b6b; +} + +.recommendation-title { + font-weight: bold; + color: #333; + margin-bottom: 0.5rem; + font-size: 0.9rem; +} + +.recommendation-text { + font-size: 0.8rem; + color: #666; + line-height: 1.4; +} + +/* 追蹤頁面 */ +.stats-grid { + display: grid; + grid-template-columns: 1fr 1fr; + gap: 1rem; + margin-bottom: 2rem; +} + +.stat-card { + background: white; + padding: 1.5rem; + border-radius: 15px; + box-shadow: 0 5px 15px rgba(0, 0, 0, 0.1); + text-align: center; +} + +.stat-number { + font-size: 2rem; + font-weight: bold; + color: #667eea; + margin-bottom: 0.5rem; +} + +.stat-label { + font-size: 0.9rem; + color: #666; +} + +.chart-container { + background: white; + padding: 1.5rem; + border-radius: 15px; + box-shadow: 0 5px 15px rgba(0, 0, 0, 0.1); + margin-bottom: 2rem; +} + +.today-meals { + background: white; + padding: 1.5rem; + border-radius: 15px; + box-shadow: 0 5px 15px rgba(0, 0, 0, 0.1); +} + +.meal-item { + display: flex; + justify-content: space-between; + align-items: center; + padding: 1rem; + border-bottom: 1px solid #f0f0f0; +} + +.meal-item:last-child { + border-bottom: none; +} + +.meal-info h4 { + color: #333; + margin-bottom: 0.3rem; +} + +.meal-info span { + color: #666; + font-size: 0.8rem; +} + +.meal-calories { + font-weight: bold; + color: #667eea; +} + +.welcome-message { + background: linear-gradient(45deg, rgba(67, 206, 162, 0.1), rgba(24, 90, 157, 0.1)); + padding: 1rem; + border-radius: 10px; + margin-bottom: 2rem; + border-left: 4px solid #43cea2; +} + +.no-profile { + text-align: center; + color: #666; + padding: 2rem; +} diff --git a/frontend/src/pages/AIFoodAnalyzer.jsx b/frontend/src/pages/AIFoodAnalyzer.jsx new file mode 100644 index 0000000000000000000000000000000000000000..a0fa2c8253a4c475befed17b7d90bc654c9346d7 --- /dev/null +++ b/frontend/src/pages/AIFoodAnalyzer.jsx @@ -0,0 +1,667 @@ +import React, { useState, useEffect, useRef } from 'react'; +import { XMarkIcon, InboxArrowDownIcon, CalendarIcon, ClockIcon, ChartBarIcon } from '@heroicons/react/24/outline'; +import Chart from 'chart.js/auto'; +import './AIFoodAnalyzer.css'; + +export default function AIFoodAnalyzer() { + // 應用狀態管理 + const [userProfile, setUserProfile] = useState(null); + const [dailyMeals, setDailyMeals] = useState([]); + const [activeTab, setActiveTab] = useState('profile'); + const [selectedImage, setSelectedImage] = useState(null); + const [isAnalyzing, setIsAnalyzing] = useState(false); + const [currentFoodAnalysis, setCurrentFoodAnalysis] = useState(null); + const videoRef = useRef(null); + const canvasRef = useRef(null); + const chartRef = useRef(null); + const chartInstance = useRef(null); + + // 後端 API 基礎 URL + const API_BASE_URL = 'http://localhost:8000/api'; + + // 狀態追蹤錯誤信息 + const [error, setError] = useState(null); + + // 初始化 - 載入儲存的資料 + useEffect(() => { + loadStoredData(); + }, []); + + // 當 tab 切換時檢查頁面狀態 + useEffect(() => { + checkPageAccess(activeTab); + }, [activeTab, userProfile]); + + // 初始化營養圖表 + useEffect(() => { + if (activeTab === 'tracking' && userProfile && chartRef.current) { + initNutritionChart(); + } + }, [activeTab, userProfile]); + + // 載入儲存的資料 + const loadStoredData = () => { + try { + const storedProfile = localStorage.getItem('userProfile'); + const storedMeals = localStorage.getItem('dailyMeals'); + + if (storedProfile) { + setUserProfile(JSON.parse(storedProfile)); + } + + if (storedMeals) { + setDailyMeals(JSON.parse(storedMeals)); + } + } catch (error) { + console.error('Error loading stored data:', error); + } + }; + + // 檢查頁面訪問權限 + const checkPageAccess = (tabId) => { + if (tabId === 'analyze') { + if (userProfile) { + initCamera(); + } + } else if (tabId === 'tracking' && userProfile) { + updateTrackingStats(); + } + }; + + // 初始化相機 + const initCamera = async () => { + try { + if (videoRef.current && navigator.mediaDevices && navigator.mediaDevices.getUserMedia) { + const stream = await navigator.mediaDevices.getUserMedia({ video: true }); + videoRef.current.srcObject = stream; + } + } catch (error) { + console.error('Error accessing camera:', error); + } + }; + + // 拍照功能 + const captureImage = () => { + if (videoRef.current && canvasRef.current) { + const context = canvasRef.current.getContext('2d'); + canvasRef.current.width = videoRef.current.videoWidth; + canvasRef.current.height = videoRef.current.videoHeight; + context.drawImage(videoRef.current, 0, 0, canvasRef.current.width, canvasRef.current.height); + + const imageData = canvasRef.current.toDataURL('image/png'); + setSelectedImage(imageData); + analyzeImage(imageData); + } + }; + + // 處理檔案上傳 + const handleFileUpload = (event) => { + const file = event.target.files[0]; + if (file) { + const reader = new FileReader(); + reader.onloadend = () => { + setSelectedImage(reader.result); + analyzeImage(reader.result); + }; + reader.readAsDataURL(file); + } + }; + + // 分析圖片 + const analyzeImage = async (imageData) => { + setIsAnalyzing(true); + setError(null); + + try { + // 將 base64 圖片數據轉換為 Blob + const base64Response = await fetch(imageData); + const blob = await base64Response.blob(); + + // 創建 FormData 並添加圖片 + const formData = new FormData(); + formData.append('file', blob, 'food-image.jpg'); + + // 發送到後端 API + const response = await fetch(`${API_BASE_URL}/recognize-food`, { + method: 'POST', + body: formData, + // 注意：不要手動設置 Content-Type，讓瀏覽器自動設置並添加 boundary + }); + + if (!response.ok) { + const errorData = await response.json().catch(() => ({})); + throw new Error(errorData.detail || '分析圖片時發生錯誤'); + } + + const data = await response.json(); + + setCurrentFoodAnalysis({ + name: data.food_name, + nutrition: data.nutrition || {}, + timestamp: new Date().toISOString() + }); + + // 如果後端返回了營養分析，也更新追蹤數據 + if (data.nutrition) { + addMealToTracker(data.food_name, data.nutrition); + } + + } catch (err) { + console.error('分析圖片時出錯:', err); + setError(err.message || '分析失敗，請重試！'); + } finally { + setIsAnalyzing(false); + } + }; + + // 儲存個人資料 + const saveProfile = () => { + const name = document.getElementById('userName').value; + const age = parseInt(document.getElementById('userAge').value); + const gender = document.getElementById('userGender').value; + const height = parseInt(document.getElementById('userHeight').value); + const weight = parseInt(document.getElementById('userWeight').value); + const activityLevel = document.getElementById('activityLevel').value; + const healthGoal = document.getElementById('healthGoal').value; + + // 驗證必填欄位 + if (!name || !age || !gender || !height || !weight || !activityLevel || !healthGoal) { + alert('請填寫所有欄位！'); + return; + } + + const profile = { + name, + age, + gender, + height, + weight, + activityLevel, + healthGoal + }; + + // 計算基礎代謝率 (BMR) 和每日熱量需求 + profile.bmr = calculateBMR(profile); + profile.dailyCalories = calculateDailyCalories(profile); + profile.bmi = (profile.weight / Math.pow(profile.height / 100, 2)).toFixed(1); + + setUserProfile(profile); + + // 儲存到 localStorage + localStorage.setItem('userProfile', JSON.stringify(profile)); + localStorage.setItem('dailyMeals', JSON.stringify([])); // 重置每日記錄 + setDailyMeals([]); + + alert('個人資料已儲存成功！✅'); + + // 切換到分析頁面 + setActiveTab('analyze'); + }; + + // 計算基礎代謝率 (BMR) + const calculateBMR = (profile) => { + let bmr; + if (profile.gender === 'male') { + bmr = 88.362 + (13.397 * profile.weight) + (4.799 * profile.height) - (5.677 * profile.age); + } else { + bmr = 447.593 + (9.247 * profile.weight) + (3.098 * profile.height) - (4.330 * profile.age); + } + return Math.round(bmr); + }; + + // 計算每日熱量需求 + const calculateDailyCalories = (profile) => { + const activityMultipliers = { + sedentary: 1.2, + light: 1.375, + moderate: 1.55, + active: 1.725, + extra: 1.9 + }; + + let calories = profile.bmr * activityMultipliers[profile.activityLevel]; + + // 根據健康目標調整 + if (profile.healthGoal === 'lose') { + calories -= 300; // 減重 + } else if (profile.healthGoal === 'gain') { + calories += 300; // 增重 + } + + return Math.round(calories); + }; + + // 顯示營養資訊 + const displayNutrition = (foodName, nutrition) => { + return ( +
+
+
{foodName} (每100g)
+ +
+
+
+
熱量
+
{nutrition.calories} 卡
+
+
+
蛋白質
+
{nutrition.protein} g
+
+
+
碳水化合物
+
{nutrition.carbs} g
+
+
+
脂肪
+
{nutrition.fat} g
+
+
+
纖維
+
{nutrition.fiber} g
+
+
+
糖分
+
{nutrition.sugar} g
+
+
+
+
💡 個人化建議
+
+ {getPersonalizedRecommendation(nutrition)} +
+
+
+ ); + }; + + // 獲取個人化建議 + const getPersonalizedRecommendation = (nutrition) => { + if (!userProfile) return ''; + + const dailyCaloriesNeeded = userProfile.dailyCalories; + const caloriePercentage = ((nutrition.calories / dailyCaloriesNeeded) * 100).toFixed(1); + + let recommendationText = `這份食物提供您每日所需熱量的 ${caloriePercentage}%。`; + + if (userProfile.healthGoal === 'lose' && nutrition.calories > 200) { + recommendationText += ' 由於您的目標是減重，建議控制份量或搭配蔬菜一起食用。'; + } else if (userProfile.healthGoal === 'muscle' && nutrition.protein < 10) { + recommendationText += ' 建議搭配高蛋白食物，有助於肌肉成長。'; + } else if (nutrition.fiber > 3) { + recommendationText += ' 富含纖維，對消化健康很有幫助！'; + } + + return recommendationText; + }; + + // 加入飲食記錄 + const addToDiary = () => { + if (currentFoodAnalysis) { + const newMeals = [...dailyMeals, { + ...currentFoodAnalysis, + id: Date.now() + }]; + + setDailyMeals(newMeals); + localStorage.setItem('dailyMeals', JSON.stringify(newMeals)); + alert('已加入今日飲食記錄！✅'); + + // 更新統計 + updateTrackingStats(); + } + }; + + // 更新追蹤統計 + const updateTrackingStats = () => { + // 計算今日總熱量 + const todayCalories = dailyMeals.reduce((total, meal) => { + return total + meal.nutrition.calories; + }, 0); + + // 模擬週平均 + const weeklyAvg = Math.round(todayCalories * (0.8 + Math.random() * 0.4)); + + // 更新 DOM + const todayCaloriesElement = document.getElementById('todayCalories'); + const weeklyAvgElement = document.getElementById('weeklyAvg'); + + if (todayCaloriesElement) todayCaloriesElement.textContent = todayCalories; + if (weeklyAvgElement) weeklyAvgElement.textContent = weeklyAvg; + + // 更新餐點列表 + updateMealsList(); + }; + + // 更新餐點列表 + const updateMealsList = () => { + const mealsListElement = document.getElementById('mealsList'); + + if (mealsListElement) { + if (dailyMeals.length === 0) { + mealsListElement.innerHTML = ` +
+
+

尚無飲食記錄

+ 開始分析食物來建立您的飲食記錄吧！ +
+
+ `; + } else { + mealsListElement.innerHTML = dailyMeals.map(meal => ` +
+
+

${meal.name}

+ ${new Date(meal.timestamp).toLocaleTimeString()} +
+
${meal.nutrition.calories} 卡
+
+ `).join(''); + } + } + }; + + // 初始化營養圖表 + const initNutritionChart = () => { + if (chartInstance.current) { + chartInstance.current.destroy(); + } + + const ctx = chartRef.current.getContext('2d'); + + // 模擬一週的數據 + const days = ['週一', '週二', '週三', '週四', '週五', '週六', '週日']; + const caloriesData = Array(7).fill(0).map(() => Math.floor(Math.random() * 1000 + 1000)); + const proteinData = Array(7).fill(0).map(() => Math.floor(Math.random() * 30 + 50)); + const carbsData = Array(7).fill(0).map(() => Math.floor(Math.random() * 50 + 150)); + + // 今天的數據 + const today = new Date().getDay() || 7; // 0 是週日，轉換為 7 + caloriesData[today - 1] = dailyMeals.reduce((total, meal) => total + meal.nutrition.calories, 0) || + Math.floor(Math.random() * 1000 + 1000); + + chartInstance.current = new Chart(ctx, { + type: 'line', + data: { + labels: days, + datasets: [ + { + label: '熱量 (卡)', + data: caloriesData, + borderColor: 'rgb(255, 99, 132)', + backgroundColor: 'rgba(255, 99, 132, 0.2)', + tension: 0.3 + }, + { + label: '蛋白質 (g)', + data: proteinData, + borderColor: 'rgb(54, 162, 235)', + backgroundColor: 'rgba(54, 162, 235, 0.2)', + tension: 0.3 + }, + { + label: '碳水 (g)', + data: carbsData, + borderColor: 'rgb(75, 192, 192)', + backgroundColor: 'rgba(75, 192, 192, 0.2)', + tension: 0.3 + } + ] + }, + options: { + responsive: true, + plugins: { + legend: { + position: 'top', + }, + tooltip: { + mode: 'index', + intersect: false, + } + }, + scales: { + y: { + beginAtZero: true + } + } + } + }); + }; + + // 獲取 BMI 狀態 + const getBMIStatus = (bmi) => { + if (bmi < 18.5) return '體重過輕'; + if (bmi < 24) return '正常範圍'; + if (bmi < 27) return '體重過重'; + if (bmi < 30) return '輕度肥胖'; + if (bmi < 35) return '中度肥胖'; + return '重度肥胖'; + }; + + // 更新歡迎訊息 + const updateWelcomeMessage = () => { + if (!userProfile) return ''; + + const bmiStatus = getBMIStatus(userProfile.bmi); + return ( +
+

👋 你好，{userProfile.name}！

+

BMI: {userProfile.bmi} ({bmiStatus}) | 每日建議熱量: {userProfile.dailyCalories} 卡

+
+ ); + }; + + return ( +
+
+

🍎 AI營養分析器

+

個人化健康管理助手

+
+ + + + {/* 個人資料頁面 */} +
+
+
+ + +
+ +
+
+ + +
+
+ + +
+
+ +
+
+ + +
+
+ + +
+
+ +
+ + +
+ +
+ + +
+ + +
+
+ + {/* 食物分析頁面 */} +
+ {!userProfile ? ( +
+

請先完成個人資料設定，以獲得個人化營養建議 👆

+
+ ) : ( +
+
+ + +
+ +
+ + + +
+ + {isAnalyzing && ( +
+
+

AI正在分析食物...

+
+ )} + + {error && !isAnalyzing && ( +
+ ⚠️ +
+ 錯誤： +

{error}

+
+
+ )} + + {currentFoodAnalysis && !isAnalyzing && ( + displayNutrition(currentFoodAnalysis.name, currentFoodAnalysis.nutrition) + )} +
+ )} +
+ + {/* 追蹤記錄頁面 */} +
+ {!userProfile ? ( +
+

請先完成個人資料設定，開始追蹤您的營養攝取 👆

+
+ ) : ( +
+ {updateWelcomeMessage()} + +
+
+
+ {dailyMeals.reduce((total, meal) => total + meal.nutrition.calories, 0)} +
+
今日熱量
+
+
+
+ {Math.round(dailyMeals.reduce((total, meal) => total + meal.nutrition.calories, 0) * (0.8 + Math.random() * 0.4)) || 0} +
+
週平均
+
+
+ +
+

本週營養攝取趨勢

+ +
+ +
+

今日飲食記錄

+
+ {dailyMeals.length === 0 ? ( +
+
+

尚無飲食記錄

+ 開始分析食物來建立您的飲食記錄吧！ +
+
+ ) : ( + dailyMeals.map(meal => ( +
+
+

{meal.name}

+ {new Date(meal.timestamp).toLocaleTimeString()} +
+
{meal.nutrition.calories} 卡
+
+ )) + )} +
+
+
+ )} +
+
+ ); +} diff --git a/frontend/src/pages/Dashboard.jsx b/frontend/src/pages/Dashboard.jsx new file mode 100644 index 0000000000000000000000000000000000000000..33a4b651c22e0bfaf0d50a4aaa15703c1c36e76b --- /dev/null +++ b/frontend/src/pages/Dashboard.jsx @@ -0,0 +1,177 @@ +import React, { useState } from 'react'; +import { UserIcon, CalculatorIcon, CalendarIcon, CloudArrowUpIcon, ClipboardDocumentListIcon, ArrowPathIcon } from '@heroicons/react/24/outline'; + +export default function Dashboard() { + // 狀態區 + const [user, setUser] = useState({ name: '', age: '', height: '', weight: '', id: '' }); + const [userCreated, setUserCreated] = useState(false); + const [foodDate, setFoodDate] = useState(''); + const [foodCal, setFoodCal] = useState(''); + const [foodCarb, setFoodCarb] = useState(''); + const [foodProtein, setFoodProtein] = useState(''); + const [foodMsg, setFoodMsg] = useState(''); + const [bmi, setBmi] = useState(null); + const [bmiMsg, setBmiMsg] = useState(''); + const [waterDate, setWaterDate] = useState(''); + const [water, setWater] = useState(''); + const [waterMsg, setWaterMsg] = useState(''); + const [aiResult, setAiResult] = useState(''); + const [history, setHistory] = useState([]); + + // 處理邏輯 + const handleCreateUser = (e) => { e.preventDefault(); setUserCreated(true); setUser({ ...user, id: '6' }); }; + const handleAddFood = (e) => { e.preventDefault(); setFoodMsg('Food log added successfully.'); setHistory([...history, { date: foodDate, cal: foodCal, carb: foodCarb, protein: foodProtein }]); }; + const handleCalcBmi = () => { + if (user.height && user.weight) { + const bmiVal = (user.weight / ((user.height / 100) ** 2)).toFixed(2); + setBmi(bmiVal); + let msg = ''; + if (bmiVal < 18.5) msg = '體重過輕，建議均衡飲食與適度運動'; + else if (bmiVal < 24) msg = '正常範圍，請繼續保持'; + else msg = '體重過重，建議增加運動與飲食控制'; + setBmiMsg(msg); + } + }; + const handleAddWater = (e) => { e.preventDefault(); let msg = ''; if (water >= 2000) msg = '今日補水充足！'; else msg = '今日飲水量不足，請多補充水分！'; setWaterMsg(msg); }; + const handleAi = () => { setAiResult('AI 分析結果：雞肉沙拉，約 350 kcal'); }; + const handleQueryHistory = () => { setHistory(history); }; + + return ( +
+
+ {/* 上方切換選單間隔 */} +
+ 用戶 + 飲食 + BMI + 水分 + AI + 歷史 +
+
+ {/* 用戶卡片 */} +
+
+ + 用戶資料 +
+
+ setUser({ ...user, name: e.target.value })} /> + setUser({ ...user, age: e.target.value })} /> + setUser({ ...user, height: e.target.value })} /> + setUser({ ...user, weight: e.target.value })} /> + +
+ {userCreated && ( + <> +
用戶ID：6
+ +
+
姓名：{user.name}
+
年齡：{user.age}
+
身高：{user.height} cm
+
體重：{user.weight} kg
+
+ + )} +
+ {/* 飲食紀錄卡片 */} +
+
+ + 飲食紀錄 +
+
+ setFoodDate(e.target.value)} /> + setFoodCal(e.target.value)} /> + setFoodCarb(e.target.value)} /> + setFoodProtein(e.target.value)} /> + +
+ {foodMsg &&
{foodMsg}
} +
+ {/* BMI 卡片 */} +
+
+ + BMI 與建議 +
+ + {bmi &&
BMI：{bmi}
} + {bmiMsg &&
{bmiMsg}
} +
+ {/* 水分卡片 */} +
+
+ + 水分攝取 +
+
+ setWaterDate(e.target.value)} /> + setWater(e.target.value)} /> + +
+ {waterMsg &&
{waterMsg}
} +
+ {/* AI 卡片 */} +
+
+ + AI 食物辨識 +
+ + + {aiResult &&
{aiResult}
} +
+ {/* 歷史紀錄卡片 */} +
+
+ + 歷史紀錄查詢 +
+ +
+ + + + + + + + + + + {history.map((h, i) => ( + + + + + + + ))} + +
日期熱量碳水蛋白質
{h.date}{h.cal}{h.carb}{h.protein}
+
+ {userCreated && ( +
+
用戶資料：
+
姓名：{user.name}
+
年齡：{user.age}
+
身高：{user.height} cm
+
體重：{user.weight} kg
+
用戶固定測試ID：6
+
+ )} +
+
+
+ {/* 簡單樣式 */} + +
+ ); +} diff --git a/frontend/src/pages/ExerciseTracker.jsx b/frontend/src/pages/ExerciseTracker.jsx new file mode 100644 index 0000000000000000000000000000000000000000..53cde54d3d8f84865b49e110bf53570d23a3ea0d --- /dev/null +++ b/frontend/src/pages/ExerciseTracker.jsx @@ -0,0 +1,138 @@ +import React from 'react'; +import { useState } from 'react'; +import { PlusIcon } from '@heroicons/react/24/outline'; + +const exerciseTypes = [ + { id: 'walking', name: '步行', caloriesPerMinute: 4 }, + { id: 'running', name: '跑步', caloriesPerMinute: 10 }, + { id: 'cycling', name: '騎自行車', caloriesPerMinute: 7 }, + { id: 'swimming', name: '游泳', caloriesPerMinute: 8 }, + { id: 'yoga', name: '瑜伽', caloriesPerMinute: 3 }, + { id: 'weightlifting', name: '重訓', caloriesPerMinute: 6 }, +]; + +export default function ExerciseTracker() { + const [selectedExercise, setSelectedExercise] = useState(''); + const [duration, setDuration] = useState(''); + + const calculateCalories = () => { + const exercise = exerciseTypes.find(e => e.id === selectedExercise); + if (exercise && duration) { + return exercise.caloriesPerMinute * parseInt(duration); + } + return 0; + }; + + return ( +
+
+

記錄運動

+ +
+
+
+ + +
+ +
+ + setDuration(e.target.value)} + className="mt-1 block w-full rounded-md border-gray-300 shadow-sm focus:border-indigo-500 focus:ring-indigo-500" + placeholder="0" + min="0" + /> +
+ + {selectedExercise && duration && ( +
+

預計消耗卡路里: {calculateCalories()} kcal

+
+ )} + + +
+ +
+

本週運動統計

+
+
+
+ 總運動時間 + 180 分鐘 +
+
+
+
+
+
+
+ 消耗卡路里 + 1,200 kcal +
+
+
+
+
+
+
+
+
+ + {/* 運動記錄 */} +
+

最近的運動記錄

+
+ + + + + + + + + + + + + + + + + + + + + + + +
日期運動類型時間 (分鐘)消耗卡路里
2025-04-21跑步30300
2025-04-20重訓45270
+
+
+
+ ); +} diff --git a/frontend/src/pages/FoodTracker.jsx b/frontend/src/pages/FoodTracker.jsx new file mode 100644 index 0000000000000000000000000000000000000000..0c3eec8b04ac731060fb77bdd0d8dbd1cd651096 --- /dev/null +++ b/frontend/src/pages/FoodTracker.jsx @@ -0,0 +1,337 @@ +import React, { useState, useRef, useEffect } from 'react'; +import { XMarkIcon, InboxArrowDownIcon, CalendarIcon, ClockIcon, ChartBarIcon } from '@heroicons/react/24/outline'; +import axios from 'axios'; + +export default function FoodTracker() { + const [selectedImage, setSelectedImage] = useState(null); + const [isAnalyzing, setIsAnalyzing] = useState(false); + const [analysisResults, setAnalysisResults] = useState(null); + const [isDragging, setIsDragging] = useState(false); + const [mealType, setMealType] = useState('lunch'); + const [mealDate, setMealDate] = useState(new Date().toISOString().split('T')[0]); + const [mealTime, setMealTime] = useState(new Date().toLocaleTimeString('en-US', { hour12: false, hour: '2-digit', minute: '2-digit' })); + const [portionSize, setPortionSize] = useState('medium'); + const [nutritionSummary, setNutritionSummary] = useState(null); + const [recentMeals, setRecentMeals] = useState([]); + const [isLoading, setIsLoading] = useState(false); + const imageRef = useRef(null); + + const handleDrop = async (event) => { + event.preventDefault(); + setIsDragging(false); + if (event.dataTransfer.files && event.dataTransfer.files.length > 0) { + await handleImageUpload({ target: { files: event.dataTransfer.files } }); + } + }; + + const handleDragOver = (event) => { + event.preventDefault(); + setIsDragging(true); + }; + + const handleDragLeave = (event) => { + event.preventDefault(); + setIsDragging(false); + }; + + const handleImageUpload = async (event) => { + const file = event.target.files[0]; + if (file) { + const reader = new FileReader(); + reader.onloadend = () => { + setSelectedImage(reader.result); + }; + reader.readAsDataURL(file); + + setIsAnalyzing(true); + try { + const formData = new FormData(); + formData.append('file', file); + + const response = await fetch('http://localhost:8000/api/ai/analyze-food', { + method: 'POST', + body: formData, + }); + + if (!response.ok) { + throw new Error('分析請求失敗'); + } + + const data = await response.json(); + + if (data.success && data.top_prediction) { + setAnalysisResults(data); + } else { + throw new Error(data.error || 'AI 無法識別這道食物'); + } + } catch (error) { + console.error('Error analyzing image:', error); + alert(error.message || '圖片分析失敗，請稍後再試'); + } finally { + setIsAnalyzing(false); + } + } + }; + + return ( +
+
+
+
+
+
+
+

AI 智慧食物分析

+ + {/* 上傳區域 */} +
+ {selectedImage ? ( +
+ 預覽 + +
+ ) : ( +
+ +
+ 拖曳圖片到這裡，或 + +
+ +
+ )} +
+ + {/* 載入中動畫 */} + {analysisResults && ( +
+

分析結果

+

這是：{analysisResults.top_prediction}

+ + {/* 營養資訊 */} +
+
+

熱量

+

{analysisResults.nutrition?.calories || '--'} kcal

+
+
+

蛋白質

+

{analysisResults.nutrition?.protein || '--'} g

+
+
+

碳水化合物

+

{analysisResults.nutrition?.carbs || '--'} g

+
+
+

脂肪

+

{analysisResults.nutrition?.fat || '--'} g

+
+
+ + {/* 用餐記錄 */} +
+
+
+ + +
+
+ + +
+
+ +
+
+ +
+ + setMealDate(e.target.value)} + className="w-full pl-10 rounded-md border-gray-300 shadow-sm focus:border-indigo-500 focus:ring-indigo-500" + /> +
+
+
+ +
+ + setMealTime(e.target.value)} + className="w-full pl-10 rounded-md border-gray-300 shadow-sm focus:border-indigo-500 focus:ring-indigo-500" + /> +
+
+
+ + +
+
+ )} + + {/* 分析結果 */} + {analysisResults && analysisResults.success && ( +
+ {/* 主要預測結果 */} +
+

+ 分析結果 +

+

+ {analysisResults.top_prediction?.label} + + ({Math.round(analysisResults.top_prediction?.confidence)}% 確信度) + +

+
+ + {/* 營養資訊 */} + {analysisResults.top_prediction?.nutrition && ( +
+

營養資訊

+
+
+ 熱量: + {analysisResults.top_prediction.nutrition.calories} kcal +
+
+ 蛋白質: + {analysisResults.top_prediction.nutrition.protein}g +
+
+ 碳水化合物: + {analysisResults.top_prediction.nutrition.carbs}g +
+
+ 脂肪: + {analysisResults.top_prediction.nutrition.fat}g +
+
+
+ )} + + {/* 食物描述 */} + {analysisResults.top_prediction?.description && ( +
+

食物描述

+

+ {analysisResults.top_prediction.description} +

+
+ )} + + {/* 分析時間 */} +
+ 分析時間: {new Date(analysisResults.analysis_time).toLocaleString()} +
+
+ )} +
+
+
+
+
+
+
+ ); +} diff --git a/frontend/src/pages/Login.jsx b/frontend/src/pages/Login.jsx new file mode 100644 index 0000000000000000000000000000000000000000..703fe74f0168964cf7f5f199ce58e47b6804e86a --- /dev/null +++ b/frontend/src/pages/Login.jsx @@ -0,0 +1,20 @@ +import React from 'react'; + +export default function Login() { + return ( +
+

登入

+
+
+ + +
+
+ + +
+ +
+
+ ); +} diff --git a/frontend/src/pages/Profile.jsx b/frontend/src/pages/Profile.jsx new file mode 100644 index 0000000000000000000000000000000000000000..9e1f9ebd1c453f4ace6172135a7d05626a78cb9b --- /dev/null +++ b/frontend/src/pages/Profile.jsx @@ -0,0 +1,10 @@ +import React from 'react'; + +export default function Profile() { + return ( +
+

個人資料

+

這裡可以顯示和編輯您的個人資訊。

+
+ ); +} diff --git a/frontend/src/pages/Register.jsx b/frontend/src/pages/Register.jsx new file mode 100644 index 0000000000000000000000000000000000000000..4273ec32768a8fb50217180f8d7f1f6cc5e17857 --- /dev/null +++ b/frontend/src/pages/Register.jsx @@ -0,0 +1,20 @@ +import React from 'react'; + +export default function Register() { + return ( +
+

註冊

+
+
+ + +
+
+ + +
+ +
+
+ ); +} diff --git a/frontend/src/pages/WaterTracker.jsx b/frontend/src/pages/WaterTracker.jsx new file mode 100644 index 0000000000000000000000000000000000000000..fc67728beca250573308636fb637680f92a3371b --- /dev/null +++ b/frontend/src/pages/WaterTracker.jsx @@ -0,0 +1,123 @@ +import React from 'react'; +import { useState } from 'react'; +import { PlusIcon, MinusIcon } from '@heroicons/react/24/outline'; + +export default function WaterTracker() { + const [waterIntake, setWaterIntake] = useState(0); + const goal = 2000; // 每日目標 (ml) + + const addWater = (amount) => { + setWaterIntake(prev => Math.min(prev + amount, 5000)); + }; + + const removeWater = (amount) => { + setWaterIntake(prev => Math.max(prev - amount, 0)); + }; + + const progress = (waterIntake / goal) * 100; + + return ( +
+
+

今日飲水追蹤

+ + {/* 進度顯示 */} +
+
+ 目前進度 + {waterIntake} / {goal} ml +
+
+
+
+
+ + {/* 快速添加按鈕 */} +
+ + + + +
+ + {/* 自定義輸入 */} +
+
+ + +
+ +
+
+ + {/* 今日記錄 */} +
+

今日記錄

+
+ + + + + + + + + + + + + + + + + + + + + +
時間數量 (ml)
14:30300
12:00500
09:15200
+
+
+
+ ); +} diff --git a/frontend/tailwind.config.js b/frontend/tailwind.config.js new file mode 100644 index 0000000000000000000000000000000000000000..dca8ba02d37dfd402f17d6004160b24574432251 --- /dev/null +++ b/frontend/tailwind.config.js @@ -0,0 +1,11 @@ +/** @type {import('tailwindcss').Config} */ +export default { + content: [ + "./index.html", + "./src/**/*.{js,ts,jsx,tsx}", + ], + theme: { + extend: {}, + }, + plugins: [], +} diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000000000000000000000000000000000000..3b522f444b89f7b88717f76dfed5146c19f3d4a5 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,7 @@ +[pytest] +python_files = test_*.py +testpaths = tests +pythonpath = . backend +addopts = -v --cov=backend --cov-report=term-missing +env = + LOAD_MODELS=false diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..c67360d78ba1f1bfb12f070c014df7618f6e88bb --- /dev/null +++ b/requirements.txt @@ -0,0 +1,19 @@ +# Core dependencies +fastapi>=0.109.0 +uvicorn[standard]>=0.27.0 +python-multipart>=0.0.6 +Pillow>=10.3.0 +transformers>=4.40.0 +torch>=2.2.0 +pydantic>=2.5.0 +anthropic>=0.18.0 +python-dotenv>=1.0.0 +httpx>=0.27.0 + +# Development dependencies +pytest>=8.4.1 +pytest-cov>=6.2.1 +pytest-asyncio>=1.0.0 +black>=24.4.2 +isort>=5.13.2 +mypy>=1.10.0 diff --git a/setup.py b/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..b3050f4fa9a5a96587877757640152f5bf03b143 --- /dev/null +++ b/setup.py @@ -0,0 +1,30 @@ +from setuptools import setup, find_packages + +setup( + name="health_assistant", + version="0.1.0", + packages=find_packages(where="backend"), + package_dir={"": "backend"}, + install_requires=[ + "fastapi>=0.68.0", + "uvicorn>=0.15.0", + "python-multipart>=0.0.5", + "Pillow>=8.3.1", + "transformers>=4.11.3", + "torch>=1.9.0", + "python-dotenv>=0.19.0", + "httpx>=0.19.0", + "pydantic>=1.8.0", + ], + extras_require={ + "dev": [ + "pytest>=6.2.5", + "pytest-cov>=2.12.1", + "pytest-asyncio>=0.15.1", + "black>=21.7b0", + "isort>=5.9.3", + "mypy>=0.910", + ], + }, + python_requires=">=3.8", +) diff --git a/test_pytest.py b/test_pytest.py new file mode 100644 index 0000000000000000000000000000000000000000..95c5ab74551bf74921d0dddabb2907833f6791ff --- /dev/null +++ b/test_pytest.py @@ -0,0 +1,2 @@ +def test_example(): + assert 1 + 1 == 2 diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..f26a2adc77d65196373b092598547d3d2ee90119 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,45 @@ +import pytest +from fastapi.testclient import TestClient +from backend.app.main import app +from unittest.mock import patch, MagicMock + +@pytest.fixture +def test_app(): + with TestClient(app) as test_client: + yield test_client + +@pytest.fixture +def sample_image(): + """Create a sample in-memory image for testing""" + from PIL import Image + from io import BytesIO + + # Create a simple red image + img = Image.new('RGB', (100, 100), color='red') + img_byte_arr = BytesIO() + img.save(img_byte_arr, format='JPEG') + img_byte_arr.seek(0) + + # Return the image data as bytes + return img_byte_arr.getvalue() + +@pytest.fixture +def mock_analyzer(): + with patch('transformers.AutoImageProcessor') as mock_processor, \ + patch('transformers.AutoModelForImageClassification') as mock_model, \ + patch('anthropic.Anthropic') as mock_anthropic: + + # Setup mock processor + mock_processor.from_pretrained.return_value = MagicMock() + + # Setup mock model + mock_model.from_pretrained.return_value = MagicMock() + + # Setup mock anthropic + mock_anthropic.return_value = MagicMock() + + yield ( + mock_processor.from_pretrained.return_value, + mock_model.from_pretrained.return_value, + mock_anthropic.return_value + ) diff --git a/tests/test_api/__init__.py b/tests/test_api/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tests/test_api/test_main.py b/tests/test_api/test_main.py new file mode 100644 index 0000000000000000000000000000000000000000..48de39c526a6766ebb4ff4df91c4a9f2b849d573 --- /dev/null +++ b/tests/test_api/test_main.py @@ -0,0 +1,100 @@ +import pytest +import sys +import os +from pathlib import Path + +# Add the project root to the Python path +project_root = Path(__file__).parent.parent +sys.path.append(str(project_root)) + +from unittest.mock import patch, MagicMock +from fastapi import HTTPException +from fastapi.testclient import TestClient + +# Test the root endpoint +def test_read_root(test_app): + response = test_app.get("/") + assert response.status_code == 200 + assert response.json() == {"message": "Health Assistant API is running"} + +# Test food recognition endpoint +@patch('backend.app.services.food_analyzer_service.HybridFoodAnalyzer.analyze_food') +def test_recognize_food(mock_analyze_food, test_app, sample_image): + # Mock the response from the analyzer + mock_analyze_food.return_value = { + "food_name": "apple", + "chinese_name": "蘋果", + "confidence": 0.95, + "success": True + } + + # Create a test file + files = {"file": ("test_image.jpg", sample_image, "image/jpeg")} + + # Make the request + response = test_app.post("/recognize-food", files=files) + + # Check the response + assert response.status_code == 200 + data = response.json() + assert data["success"] is True + assert "food_name" in data + assert "chinese_name" in data + assert "confidence" in data + +# Test nutrition analysis endpoint +@patch('app.services.food_analyzer_service.HybridFoodAnalyzer.analyze_nutrition') +def test_analyze_nutrition(mock_analyze_nutrition, test_app): + # Mock the response from the analyzer + mock_analyze_nutrition.return_value = { + "nutrition": { + "calories": 95, + "protein": 0.5, + "fat": 0.3, + "carbohydrates": 25.0 + }, + "success": True + } + + # Test data + test_data = {"food_name": "apple"} + + # Make the request + response = test_app.post("/analyze-nutrition", json=test_data) + + # Check the response + assert response.status_code == 200 + data = response.json() + assert data["success"] is True + assert "nutrition" in data + assert "analysis" in data + +# Test error handling for invalid image +def test_recognize_food_invalid_file(test_app): + # Create an invalid file + files = {"file": ("test.txt", b"not an image", "text/plain")} + + # Make the request + response = test_app.post("/recognize-food", files=files) + + # Check the response + assert response.status_code == 400 + data = response.json() + assert data["detail"] == "Invalid image file" + +# Test error handling for missing food name +@patch('app.services.food_analyzer_service.HybridFoodAnalyzer.analyze_nutrition') +def test_analyze_nutrition_missing_food(mock_analyze_nutrition, test_app): + # Configure the mock to raise an exception + mock_analyze_nutrition.side_effect = ValueError("Food name is required") + + # Test data with missing food name + test_data = {} + + # Make the request + response = test_app.post("/analyze-nutrition", json=test_data) + + # Check the response + assert response.status_code == 400 + data = response.json() + assert "detail" in data diff --git a/tests/test_services/__init__.py b/tests/test_services/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tests/test_services/test_food_analyzer_service.py b/tests/test_services/test_food_analyzer_service.py new file mode 100644 index 0000000000000000000000000000000000000000..7fafef93a79547a5582a9a9a7184aa756db6d19c --- /dev/null +++ b/tests/test_services/test_food_analyzer_service.py @@ -0,0 +1,179 @@ +import pytest +from unittest.mock import patch, MagicMock, ANY +from PIL import Image +from io import BytesIO +import json +import sys +import os +import torch + +# Add project root to path +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..'))) + +from backend.app.services.food_analyzer_service import HybridFoodAnalyzer + +# Test data +SAMPLE_NUTRITION_DATA = { + "calories": 95, + "protein": 0.5, + "fat": 0.3, + "carbohydrates": 25.0, + "fiber": 4.4, + "sugar": 19.0, + "sodium": 2, + "cholesterol": 0, + "saturated_fat": 0.1, + "calcium": 1, + "iron": 1, + "potassium": 195, + "vitamin_a": 2, + "vitamin_c": 14, + "vitamin_d": 0 +} + +SAMPLE_IMAGE = None + +def create_test_image(): + """Helper function to create a test image""" + img = Image.new('RGB', (100, 100), color='red') + img_byte_arr = BytesIO() + img.save(img_byte_arr, format='JPEG') + return img_byte_arr.getvalue() + +@pytest.fixture +def mock_analyzer(): + with patch('transformers.AutoImageProcessor.from_pretrained') as mock_processor, \ + patch('transformers.AutoModelForImageClassification.from_pretrained') as mock_model, \ + patch('anthropic.Anthropic') as mock_anthropic: + + # Setup mock processor + mock_processor.return_value = MagicMock() + + # Setup mock model + mock_model.return_value = MagicMock() + mock_model.return_value.config.id2label = {0: "apple"} + mock_model.return_value.return_value = MagicMock(logits=torch.tensor([[0.9, 0.1]])) + + # Setup mock anthropic + mock_anthropic.return_value = MagicMock() + mock_anthropic.return_value.messages.create.return_value.content = [ + MagicMock(text=json.dumps(SAMPLE_NUTRITION_DATA)) + ] + + analyzer = HybridFoodAnalyzer("test_api_key") + analyzer.processor = mock_processor.return_value + analyzer.model = mock_model.return_value + analyzer.claude_client = mock_anthropic.return_value + + yield analyzer + +def test_hybrid_food_analyzer_init(mock_analyzer): + """Test initialization of HybridFoodAnalyzer""" + assert mock_analyzer is not None + assert hasattr(mock_analyzer, 'processor') + assert hasattr(mock_analyzer, 'model') + assert hasattr(mock_analyzer, 'claude_client') + +def test_recognize_food_success(mock_analyzer): + """Test successful food recognition""" + # Create test image + img = Image.new('RGB', (100, 100), color='red') + img_byte_arr = BytesIO() + img.save(img_byte_arr, format='JPEG') + + # Call method + result = mock_analyzer.recognize_food(img_byte_arr.getvalue()) + + # Assertions + assert result["success"] is True + assert "food_name" in result + assert "chinese_name" in result + assert "confidence" in result + assert result["food_name"] == "apple" + assert result["chinese_name"] == "蘋果" + +def test_recognize_food_error(mock_analyzer): + """Test error handling in food recognition""" + # Setup mock to raise exception + mock_analyzer.model.side_effect = Exception("Test error") + + # Call method with invalid image + result = mock_analyzer.recognize_food(b"invalid_image") + + # Assertions + assert result["success"] is False + assert "error" in result + +def test_analyze_nutrition_success(mock_analyzer): + """Test successful nutrition analysis""" + # Call method + result = mock_analyzer.analyze_nutrition("apple") + + # Assertions + assert result["success"] is True + assert "nutrition" in result + assert result["nutrition"] == SAMPLE_NUTRITION_DATA + mock_analyzer.claude_client.messages.create.assert_called_once() + +def test_analyze_nutrition_error(mock_analyzer): + """Test error handling in nutrition analysis""" + # Setup mock to raise exception + mock_analyzer.claude_client.messages.create.side_effect = Exception("API error") + + # Call method + result = mock_analyzer.analyze_nutrition("invalid_food") + + # Assertions + assert result["success"] is False + assert "error" in result + +def test_process_image_success(mock_analyzer): + """Test successful image processing""" + # Setup + test_image = create_test_image() + + # Call method + result = mock_analyzer.process_image(test_image) + + # Assertions + assert result["success"] is True + assert "food_name" in result + assert "nutrition" in result + assert "analysis" in result + assert "healthScore" in result["analysis"] + assert "recommendations" in result["analysis"] + assert "warnings" in result["analysis"] + +def test_calculate_health_score(mock_analyzer): + """Test health score calculation""" + # Test with sample nutrition data + score = mock_analyzer.calculate_health_score(SAMPLE_NUTRITION_DATA) + + # Assert score is within expected range + assert isinstance(score, (int, float)) + assert 0 <= score <= 100 + +def test_generate_recommendations(mock_analyzer): + """Test generation of dietary recommendations""" + # Call method + recommendations = mock_analyzer.generate_recommendations(SAMPLE_NUTRITION_DATA) + + # Assertions + assert isinstance(recommendations, list) + assert all(isinstance(rec, str) for rec in recommendations) + +def test_generate_warnings(mock_analyzer): + """Test generation of dietary warnings""" + # Call method + warnings = mock_analyzer.generate_warnings(SAMPLE_NUTRITION_DATA) + + # Assertions + assert isinstance(warnings, list) + assert all(isinstance(warning, str) for warning in warnings) + +def test_calculate_health_score_incomplete_data(mock_analyzer): + """Test health score calculation with incomplete nutrition data""" + # Test with incomplete nutrition data + incomplete_nutrition = {"calories": 100} + health_score = mock_analyzer.calculate_health_score(incomplete_nutrition) + assert 0 <= health_score <= 100 diff --git a/tests/test_simple.py b/tests/test_simple.py new file mode 100644 index 0000000000000000000000000000000000000000..82c56be3d0801144b689b585331c55250878ffb2 --- /dev/null +++ b/tests/test_simple.py @@ -0,0 +1,2 @@ +def test_simple(): + assert 1 + 1 == 2 diff --git a/tests/test_verify.py b/tests/test_verify.py new file mode 100644 index 0000000000000000000000000000000000000000..d787d6e7a9a681b46c0be4a938a0dc9778c33456 --- /dev/null +++ b/tests/test_verify.py @@ -0,0 +1,5 @@ +def test_verify(): + """Simple test to verify pytest is working""" + print("\n=== Running verify test ===") + assert 1 + 1 == 2 + print("=== Verify test passed ===\n") diff --git a/updated_addToDiary.js b/updated_addToDiary.js new file mode 100644 index 0000000000000000000000000000000000000000..43f1e426257de200947d3dd1fd49bc33c44d9c83 --- /dev/null +++ b/updated_addToDiary.js @@ -0,0 +1,123 @@ +// 將食物加入飲食記錄 +function addToDiary(foodName) { + if (!foodName) { + console.warn('無法加入空的食物名稱'); + return false; + } + + try { + // 確保 dailyMeals 是陣列 + if (!Array.isArray(dailyMeals)) { + dailyMeals = []; + } + + const today = new Date().toISOString().split('T')[0]; + const now = new Date(); + + // 從營養資料庫獲取食物資訊，如果沒有則使用預設值 + const foodInfo = nutritionDatabase[foodName] || { + calories: 200, + protein: 10, + carbs: 30, + fat: 5, + fiber: 2, + sugar: 5 + }; + + const meal = { + id: Date.now(), + name: foodName, + ...foodInfo, // 展開營養資訊 + date: today, + time: now.toLocaleTimeString('zh-TW', {hour: '2-digit', minute:'2-digit'}), + timestamp: now.toISOString() + }; + + // 加入飲食記錄 + dailyMeals.push(meal); + + // 儲存到 localStorage + localStorage.setItem('dailyMeals', JSON.stringify(dailyMeals)); + + // 顯示成功訊息 + showNotification(`已將「${foodName}」加入飲食記錄`, 'success'); + + // 更新 UI + if (typeof updateMealsList === 'function') { + updateMealsList(); + } + + if (typeof updateTrackingStats === 'function') { + updateTrackingStats(); + } + + // 觸發自定義事件，通知其他組件 + document.dispatchEvent(new CustomEvent('mealAdded', { detail: meal })); + + return true; + + } catch (error) { + console.error('加入飲食記錄失敗:', error); + showNotification('加入飲食記錄時發生錯誤', 'error'); + return false; + } +} + +// 顯示通知訊息 +function showNotification(message, type = 'info') { + // 檢查是否已經存在通知容器 + let notificationContainer = document.getElementById('notification-container'); + + if (!notificationContainer) { + // 創建通知容器 + notificationContainer = document.createElement('div'); + notificationContainer.id = 'notification-container'; + notificationContainer.style.position = 'fixed'; + notificationContainer.style.top = '20px'; + notificationContainer.style.right = '20px'; + notificationContainer.style.zIndex = '1000'; + document.body.appendChild(notificationContainer); + } + + // 創建通知元素 + const notification = document.createElement('div'); + notification.className = `notification ${type}`; + notification.style.padding = '12px 20px'; + notification.style.marginBottom = '10px'; + notification.style.borderRadius = '4px'; + notification.style.color = 'white'; + notification.style.opacity = '0'; + notification.style.transition = 'opacity 0.3s ease-in-out'; + notification.style.boxShadow = '0 2px 10px rgba(0,0,0,0.1)'; + + // 根據類型設置背景色 + const colors = { + success: '#4CAF50', + error: '#F44336', + warning: '#FF9800', + info: '#2196F3' + }; + + notification.style.backgroundColor = colors[type] || colors.info; + notification.textContent = message; + + // 添加到容器 + notificationContainer.appendChild(notification); + + // 觸發動畫 + setTimeout(() => { + notification.style.opacity = '1'; + }, 10); + + // 3秒後自動移除 + setTimeout(() => { + notification.style.opacity = '0'; + setTimeout(() => { + notification.remove(); + // 如果容器為空，則移除容器 + if (notificationContainer && notificationContainer.children.length === 0) { + notificationContainer.remove(); + } + }, 300); + }, 3000); +}