AI-Enabled Nutrition Management Web App
Track your daily food intake, calculate calories, and stay aligned with your fat-loss goals using AI-powered insights built on USDA datasets.
DietTracker is a full-stack AI-enabled web application built with the latest web technologies like Next.js, React, Prisma ORM, MongoDB, Tailwind CSS, Clerk, and AG Grid. It integrates Google Gemini 2.0 Flash via Vertex AI Studio—fine-tuned on the USDA dataset—to help users track and analyze their diet with minimal manual input while maintaining a calorie deficit cycle for fat loss.
- Google Authentication (OAuth via Clerk)
- Goal Setting: Age, Weight, Height, BMR, TDEE, Calorie Deficit
- Manual or AI-Powered Food Logging (USDA or Image-to-Nutrition via AI Agent)
- Category Tracking: Breakfast, Lunch, Dinner, Snacks, Other
- Live Tracker Tables (AG Grid) with real-time nutrient subtotals
- Quick Add Buttons and floating AI assistant
- Persistent Food Lists for faster future tracking
- Dynamic Nutrient Analysis and Calorie Goal Comparison
- Account Editing, Goal Reset & Deletion (Clerk)
-
AI Assistant:
- Get nutritional values of food items from the USDA dataset.
- Explain nutritional values in detail.
- Auto-fill the Add Food Item form with JSON data generated by the AI model.
-
Image Parsing: Upload or capture nutritional labels for automatic parsing and form-filling using Google Gemini 2.0 Flash.
- Add Food Items: Fill a form with food name and nutritional values manually or upload a product nutrition label image for AI parsing.
- Categorize Food Items: Track food under predefined categories (Breakfast, Lunch, Dinner, Snacks, and Other) using enums.
- Reusable Food List: Save frequently used food items for quick reuse without re-entering details.
- Update or Delete Food Items: Easily modify or remove items directly from the tracker.
- AG Grid Tracker: Displays food items in a table with rows for items and columns for nutrients (Carbs, Protein, Fat, Sugar, etc.).
- Category-Specific Tracking: Maintain separate trackers for each category.
- Nutrition Subtotals: Automatically calculates subtotal for nutrients in each tracker.
- Total Nutrition Calculation: Calculate total nutrients across all categories to compare against calorie deficit goals.
- Debounced Input: Avoids unnecessary API calls for fetching food items by implementing a debouncing algorithm.
- Recent Item Fetching: Quickly fetches recently added items from diet state management to avoid redundant API calls.
- USDA API Integration: Fetch nutritional data from the USDA API.
- Account Management: Delete the user account directly from the profile settings.
- Goal Updates: Update personal data and calorie goals from the Goal page.
flowchart TD
A[Google Sign-In via Clerk] --> B{New User?}
B -- Yes --> C[Enter Age, Weight, Height]
C --> D[Calculate BMR, TDEE, Calorie Deficit]
B -- No --> E[Go to Dashboard]
D --> E
E --> F[Add Food Item]
F --> G{Input Method}
G -- Manual --> H[Enter Nutrients]
G -- AI --> I[Extract from Image/USDA]
H --> J[Save to Food List]
I --> J
J --> K{Select Category}
K --> L[Breakfast]
K --> M[Lunch]
K --> N[Dinner]
K --> O[Snacks]
K --> P[Other]
L --> Q[Add to Tracker Table]
M --> Q
N --> Q
O --> Q
P --> Q
Q --> R[View Subtotal per Category]
R --> S[Calculate Daily Totals]
S --> T[Compare with Goal]
T --> U[Use Quick Actions]
U --> V[AI Chat Assistant]
V --> W[Fetch USDA Info]
V --> X[Auto-fill Form]
T --> Y[Track Progress]
Q --> Z[Edit/Delete Items]
Z --> AA[Update Goals / Delete Account]
| Layer | Technologies & Services |
|---|---|
| Front-end Framework | Next.js 14 (App Router), React 19 |
| UI & Styling | Shadcn UI, Tailwind CSS, AG Grid |
| Language & Typing | TypeScript, Javascript, ES2024+ |
| State Management | React Context & Hooks, Debounce Utility |
| Authentication | Clerk (OAuth2 / JWT) |
| Back-end & ORM | Next.js API Routes, Next.js Server Action, Prisma ORM |
| Database | MongoDB Atlas |
| AI & ML | Google Vertex AI Studio, Goole AI Studio, Gemini 2.0 Flash model |
| APIs | USDA API, Custom REST Endpoints |
| Storage | Cloudinary |
| Deployment | Vercel (Front-end), Google Cloud (Vertex AI + Functions) |
-
Clone the repository:
git clone https://github.com/your-username/diet-tracker.git cd diet-tracker -
Install dependencies:
npm install
-
Set up environment variables:
-
Create a
.envfile in the root directory. -
Add your API keys and configuration settings for:
DATABASE_URL=your_mongodb_connection_string NEXT_PUBLIC_URL=your_public_url NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY=your_clerk_publishable_key CLERK_SECRET_KEY=your_clerk_secret_key SIGNING_SECRET=your_clerk_google_signing_secret NEXT_PUBLIC_CLERK_SIGN_IN_URL=your_signin_url GEMINI_API_KEY=google_gemini_api CLOUDINARY_CLOUD_NAME=your_cloud_name CLOUDINARY_API_KEY=your_api_key CLOUDINARY_API_SECRET=your_api_secret
-
-
Run the development server:
npm run dev
-
Open the application in your browser:
http://localhost:3000
- Log in and set your personal goals (optional).
- Add food items manually or via AI-assisted form filling.
- Track your diet using category-specific trackers.
- Use AI chat for nutritional insights and quick actions.
Contributions are welcome! Please follow the steps below:
- Fork the repository.
- Create a new branch:
git checkout -b feature/your-feature-name
- Commit your changes:
git commit -m 'Add your feature description' - Push to the branch:
git push origin feature/your-feature-name
- Open a Pull Request.
This project is licensed under the MIT License.