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https://github.com/ezahpizza/nexafit

A free web app that helps users plan healthy meals and track calories burned after workouts using smart predictions.
https://github.com/ezahpizza/nexafit

machine-learning reactjs spoonacular-api web-development xgboost

Last synced: about 2 months ago
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A free web app that helps users plan healthy meals and track calories burned after workouts using smart predictions.

Awesome Lists containing this project

README 

          
# nexaFit Monorepo



Welcome to the **nexaFit Monorepo**, a unified repository for both the backend and frontend components of the nexaFit platform. nexaFit is a comprehensive nutrition and wellness platform that offers calorie prediction, meal planning, community forums, and now an **LLM-powered chatbot assistant** to guide users in utilizing the platform effectively.



---



## Features



### Backend Features



1. **Post-Workout Burnt Calorie Prediction**

    - Uses a trained **XGBoost model** to predict calories burnt based on user input.

    - Inputs include gender, age, height, weight, workout duration, heart rate, and body temperature.

2. **Meal Planning**

    - Integrates with the **Spoonacular API** to generate weekly meal plans.

    - Supports dietary preferences, calorie limits, and intolerance filtering.

3. **Forum**

    - Allows users to create posts, comment on discussions, and engage with the community.

    - Features tagging, pagination, and comment tracking for meaningful discussions.

4. **User Management**

    - Handles user profile creation, updates, and retrieval.

5. **Chatbot Assistant**

    - Powered by an LLM (Large Language Model) to assist users in navigating the platform.

    - Provides guidance on calorie prediction inputs, meal planning options, forum usage, and more.



---



### Frontend Features



1. **User Dashboard**

    - Displays daily caloric intake, nutritional data, and suggested meal plans.

    - Visualizes calorie predictions using charts.

2. **Authentication**

    - User registration and login powered by **Clerk**.

3. **Meal Planning**

    - Fetches weekly meal plans from the backend.

4. **Forum**

    - Enables users to participate in discussions, share motivation, and engage with the community.

5. **Chatbot Integration**

    - Frontend interface for interacting with the LLM-powered assistant.

6. **Responsive Design**

    - Optimized for mobile and desktop views.



---



## Tech Stack



### Backend



- **Framework**: FastAPI

- **Database**: MongoDB Atlas

- **Machine Learning Model**: XGBoost

- **Containerization**: Docker

- **LLM Integration**: OpenAI GPT or similar language model



### Frontend



- **Framework**: React

- **Styling**: TailwindCSS

- **Deployment**: Vercel

- **HTTP Client**: Axios

- **Authentication**: Clerk



---



## Monorepo Directory Structure



```plaintext

.

├── backend/                  # Backend directory

│   ├── .dockerignore         # Files ignored by Docker

│   ├── .env                  # Environment variables (not included in repo)

│   ├── .gitignore            # Git ignore rules

│   ├── Dockerfile            # Docker build instructions

│   ├── LICENSE               # License file

│   ├── main.py               # FastAPI entry point

│   ├── README.Docker.md      # Docker-specific instructions

│   ├── README.md             # Backend documentation

│   ├── render.yaml           # Render deployment blueprint file

│   ├── requirements.txt      # Python dependencies

│   ├── database/             # Database management files

│   │   └── database.py       # MongoDB database manager

│   ├── ml/                   # Machine learning-related files

│   │   ├── calorie_predictor.json  # Trained XGBoost model

│   │   ├── ml_predictor.py         # Prediction endpoint handler

│   │   └── std_scaler.bin          # Fitted StandardScaler for normalization

│   ├── routes/               # API route handlers

│   │   ├── calorie_prediction.py  # Calorie prediction routes

│   │   ├── chat_assistant.py       # Chatbot assistant routes

│   │   ├── forum.py                # Forum routes

│   │   ├── meal_planning.py        # Meal planning routes

│   │   └── user_profile.py         # User profile routes

│   └── validation/           # Validation models using Pydantic

│       └── models.py         # Pydantic validation models for API requests/responses

├── frontend/                 # Frontend directory

│   ├── public/               # Static assets (e.g., images, favicon)

│   ├── src/

│   │   ├── components/       # Reusable React components

│   │   ├── pages/            # Page-level components (e.g., Dashboard, Forum)

│   │   ├── styles/           # TailwindCSS configuration files

│   │   └── App.js            # Main React application entry point

│   ├── .env                  # Environment variables (not included in repo)

│   ├── package.json          # Project metadata and dependencies

│   └── tailwind.config.js    # TailwindCSS configuration file

└── README.md                 # Monorepo documentation (this file)

```



---



## Installation



### Prerequisites



- Python 3.9+

- Node.js (v16+)

- Docker (optional for containerization)

- MongoDB Atlas account (for backend database)

- Spoonacular API key (for meal planning)

- OpenAI API key or equivalent (for LLM chatbot assistant)



---



### Backend Setup



1. Navigate to the `backend` directory:



```bash

cd backend/

```



2. Install dependencies:



```bash

pip install -r requirements.txt

```



3. Set up environment variables:

Create a `.env` file in the `backend` directory with the following keys:



```env

MONGODB_URI=<your_mongodb_uri>

DATABASE_NAME=<your_database_name>

SPOONACULAR_API_KEY=<your_spoonacular_api_key>

CALORIE_MODEL_PATH=ml/calorie_predictor.json

OPENAI_API_KEY=<your_openai_api_key>

```



4. Run the application:



```bash

uvicorn main:app --reload

```



5. Access the API documentation at `http://127.0.0.1:8000/docs`.



---



### Frontend Setup



1. Navigate to the `frontend` directory:



```bash

cd frontend/

```



2. Install dependencies:



```bash

npm install

```



3. Set up environment variables:

Create a `.env` file in the `frontend` directory with the following keys:



```bash

REACT_APP_API_URL=<your-backend-api-url>

REACT_APP_CLERK_FRONTEND_API=<your-clerk-frontend-api-key>

REACT_APP_OPENAI_API_KEY=<your-openai-api-key>

```



4. Start the development server:



```bash

npm run dev

```



5. Build for production:



```bash

npm run build

```



6. Run production build locally:



```bash

npm start

```



---



## Running with Docker



### Backend



1. Navigate to the `backend` directory:



```bash

cd backend/

```



2. Build the Docker image:



```bash

docker build -t nexafit-backend .

```



3. Run the container:



```bash

docker run --env-file .env -p 8000:8000 nexafit-backend 

```



4. Access the API at `http://127.0.0.1:8000`.



---



## Deployment



### Backend Deployment



The backend can be deployed on platforms like Render.com using `render.yaml` or any cloud provider supporting Docker containers.



### Frontend Deployment



The frontend is optimized for deployment on Vercel or Netlify.



To deploy on Vercel:



1. Push changes to your GitHub repository.

2. Link your repository to Vercel.

3. Configure environment variables in Vercel settings.

4. Deploy!



---



## Contributing



We welcome contributions! To contribute:



1. Fork this repository.

2. Create a new branch (`git checkout -b feature-name`).

3. Commit your changes (`git commit -m "Add feature"`).

4. Push to your branch (`git push origin feature-name`).

5. Open a pull request.



---



## License



This project is licensed under the MIT License.



---



## Contact



For questions or support, please contact [ezahpizza](https://github.com/ezahpizza).