Cred-Hack is a full-stack application designed to streamline financial management. It features a modern frontend built with Next.js and Tailwind CSS, and a robust backend powered by FastAPI. The application includes tools for bank statement ingestion, categorization, and analysis, as well as AI-powered chat capabilities using OpenAI's API.

• Modern UI: Built with Next.js and styled using Tailwind CSS.
• Bank Statement Upload: Users can upload bank statements in .xlsx format for analysis.
• Authentication: User authentication and onboarding flows.
• AI Chat: Chat interface powered by OpenAI for financial advice.

• Bank Statement Processing: Parses and categorizes transactions from uploaded bank statements.
• Data Analysis: Provides summaries of income, expenses, and monthly surpluses.
• API Integration: Exposes endpoints for frontend interaction.

The repository is organized as follows:

cred-hack/
├── backend/                # Backend codebase
│   ├── app/                # FastAPI application
│   │   ├── main.py         # Entry point for the backend server
│   │   ├── services/       # Business logic and services
│   │   └── __init__.py     # Package initialization
│   ├── requirements.txt    # Python dependencies
│   └── .env                # Environment variables for the backend
├── frontend/               # Frontend codebase
│   ├── src/                # Source code for the Next.js application
│   │   ├── app/            # Application pages and API routes
│   │   ├── components/     # Reusable UI components
│   │   └── styles/         # Global styles
│   ├── package.json        # Node.js dependencies
│   ├── .env.local          # Environment variables for the frontend
│   └── tailwind.config.js  # Tailwind CSS configuration
├── .gitignore              # Files and directories to ignore in Git
├── README.md               # Project documentation
└── LICENSE                 # License for the project

This structure separates the backend and frontend codebases, ensuring modularity and maintainability.

• Node.js 18.x or later
• Python 3.11 or later
• Redis (for caching)
• An OpenAI API key

1. Navigate to the backend directory:

   cd backend

2. Create a virtual environment and activate it:

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up the .env file with the required environment variables:

   JWT_SECRET_KEY=your_jwt_secret_key
   OPENAI_API_KEY=your_openai_api_key

5. Run the backend server:

   uvicorn app.main:app --reload

1. Navigate to the frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Set up the .env.local file with the required environment variables:

   OPENAI_API_KEY=your_openai_api_key

4. Run the development server:

   npm run dev

1. Start the backend server by following the steps in the Backend section.
2. Start the frontend server by following the steps in the Frontend section.
3. Open your browser and navigate to http://localhost:3000 to access the application.

1. Start the backend server by following the steps in the Backend section of the installation guide.
2. Start the frontend server by following the steps in the Frontend section of the installation guide.
3. Open your browser and navigate to http://localhost:3000 to access the application.
4. Upload a bank statement in .xlsx format for analysis.
5. Use the AI-powered chat interface to get financial advice or insights.

Cred-Hack is a full-stack application with the following architecture:

• Framework: Built with Next.js for server-side rendering and React-based UI.
• Styling: Tailwind CSS for modern and responsive design.
• Features:
  • Bank statement upload and analysis.
  • User authentication and onboarding flows.
  • AI-powered chat interface for financial advice using OpenAI's API.

• Framework: FastAPI for building RESTful APIs.
• Data Processing: Utilizes Pandas for parsing and analyzing bank statements.
• Caching: Redis for caching frequently accessed data.
• Features:
  • Bank statement ingestion and categorization.
  • Income, expense, and surplus analysis.
  • API endpoints for frontend interaction.

• OpenAI API: Used for generating financial advice and insights in the chat interface.

• Storage: SQLite or other relational databases for storing user data and transaction records.

This architecture ensures a seamless user experience with a modern frontend, a robust backend, and AI-powered features for financial management.