MailBrief

A lightweight web-based AI assistant built with Flask. It can answer general questions and summarize emails using an LLM served through the Groq API.

Features

• Ask Anything – Submit any question and get an AI-generated response.
• Email Summarizer – Paste an email and receive a concise 6–7 sentence summary.
• Simple, responsive web interface (HTML, CSS, JavaScript).
• Asynchronous requests with loading indicators for a smooth user experience.

Screenshots

Tech Stack

Layer	Technology
Backend	Python, Flask
AI Model	Groq API (openai/gpt-oss-20b)
Frontend	HTML, CSS, JavaScript
Config	python-dotenv

Project Structure

Personalized-AI-Assistant/
├── main.py                # Flask app and API routes
├── templates/
│   └── index.html         # Main page
├── static/
│   ├── script.js           # Frontend logic (fetch requests)
│   └── style.css           # Styling
└── .gitignore

Prerequisites

• Python 3.9 or higher
• A Groq API key

Installation

1. Clone the repository

   git clone https://github.com/Samir-BK/Personalized-AI-Assistant.git
   cd Personalized-AI-Assistant

2. Create and activate a virtual environment (recommended)

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

3. Install dependencies

   pip install flask openai python-dotenv

4. Set up environment variables

   Create a .env file in the project root and add your Groq API key:

   GROQ_API_KEY=your_groq_api_key_here
   

Usage

Start the Flask development server:

python main.py

The app will be available at http://127.0.0.1:5000.

• Use the Ask Anything form to send a question to the assistant.
• Use the Summarize Email form to paste in an email and get a summary.

API Endpoints

Endpoint	Method	Description	Body Parameter
/	GET	Renders the main page	–
/ask	POST	Returns an AI-generated answer to a query	question
/summarize	POST	Returns a summary of the provided email	email

Configuration Notes

• The application uses the Groq-hosted OpenAI-compatible API. If you'd like to use a different provider or model, update the base_url and model values in main.py.
• app.run(debug=True) is intended for local development. Disable debug mode before deploying to production.

License

This project is licensed under the MIT License.

Author

Samir B K GitHub: @Samir-BK