AI Flight Recommender 🛫

A smart flight search and recommendation system that combines real-time flight data with AI-powered analysis to help users find their ideal flights based on personal preferences.

Features ✨

• Real-time flight search using Google Flights data
• AI-powered flight analysis and recommendations
• Customizable search parameters
• Interactive user interface
• Multi-currency support
• Layover analysis
• Detailed flight segment information
• Personalized recommendations based on preferences

Prerequisites 📋

Before running the application, make sure you have:

• Python 3.8+
• SerpAPI API key
• Groq API key
• Docker (optional, for containerized deployment)

Installation 🚀

Local Installation

1. Clone the repository:

git clone https://github.com/We4TechAI/AI-Flight-Recommender.git
cd AI-Flight-Recommender

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install required packages:

pip install -r requirements.txt

4. Create a .env file in the project root:

SERP_API_KEY=your_serpapi_key_here
GROQ=your_groq_api_key_here

Docker Installation

1. Build the Docker image:

docker build --tag ai_flight_recommender:latest .

2. Run the container:

docker run -d --name ai_flight_recommender -p 8501:8501 ai_flight_recommender:latest

Usage 📖

1. Start the application:

   • Local: streamlit run main.py
   • Docker: Access through http://localhost:8501

2. Enter flight search parameters:

   • Departure airport code
   • Arrival airport code
   • Travel dates
   • Number of passengers
   • Currency preference

3. Specify your travel preferences in the text area (e.g., "I prefer morning flights with minimal layovers")

4. Click "Search Flights" to get results and AI analysis

Project Structure 📁

AI-Flight-Recommender/
├── main.py              # Main application file
├── Dockerfile          # Docker configuration
├── requirements.txt    # Python dependencies
├── .env               # Environment variables
├── banner.png         # Project banner
└── README.md          # Project documentation

Environment Variables 🔐

Create a .env file with the following variables:

GROQ=your_groq_api_key_here
SERPAPI_KEY=your_serpapi_key_here

Docker Support 🐳

The application can be containerized using Docker. The included Dockerfile sets up the environment and runs the Streamlit application.

Build Image

docker build --tag ai_flight_recommender:latest .

Run Container

docker run -d --name ai_flight_recommender -p 8501:8501 ai_flight_recommender:latest

API Documentation 📚

SerpAPI

• Used for fetching real-time flight data
• Documentation: SerpAPI Google Flights

Groq

• Used for AI-powered flight analysis
• Documentation: Groq API

Contributing 🤝

1. Fork the repository
2. Create a feature branch: git checkout -b feature/new-feature
3. Commit changes: git commit -am 'Add new feature'
4. Push to branch: git push origin feature/new-feature
5. Submit a pull request

License 📄

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments 🙏

• SerpAPI for flight data
• Groq for AI capabilities
• Streamlit for the UI framework

Support 💬

For support, please open an issue in the GitHub repository or contact the maintainers.