Gevans4352/Design-and-implementation-of-Multilingual-system-

FluentRoot is a multilingual AI chatbot developed as a final-year project. It supports five languages English, French, Yoruba, Igbo, and Hausa making it especially relevant to Nigeria’s diverse linguistic landscape. Built with a React/TypeScript frontend and a python backend. # Fluent FluentRoot is a multilingual AI-powered chatbot that enables users to communicate and learn across five languages: English, French, Yoruba, Igbo, and Hausa. Built as a final-year capstone project, it focuses on bridging the gap in natural language processing support for Nigerian indigenous languages. --- ## Table of Contents - Overview - Features - Tech Stack - Getting Started - Language Support - Acknowledgements --- ## Overview FluentRoot addresses a significant gap in NLP tooling: the underrepresentation of Nigerian languages in conversational AI. By combining modern AI APIs with a clean, accessible interface, FluentRoot allows users to hold conversations and receive responses in their preferred language, including Yoruba, Igbo, and Hausa alongside English and French. --- ## Features - Multilingual chat interface supporting English, French, Yoruba, Igbo, and Hausa - AI-powered responses using OpenAI and Google Gemini APIs - Real-time language switching within conversations - User authentication and session management via Supabase - Clean, responsive UI built with React and TypeScript - FastAPI backend for efficient request handling --- ## Tech Stack **Frontend** - React - TypeScript **Backend** - FastAPI (Python) **Database and Auth** - Supabase (PostgreSQL + authentication) **AI APIs** - OpenAI API - Google Gemini API --- ## Getting Started ### Prerequisites - Node.js (v18 or above) - Python 3.10 or above - A Supabase project - OpenAI API key - Google Gemini API key ### Installation 1. Clone the repository: ```bash git clone github.com cd fluentroot ``` 2. Install frontend dependencies: ```bash cd frontend npm install ``` 3. Install backend dependencies: ```bash cd backend pip install -r requirements.txt ``` 4. Set up your environment variables (see below). 5. Start the backend: ```bash uvicorn main:app --reload ``` 6. Start the frontend: ```bash npm run dev ``` --- ## --- ## Langu …