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vary-dev/varygo-ai

Domain:

natural language processing

Record type:

softwareproject
Creator:
var
Host:
πŸπŸ€– VaryGo AI – Interactive Rwanda Tourism Chatbot A smart, responsive chatbot built with React + Tailwind CSS and Flask + Python AI, trained to guide users through Rwanda’s attractions, wildlife, culture, and travel tips – ask questions and explore instantly! πŸ¦πŸžοΈπŸ’¬ # 🌍 VaryGo AI β€” Rwanda Tourism Chatbot πŸ€–βœ¨ > 🧠 **AI-powered chatbot built with React (frontend) + Flask (backend) + Python NLP**, designed to help users explore the tourism and cultural attractions of Rwanda πŸ‡·πŸ‡Ό. > Open-source, free to use, and ready for your next adventure. 🌿🦍🏞️ --- ## πŸ–Ό Project Banner --- ## 🧭 Table of Contents - ✨ Features - πŸ“‚ Project Structure - ⚑ Installation - Frontend (React) - Backend (Flask + Python) - πŸ€– Training the AI - πŸ§ͺ Running the Project - 🌐 API Endpoint - 🀝 Contributing - πŸ“„ License --- ## ✨ Features - πŸ’¬ **Real-time text chat** powered by AI trained on Rwanda tourism Q&A - 🌍 Covers gorilla trekking, national parks, culture, accommodations & travel tips - πŸ”₯ Modern UI with **React + TailwindCSS** - ⚑ Fast & lightweight Flask backend using Python + NLP (NLTK + scikit-learn) - πŸ“‘ Simple REST API between frontend & backend - 🧠 Extensible β€” you can train the AI with your own dataset --- ## πŸ“‚ Project Structure tour-with-vary/ β”œβ”€β”€ client/ ← React frontend β”‚ β”œβ”€β”€ public/ β”‚ β”œβ”€β”€ src/ β”‚ β”œβ”€β”€ package.json β”‚ └── vite.config.js β”‚ β”œβ”€β”€ server/ ← Flask backend + Python AI β”‚ β”œβ”€β”€ app.py β”‚ β”œβ”€β”€ requirements.txt β”‚ β”œβ”€β”€ models/ β”‚ β”œβ”€β”€ routes/ β”‚ β”œβ”€β”€ db/ β”‚ └── venv/ β”‚ β”œβ”€β”€ .gitignore β”œβ”€β”€ README.md └── package.json (optional for monorepo) --- ## ⚑ Installation ### Frontend (React) ```bash cd client npm install npm run dev Backend (Flask + Python) cd server python3 -m venv venv source venv/bin/activate # On Linux / macOS pip install -r requirements.txt πŸ€– Training the AI Prepare your tourism Q&A data in JSON or CSV format inside server/models/data/. Run the training script to generate the model: python ai/train.py The trained model will be saved and used for chatbot responses. πŸ§ͺ Running the Project πŸ‘‰ In one terminal, run the backend: cd server source venv/bin/activate flask run πŸ‘‰ In another terminal, run the frontend: cd client npm run dev Visit: localhost (or similar port) to chat with VaryGo AI πŸ¦πŸ’¬ 🌐 API Endpoint POST htt …