Askiit

A university Q&A platform empowering students with instant answers, project insights, and internship tracking—all enhanced by AI.

🚀 Description

Askiit is a full-stack web platform designed for universities, connecting students, teachers, and admins for seamless knowledge-sharing. Built with Next.js (frontend) and FastAPI (backend), it offers:

• Fast, secure student authentication and profile management
• Project (SPL) coordination and tracking
• Internship opportunities overview
• An AI-powered chatbot via Gemini API (through LangChain) for smart responses
• Modern, containerized deployment

Preview

Live Preview

✨ Features

• Student Registration & Session Management: Secure signup/login and account management
• SPL Listings: View and manage Software Project Lab (SPL) projects and details
• Internship Company Tracking: Keep records of companies, roles, and internship status
• AI-Powered Chatbot: Get quick answers with Gemini-powered AI integration using LangChain
• Admin Panel: Manage users, SPL projects, internship listings (optional/enabled)
• Dockerized Setup: Ready-to-launch Docker Compose configuration for both frontend and backend

🛠️ Tech Stack

Layer	Technologies
Frontend	Next.js, Tailwind CSS
Backend	FastAPI, SQLAlchemy
Database	PostgreSQL
AI	Gemini API (Google) via LangChain
DevOps	Docker, Render or self-host

📉 LangGraph

🚦 Getting Started

Prerequisites

• Docker & Docker Compose
• Node.js (v18+) for frontend local dev (optional)
• Python 3.10+ (for backend local dev, optional)
• PostgreSQL database (local/remote)

Clone the Repository

git clone https://github.com/masum184e/askiit.git
cd askiit

🔧 Setup Instructions

1. Environment Variables

Create .env files using these samples:

Frontend (client/.env)

NEXT_PUBLIC_API_URL=http://localhost:8000
NEXT_PUBLIC_GEMINI_API_KEY=your-gemini-api-key

Backend (server/.env)

DATABASE_URL=postgresql://user:password@localhost:5432/askiit
SECRET_KEY=your-super-secret-key
ALLOWED_HOSTS=*
GEMINI_API_KEY=your-gemini-api-key

2. Local Development

Start the backend:

cd server
pip install -r requirements.txt
uvicorn app.main:app --reload

Start the frontend:

cd client
npm install
npm run dev

3. Dockerized Deployment

With Docker Compose (recommended):

docker-compose up --build

• The client runs at http://localhost:3000
• The API runs at http://localhost:8000

📚 Usage

• Access frontend in browser and register/login as student or admin
• Explore SPL project listings, internship tracking, and chatbot features
• Use the admin panel (if enabled) for full management
• Test AI Q&A in the chatbot interface

📝 API Documentation

• Visit http://localhost:8000/docs for autogenerated Swagger/OpenAPI documentation.

🤝 Contribution

Contributions welcome! To get started:

1. Fork the project
2. Create your feature branch (git checkout -b feature/awesome-feature)
3. Commit your changes (git commit -m 'feat: add awesome feature')
4. Push to the branch (git push origin feature/awesome-feature)
5. Open a Pull Request

Please follow conventional commit messages and keep PRs focused.

💡Contribution Ideas

• Update Prompt
• Faculty Database
• Question Database
• Global Error Handling
  • Client
  • Server
• No Result Found UI
• Sort the response with recent update
• Improve Pagination Performance
• SPL
  • Filter By Batch
• Student
• Achivement

🪪 License

MIT

👤 Author / Contact

Developed by Masum Billah.

For issues or suggestions, please open an issue or contact via GitHub.

Empowering campus knowledge, together!

⁂