📚 AI Study Assistant

An AI-powered web application that helps students study smarter by summarizing notes and generating quiz questions using Google Gemini.

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📖 Description

AI Study Assistant is a Flask-based web application built for students who want to study more efficiently. Users can paste their study notes and instantly receive AI-generated summaries and quiz questions powered by the Google Gemini API. Study sessions are saved to a MongoDB database so users can review their progress over time.

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🌐 Live URL

🔗 https://ai-study-buddy-latest-4114.onrender.com

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✨ Features

Feature	AI-Powered
Paste study notes	❌
AI-generated note summary	✅ Gemini API
AI-generated quiz questions	✅ Gemini API
Save and view past study sessions	❌
Dedicated sessions history page	❌
Health check endpoint	❌

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🛠️ Tech Stack

Layer	Technology	Justification
Backend	Python + Flask	Lightweight, easy to structure REST APIs
AI / LLM	Google Gemini API	Provided by instructor, strong summarization
Database	MongoDB Atlas	Free tier, flexible document storage
Frontend	HTML + CSS + JS	Simple, fast, no framework overhead
CI/CD	GitHub Actions	Course standard, integrates natively with GitHub
Deployment	Render.com	Free tier, supports Docker, easy deploy hooks
Containerization	Docker	Ensures consistent environment across machines
Image Registry	Docker Hub	Stores built images for deployment

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👥 Team Members

Name	Student ID	Role	Ownership
Hamad Almansouri	60302091	Backend & Database	Flask API routes, MongoDB integration
Abdulrahman Al-Mutawah	60102286	AI & Testing	Gemini integration, prompt design, unit tests
Saeed Abdullah Dar	60307149	Frontend & DevOps	UI templates, GitHub Actions CI/CD, Render deployment

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🏗️ Architecture

Push to main
→ GitHub Actions CI runs:
   - Linter (flake8)
   - Unit tests (pytest — 12 tests)
   - Docker image build
   - Smoke test (/health endpoint)
→ CI passes
→ CD runs:
   - Builds and pushes image to Docker Hub
     (cipher974/ai-study-buddy:latest)
   - Triggers Render deploy hook
→ Render pulls latest image and deploys
→ Live URL updates automatically

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📁 Project Structure

ai-study-buddy/
│
├── app.py                  # Main Flask application
├── requirements.txt        # Python dependencies
├── Dockerfile              # Container configuration
├── .env.example            # Environment variable template
├── .gitignore              # Git ignore rules
│
├── templates/              # HTML templates (Jinja2)
│   ├── base.html           # Base layout with navbar
│   ├── index.html          # Homepage
│   ├── dashboard.html      # Dashboard page
│   ├── summarize.html      # AI summarize page
│   ├── quiz.html           # AI quiz page
│   └── sessions.html       # Past sessions page
│
├── static/
│   ├── css/
│   │   └── style.css       # Cyberpunk dark theme stylesheet
│   └── js/
│       └── script.js       # Frontend JavaScript
│
├── tests/
│   ├── __init__.py
│   └── test_app.py         # Unit tests (12 tests)
│
└── .github/
    └── workflows/
        └── ci.yml          # GitHub Actions CI/CD pipeline

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⚙️ Local Setup Instructions

1. Clone the Repository

git clone https://github.com/HBS-oc/ai-study-buddy.git
cd ai-study-buddy

2. Create Virtual Environment

python -m venv venv

Activate it:

• Windows:

venv\Scripts\activate

• Mac/Linux:

source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Set Up Environment Variables

cp .env.example .env

Open .env and fill in your values:

FLASK_ENV=development
FLASK_DEBUG=1
GEMINI_API_KEY=your-gemini-api-key-here
MONGODB_URI=your-mongodb-connection-string-here

5. Run the Application

python app.py

6. Open in Browser

http://127.0.0.1:5000/

7. Verify Health Endpoint

http://127.0.0.1:5000/health

Expected response:

{"status": "ok"}

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🐳 Run with Docker

Build the Image

docker build -t ai-study-buddy .

Run the Container

docker run -p 5000:5000 --env-file .env ai-study-buddy

Open in Browser

http://localhost:5000/

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🧪 Run Tests

pip install pytest mongomock
pytest tests/ -v

Expected: 12 tests passing

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🔀 Branch Strategy

main          → production-ready code only
feature/name  → one branch per feature or task
fix/issue-N   → bug fixes
docs/name     → documentation updates
chore/name    → DevOps and configuration tasks

All work is done via feature branches and pull requests. No direct commits to main.

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📅 Timeline

Milestone	Due Date	Goals
Foundation	April 2, 2026	Repo setup, proposal, app skeleton, Docker, CI
Core Development	April 4, 2026	All features, AI integration, unit tests, CI green
Deployment & Presentation	April 6, 2026	CD pipeline, live URL, presentation ready

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📝 API Endpoints

Method	Endpoint	Description
GET	/	Homepage
GET	/dashboard	Dashboard page
GET	/summarize	Summarize page
GET	/quiz	Quiz page
GET	/sessions	Sessions history page
GET	/health	Health check
GET	/api/test-db	Test database connection
POST	/api/summarize	Generate AI summary from notes
POST	/api/quiz	Generate quiz questions from notes
GET	/api/sessions	Get all past study sessions
POST	/api/sessions	Save a new study session