An intelligent, mood‑driven study planning app that helps students manage their time, connect with friends, and stay motivated. Powered by reinforcement learning, personalized recommendations, and social features.
AI‑Powered Study Planner leverages state‑of‑the‑art AI and data insights to:
- Automatically generate optimal daily/weekly study schedules based on past performance, real‑time mood input, and sleep patterns.
- Continuously learn and adapt via Proximal Policy Optimization (PPO) to improve time allocations that maximize academic success.
- Facilitate social engagement through friend recommendations, competitions, and collaborative goals.
- Encourage healthy habits with personalized study breaks, sleep/rest planning, and motivational content.
- PPO optimization loop: updates policy to converge on schedules that maximize academic reward.
- Adaptive planning: refines daily/weekly plans as new performance and mood data arrives.
- Sleep‑aware scheduling: integrates historical sleep patterns to ensure study sessions balance productivity and rest.
- Study‑habit clustering: analyzes duration, frequency, subject preferences to find peers with similar habits.
- Weekly friend goals: sets collaborative challenges and tracks progress with recommended friends.
- Contest mode: friendly competitions that rank users locally or among friends on metrics like hours studied or topics mastered.
- Gemini AI integration: powers an interactive chatbot that can parse documents and generate context‑aware responses.
- Document upload & analysis: users can submit PDFs, text files, or DOCX; the chatbot ingests the content, indexes it with FAISS, and enables targeted QA.
- Interactive queries: ask the chatbot questions about the uploaded document—definitions, summaries, or clarifications—and receive precise, AI‑generated answers.
- User mood input: options like “stressed,” “focused,” or “relaxed” guide session intensity.
- Dynamic session design: adjusts length, break type, and resource difficulty to suit emotional state.
- Study‑history analytics: ML‑driven recommendations for reinforcing weaker subjects or advancing strong areas.
- Creative breaks: mini‑games, mindfulness exercises, guided stretches—solo or social.
- Historical sleep data: tracks and analyzes nightly rest via user input or wearable integration.
- Optimal rest recommendations: suggests bedtimes and wake‑up times to align study intensity with recovery needs.
- Schedule adjustment: proactive rebalancing of study sessions if sleep patterns deviate from healthy norms.
- Leaderboards: rank friends and local users by study metrics (hours, streaks, quiz scores).
- Achievements & badges: earn rewards for consistency, milestones, and collaborative wins.
- Local challenges: participate in community‑wide study events and compare performance on area‑specific boards.
- Interactive timeline: visual map of completed sessions, upcoming tasks, and milestones.
- Social feed: showcases friends’ streaks, contest highlights, and shared achievements.
- Frontend: React (Vite) with JavaScript, Tailwind CSS, Framer Motion, and Recharts.
- Backend: Python with Flask for REST APIs.
- Vector Search: FAISS for fast document embedding retrieval.
- AI Services: Gemini AI for natural‑language understanding and response generation.
- Database: Firebase Realtime Database and Firestore for user data, schedules, social graphs, and sleep logs.
Contributions, issues, and feature requests are welcome! Please follow these steps:
- Fork the repository.
- Create a feature branch (
git checkout -b feature/YourFeature). - Commit your changes (
git commit -m 'Add YourFeature'). - Push to the branch (
git push origin feature/YourFeature). - Open a Pull Request and describe your changes.
Please adhere to the project’s code style and ensure new features include tests.