🧠 AI Smart Revision Scheduler

An AI-powered revision planning system designed to optimize long-term retention for competitive exam aspirants by intelligently scheduling, prioritizing, and automating revision cycles.

This project was initially developed using Django, Django REST Framework, and SQL-based databases, and deployed on Render.
It is currently being modernized and re-architected using a more performant, cloud-native stack with FastAPI, Docker, MongoDB, and NGINX.

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🚀 Key Features

• 📅 Intelligent Revision Scheduling
  Dynamically prioritizes topics based on revision history, frequency, and importance.

• 🤖 AI-Assisted Content Handling
  NLP-based summarization to help learners focus on high-impact revision content.

• ⚙️ Asynchronous Background Processing
  Automated revision reminders and scheduling using background tasks.

• 🌐 RESTful API Architecture
  Clean, modular APIs designed for seamless frontend or client integration.

• ☁️ Cloud-Native & Scalable Design
  Built with scalability, maintainability, and production-readiness in mind.

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🏗️ Tech Stack Evolution

🟢 Version 1 – Initial Implementation

• Django
• Django REST Framework
• SQL Database
• Render (Cloud Deployment)

🔵 Version 2 – Ongoing Modernization

• FastAPI
• MongoDB
• Docker
• NGINX
• Render (Production Hosting & CI/CD)

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🔄 System Architecture (Current)

1: Client (Web / Mobile)

2: NGINX

3: FastAPI Backend

4: MongoDB

5: Background Workers (Async Tasks)

6: Reverse Proxy NGINX

7: Client (Web / Mobile)

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🔁 CI/CD & Deployment Workflow

• Source code is managed using GitHub.
• The GitHub repository is directly linked to Render.
• On every push to the main branch:
  • Render automatically pulls the latest code
  • Builds Docker images
  • Deploys updated services without manual intervention

CI/CD is handled using Render’s native GitHub integration, ensuring automated and reliable deployments.

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🧪 API Testing & Developer Experience

• Designed and validated REST APIs using Postman for end-to-end testing.
• Created structured Postman collections to test authentication, CRUD operations, and edge cases.
• Used environment variables in Postman to simulate multiple deployment environments (local, staging, production).
• Improved API reliability by identifying request/response mismatches and handling error scenarios early in development.

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🛠️ Local Development Setup

git clone https://github.com/your-username/ai-smart-revision-scheduler.git
cd ai-smart-revision-scheduler