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ReviseX-ai Backend

🚀 Overview

ReviseX-ai is an AI-powered educational platform designed to optimize learning through intelligent question generation and personalized content delivery. This repository serves as the backend engine, built using Python, powering the intelligence and integration layers of the platform.

📁 Repository Structure

🔹 Personalised_Learning_Guide/

A personalized learning recommendation engine tailored to user performance and behavior.

  • Learning pattern analytics
  • Content recommendation algorithms
  • Progress tracking
  • Adaptive difficulty handling

🎯 Personalized Learning Pipeline

Personalized Learning Pipeline

🔹 Question_Generation_Guide/

A module that generates intelligent and diverse question types for active learning.

  • Multi-format question generation (MCQ, fill-in-the-blank, etc.)
  • Concept-to-question mapping
  • Difficulty level calibration
  • Auto-answer validation

🧠 Question Generation Pipeline

Question Generation Pipeline

🔹 YouTube_Tutorial/

Integration of video-based learning through YouTube.

  • Content summarization from videos
  • Timestamp-based topic segmentation
  • AI-driven video recommendations

⚙️ Getting Started

✅ Prerequisites

Ensure the following are installed on your system:

  • Python 3.8 or higher
  • pip (Python package installer)
  • Git
  • Virtual environment tool (optional but recommended)
  • API access credentials (contact project admin)

🛠️ Installation

Follow these steps to set up the project locally:

1. Clone the repository

git clone https://github.com/ReviseX-ai/Backend.git
cd Backend

2. Create and activate a virtual environment

python -m venv venv

# Windows
venv\Scripts\activate
# macOS/Linux
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

The sections have seperate UI component, those will be furthur expanded to create a complete application.

🤝 Contributing

We welcome contributions from the community! To contribute:

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature-name)
  3. Commit your changes (git commit -m 'Add feature')
  4. Push to the branch (git push origin feature-name)
  5. Open a pull request

Please ensure all pull requests are accompanied by appropriate documentation and test coverage.

📜 License

This project is licensed under the MIT License.