Mentora - The AI Course Creator

Mentora is an AI-powered platform designed to revolutionize corporate training by transforming course creation from a process that takes weeks into one that takes minutes.

This project was developed by the team Strawhats for the Code O'Clock 24-Hour National Level Hackathon, where it proudly secured 3rd place.

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🎯 The Problem

In corporate training, course creation is slow, rigid, and disconnected from learners needs. Trainers spend weeks writing objectives, splitting content into lessons, preparing slides, designing quizzes, and recording videos. By the time the course is complete, learners have often moved ahead, leaving the material outdated and irrelevant. Moreover, the lack of reusability makes the process repetitive and inefficient, while the final courses frequently fail to connect with real-world job requirements.

✨ Our Solution

We propose an AI-powered course creation system that enables users to generate fully customized learning experiences from diverse content inputs such as text, PDFs, and videos. The system analyzes the uploaded material and automatically designs a tailored course, while giving users complete control over personalization.

🚀 Key Features

Flexible Content Formats: Choose between micro-lessons, short videos, PPTs or PDFs. Continuous Assessments: Add quizzes that are essential for the user to test their skills. Multilingual Support: Generate courses in multiple languages for global accessibility. AI-Monitored Quizzes: An innovative feature to ensure learner engagement and assessment integrity. Interactive Chatbot (Future Scope): Enhance learner engagement with conversational support.

📸 Screenshots

Here is a look at the Mentora prototype in action.

Step 1: Describe Course

Course With Quiz

Trainer Review

🛠️ Technical Architecture

Mentora is powered by a modern, multi-stage AI pipeline to deliver high-quality, relevant content.

Architecture Diagram

Text Extraction: We use PyMuPDF and BeautifulSoup to parse and extract text from PDFs and websites. [cite: 22] Orchestration Framework: The entire workflow is managed and orchestrated by LangChain. [cite: 22] Vector Storage: Content is processed, chunked, and stored in ChromaDB for efficient retrieval. [cite: 22] Generative Core: Course generation is handled by API calls to powerful Large Language Models, including Google's Gemini API and the GROK API.

⚙️ Getting Started

To run this project locally, follow these steps:

1. Clone the repository:

git clone [https://github.com/thilak0105/Code-O-Clock.git](https://github.com/thilak0105/Code-O-Clock.git)
cd Code-O-Clock

2. Create and activate a virtual environment:

python3 -m venv venv
source venv/bin/activate

3. Install dependencies: (Note: You need to create a requirements.txt file first by running pip freeze > requirements.txt)

pip install -r requirements.txt

4. Set up environment variables: Create a file named .env in the root directory and add your API keys:

GOOGLE_API_KEY="YOUR_GEMINI_API_KEY"
GROK_API_KEY="YOUR_GROK_API_KEY"

5. Run the application:

python app.py

6. Run the frontend in other Terminal:

npm start

The Team (Strawhats)

• Gopi M - GitHub Profile
• Thilak L - GitHub Profile
• Vidhun KS - GitHub Profile
• Loganand S - GitHub Profile

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.