Shiksha Sahayak (meaning "Education Assistant") is a full-stack web application that transforms any content—PDFs, websites, or YouTube videos—into a dynamic and interactive learning experience. It leverages a powerful AI backend to provide users with summaries, direct Q&A, and auto-generated quizzes to solidify their understanding.
- Multi-Format Content Ingestion: Start a learning session by providing a:
- 📄 PDF Document: Upload any PDF file.
- 🌐 Website URL: Paste a link to an article, blog, or documentation.
▶️ YouTube Video: Provide a video URL to learn from its transcript.
- AI-Powered Chat: Ask specific, contextual questions about the source material and receive instant, accurate answers.
- Automated Summaries: Get a concise summary of the key points and main ideas from your content with a single click.
- Interactive Quizzing: Test your knowledge with multiple-choice quizzes that are automatically generated based on the content.
- Clean, Modern UI: A fully responsive and intuitive interface designed for a seamless user experience on any device.
This project is built on a modern, decoupled, two-part architecture: a Next.js Frontend and a FastAPI Backend. This separation allows for independent development, scaling, and deployment.
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Frontend: A Next.js application serves the user interface. It is responsible for all visual components and for making API calls to the backend. It's designed to be deployed as a standalone Node.js service.
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Backend: A Python-based FastAPI server acts as the application's brain. It exposes a RESTful API and handles all core logic, including the Retrieval-Augmented Generation (RAG) pipeline, interactions with the Google Gemini LLM, and managing user sessions.
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Hosting: Backend services are containerized and deployed on Google Cloud Platform (GCP)
The complete system is split into two dedicated repositories. Please visit them for detailed technical information, setup instructions, and the source code for each part.
The frontend is a server-side rendered (SSR) application built with Next.js, providing a fast and responsive user experience.
- Repository: ➡️ pyandcpp-coder/Shikshak_Sahayak_Frontend
- Technology: Next.js, React, TypeScript, Tailwind CSS, shadcn/ui
- Deployment: Hosted on Google Cloud Platform.
The backend is a high-performance, asynchronous API built with FastAPI. It handles all the heavy lifting, including document processing, AI model interactions, and the RAG pipeline.
- Repository: ➡️ pyandcpp-coder/Shikshak_Sahayak_Backend
- Technology: FastAPI, Python, LangChain, Google Gemini, ChromaDB
- Deployment: Hosted on Google Cloud Platform.
To run the full application on your local machine, you need to clone and run both repositories concurrently.
First, set up and start the backend server.
# Clone the backend repository
git clone https://github.com/pyandcpp-coder/Shikshak_Sahayak_Backend.git
cd Shikshak_Sahayak_Backend
# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate
# Install dependencies
pip install -r requirements.txt
# Create a .env file and add your GOOGLE_API_KEY
echo "GOOGLE_API_KEY='YOUR_API_KEY_HERE'" > .env
# Run the server
uvicorn main:app --reload# Clone the frontend repository
git clone https://github.com/pyandcpp-coder/Shikshak_Sahayak_Frontend.git
cd Shikshak_Sahayak_Frontend
# Install dependencies
npm install
# Create a .env.local file pointing to your local backend
echo "NEXT_PUBLIC_API_URL=http://localhost:8000" > .env.local
# Run the development server
npm run devThe frontend will now be available at http://localhost:3000. Open this URL in your browser to use the application.