https://github.com/jeevanbabu7/eduarc-client
https://github.com/jeevanbabu7/eduarc-client
Last synced: 30 days ago
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- Host: GitHub
- URL: https://github.com/jeevanbabu7/eduarc-client
- Owner: jeevanbabu7
- Created: 2024-12-16T05:14:13.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-03T19:31:55.000Z (over 1 year ago)
- Last Synced: 2025-04-03T20:31:26.467Z (over 1 year ago)
- Language: JavaScript
- Size: 5.71 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
# EduArc : AI-Powered Learning and Assessment Tool
A comprehensive app that combines learning aids and performance assessment tools into a unified educational ecosystem built with React Native. Designed to support students in personalized learning, the app offers features such as intelligent text summarization, automated flashcard and quiz generation, a live chatbot for real-time academic assistance, and predictive analytics to highlight key exam topics — providing a holistic and adaptive solution to enhance retention, comprehension, and performance in academic settings.
## Features
- Summarize lengthy study materials into short, easy-to-understand notes.
- Generate flashcards automatically from uploaded notes or text.
- Create quizzes from documents to reinforce learning.
- Use a live AI chatbot to clear doubts in real time.
- Predict high-weightage topics using past exam data.
- Suggest additional resources to aid learning.
## Tech Stack
- **Frontend :** React Native
- **Backend :** Express.js, Flask
- **AI/ML Components :** Llama 3.1, Command-R
- **Database and Storage :** MongoDB, Chroma
- **Frameworks :** Unsloth
- **Other tools :** Appwrite, FFmpeg, LangChain, Firebase, Whisper, Google Colab
## Architecture
System Architecture
Detailed Architecture
RAG Architecture
Dataflow
## 📦 Installation & Setup
### Prerequisites
Make sure the following are installed:
- [Node.js](https://nodejs.org/) (LTS recommended)
- npm or yarn
- [Expo CLI](https://docs.expo.dev/get-started/installation/)
- Git
- Python 3.8+
- Android Studio or Xcode (for emulators)
*OR* install **Expo Go** on a physical Android/iOS device
---
### ✅ Frontend Setup (EduArc App)
1. **Clone the frontend repository**
```bash
[git clone https://github.com/jeevanbabu7/EduArc-client.git]
cd eduarc-frontend
```
2. **Install Expo CLI globally**
```bash
npm install -g expo-cli
```
3. **Install frontend dependencies**
```bash
npm install
# or
yarn install
```
4. **Start the development server**
```bash
expo start
```
- Press `i` to run on iOS simulator (macOS only)
- Press `a` to run on Android emulator
- Or scan the QR code with **Expo Go** on your mobile device
5. **(Optional) Setup environment variables**
```bash
touch .env
```
Add the following to `.env`:
```
API_URL=https://your-backend-url.com/api
```
---
### ⚙️ Backend Setup (Flask + Express)
1. **Clone the backend repository**
```bash
https://github.com/jeevanbabu7/EduArc-server.git
cd server
```
2. **Set up Flask server**
```bash
cd RAG
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
```
3. **Set up Express server**
```bash
cd server
npm install
```
4. **Run both backend servers**
- Start Flask server:
```bash
cd server/RAG
python app.py
```
- Start Express server:
```bash
cd server
npm start
```
---
## UI Design
Dashboard
Courses
Materials
Tools
Summarization
Chatbot
Questions
Flashcards
Results
## 🤖 Model and Framework Details
EduArc integrates several powerful AI models and frameworks to deliver its intelligent features:
- **LLaMA 3.1 (Meta AI):**
Used for contextual understanding, summarization, and generating natural-language responses in the chatbot module.
- **Command-R (Reka AI):**
Supports robust question generation and flashcard creation by analyzing semantic meaning from uploaded content.
- **LangChain:**
Acts as an orchestration layer for chaining LLM calls with document processing, especially in RAG pipelines.
- **Unsloth:**
Helps in fine-tuning and accelerating LLM training for lightweight deployment and responsiveness on smaller instances.
- **Whisper (OpenAI):**
Integrated for converting spoken lecture recordings or audio content into accurate transcriptions, enabling text-based summarization and search.
- **Chroma DB:**
Vector database used for storing embeddings and performing semantic search over documents and past queries.
- **FFmpeg:**
Used in preprocessing audio/video inputs for Whisper transcription.
These models collectively power the learning assistant’s capabilities — from extracting insights and summarizing data to answering academic queries and generating quizzes tailored to a student’s course material.
---
## Training Notebooks
- Topic Weightage Analysis : [Open in Google Colab](https://colab.research.google.com/drive/1RclTiAr8_MUMUVlun5CsPiwQixlGOF45?usp=sharing)
- Question Generation : [Open in Google Colab](https://colab.research.google.com/drive/1zZNdrRlQJtcBKQ_O30Gs4mvr24BKkSQe?usp=sharing)