An AI-driven academic survival platform built for African universities to bridge the gap between incomplete lecture delivery and exam readiness through semantic syllabus analysis and RAG-powered tutoring.
# Lighthub.ed
Lighthub.ed is a student study assistant repository with a Next.js frontend and a Python FastAPI backend. The workspace includes a study dashboard, course workspace, gap detector, panic mode UI, and a backend AI engine with document upload and vector search support.
## What this repo contains
- `app/`: Next.js App Router frontend for the study assistant UI
- `Backend/`: FastAPI backend service with document ingestion, PDF parsing, ChromaDB vector storage, and chat endpoint logic
- `render.yaml`: Render service configuration for hosting the backend
- frontend pages: dashboard, course workspace, gap detector, panic mode, and login
## What is implemented
- frontend navigation and branded Lighthub.ed interface
- course dashboard and workspace UI
- panic mode and gap detector pages
- FastAPI backend service at `Backend/app/main.py`
- document upload endpoint and PDF text extraction pipeline
- local ChromaDB vector store integration in `Backend/app/processing.py`
- deployment-ready Render configuration with `root: Backend`
## Run locally
### Frontend
```bash
npm install
npm run dev
```
Open
localhost to view the Next.js app.
### Backend
```bash
cd Backend
python -m pip install -r requirements.txt
uvicorn app.main:app --host 0.0.0.0 --port 8000
```
Open
localhost to verify the FastAPI backend.
## Deployment
This repo includes a Render configuration file at `render.yaml`.
The backend deploys from the `Backend` directory with:
- Build command: `pip install -r requirements.txt`
- Start command: `uvicorn app.main:app --host 0.0.0.0 --port $PORT`
- Environment variable: `PYTHONUNBUFFERED=1`
## Notes
The frontend is a working Next.js app, and the backend is configured for Render deployment. Some features are still prototype-level and may require further integration for production readiness.