Educational AI Platform & Learning Management System (LMS) - Production Version

This project is a sophisticated, multi-component Educational AI Platform architected to function as a full-fledged, multi-tenant Learning Management System (LMS) for individuals, educators, and institutions. It is built with a production-ready, secure, and scalable architecture.

🏛️ Architecture

• Backend: A secure API built with FastAPI, featuring JWT token-based authentication.
• Frontend: An interactive web application built with Streamlit, acting as a pure client to the backend.
• Core Logic: A modular library of functions separated by concern (database.py, ai.py, rag.py, auth.py, llm_config.py).
• Data Stores: Google Firestore for all application metadata and Google Cloud Storage for persistent, secure storage of user-uploaded documents and AI vector stores.

✨ Key Features

• Secure Authentication: Production-grade login/registration system using password hashing and JWT access tokens.
• Multi-Tenant Hierarchy: Full support for School -> Educator -> Student relationships, as well as independent educators and individual learners.
• Secure Classroom Management: A complete workflow for educators to manage their classroom with unique join codes and an approval system.
• Multi-LLM Support: A flexible backend that can use multiple AI providers (Together AI, OpenAI, Google, etc.), selectable by the user in the UI.
• Persistent & Categorized RAG: A powerful RAG system where users can upload multiple documents, assign them to categories, and have them permanently stored and secured in Google Cloud Storage.
• Configurable Storage Quotas: A universal, per-user storage limit, configurable by the platform owner, prevents misuse.
• Full Assessment & Grading Cycle: Educators can create assignments with deadlines, and students can submit their work to be graded instantly by the AI.
• Comprehensive Documentation: Includes in-app user guides and a full suite of project documents in the /docs directory.

🚀 Production Setup and Installation

1. Set Up Credentials & Environment

Create a .env file and populate it with your secret keys and configuration:

# .env

# --- LLM API Keys ---

TOGETHER_API_KEY="your_key_here"
OPENAI_API_KEY="your_key_here"
GOOGLE_API_KEY="your_key_here"


# --- JWT Secret ---
# Generate a strong, random string for this in production, e.g., openssl rand -hex 32
JWT_SECRET_KEY="your_super_secret_jwt_key"

# --- Backend & Storage Config ---
API_BASE_URL="http://127.0.0.1:8000"
GCS_BUCKET_NAME="your-gcs-bucket-name-for-rag-storage"
USER_STORAGE_LIMIT_MB="100"

# --- Google Cloud Service Account ---

# Provide the absolute path to your service account key file
GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/service-account-key.json"

2. Install Dependencies

pip install -r requirements.txt

3. Run the Application

You must run the backend and frontend servers in two separate terminals.

Terminal 1: Start Backend Server

uvicorn main:app --host 0.0.0.0 --port 8000

Terminal 2: Start Frontend Application

streamlit run app.py

Your application will be accessible at the local URL provided by Streamlit.