QuizMLOps: Automated Quiz Pipeline with MLOps

MY App on App Store Called Quiz Me

in this task i have used the (staging data) on the app to complete the task instead of relying on separate api's

the app url

https://apps.apple.com/sa/app/quizme-%D8%A7%D8%AE%D8%AA%D8%A8%D8%B1%D9%86%D9%8A/id6739629798

Overview

This project automates quiz processing using MLOps practices. It fetches quiz data from a Firebase API, cleans it, integrates with OpenAI for question answering, and deploys the pipeline via FastAPI. Designed for educational use cases.

Features

• Data Extraction: Fetch quiz data from Firebase Firestore
• Text Cleaning: Format questions/answers for LLM compatibility
• LLM Integration: OpenAI GPT-3.5/4 for answer generation
• API Deployment: FastAPI endpoints for pipeline interaction
• MLOps Practices: Modular code

Installation

Prerequisites

• Python 3.9+
• Firebase credentials (service-account-file.json)
• OpenAI API key

Setup

# Clone repository
git clone https://github.com/your-username/QuizMLOps.git
cd QuizMLOps

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Add your OpenAI API key to .env
#Run the application by executing:

python main.py

Project Structure
📦QuizMLOps
┣ 📂app
┃ ┣ 📂api
┃ ┃ ┗ 📜endpoints.py 
┃ ┣ 📂models
┃ ┃ ┗ 📜qa_model.py 
┃ ┣ 📂services
┃ ┃ ┣ 📜data_cleaner.py 
┃ ┃ ┗ 📜data_fetcher.py 
┣ 📂pipeline
┃ ┗ 📜quiz_pipeline.py 
┣ 📂tests
┣ 📜requirements.txt 
┗ 📜README.md

API Reference

GET /tests

Fetch processed quiz data:

curl http://localhost:8000/tests

POST /ask

Generate answers using OpenAI:

curl -X POST http://localhost:8000/ask \
  -H "Content-Type: application/json" \
  -d '{"question": "What is CMOS?", "context": "CMOS technology reduces power consumption..."}'

Acknowledgments

OpenAI API for LLM integration

Firebase for data retrieve

FastAPI for deployment