README.md

metadata

title: Quiz Generation
emoji: 📝
colorFrom: blue
colorTo: green
sdk: docker
pinned: false

AI Exam Generator

A FastAPI-based microservice that generates quizzes from uploaded documents using an AI model.

The API accepts PDF, DOCX, or TXT files, extracts their text, and generates different types of questions such as:

• Multiple Choice Questions (MCQ)
• Fill in the Blank
• Explanation Questions

The questions are generated using the Groq LLM (Llama 3.3 70B) via LangChain.

---

FEATURES

---

• Upload documents (PDF, DOCX, TXT)
• Automatic text extraction
• AI-generated quizzes
• Multiple question types
• Separate answer key generation
• Optional download as TXT or PDF
• REST API with FastAPI
• Interactive API documentation

---

INSTALLATION

---

1. Clone the repository
2. Create a virtual environment python -m venv .venv
3. Activate the environment (Windows PowerShell) .\.venv\Scripts\Activate.ps1
4. Install dependencies pip install -r requirements.txt

---

ENVIRONMENT VARIABLES

---

Create a .env file in the project root:

MY_API_KEY=your_groq_api_key_here

This key is required to access the Groq LLM.

---

RUNNING THE API

---

Start the server:

uvicorn app.main:app --reload

The API will run at:

http://127.0.0.1:8000

Interactive API documentation:

http://127.0.0.1:8000/docs

---

API ENDPOINTS

---

• POST /api/v1/upload Upload a document (PDF, DOCX, TXT)
• POST /api/v1/generate Generate questions from uploaded text
• GET /api/v1/quiz/{quiz_id} Returns generated quiz with answer key
• GET /api/v1/download/{filename} Download generated quiz file
• GET /api/v1/uploads List uploaded documents
• DELETE /api/v1/upload/{upload_id} Delete uploaded document

---

EXAMPLE WORKFLOW

---

1. Upload a file
2. Generate a quiz
3. Retrieve the quiz results

---

TECHNOLOGIES USED

---

FastAPI, LangChain, Groq LLM (Llama 3.3 70B), Python, pdfplumber, python-docx, FPDF

---

NOTES

---

• Uploaded text and quizzes are stored locally in JSON files.
• This implementation uses in-memory storage.
• For production use, a database is recommended.