🧠 SmartQuizzer Live

SmartQuizzer Live is an AI-powered quiz generator built using Python and Streamlit. The application allows users to upload study materials (PDFs) and automatically extract text to generate interactive quizzes, complete with real-time analytics and tracking.

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📑 Table of Contents

• Features
• Target Audience
• Tech Stack
• Getting Started
• Usage
• Project Structure
• Backend Database
• GitHub Setup
• Future Improvements
• Team Members & Contributions

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🚀 Features

• 📄 PDF Text Extraction: Generate quizzes seamlessly from uploaded study materials.
• 🧠 AI-Based Generation: Automated context-aware quiz generation.
• 📊 Interactive Analytics Dashboard: Beautiful charts and statistics to track performance over time.
• 🗂 Automated Storage: Backend SQLite database to manage history without manual setup.
• 📈 Live Performance Tracking: View dynamic, live updates on your quiz attempts.
• 🧪 Attempt History: Stores past quiz attempts, scores, and metadata for review.
• ✨ Interactive Splash Screen: Beautiful 2-second animated entrance for the application.
• ⚡ Instant Quiz Viewing: Automatically redirects to the quiz immediately after generation.
• 📑 Single Question View: Enhanced user experience by showing one question at a time.

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🎯 Target Audience

• Students: Quickly generate practice quizzes from lecture notes and study materials to prepare for exams.
• Educators & Teachers: Effortlessly create assessment materials and interactive quizzes for students based on lesson plans.
• Self-Learners & Professionals: Test knowledge retention on manuals, academic papers, or any PDF document.

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🛠 Tech Stack

• Frontend / UI: Streamlit
• Backend Language: Python
• Database: SQLite (built-in)
• Data Visualization: Matplotlib / Plotly
• AI / Data Processing: NLP Concepts

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🏗 Architecture

graph TD
    User((User)) <--> |React / Tailwind| Frontend[Frontend UI Layer]
    Frontend <--> |REST API| Backend[Flask Server Engine]
    subgraph AI_CLOUD[Groq AI Infrastructure]
    Backend <--> |Secure API Stream| Llama[Llama 3.3 70B Model]
    end
    Backend <--> |Query/Store| DB[(SQLite Database)]

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🔄 Workflow

flowchart TD
    Start([User Request]) --> Parser[Content Parsing & Sanitization]
    Parser --> PromptEngine[Dynamic Prompt Construction]
    
    subgraph AI_Core [Inference Layer]
    PromptEngine --> Groq[Groq Llama 3.3 API]
    Groq --> Schema[JSON Schema Validation]
    end
    
    Schema --> LogicGate{Logic Gate}
    LogicGate -->|Success| StudyHub[Generate Study Aids]
    LogicGate -->|Success| QuizGen[Generate Adaptive Questions]
    LogicGate -->|Error| Retry[Prompt Refinement]
    
    QuizGen --> DB[(SQLite Storage)]
    StudyHub --> UI[Frontend React Delivery]

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▶️ Getting Started

Follow these steps to run the application locally on your machine.

1. Clone the repository (if applicable)

git clone https://github.com/<your-username>/<your-repo>.git
cd <your-repo>

2. Install dependencies

Ensure you have Python installed, then install the required Python packages:

pip install -r requirements.txt

3. Run the application

Start the Streamlit server:

streamlit run app.py

The application should now be accessible in your browser at http://localhost:8501.

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📖 Usage

1. Upload Material: Navigate to the upload section and provide your PDF notes.
2. Generate Quiz: Click the "Generate" button and let the app process the text into questions.
3. Take the Quiz: Answer the generated questions interactively and submit.
4. View Analytics: Head over to the Analytics Dashboard to see your score, historical performance, and insights.

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📂 Project Structure

SmartQuizzer/
│
├── app.py                   # Main Streamlit application entry point
├── text_extractor.py        # Logic for extracting text from uploaded PDFs
├── question_generator.py    # AI logic for generating quiz questions from text
├── quiz_engine.py           # Core logic for handling quiz sessions and scoring
├── analytics.py             # Analytics generation and dashboard visualizations
│
├── data/                    # Local data storage directory
│   ├── smartquizzer.db      # SQLite database for storing history
│   ├── questions.json       # Temporary/cached question storage
│   └── attempts.json        # Temporary/cached metadata for attempts
│
├── utils/                   # Utility modules
│   └── storage.py           # Database handling and file IO utilities
│
└── requirements.txt         # Project dependencies

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🗄 Backend Database

The backend database is automatically instantiated on the first run. By default, it is created at:

data/smartquizzer.db

It reliably stores:

• Generated quizzes and questions
• User attempts and timestamps
• Scores and performance metrics
• Raw analytics data

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📦 GitHub Setup

If you are pushing the project for the first time:

git init
git add .
git commit -m "Initial commit ✨"
git branch -M main
git remote add origin https://github.com/<your-username>/<your-repo>.git
git push -u origin main

If the repository already exists:

git add .
git commit -m "Update README and refine structure"
git push

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📊 Future Improvements

• 🤖 Integration with advanced LLMs (like OpenAI/Claude) for more nuanced question generation.
• ☁️ Online deployment with Streamlit Cloud or Heroku.

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👥 Team Members & Contributions

This project was developed by a team of four members:

• Sujal Gupta (Team Leader) – Lead System Architect, Data Analytics

  • Led the project planning, task distribution, and overall execution
  • Designed system architecture and workflow
  • Worked on data processing, analytics, and module integration
  • Ensured smooth collaboration between AI, backend, and frontend teams

• Shuchi Makhija – AI Engineer

  • Developed AI/ML models for quiz generation
  • Implemented adaptive difficulty logic
  • Integrated NLP and LLM APIs

• Shiva – Backend Developer

  • Built APIs and backend services
  • Managed server-side logic and database communication
  • Handled system integration

• Lithika D – UI Developer

  • Designed and developed user interface
  • Improved user experience (UX)
  • Integrated frontend with backend

• Santhosh S – UI Developer

  • Designed and developed user interface
  • Improved user experience (UX)
  • Integrated frontend with backend