Build a Quiz App with Streamlit
A quiz application with questions, scoring, and results. Using Streamlit's Streamlit data apps with interactive widgets and charts, LoomCode AI generates a production-ready quiz app with clean code structure, proper state management, and a polished user interface — all from a single text description in seconds. No prior coding experience required.
Build This App NowHow to Build a Quiz App with Streamlit
Select Streamlit
Open LoomCode AI and choose the Streamlit template from the template picker.
Describe your app
Type a description of your quiz app and click submit.
Preview & deploy
Watch the AI generate code and preview your working app live. Deploy with one click.
Why Build a Quiz App with Streamlit
Streamlit makes educational tools like a quiz app easy to build and share. Students and teachers interact through simple widgets while Streamlit handles the logic.
What the AI Generates for This Quiz App
- Interactive Streamlit widgets for user input and filtering
- Data processing with Pandas, NumPy, Plotly
- Auto-generated charts and visualizations
- File upload support for CSV/Excel data
- Progress bars and completion tracking for learning milestones
- Interactive feedback elements (correct/incorrect states) for quiz app exercises
Example Prompt
Copy this prompt and paste it into LoomCode AI:
What You Get
LoomCode AI generates a quiz app with interactive exercises, progress tracking, and feedback states. Users navigate through content, receive immediate feedback on answers, and see their progress throughout. The output is properly structured Streamlit code using Streamlit, Pandas, NumPy, Plotly with data processing pipelines, interactive widgets, and visualization libraries. The app runs immediately in a live sandbox — interact with it, test every feature, then iterate with follow-up prompts or deploy to a shareable URL.
Tips for Better Results
- Describe the learning flow: "show question, accept answer, reveal correct/incorrect, track score, show final results"
- Ask for progress indicators: "progress bar showing 3 of 10 completed, score counter, streak tracking"
- Include "hint system" or "show explanation after answer" for a better learning experience
- Upload a sample CSV or describe your data schema in the prompt for more accurate data handling
Tech Stack
FAQ
Can AI build a Quiz App with Streamlit?
Yes. LoomCode AI generates a complete quiz app with Streamlit, Pandas, NumPy, Plotly from a text description. The AI understands interactive learning, quizzes, progress tracking, and feedback and produces working code that runs immediately in a live sandbox. Streamlit's built-in widgets and Python data libraries handle interactive learning, quizzes, progress tracking, and feedback with interactive controls and visualizations. You can iterate with follow-up prompts to refine features or deploy with one click.
How long does it take to build a Quiz App with AI?
A working quiz app typically generates in 30-60 seconds. The initial version includes interactive learning, quizzes, progress tracking, and feedback with a polished UI. From there, you can add features incrementally — each follow-up prompt takes another 15-30 seconds. Most users go from idea to a deployable quiz app in under 10 minutes, compared to hours or days of manual development.
Can I customize the generated Quiz App?
Yes, in two ways. First, use natural language follow-up prompts: "add dark mode", "change the layout to tabs", or "add a search filter" — the AI modifies the existing code. Second, copy the full source code and edit it directly. The output is standard Streamlit code using Streamlit and Pandas that works in any Python environment.
Which AI model works best for a Quiz App?
For a quiz app, GPT-4o offers the best speed-to-quality balance for quick iterations. Claude 3.5 Sonnet produces more polished code for complex features. DeepSeek V3 is a cost-effective alternative for simpler versions. You can switch models anytime.
Is the generated quiz app production-ready?
For prototypes and MVPs, the generated quiz app is typically ready to use immediately. The code includes data validation, error handling, and interactive widgets. For production deployment at scale, you may want to add automated tests, error boundaries, and monitoring.
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