Build a Todo App with Streamlit
A task management app with add, edit, delete, and status tracking. Using Streamlit's Streamlit data apps with interactive widgets and charts, LoomCode AI generates a production-ready todo 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 Todo 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 todo app and click submit.
Preview & deploy
Watch the AI generate code and preview your working app live. Deploy with one click.
Why Build a Todo App with Streamlit
Streamlit turns a todo app into an interactive data app without needing frontend code. Widgets like sliders, checkboxes, and date pickers make the interface interactive, while Streamlit, Pandas, NumPy, Plotly handle data processing.
What the AI Generates for This Todo 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
- Persistent state so work is not lost on page refresh
- Keyboard shortcuts and quick-action buttons for common todo app operations
Example Prompt
Copy this prompt and paste it into LoomCode AI:
What You Get
LoomCode AI generates a todo app with full CRUD operations — create, read, update, and delete. Items persist in state, support status toggling and filtering, and the interface responds instantly to user actions. 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 your ideal workflow: "add task, set priority, mark complete, filter by status" gives the AI clear action patterns to implement
- Ask for keyboard shortcuts if power-user efficiency matters — the AI can wire up Ctrl+N for new items, Delete for removal, etc.
- Mention "drag and drop" or "reorder" explicitly if you want sortable lists — the AI uses the appropriate library for Streamlit
- Upload a sample CSV or describe your data schema in the prompt for more accurate data handling
Tech Stack
FAQ
Can AI build a Todo App with Streamlit?
Yes. LoomCode AI generates a complete todo app with Streamlit, Pandas, NumPy, Plotly from a text description. The AI understands task management, organization, and workflow features and produces working code that runs immediately in a live sandbox. Streamlit's built-in widgets and Python data libraries handle task management, organization, and workflow features 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 Todo App with AI?
A working todo app typically generates in 30-60 seconds. The initial version includes task management, organization, and workflow features 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 todo app in under 10 minutes, compared to hours or days of manual development.
Can I customize the generated Todo 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 Todo App?
For a todo 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 todo app production-ready?
For prototypes and MVPs, the generated todo 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.
Build a Todo App with Other Frameworks
Related Streamlit Apps