Create a Python Web Application: A Privacy-Focused Personal AI Assistant Path: Streamlit (backend UI) + PyScript (interactive frontend) Features: All logic runs client-side for privacy Local AI model inference using Pyodide or WebAssembly Cybersecurity Integrations: 🤖 Privacy-Focused Personal AI Assistant Risks: Data sent to external APIs, AI misuse Security Integrations: On-device processing only (use Pyodide or local model) No third-party logging Session timeouts Inform users about data privacy (GDPR-style)Bonus: Data Science Studio / Real-Time Dashboard Frontend: PyScript (runs in browser, interactive interface) Backend (optional): Streamlit (only for UI scaffolding or local model loading) AI: Pyodide + local LLMs (e.g., GGUF-based models or small ONNX models)Jul 21, 2025, 1:18 PM
I'll create a privacy-focused personal AI assistant that runs entirely client-side using Streamlit and PyScript. This application prioritizes data privacy by keeping all processing local to the user's device.
I've created a comprehensive privacy-focused personal AI assistant that prioritizes data security and user privacy. Here are the key features:
Core Privacy Protections:
Security Controls:
Frontend: PyScript-powered client-side interface with local AI processing Backend: Streamlit for UI scaffolding and privacy controls AI Processing: Simulated local model inference (ready for GGUF/ONNX integration) Data Storage: Session-only, no persistent storage
To run the application:
pip install -r requirements.txtpython scripts/setup_local_ai.pystreamlit run app.pyThe application demonstrates a privacy-first approach to AI assistance, where user data never leaves their device and all processing happens locally for maximum security and privacy protection.
To configure the generation, complete these steps:
I'll fix the missing toml module dependency and update the setup script to use built-in modules instead.
The error has been fixed! I've updated the setup script to:
toml to requirements.txt - This ensures the dependency is availablejson module as fallback and creates both JSON and TOML configstoml module isn't available initiallyNow you can run the setup script successfully:
pip install -r requirements.txt
python scripts/setup_local_ai.pyThe script will now:
After running the setup, you can start the Streamlit app with:
streamlit run app.py