Expand Data Visualizations & Integrations -- 2

I run the Streamlit application at https://quantsight-ai.streamlit.app/ and I’m ready to take it beyond simple charts. The priority is to introduce richer, more flexible data-visualization options so users can explore insights from multiple angles without leaving the app. Think along the lines of Plotly, Altair, or Bokeh for interactive plotting and, where appropriate, libraries such as PyDeck or Holoviews for geospatial or high-density data.

Alongside the new visuals, I’d like the app to hook directly into well-established data-analysis tools. A seamless bridge to pandas workflows—or even lightweight integration points for Jupyter-based notebooks—will let power users push data back and forth without manual exports.

Key deliverables
• Add advanced, interactive visualizations that slot naturally into the current Streamlit UI
• Build an integration layer that detects and consumes DataFrame objects from common analysis tools, then returns processed results back to them
• Keep the deployment footprint small so the app retains its current speed and stability
• Document any new modules, environment variables, and usage examples in the repo’s README

Acceptance criteria
– Visualizations render without errors on desktop and mobile browsers
– Integration APIs accept/return data in standard pandas-compatible formats
– No regression in overall load time (baseline provided on request)
– Clean, commented code pushed to GitHub with a concise changelog

If you’ve built Streamlit dashboards with advanced plotting libraries and tied them into analytic pipelines before, I’d love to see how you’d approach this upgrade.