Streamlit Examples on Notebooks Hub

Notebooks Hub features various Streamlit apps showcasing a broad range of applicability. Current examples include: - AI chat bots that help answer academic questions and provide citations for suggested text - A python code-generator for spreadsheet editing and graphing - Imaging tools - Neural network models - Educational teaching demos - Platform for exploratory data analysis.

Example: Polus Flatfield Comparison

The Polus Flatfield Comparison app allows users to compare raw and flat-field corrected (FFC) images interactively. FFC is a digital technique that renders even illumination across an image to mitigate inconsistent brightness due to imperfections in the workspace and equipment, including specks of dust.

A screenshot of the Streamlit Polus FFC App

The app enables users to select a pair of images that are then displayed side-by-side using a Streamlit Image Comparison module. An adjustible slider provides the interactive component and enhances the user’s ability to visualize differences. The sample image dataset was provided by the Polus AI team @ Axle Informatics.

A gif showing interactive slider in FFC app

Example: Polus Datasheet

The Polus Datasheet app integrates Mito, an open-source data science tool that helps users easily edit spreadsheets, generate graphs, and render python code.

A screenshot of the Streamlit Polus Datasheet App

Example: Polus Ask Athena

PolusAI created a set of AI chat bots named Athena, or AIthena, to help provide users with literature references from select databases to help answer questions and provide citations. AIthena is not to be used to answer general questions, as a sole source of truth, or as a companion for hypothesis generation due to intentional restrictions placed on limiting its knowledge base to primary publications and preprints.

A screenshot of the Streamlit Ask Athena App

A screenshot of the Streamlit Advisor Athena App

Example: Neural Networks Using Streamlit

The Neural Network Example a teaching demo to show an example of the deep learning process using Tensorflow. See original project here.