We are happy to have you as a contributor 🎈

Articles to be published in TST should include the use of Streamlit in some way, preferably providing clear explanations about the implementation or discussing interesting use cases or reflections about the impact of this amazing tool. We are open to any style, whenever the document is well written. …

We Streamlit enthusiasts know that our creations deserve to be shared with others. We love to see that what we do with creativity can be helpful to someone else. This is part of the appeal of Streamlit, to convert simple python scripts into great web apps that could be used…

Set up a Keras Model and build a web app easily I believe Streamlit is a fantastic tool, and I try to spread my knowledge and experience building web apps in my community, particularly in my faculty. In a recent workshop, I taught the basics of Artificial Neural Networks to a group of chemistry students, and of course, I used Streamlit…

Py3DMol and stmol component — Follow this link for the first part: https://towardsdatascience.com/molecular-visualization-in-streamlit-using-rdkit-and-py3dmol-4e8e63488eb8 1. Introduction When I asked Adrien Treuille how to deploy 3D structures on Streamlit, I was soon contacted by TC Ricks, and he proposed me to develop a Streamlit Component, when components were not yet released to the public. I worked on that and…

Part 1: RDKit — 1. Introduction Soon after I knew about Streamlit [1], my first question [2] was, how it could be possible to deploy molecular structures? In this post AND the next, I will contribute what I have learned, hoping that this could be of help to others. In this two-part series, I will concentrate…

An end to end project combining Chemistry and Deep Learning on Graphs — Here what we are going to accomplish are the following tasks: Build a custom graph dataset formatted to work in DGL (Main subject in this post 😎) Prepare Train and Test datasets randomly (Good to know 👍) Define the Graph Conv Network (Piece of cake 🍰) Train and evaluate Accuracy…

A Painless Introduction — I have reviewed some of the existing libraries to do Graph Convolutional Neural Networks (GCNN’s) and, although in general they are very good, I always return to DGL because it has excellent documentation and many examples, among other things [1]. Here, I want to share my review of a classic…

A beginner friendly use of cosine similarity — Whenever you start with machine learning, there are several well known datasets ( Iris dataset for example 🥀 ). Among them there is one for images that is simple, popular and interesting, namely the MNIST dataset which consist of a large number of handwritten digits 📝 [1]. Here we are…