Merge pull request #7 from kameshsampath/review

Review Comments and Basic Styles

README.md

@@ -1,5 +1,7 @@
-# Zero To Streamlit
+# Bootstrapping Your Transition from Streamlit OSS to Streamlit in Snowflake(SiS)
 
 Getting started with Streamlit and deploying to Streamlit in Snowflake(SiS). This tutorial is adapted from <https://www.youtube.com/watch?v=LJ6DcLGQ4vY>
 
-[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://kameshsampath-ml-app.streamlit.app/)
+[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://kameshsampath-ml-app.streamlit.app/)
+
+The complete HTML documentation of this lab is available at <https://snowflake-labs.github.io/streamlit-oss-to-sis-bootstrap/>

docs/explore_dataset.md

@@ -1,14 +1,14 @@
 # Exploring the Penguins Dataset with Streamlit
 
-In this chapter, we'll explore the Penguins dataset{:target=_blank} and use it as a base to build an interactive ML application using Streamlit.
+In this chapter, we'll explore the [Penguins dataset](https://github.com/dataprofessor/data/blob/master/penguins_cleaned.csv{:target=_blank} and use it as a base to build an interactive ML application using Streamlit.
 
 By the end of this chapter you will,
 
 - [x] Loading, preprocessing, and preparing the dataset for visualization
 - [x] Using Streamlit Expander to display:
     * Dataset Information
     * Machine Learning Model Features (**X**)
-    * Prediction Target(`species`) variable (**y**)
+    * Prediction Target (`species`) variable (**y**)
 - [x] Creating interactive scatter plots to identify patterns and relationships
 
 ## Download Dataset
@@ -40,7 +40,7 @@ st.write("Welcome to world of Machine Learning with Streamlit.")
 df = pd.read_csv("data/penguins_cleaned.csv")
 df
 ```
-Stage, commit and push the code to the repository. In few seconds you should notice that your application on Streamlit Community Cloud refreshed with all your changes.
+Stage, commit and push the code to the repository. In a few seconds you’ll see that your deployed app on the Streamlit Community Cloud refreshes as the code changes.
 
 ## Application Overview
 
@@ -73,7 +73,7 @@ with st.expander("Data"):
 
 ## Displaying the Variables
 
-Let us create and display the input features(**X**) and target(**y**), edit and update the `$TUTORIAL_HOME/streamlit_app.py` with the following code,
+Let us create and display the input features (**X**) and target (**y**), edit and update the `$TUTORIAL_HOME/streamlit_app.py` with the following code,
 
 ```py title="streamlit_app.py" linenums="1" hl_lines="16-22"
 import streamlit as st

docs/index.md

@@ -1,7 +1,7 @@
 # Bootstrapping Your Transition from Streamlit OSS to Streamlit in Snowflake(SiS)
 
 ## Get Ready to Build!
-Ready to transform a simple Streamlit application into an enterprise-grade solution in to SiS? In this hands-on tutorial, you'll explore Streamlit's versatility while building an interactive data application. Using a Machine Learning example, you'll discover how easily Streamlit can evolve from your local machine to a fully integrated Snowflake application.
+Ready to transform a simple Streamlit application into an enterprise-grade solution on SiS? In this hands-on tutorial, you'll explore Streamlit's versatility while building an interactive data application. Using a Machine Learning example, you'll discover how easily Streamlit can evolve from your local machine to a fully integrated Snowflake application.
 
 ## What You'll Build
 
@@ -48,10 +48,9 @@ Your journey will take you through the complete Streamlit development lifecycle.
 
 Watch your Streamlit application evolve:
 
-- From your laptop to the cloud
-- Through Snowflake integration
-- Into native Snowflake deployment
-- With surprisingly few code changes
+- Going from local development to cloud deployment
+- Connect to Snowflake’s data platform
+- Deploy natively within Snowflake with minimal code changes
 
 ## Your Learning Goals
 

docs/setup.md

@@ -1,6 +1,6 @@
 # Setup
 
-By the end of this chapter, you will have successfully created and set up a GitHub project using the [Stream Starter Kit](https://github.com/streamlit/app-starter-kit){:target=_blank}. This starter kit provides a solid foundation for building interactive web applications with Streamlit, a powerful Python library for creating data-driven apps.
+By the end of this chapter, you will have successfully created and set up a GitHub project using the [Streamlit Starter Kit](https://github.com/streamlit/app-starter-kit){:target=_blank}. This starter kit provides a solid foundation for building interactive web applications with Streamlit, a powerful Python library for creating data-driven apps.
 
 In this chapter, we will guide you through the following steps:
 
@@ -19,15 +19,15 @@ To follow along with this tutorial and set up your project successfully, you'll
 
 * [Docker for Desktop](https://www.docker.com/products/docker-desktop/){:target=_blank}
 
-* A GitHub account: If you don't already have a GitHub account, you'll need to create one. 
+* A [GitHub account](https://github.com/signup){:target=_blank}: If you don't already have a GitHub account, you'll need to create one. 
 
 * [Miniconda](https://docs.anaconda.com/miniconda/){:target=_blank}
 
 * [Snowflake Account](https://signup.snowflake.com){:target=_blank}
 
 * [Snowflake CLI](https://docs.snowflake.com/en/developer-guide/snowflake-cli/index){:target=_blank}
 
-* And last but not the least latest Google Chrome browser
+* And last but not the least latest [Google Chrome](https://www.google.com/chrome/index.html){:target=_blank} browser
 
 Please ensure you have all these tools and accounts set up before proceeding with the tutorial. In the following sections, we'll guide you through the process of setting up your GitHub project using the Streamlit Starter Kit.
 

docs/stylesheets/extra.css

@@ -1,6 +0,0 @@
-:root {
-    --md-primary-fg-color: #249edc;
-    --md-primary-fg-color--light: #FFFFFF;
-    --md-primary-fg-color--dark: #11567F;
-    --md-code-hl-color--light: #fbf719;
-}

mkdocs.yml

@@ -12,11 +12,10 @@ extra_css:
 theme:
     name: material
     palette:
-        primary: custom
-
-    font:
-        text: Roboto
-        code: Fira Code
+        primary: white
+    logo: assets/snowflake-logo-blue.svg
+    favicon: assets/favicon.ico
+    font: false
     features:
         - content.code.copy
         - content.code.select