Streamlit Bootcamp

Explore the diverse text elements in Streamlit, including title, header, sub header, markdown, code blocks, text, and write, to display text on your Streamlit web app.

Explore how to work with data frames in Streamlit, create pandas data frames with numpy random data, and display an interactive table with sort by column and adjustable width/height.

Explore tables in streamlit and distinguish static tables from interactive dataframes. Create a pandas data frame with numpy random data and display it as a static tabular output using st.table.

Display json data in a streamlit app using the json display function with a body object and optional expanded flag to control its initial state, sample json ABC:1, XYZ:2, PQR:3.

Plot a line chart in Streamlit with the line_chart function, using a pandas data frame and specifying x, y, width, and height for interactive visualization.

Learn to plot area charts in Streamlit with area_chart, using a pandas data frame to define x, y, and z axes, and set height and width for interactive features.

Learn to plot a bar chart in stream lit by creating a pandas data frame with numpy random values, then use st.bar_chart with x and y data and run locally.

Learn to plot a histogram on a Streamlit web app using matplotlib.pyplot and numpy by generating normal data, creating a figure with subplots, and rendering it in the app.

Learn to use the button element in streamlit by implementing start button with a label, optional markdown, and an on-click callback, then run a simple hello/goodbye demo.

Learn to implement a streamlit download button using sd.download_button to enable downloading a text file, an image, or a csv from a dataframe in your web app.

Explore the Streamlit checkbox widget, including label, value (default false), key, and on_change callback, with a hands-on demo showing a message when checked.

Create radio buttons in Streamlit using the radio widget, present options such as comedy, drama, and documentary, and display messages based on the selected choice.

learn to build a simple select box widget in streamlit, display a label and options (email, home phone, mobile phone), and show the selected value in the app.

Explore the streamlit slider widget through hands-on examples, including single value sliders, range sliders, time sliders, and date-time sliders with labeled outputs.

Explore text input in Streamlit by labeling with a default value, storing user input in a title variable, and displaying the current movie title on the local host.

Learn how to use st.number_input to capture user numbers, store them in a variable called num, and observe live updates with plus minus controls and built-in numeric validation.

Learn to capture a user’s date input with Streamlit’s date_input widget and store it in a variable, then display the chosen birthday.

Learn to add images to your stream web app by importing PIL, loading a local image, and displaying it with a caption like Nice picture.

Learn to work with video files in a streamlit app by importing streamlit, reading a local mp4 in binary mode, and displaying it with st.video in a runnable streamlit project.

Learn to use Streamlit sidebars by adding a select box and a radio button with labels and options, and run the app on local host to explore the interactive widgets.

Learn to build a three-column layout in Streamlit using the columns container, populate columns with headers and text, and run the app locally.

Learn how to use an expander in Streamlit to wrap a bar chart with data, set the width, and reveal the label 'expand me to see some text'.

Develop an insurance premium predictor app using a random forest regression model in streamlit. Encode categorical features, train on insurance.csv, and deploy a user-friendly predict interface.

Train a random forest regressor to predict calories burned using gender, age, height, weight, duration, heart rate, and body temperature, serialize with pickle, and build a streamlit app for predictions.

Develop a Streamlit web app that deploys a machine learning model to predict calories burned from inputs like gender, height, weight, duration, heart rate, and body temperature.

Create a Streamlit app that predicts a stock market index using economic growth rate and unemployment rate data, training a linear regression model and visualizing results with scatter plots.