Developing and Deploying Applications with Streamlit

Explore displaying text information with Streamlit using built-in functions such as title, header, text, markdown, caption, and latex, and preview data types like metric, data frame, table, and json.

Display media in streamlit using st.image, embed videos via URLs, and include audio from SoundCloud. Demonstrate an interactive image app to rotate, flip, and resize images for quick prototyping.

Learn how a button in a Streamlit app becomes powerful when paired with an action, filtering a mammals and birds dataframe to display mammals or birds.

Demonstrate how checkboxes paired with cascading actions enable meaningful interactivity by showing a bar chart when selected and contrasting a dummy checkbox that does nothing.

Explore single line and multi line inputs in Streamlit using text input and text area, enter names and a bio, press control enter to print results, and preview numeric input.

Explore the Streamlit number input widget that accepts integers or floats, with configurable steps and a min-max range plus validation for out-of-range input.

Explore the file uploader in Streamlit to upload PDFs, images, or Word files, set a 200 MB size limit, restrict accepted types, and validate input with feedback.

Explore four Streamlit UI concepts—sidebar to declutter the app, forms for input-driven decisions, expanders for on-demand charts, and columns for side-by-side layouts and responsive sizing.

Explore mutating data and charts in Streamlit by appending rows in place with add rows, demonstrating in-place data and chart updates without re-creating structures.

Develop a Streamlit app that accepts user image uploads, provides a filter selection (pencil sketch initially), and lays out a sidebar with input controls using OpenCV and the Streamlit library.

Learn to convert an input image into a pencil sketch in a streamlit app by applying grayscale conversion, image inversion, gaussian blur, and dodge division using OpenCV.

Learn to add an image download link in a streamlit workflow by encoding an image to base64, saving it to a buffer, and returning an HTML hyperlink for download.

Build an animated GDP per capita chart using Gap Minder data in Streamlit, adding interactive year selection and an animation frame by country for a production-ready visualization.

Create a ChatGPT account by signing up with Google or Microsoft, entering basic profile details, selecting your country, verifying your phone with a code, and gaining access to ChatGPT.

Explore chatgpt example search queries, model capabilities, and how it declines inappropriate requests, with prompts on protecting bank accounts and homes and comparing stackoverflow solutions for a streamlined ui integration.

Set up a virtual environment for ChatGPT integration with a simulator using Anaconda or a native virtual env, install OpenAI and Streamlit, and verify imports to ensure everything works.

Learn how to use ChatGPT to solve LeetCode problems in Python, compare iterative and recursive approaches, test solutions, explain space and time complexity, and preview Streamlit UI integration.

Explore integrating ChatGPT with Streamlit to fetch and display Python code solving LeetCode problems, view and export generated solutions, and dynamically adjust prompts and token limits.

Learn to build multi-page Streamlit apps with set_page_config, organizing GDP per capita visuals into three pages: basic_data, basic_plot, and animated_plot, and managing a pages directory for data and plots.