How to build multi threaded apps in streamlit with ChatGPT

The video explains how to build multi-threaded apps in Streamlit using ChatGPT, showcasing a workaround to achieve concurrency through threading and session state. By leveraging queues for communication between threads, developers can run multiple tasks concurrently, improving app efficiency and user experience.

The video discusses how to create multi-threaded apps in Streamlit using ChatGPT. Multi-threading allows running parallel processes such as chat processing, question answering, and summarization concurrently. While Streamlit does not inherently support multi-threaded apps, the video presents a workaround to achieve concurrency using threading and session state. The demonstration involves initializing objects in the session state, such as GPT models for chat and summarization. User inputs trigger responses from GPT, which are then summarized and written to files. The use of queues facilitates communication between the main script and threaded processes.

The tutorial highlights the benefits of leveraging multi-threading for running multiple tasks simultaneously in Streamlit apps. By using threading and queues, developers can create apps that perform tasks concurrently, enhancing efficiency and user experience. The video demonstrates how to set up multiple GPT instances for different tasks like question answering and summarization, leveraging session state to persist objects across script runs. Threading enables the processing of user inputs, responses, and summarization in parallel, showcasing the power of multi-threading in app development.

The implementation involves initializing GPT instances, queues, and a function for summarization within a while loop. The function continuously checks the chat queue for inputs, processes them using the summary GPT model, and writes the summaries to files and another queue. Threading is utilized to run this function concurrently with the main script, showcasing how multi-threading can handle multiple tasks efficiently. The video emphasizes passing GPT objects as arguments to threaded functions to prevent errors and ensure smooth execution.

The tutorial explains the step-by-step process of setting up multi-threaded apps in Streamlit, emphasizing the importance of using queues for communication between threads. By demonstrating how to handle user inputs, responses, and summarization concurrently, the video provides a practical example of leveraging multi-threading for enhanced app functionality. The use of session state to persist objects and threading to run parallel processes adds a new dimension to Streamlit app development, enabling developers to create more dynamic and responsive applications.

In conclusion, the video showcases a workaround for building multi-threaded apps in Streamlit using ChatGPT, offering insights into the benefits and implementation of multi-threading for concurrent task execution. By combining threading, session state, and queues, developers can create apps that handle multiple tasks simultaneously, improving performance and user interaction. The tutorial provides a comprehensive guide on how to set up and debug multi-threaded apps in Streamlit, empowering developers to enhance the functionality and responsiveness of their applications.