Create Your Own AI App in Just 6 Hours!
Table of Contents
- Introduction
- Streamlit Hackathon Announcement
- The Problem and Goal
- My Inspiration to Create the App
- Overview of the App
- Retrieving the YouTube Video ID
- Extracting Captions from the Video
- Feeding the Text to the Language Model
- Utilizing the pytube Module for Extracting the Video ID
- Extracting Captions from the Video using a Python Library
- Using LangChain and their Prompt Template
- Formatting the Output for the Quiz
- Creating the Streamlit App
- Importing Helper Functions
- Basic Page Configurations
- Toast Messages with ChatGPT
- Gathering Information Using Input Fields
- Calling Helper Functions
- Loading Quiz Data and Displaying to the User
- Adding User-Friendly Features - A Short Tutorial
- Expander Element and Tutorial Video
- AI Voiceovers for the Tutorial
- Conclusion
- Source Code
Streamlit App Development: Behind the Scenes
In this article, I will provide an in-depth look at how I developed a Streamlit app for a hackathon announcement. The app utilizes a large language model and addresses a real-world problem. I will take You through the process step by step, from the initial inspiration to the final product.
1. Introduction
Streamlit recently announced a hackathon on their blog, and I was intrigued by the opportunity to showcase my skills. I decided to create a Streamlit app using a large language model and submit it as my entry for the hackathon. In this article, I will share the Journey of creating the app and provide insights into the development process.
2. Streamlit Hackathon Announcement
Streamlit's blog post announced the hackathon and provided guidelines for the participants. The app had to utilize a large language model and address a real-world problem. The goal was to inspire developers to create their own apps and add them to their portfolios or simply make them for fun.
3. The Problem and Goal
The first step in developing the app was to identify a real-world problem that the app could address. After brainstorming ideas, I decided to focus on the abundance of educational content available on YouTube. Many users watch educational videos but struggle to gauge their understanding of the content. The goal was to create an app that would allow users to test their knowledge by creating quizzes Based on YouTube videos.
4. My Inspiration to Create the App
The idea to create a Streamlit app for the hackathon came to me when I realized the potential of using a language model to generate quizzes based on YouTube videos. I spent around 5 to 6 hours over a weekend creating the initial version of the app. My main motivation was to inspire others to create their own apps by sharing my process and the solutions I discovered along the way.
5. Overview of the App
The app, called QuizTube, allows users to input a YouTube video link and their OpenAI API key. It then creates a quiz based on the video's content. The app provides Instant feedback by animating a balloon for correct answers and listing incorrect answers below. In the sidebar, additional information about the app and my social links is displayed.
To give you a better understanding of how the app works, let's dive into the step-by-step process of its development.
6. Retrieving the YouTube Video ID
The first step in creating the app was to extract the YouTube video ID from the inputted link. Instead of writing a complex function to extract the ID, I used the pytube module, which conveniently offers that functionality. This allowed me to obtain the video ID easily and move on to the next step.
7. Extracting Captions from the Video
To generate quizzes based on the video's content, I needed the transcript of the video. Instead of downloading the video and converting it to audio, I discovered a Python library that extracts captions directly from the video. Of course, this method only works if the video has captions available. As I wanted to keep the app simple, I deemed this solution to be quick and effective.
8. Feeding the Text to the Language Model
With the video transcript in place, I shifted my focus to utilizing a large language model to generate the quiz. I chose to use LangChain and their prompt template for this purpose. I experimented with the template text and decided to store the quiz as a list of lists, which yielded better results compared to a dictionary format.
9. Formatting the Output for the Quiz
The output generated by the language model was initially in the form of a STRING that resembled a list of lists. However, with the help of a built-in Python module, I transformed the string into an actual list. This allowed me to clean up the data and ensure it was in the desired format.
10. Creating the Streamlit App
With all the necessary components in place, it was time to create the Streamlit app. The app consisted of various elements such as importing helper functions, configuring the page, displaying toast messages, gathering input information, calling helper functions, and displaying the quiz to the user. ChatGPT played a significant role in automating these processes and streamlining the development workflow.
11. Adding User-Friendly Features - A Short Tutorial
To make the app more user-friendly, I included a short tutorial hosted on YouTube. The tutorial was embedded in an expander element, allowing users to quickly access it for guidance. To avoid the effort of recording a voiceover myself, I utilized free AI voiceovers from ElevenLabs, which added a professional touch to the tutorial.
12. Conclusion
Developing the Streamlit app for the hackathon was an exciting and fulfilling experience. With the help of various Python libraries and the power of a large language model, I was able to create an app that streamlined the process of generating quizzes based on YouTube videos. I hope this behind-the-scenes look at the app's development has inspired you to explore your own ideas and create innovative projects.
13. Source Code
If you're interested in reusing the source code of the app or want to Delve deeper into the implementation details, you can find the link to the source code in the description below. Feel free to explore, modify, and adapt the code for your own app development endeavors.
Highlights:
- Development of a Streamlit app for a hackathon
- Utilizing a large language model to generate quizzes
- Addressing the real-world problem of assessing understanding from YouTube videos
- Retrieving the YouTube video ID using the pytube module
- Extracting captions from videos using a Python library
- Formatting the output from the language model for quiz creation
- Creating a user-friendly Streamlit app with additional features
- Including a short tutorial with AI voiceover
- Sharing the source code for reuse and inspiration
FAQs (Frequently Asked Questions)
Q: Can I use the app for any YouTube video?
A: The app requires the YouTube video to have captions available. If the video has captions, you can use the app to generate quizzes based on its content.
Q: How accurate is the quiz generated by the app?
A: The accuracy of the quizzes depends on the quality of the video's captions and the performance of the language model used. The quizzes strive to test the user's understanding, but slight variations and ambiguity may exist.
Q: Can I customize the quiz format or questions?
A: As of the current version, the app generates quizzes based on a predefined template. Customization options may be added in future updates to provide more flexibility to users.
Q: How can I provide feedback or report issues with the app?
A: You can reach out to the app developer through the provided social links in the app's sidebar. Feedback and bug reports are highly appreciated to improve the app's functionality.
Q: Is the app suitable for educational purposes?
A: The app can be a useful tool for self-assessment and testing understanding of YouTube videos. However, it should not be considered a substitute for comprehensive learning resources or educational materials.