Harness the power of LangChain and OpenAI's GPT3.5 Turbo model
In today's fast-paced world, people are inundated with an overwhelming amount of information. As a result, the need for quick and efficient access to essential content is more important than ever. This is particularly true for video content, as YouTube alone has over 2 billion users, and countless hours of videos are uploaded every day. I have developed a Streamlit app that leverages the power of OpenAI's GPT3.5 Turbo model and Langchain to summarize YouTube videos, making it easier for anyone to consume information at a glance.
This app uses a combination of libraries and technologies to achieve its goal. The main components are the Langchain library, OpenAI's GPT3.5 Turbo model and Streamlit. The Langchain library provides an interface to interact with OpenAI's GPT 3.5 Turbo model and manage the pipeline for summarizing the video transcript. Streamlit is a popular web framework that allows developers to create web apps with Python easily and also deploy them free of cost.
The code begins by importing the necessary libraries and defining utility functions. The 'youtube_video_url_is_valid' function checks whether the provided YouTube URL is valid. The main function, 'find_insights', is responsible for loading the video transcript, creating a language model, and running the summarization process.
The 'find_insights' function starts by loading the video transcript using the 'YoutubeLoader' from Langchain. If an error occurs during this process, a corresponding error message is returned. Next, the OpenAI language model (LLM) is instantiated with the appropriate parameters. The 'PromptTemplate' objects are then created for both the mapping and combining stages of the summarization process. The 'RecursiveCharacterTextSplitter' is used to split the transcript into smaller chunks for processing.
Finally, the summarization chain is loaded and executed using the 'load_summarize_chain' function from Langchain. The chain consists of a 'map' stage, where each chunk of text is summarized individually, followed by a 'reduce' stage, where the summarized text is combined into a single, concise summary. If any error occurs during these steps, an error message is returned.
You can view the complete code in my github repo.
In conclusion, this Streamlit app harnesses the power of OpenAI and Langchain to provide users with a convenient way to summarize YouTube videos. This innovative solution addresses the challenge of information overload and allows users to focus on the most important content. By making it easier to consume and understand video content, tgis app has the potential to revolutionize the way people interact with online videos and streamline the learning process, ultimately contributing to a more knowledgeable and well-informed society.