Create a YouTube Video Summarization App with Haystack, Llama2, Whisper, and Streamlit

Create a YouTube Video Summarization App with Haystack, Llama2, Whisper, and Streamlit

Table of Contents:

1. Introduction
2. The Problem with Closed Source Models and APIs
3. An Open Source Solution Using Haystack and Llama2
4. Getting Started with the Streamlit Application
5. Summarizing YouTube Videos Using HeyStack and Whisper
6. Building the YouTube Summarization App Step by Step
   1. Downloading the YouTube Video
   2. Initializing the Llama2 Model
   3. Initializing the Prompt Node
   4. Transcribing the Audio
   5. Running the Pipeline
   6. Displaying the Video and Summary
7. Conclusion

Building a YouTube Summarization App with Llama2 and HeyStack

In this article, we will explore how to build a Streamlit application that can summarize YouTube videos. This application will leverage the power of the HeyStack framework and the Llama2 language model to retrieve Relevant information from the videos. The best part is that this project will be entirely open source, meaning you won't have to rely on any closed source models or APIs. Let's dive in and see how you can develop this application step by step.

Introduction

YouTube has become a treasure trove of information, with millions of videos covering a wide range of topics. However, watching lengthy videos can be time-consuming, particularly when you're looking for specific information. That's where a YouTube summarization app can come in handy.

The goal of our project is to develop a Streamlit application that takes a YouTube URL as input and generates a summary of the video. To achieve this, we will combine two powerful models: Llama2 and Whisper. Llama2 is a large language model that can process and summarize text, while Whisper is an AI model for converting speech to text.

The Problem with Closed Source Models and APIs

Usually, when developers want to implement summarization features, they rely on closed source models or APIs. This can be a costly and inflexible solution, as you often have to pay to use these services, and it can be challenging to customize them to fit your specific needs.

Instead of relying on closed source models and APIs, we can leverage open source alternatives like the HeyStack framework and the Llama2 model. Open source solutions provide more flexibility, allowing you to tweak and customize the models to suit your requirements without incurring extra costs.

An Open Source Solution Using HeyStack and Llama2

HeyStack is an open source framework developed by Deepset that allows you to build production-ready applications powered by Llama2 and other AI models. It provides various tools, such as semantic kernels, Llama indices, and more, to help you process and retrieve information from text documents efficiently.

For our YouTube summarization app, we will leverage the power of HeyStack's semantic kernels and Llama indices to build our summarization pipeline. This pipeline will take a YouTube video as input, transcribe the audio using Whisper, and then use Llama2 to generate a summary of the video's content.

By using open source frameworks like HeyStack and models like Llama2, we can create a powerful, cost-effective, and customizable solution for summarizing YouTube videos.

Getting Started with the Streamlit Application

To begin building our Streamlit application, we need to set up our development environment. First, install the required libraries and dependencies. These include Streamlit, Torch, Sentence Transformers, and more. You can find the complete list of dependencies in the requirements.txt file provided with the code.

Next, import the necessary libraries and modules at the beginning of your script, including Streamlit, YouTube from piTube, nodes, pipelines, and transcribers from HeyStack, and more.

Summarizing YouTube Videos Using HeyStack and Whisper

To summarize a YouTube video using our Streamlit application, we follow a simple process. The user enters the YouTube video URL, and the application handles the rest. Let's break down the steps involved:

1. Downloading the YouTube Video: The application uses the piTube library to download the video from the provided URL. It utilizes the piTube stream feature to filter and retrieve the desired video stream.

2. Initializing the Llama2 Model: We initialize the Llama2 model using the HeyStack framework. This ensures we have access to the powerful text processing capabilities of Llama2.

3. Initializing the Prompt Node: A prompt node represents a specific task or question that we want the model to answer. In our case, the prompt node will focus on summarization. We configure the prompt node with the Llama2 model and a default summarization prompt.

4. Transcribing the Audio: We use the Whisper model, an AI model for Speech-to-Text conversion, to transcribe the audio content of the YouTube video. HeyStack's Transcriber module simplifies this process.

5. Running the Pipeline: HeyStack provides a pipeline module that allows us to chain together different nodes and create a data processing workflow. We add the whisper transcriber and the prompt node to the pipeline and then run the pipeline to execute the nodes and generate the results.

6. Displaying the Video and Summary: Finally, we display the video and the generated summary in separate columns using Streamlit's built-in components. The user can see the original YouTube video and read the summarized content side by side.

Building the YouTube Summarization App Step by Step

Now let's go through the steps to build the YouTube summarization app using Streamlit, HeyStack, Llama2, and Whisper. We will break down the code into functions and call them in a logical order to achieve our goal.

Step 1: Downloading the YouTube Video

To download the YouTube video, we create a function called download_video(url) which takes the YouTube video URL as input. This function uses the piTube library to download the video from the URL and returns the video object.

Step 2: Initializing the Llama2 Model

Next, we initialize the Llama2 model by creating a function called initialize_model(model_path). This function takes the full path of the Llama2 model as input and returns the initialized Llama2 model.

Step 3: Initializing the Prompt Node

To initialize the prompt node, we create a function called initialize_prompt_node(model). This function takes the initialized Llama2 model as input and configures the prompt for summarization. We can customize the prompt as needed for our application.

Step 4: Transcribing the Audio

To transcribe the audio of the YouTube video, we create a function called transcribe_audio(file_path, prompt_node). This function takes the file path of the downloaded YouTube video and the initialized prompt node as input. It utilizes the Whisper transcriber to convert the audio into text.

Step 5: Running the Pipeline

HeyStack provides a pipeline module for chaining different nodes to create a processing workflow. We create a function called run_pipeline(file_path, prompt_node) to run the pipeline. This function executes the nodes in the pipeline, including the Whisper transcriber and the prompt node.

Step 6: Displaying the Video and Summary

Finally, we display the YouTube video and the generated summary using Streamlit. We use the column component from Streamlit to create two columns. In column one, we display the YouTube video, and in column two, we display the summary fetched from the prompt node.

With these steps implemented, we have successfully built the YouTube summarization app using Streamlit, HeyStack, Llama2, and Whisper.

Conclusion

In this article, we have explored the process of building a YouTube summarization app using the HeyStack framework, Llama2 models, and the Whisper transcriber. The app allows users to input a YouTube video URL and generates a summary of the video content. By leveraging open source technologies, we avoid the need for expensive closed source models or APIs.

We went through the step-by-step process of building the app, including downloading the video, initializing the models and prompt nodes, transcribing the audio, running the pipeline, and displaying the video and summary. The result is an efficient, cost-effective, and customizable solution for summarizing YouTube videos.

Building on this foundation, you can further extend the app's functionality, such as adding support for local video files, PDF documents, or even chat-based interactions with the video content. The possibilities are endless, and with the power of open source frameworks, you can create tailored solutions to meet your specific requirements.

That wraps up our journey of building a YouTube summarization app using open source technologies. We hope you found this article informative and inspiring for your own projects. Happy coding!

Additional Resources:

• HeyStack Documentation
• Llama2 Model
• Whisper Model
• Streamlit Documentation

FAQ

Q: Can this app summarize videos in languages other than English? A: Yes, the Whisper transcriber can handle multiple languages. However, the Llama2 model used for summarization has been trained mainly on English text, so its performance on other languages may vary.

Q: Can I use this app to summarize local video files instead of YouTube videos? A: Yes, by modifying the download_video(url) function to accept a local file path as input, you can use this app to summarize local video files as well.

Q: Is the Llama2 model suitable for large videos? A: The Llama2 model can handle large videos, but the processing time will depend on the video's duration and the hardware resources available. For optimal performance, using GPUs can significantly speed up the processing time.

Q: Can I customize the summarization prompt used by the app? A: Yes, you can customize the summarization prompt by modifying the initialize_prompt_node(model) function and providing your own prompt text.

Q: Is this app compatible with other language models besides Llama2? A: The app is designed to work specifically with the Llama2 model. However, with some modifications to the code, you can adapt it to work with other language models supported by HeyStack.

Q: How can I deploy this app to a web server? A: To deploy the app to a web server, you can follow the deployment guides provided by Streamlit or deploy it using cloud services like Azure App Service or Heroku.