Master AI Engineering with Your First YouTube Chat Project

Master AI Engineering with Your First YouTube Chat Project

Table of Contents

1. Introduction
2. Project Overview
3. Selecting Tools and Technologies
4. Backend Development
   1. Downloading the YouTube Video
   2. Converting Video to MP3
   3. Transcribing Audio to Text
   4. Creating a Chatbot Context
   5. Chatting with the Chatbot
5. Frontend Development
   1. Setting up the Flask App
   2. Designing the UI
   3. Styling the UI
6. Conclusion

Introduction

In today's video, We Are going to embark on our first project in the AI Engineer Skills for Beginners series. Building upon what we learned in the previous episodes about the OpenAI API and Python, we will explore how AI Tools can be utilized to generate code. Our project will involve creating a chatbot that can Interact with a YouTube video, allowing users to transcribe the video content and interact with it. Using the GPT-4 model and OpenAI's ChatGPT API, we will develop a user interface and backend functionality to enable seamless interaction with the chatbot.

Project Overview

The project aims to leverage AI tools and technologies to Create a chatbot that can interact with YouTube videos. The process involves transcribing a YouTube video using OpenAI's Whisper API, converting the video to text, and utilizing the GPT-4 model to generate responses Based on user queries. The chatbot will be built as a Flask application, with a user-friendly UI that allows input of YouTube video URLs and text interactions with the chatbot. The project will be broken down into backend and frontend development stages.

Selecting Tools and Technologies

To successfully accomplish the project goals, we will utilize several tools and technologies. These include Python, the OpenAI API, Flask framework for backend development, HTML, JavaScript, and CSS for frontend development, and various libraries such as Pytube, MoviePy, and FFMpeg for video handling and conversion. We will also employ the GPT-4 model and OpenAI's ChatGPT API for natural language processing and chatbot functionalities.

Backend Development

Downloading the YouTube Video

Our first step in the backend development process will be to create a Python function that can download a YouTube video from a given URL in MP4 format. This function will utilize the Pytube library to handle the download process and the YouTube module to retrieve the video.

Converting Video to MP3

Once the video is downloaded, we will need to convert it to the MP3 format for further processing. This will involve using the MoviePy library, which is a wrapper around FFMpeg, to perform the conversion from MP4 to MP3.

Transcribing Audio to Text

To transcribe the audio from the MP3 file into text, we will utilize OpenAI's Whisper API. This API allows us to convert speech to text and will provide us with the transcribed content of the YouTube video. We will create a function that takes the MP3 file as input and outputs the transcribed text.

Creating a Chatbot Context

To enable Meaningful interactions with the chatbot, we need to set up a context for it to understand the conversation history and user queries. We will utilize the GPT-4 model and OpenAI's CreateChatContext API to create a context for the chatbot that includes the transcribed text.

Chatting with the Chatbot

Once the chatbot context is set up, we will develop a function that allows users to interact with the chatbot. Using the GPT-4 model and OpenAI's ChatCompletion API, the chatbot will be able to generate responses to user queries based on the provided context. The function will take user input and return the chatbot's response.

Frontend Development

Setting up the Flask App

To create a user-friendly interface for our chatbot, we will utilize the Flask framework to set up a web application. This will involve creating routes and endpoints that handle user requests and serve the appropriate responses.

Designing the UI

The user interface will include elements such as a text box for entering the YouTube video URL, a button to initiate the transcription process, an indicator message to Show the progress of transcription, and text input boxes for interacting with the chatbot. We will design the UI using HTML, CSS, and JavaScript, ensuring it is visually appealing and intuitive for users.

Styling the UI

To enhance the visual appeal of the UI, we will select a color palette, font styles, and background images that Align with the desired aesthetic. We will focus on creating a modern and visually engaging design inspired by the Miami Vice 80s style.

Conclusion

In this project, we have explored the process of creating a chatbot that can transcribe and interact with YouTube videos. We have leveraged AI tools, such as the GPT-4 model and OpenAI's ChatGPT API, along with Python and various libraries, to develop a backend that handles video downloading, conversion, transcription, and chatbot functionalities. The Flask framework has been used to create a user-friendly UI, which has been styled to align with the Miami Vice 80s aesthetic. This project opens up opportunities for further development and expansion, including features like video summarization, larger video support, and fine-tuning capabilities. By combining AI and web development, we have created a unique and engaging project that showcases the power of AI in solving real-world problems.

Highlights

• Leveraging AI tools to create a chatbot that interacts with YouTube videos
• Utilizing the GPT-4 model and OpenAI's ChatGPT API for natural language processing
• Developing the backend functionalities for video downloading, conversion, transcription, and chatbot interactions
• Building a user-friendly UI using HTML, CSS, and JavaScript with a Miami Vice-inspired 80s aesthetic
• Styling the UI to enhance visual appeal and user experience

FAQ

Q: Can the chatbot summarize the transcribed video content? A: While the initial implementation of the chatbot focuses on generating responses based on user queries, adding a summarization feature to condense the transcribed text is a possibility for future development.

Q: Is it possible to use the chatbot with larger videos or longer durations? A: Currently, the implementation limits the video size to ensure efficient processing. However, with further development and optimizations, it is possible to extend support for larger videos and longer durations.

Q: Can the chatbot be fine-tuned for specific use cases? A: Yes, fine-tuning the chatbot for specific use cases can enhance its performance and make it more specialized in generating responses relevant to a particular domain or topic. Fine-tuning capabilities can be explored in future iterations of the project.

Q: Are there plans to expand the project and incorporate additional features? A: Yes, the project has opportunities for expansion, such as incorporating rag systems for retrieving information from the transcribed text, implementing video summarization, and fine-tuning the chatbot for improved accuracy and context relevance. These features can enhance the overall functionality and user experience.