How to Build an AI-Powered Video Search App

Introduction

In this article, we'll explore how to create an intelligent search application for video content using Natural Language Processing (NLP), specifically for YouTube. YouTube has come a long way since its inception in 2005, where it started with a simple 19-second video titled "I'm at the zoo." Fast-forward to today, YouTube hosts a vast array of content created by everyday people, and building a sophisticated search tool for this content can greatly enhance user experience.

The Vision for Our Search Tool

The primary goal of this application is to develop a search tool that allows users to find engaging and relevant video content efficiently. While services like Google Search or the default YouTube search are available, this custom-built tool can potentially offer more refined results.

Demonstration of Functionality

The application will leverage a user-friendly interface using Streamlit. Users can input queries such as "What is deep learning?" and receive a list of relevant videos, complete with timestamps that link directly to the relevant sections of the video. This provides a more interactive way to consume video content, essentially transforming the user experience into one that feels intuitive and intelligent.

Sourcing the Data

For this project, we will work with a dataset containing 116,000 text files and audio files related to the video segments. However, our focus will primarily be on the text files which contain subtitles for each segment.

Data Preparation

Kaggle Account Setup: Begin by creating a Kaggle account and downloading the Kaggle API token (kaggle.json). This will allow us to retrieve datasets directly from Kaggle.
Data Structure: Each data file consists of timestamps and corresponding subtitles. We’ll extract this information for further processing.
Scraping Additional Metadata: To enhance the searchability of the videos, we will also scrape metadata such as video titles, thumbnails, and descriptions using libraries like Beautiful Soup.

Data Processing

After gathering the necessary data, the next step involves processing it. This entails:

Looping through each video segment to compile relevant chunks of text.
Structuring documents that include video IDs, subtitles, timestamps, and URLs.
Saving these documents to a format suitable for further use, such as JSON Lines.

Indexing with Pinecone

To facilitate quick and efficient searches, we will use Pinecone, a vector database, to index our documents.

Initialization: This involves setting up a Sentence Transformer model to convert text into vectors for indexing.
Embedding Process: The metadata and documents are encoded for insertion into the Pinecone index, allowing for semantic search capabilities.

User Application Interface

Finally, we will integrate all the functionalities into a simple yet effective web application using Streamlit:

Users can input search terms, and results will be displayed in a card format.
Each result will provide direct links to timestamps within the videos, enabling a seamless transition between search results and video playback.

Conclusion

Creating an AI-powered video search application tailored for platforms like YouTube is not only exciting but also feasible using modern ML techniques and open-source libraries.

Keywords

AI-Powered
Video Search
YouTube
NLP
Streamlit
Dataset
Metadata
Pinecone
Embedding
Web Scraping

FAQ

1. What is the main purpose of the AI-Powered Video Search App?

The primary purpose is to allow users to efficiently search for relevant video content on platforms like YouTube by providing direct links to specific segments of the videos.

2. How do I set up the data for this application?

You need to create a Kaggle account to download the video dataset and use APIs to scrape additional metadata, such as video titles and thumbnails.

3. What technologies are used to build this application?

The application employs Python libraries like Streamlit for the web interface, Beautiful Soup for web scraping, and Pinecone for vector database indexing.

4. How can I implement the search function?

After processing the data and indexing it via Pinecone, implement a search bar in Streamlit that queries the indexed data and displays results in a user-friendly format.

5. What are the advantages of using an AI-powered search tool over the existing YouTube search?

The AI-powered search tool can provide more relevant and precise results tailored to users' queries and can directly link to specific moments in videos, improving the user experience.