TLDR;
This guide provides a step-by-step approach to building a Gemini-powered YouTube summarizer web application. It covers setting up a Google Cloud project, creating a Python Flask app on Cloud Run, building the front-end with HTML, and integrating the Gemini API for back-end summarization. Finally, it guides you through deploying the application on Cloud Run.
- Setting up a Google Cloud project and enabling necessary APIs.
- Creating a Flask app and designing a user interface for submitting YouTube links.
- Integrating Gemini API to summarize video content.
- Deploying the application on Cloud Run for public access.
Introduction
YouTube summarizers are valuable tools that condense lengthy videos into concise summaries, saving time and enabling users to quickly grasp the core content. This is especially useful for students, professionals, and anyone seeking to efficiently extract key information from online video content. By the end of this lab, you will have a working web application that can generate summary from YouTube videos. You will also have a better understanding of how to use Gemini API, Google Gen AI SDK, and integrate them together to build a web application.
Before You Begin
To start, you need a Google Cloud project with billing enabled. The guide explains how to select or create a project and ensure billing is active. It also introduces Cloud Shell, a command-line environment in Google Cloud, and provides commands to authenticate and set your project ID. Additionally, it lists the APIs that need to be enabled (Vertex AI API, Cloud Run Admin API, Cloud Build API, Cloud Resource Manager API) and provides commands to enable them via Cloud Shell or the Google Cloud Console.
Create a Python Flask App on Cloud Run
This chapter details how to create a Python Flask App on Cloud Run using the auto-generated template from Cloud Shell. It involves navigating to the Cloud Shell Terminal, opening the editor, and ensuring the Cloud Code project is set to the active Google Cloud project. The guide walks through selecting "New Application," choosing the Cloud Run Application and Python Flask template, and naming the project.
Build the Front-End
The front-end of the web application includes an input field for the YouTube link, an option to choose a different model family, a textarea for additional prompts, and a submit button. You can design your own form or use the provided HTML code to create the user interface. To test the front-end, you can run the app.py file in the terminal and preview the application on port 8080.
Build the Back-End
To build the back-end, you need to create a service that uses the Gemini model to summarize YouTube videos. This involves adding the Google Gen AI SDK library to the requirements.txt file, creating a virtual environment, and installing the necessary packages. The guide provides Python code for the app.py file, which initializes the Gemini client, defines the generate function to summarize videos, and sets up routes for the home page and summarization.
Deploy the Web Application
To deploy the application, you need to configure the current project in the Cloud Shell Terminal and ensure the working directory is set to the project directory. The guide provides the command to deploy the application to Cloud Run, prompts for a service name and region, and asks to allow unauthenticated invocations for the demo application. Once deployed, a link to the live application is provided.
Challenge
This section encourages you to enhance the application by allowing users to upload videos directly from their computers.
Clean Up
To avoid incurring charges, the guide provides instructions on how to delete the project in the Google Cloud console or delete the deployed service in Cloud Run.
