I built a fullstack PaLM AI app in just 2 minutes

Beyond Fireship
12 May 202303:28

TLDRIn this video, the creator demonstrates the ease of building a full-stack real-time server-rendered application using Google's PaLM 2 large language model for text summarization. The app allows users to input content from various news sites and receive a concise summary. The user interface updates in real time and stores results in a scalable Cloud database. The entire process, from project creation to deployment on Firebase, is achieved in just two minutes, showcasing the power of Firebase and its support for SSR frameworks. The video also highlights the integration of the PaLM API through Firebase Extensions, real-time UI updates using SvelteKit, and the deployment of a cloud function for text summarization. The presenter further discusses additional Firebase features, such as Python runtime for cloud functions, Firestore's new queries, and Terraform support, emphasizing the platform's versatility and ease of use.

Takeaways

  • ๐Ÿš€ Built a fullstack app using Google's PaLM 2 language model for text summarization in just 2 minutes.
  • ๐Ÿ“„ Users can paste content from news sites and receive a concise summary in a few sentences.
  • โš™๏ธ Real-time server rendering and UI updates are facilitated by Firebase's infrastructure.
  • ๐Ÿ”„ The app stores results in a scalable Cloud database, leveraging Firebase's Firestore.
  • ๐Ÿง™โ€โ™‚๏ธ Spelled Kit is used for building the app, which integrates with Firebase.
  • ๐Ÿ”— Svelte Fire is installed for seamless Firebase integration with the app's stores.
  • ๐Ÿ”ง Firebase project setup includes enabling Firestore and creating app credentials.
  • ๐Ÿ“ˆ The Firebase Extensions Hub is used to install the 'summarize text' extension, which deploys a cloud function.
  • โœ๏ธ A form is created for users to submit text, which updates the database and triggers the summary generation.
  • ๐Ÿ” The app uses `doc.store` from Svelte Fire to view documents in real-time.
  • ๐ŸŒ Deploying the app is streamlined with Firebase's command-line tools for server-side rendering.
  • ๐Ÿ“š Other Firebase features mentioned include Python runtime for Cloud Functions, improved queries, and Terraform support.
  • ๐Ÿ“น The creator also experimented with Firebase and Vonage API to build a video chat app.

Q & A

  • What is the main feature of the app mentioned in the transcript?

    -The app is a text summarizer that uses Google's PaLM 2 large language model to provide quick summaries of content from websites like CNN or Fox News.

  • How does the app update the user interface?

    -The user interface updates in real time as the text is summarized and the result is stored in a Cloud database.

  • What technology is used for the backend of the app?

    -Firebase is used for the backend, which includes a scalable Cloud database and integration with the PaLM API.

  • How long did it take to build the app?

    -The app was built in just two minutes, thanks to the ease of use and automation provided by Firebase and Spelled Kit.

  • What is Spelled Kit?

    -Spelled Kit is a framework that was used to quickly build the app, and it integrates with Firebase for real-time updates and database management.

  • How does the app handle real-time data updates?

    -The app uses a dollar sign in front of the document reference to subscribe to the data in the database, which allows it to update the UI in real time.

  • What is the purpose of the Firebase Extensions Hub?

    -The Firebase Extensions Hub allows users to install extensions that provide additional functionality, such as the 'summarize text' extension that uses the PaLM API.

  • How does the app handle the submission of new text for summarization?

    -The app includes a form with a 'handle submit' function that updates the document in the database, triggering a cloud function to call the PaLM API and generate a summary.

  • What are some other features that Firebase has recently introduced?

    -Firebase has introduced a Python runtime for Cloud Functions, improved queries for Firestore, and experimental support for deploying Flask and Django apps.

  • What is the role of Terraform in Firebase?

    -Terraform support in Firebase allows users to represent their Firebase project configuration as code, making it easier to manage and automate the setup process.

  • How can one learn more about Firebase and its capabilities?

    -To become proficient with Firebase, one can become a pro member at Fireship IO, which offers in-depth tutorials and resources.

  • What is the significance of server-side rendering in the context of this app?

    -Server-side rendering is important as it allows the app to be live on the internet with pre-rendered content, improving load times and SEO.

Outlines

00:00

๐Ÿš€ Building a Real-Time Text Summarizer with Firebase and Palm 2

The video introduces a real-time server-rendered full stack application that uses Google's Palm 2 large language model to summarize text. The app allows users to input content from news sites like CNN or Fox News and receive a concise summary. The user interface updates in real-time and stores the results in a scalable Cloud Firestore database. The presenter demonstrates how to build this app quickly using Firebase and Spelled Kit, integrating Firebase with the app and enabling Firestore. The video also covers the installation of a Firebase extension that uses the Palm API to automatically summarize text documents. Finally, it shows how to deploy the app to Firebase and mentions other Firebase features and extensions, including Python runtime for cloud functions and terraform support.

Mindmap

Keywords

๐Ÿ’กFullstack App

A fullstack app refers to a software application that includes both the client-side (front-end) and server-side (back-end) components. In the context of the video, the speaker has built a fullstack application that uses Google's PaLM AI model to summarize text in real-time. The application is deployed on the web and is server-rendered, meaning the server generates the HTML sent to the client's browser.

๐Ÿ’กPaLM 2

PaLM 2 is Google's large language model, which stands for Pathways Language Model 2. It is an advanced AI model designed to process and understand natural language. In the video, the app leverages PaLM 2 to perform text summarization, taking in content from various news websites and generating concise summaries.

๐Ÿ’กText Summarizer

A text summarizer is a tool or application that takes a piece of text and condenses it into a shorter version while retaining the key points. In the video, the app serves as a text summarizer, allowing users to paste in content and receive a brief summary in a few sentences, which is particularly useful for condensing lengthy news articles.

๐Ÿ’กFirebase

Firebase is a platform developed by Google for creating mobile and web applications. It offers a variety of services such as real-time databases, authentication, and hosting. The video demonstrates how Firebase's services were used to quickly build and deploy the fullstack app, highlighting its ease of use and integration with other Google services.

๐Ÿ’กSvelteKit

SvelteKit is a modern framework for building web applications. It is known for its efficient approach to front-end development, as it compiles components into efficient JavaScript at build time. In the video, SvelteKit is used to create the user interface of the app, which updates in real-time as the server renders new content.

๐Ÿ’กFirestore

Firestore is a NoSQL cloud database by Firebase that allows for storing and syncing data in real-time. It is designed to scale automatically and handle large amounts of data. In the video, the app uses Firestore to store the results of the text summaries, which can be accessed and updated in real-time.

๐Ÿ’กCloud Function

A cloud function is a piece of code that runs in response to events in a cloud environment. In the context of Firebase, cloud functions can be triggered by various events, such as changes in the database. The video describes how a cloud function is used to call the PaLM API and update the summary field in the Firestore database whenever a document is updated.

๐Ÿ’กServer-Side Rendering (SSR)

Server-Side Rendering (SSR) is the process of generating HTML on the server before sending it to the client. This can improve the performance and SEO of web applications. In the video, the app is deployed with server-side rendering enabled, which allows for faster load times and better search engine optimization.

๐Ÿ’กReal-Time Updates

Real-time updates refer to the ability of an application to reflect changes immediately as they occur, without the need for manual refreshes. The video script mentions that the user interface updates in real time, which means that as the text summary is generated and stored in the database, the changes are immediately visible to the user.

๐Ÿ’กExtensions

Extensions in the context of Firebase are additional features or services that can be added to a Firebase project to extend its functionality. The video mentions installing a 'summarize text' extension that deploys a cloud function for summarizing text using the PaLM API.

๐Ÿ’กTerraform

Terraform is an infrastructure as code software that allows users to build, change, and version infrastructure safely and efficiently. In the video, it is mentioned that Firebase now supports Terraform, which means developers can define their Firebase project configuration using code, making it easier to manage and version control.

๐Ÿ’กFireship IO

Fireship IO is an online learning platform that offers tutorials and courses on Firebase and other Google Cloud technologies. The video suggests becoming a pro member at Fireship IO for those who want to become proficient with Firebase, indicating that the platform provides in-depth and expert-level instruction on using Firebase.

Highlights

A real-time server-rendered full stack app was built leveraging Google's new PaLM 2 large language model.

The app functions as a text summarizer for content from news sites like CNN or Fox News.

The user interface updates in real-time and stores results in a scalable Cloud database.

The app was constructed in just two minutes using Firebase.

Firebase now supports popular SSR Frameworks such as Next and Nuxt.

A new SpellKit project is generated and the Firebase SDK is installed.

Firestore is enabled and a new app is created in Firebase project settings.

The project credentials are used to initialize Firebase in a Spelled component.

The 'Summarize Text' Firebase extension is installed for real-time text summarization using the PaLM API.

A cloud function is deployed, triggered by document updates in the text documents collection.

The app imports 'doc store' from Spellfire to view documents in real-time.

A form is created with a 'handle submit' function to update the document and trigger the summary generation.

The app is a full stack, real-time application using the latest advancements in AI.

Firebase Experiments is used to enable web Frameworks and deploy the server-rendered app.

The app is live on the internet after provisioning a cloud function for server-side rendering.

Firebase has introduced a Python runtime for Cloud Functions, allowing the use of Python libraries.

There is experimental support for the deployment of Flask and Django apps on Firebase.

Firebase has introduced new queries that allow filtering documents server-side.

Firebase now supports Terraform for representing project configurations as code.

For mastering Firebase, becoming a pro member at Fireship IO is suggested.

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