LLaVA (Large Language-and-Vision Assistant) was updated to version 1.6 in February. I figured it was time to look at how to use it to describe an image in Node.js. LLaVA 1.6 is an advanced vision-language model created for multi-modal tasks, seamlessly integrating visual and textual data. Last month, we looked at how to use the official Ollama JavaScript Library. We are going to use the same library, today.
Basic CLI Example
Let’s start with a CLI app. For this example, I am using my remote Ollama server but if you don’t have one of those, you will want to install Ollama locally and replace const ollama = new Ollama({ host: 'http://100.74.30.25:11434' }); with const ollama = new Ollama({ host: 'http://localhost:11434' });.
To run it, first run npm i ollama and make sure that you have "type": "module" in your package.json. You can run it from the terminal by running node app.js <image filename>. Let’s take a look at the result.
Its ability to describe an image is pretty awesome.
Basic Web Service
So, what if we wanted to run it as a web service? Running Ollama locally is cool and all but it’s cooler if we can integrate it into an app. If you npm install express to install Express, you can run this as a web service.
The web service takes posts to http://localhost:4040/describe-image with a binary body that contains the image that you are trying to get a description of. It then returns a JSON object containing the description.
It's been almost a decade since I've done a live coding stream. This will be fun!
Today I'll be migrating my website from React to Lit, which is a lightweight framework built around web components. I have the scaffolding set up mostly, so now it's time to get this done.
Come watch. Ask questions in chat! You don't need to create an account, just a username is needed to participate.
trying to implement a form submission progress bar in js, but XHR follows the success redirect without telling me (I want to access it and redirect the browser).
Fetch can opt out of that but doesn't have a progress api!
I know he didn't explain his position in details, so a 1800-word article sounds a little unfair, but I think dry and sharp statements need adequate context and analysis.
🤔 I really want to figure out how to make #FediThready be able to post to a Mastodon server WITHOUT requiring a back-end.
I'm pretty sure it's possible to do OAuth and store the token locally without one, but i would love it if someone could point me to an example of this rather than figuring it out from first principles.
On s'occupe de la partie serveur du site de loterie à partir de 10h30 sur ma chaîne #Twitch. Codage en #PHP maintenant que la partie #HTML/#CSS et #JavaScript est bouclée.
Hier j'ai fait un peu de #JS, ce ne fut pas si laborieux que ça. Voici comment seront choisis les numéros de ticket de loterie par les participants : https://youtu.be/vdTp7XzNmBE