README.md

ImageBind - API Endpoint Deployment (with Baseten)

Implementation of Imagebind model with Baseten's Truss architecture. Allows anyone to host ImageBind model as an endpoint in the web using Baseten.

How to set up Baseten

You can follow along the setup.ipynb notebook as well. Here are the core steps:

import baseten
import truss

baseten.login('BASETEN_API_KEY')

my_packaged_model = truss.load('imagebind-baseten-truss/imagebind_serializable_truss/')

baseten.deploy(
    my_packaged_model,
    model_name='imageBind',
    # publish=True, # Uncomment to skip the draft stage
)

After you deploy, the console output will include a model version id. You could also get this from baseten's website under your deployed models. We take this version id and use it to retrieve the model endpoint from Baseten.

# Retrieve the uploaded model endpoint from baseten
model = baseten.deployed_model_version_id('<model_version_id>')

How to run inference

Recall that we had recieved our model version ID from our deployment. We use that ID again to retrieve the model endpoint.

# Retrieve the uploaded model endpoint from baseten
model = baseten.deployed_model_version_id('<model_version_id>')

Then, we have a suite of helper functions to be able to convert images or audio files into a JSON-Serializable format. This is necessary because Baseten only takes JSON-Serializable format as input. ImageBind is a multimodal modal that could take text, vision, or audio modalities, so converting each to a base64 string makes it straightforward.

import base64
from PIL import Image
from pydub import AudioSegment
import io

def image_paths_to_base64_strings(image_paths: list[str]) -> list[str]:
    """
    Convert a list of image paths to a list of Base64 encoded strings.

    Parameters:
    - image_paths (list[str]): List of paths to the images.

    Returns:
    list[str]: List containing Base64 encoded strings of images.
    """
    image_base64_strings = []
    for path in image_paths:
        with Image.open(path) as img:
            binary_io = io.BytesIO()
            img.save(binary_io, format="PNG")  # You can change the format if needed
            base64_encoded = base64.b64encode(binary_io.getvalue()).decode('utf-8')
            image_base64_strings.append(base64_encoded)
    return image_base64_strings

def audio_paths_to_base64_strings(audio_paths: list[str]) -> list[str]:
    """
    Convert a list of audio paths to a list of Base64 encoded strings.

    Parameters:
    - audio_paths (list[str]): List of paths to the audio files.

    Returns:
    list[str]: List containing Base64 encoded strings of audio files.
    """

    audio_base64_strings = []
    for path in audio_paths:
        audio = AudioSegment.from_file(path)
        binary_io = io.BytesIO()
        audio.export(binary_io, format="wav")  # You can change the format if needed
        base64_encoded = base64.b64encode(binary_io.getvalue()).decode('utf-8')
        audio_base64_strings.append(base64_encoded)
    return audio_base64_strings

Then finally, you can get the embeddings from ImageBind using the following structure.

text_list = ["cat meowing"]
image_paths = ["testing_data/cat.jpeg"]
audio_paths = ["testing_data/meow.mp3"]

json_serializable_input = {
    "text" : text_list,
    "vision" : image_paths_to_base64_strings(image_paths),
    "audio" : audio_paths_to_base64_strings(audio_paths)
}

model.predict(json_serializable_input)

Have fun!