radames/blip_image_embeddings

Fork of salesforce/BLIP for a feature-extraction task on 🤗Inference endpoint.

This repository implements a custom task for feature-extraction for 🤗 Inference Endpoints. The code for the customized pipeline is in the pipeline.py. To use deploy this model a an Inference Endpoint you have to select Custom as task to use the pipeline.py file. -> double check if it is selected

expected Request payload

{
  "inputs": "/9j/4AAQSkZJRgABAQEBLAEsAAD/2wBDAAMCAgICAgMC....", // base64 image as bytes
}

below is an example on how to run a request using Python and requests.

Run Request

1. prepare an image.

!wget https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg

2.run request

import json
from typing import List
import requests as r
import base64
ENDPOINT_URL = "https://api-inference.huggingface.co/models/radames/blip_image_embeddings"
HF_TOKEN = ""
def predict(path_to_image: str = None):
    with open(path_to_image, "rb") as i:
        b64 = base64.b64encode(i.read())
    payload = {"inputs": b64.decode("utf-8")}
    response = r.post(
        ENDPOINT_URL, headers={"X-Wait-For-Model": "true", "Authorization": f"Bearer {HF_TOKEN}"}, json=payload
    )
    return response.json()
prediction = predict(
    path_to_image="palace.jpg"
)

expected output

[0.016450975090265274,
  -0.5551009774208069,
  0.39800673723220825,
  -0.6809228658676147,
  2.053842782974243,
  -0.4712907075881958,...]