readme: ---
  tags:
  - feature-extraction
  - endpoints-template
  license: bsd-3-clause
  library_name: generic
  ---
  # Fork of [salesforce/BLIP](https://github.com/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](https://huggingface.co/florentgbelidji/blip-embeddings/blob/main/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
  ```json
  {
    "image": "/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. 
  ```bash
  !wget https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
  ```
  2.run request
  ```python
  import json
  from typing import List
  import requests as r
  import base64
  ENDPOINT_URL = ""
  HF_TOKEN = ""
  def predict(path_to_image: str = None):
      with open(path_to_image, "rb") as i:
          b64 = base64.b64encode(i.read())
      payload = {"inputs": {"image": b64.decode("utf-8")}}
      response = r.post(
          ENDPOINT_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json=payload
      )
      return response.json()
  prediction = predict(
      path_to_image="palace.jpg"
  )
  ```
  expected output
  ```python
  {'feature_vector': [0.016450975090265274,
    -0.5551009774208069,
    0.39800673723220825,
    -0.6809228658676147,
    2.053842782974243,
    -0.4712907075881958,...]
   }
  ```