| tags: | |
| - feature-extraction | |
| - endpoints-template | |
| license: bsd-3-clause | |
| library_name: generic | |
| duplicated_from: florentgbelidji/blip_image_embeddings | |
| # 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,...] | |
| } | |
| ``` |