| --- |
| tags: |
| - image-to-text |
| - image-captioning |
| - endpoints-template |
| license: bsd-3-clause |
| library_name: generic |
| --- |
| |
| # Blip Caption 🤗 Inference Endpoints |
|
|
| This repository implements a `custom` task for `image-captioning` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/florentgbelidji/blip_captioning/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"), "candiates": candiates}} |
| 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 |
| ['buckingham palace with flower beds and red flowers'] |
| |
| ``` |
|
|
