laion-asthetic-endpoint

Fork of Geonmo/laion-aesthetic-predictor for an Image Aesthetic Predictor).

This repository implements a custom task for Geonmo/laion-aesthetic-predictor for 🤗 Inference Endpoints. The code for the customized handler is in the handler.py.

Test Handler locally.

This model & handker can be tested locally using the hf-endpoints-emulator.

1. Clone the repository and install the requirements.

git lfs install
git clone https://huggingface.co/philschmid/laion-asthetic-endpoint

cd laion-asthetic-endpoint
pip install -r requirements.txt

1. Install hf-endpoints-emulator

pip install hf-endpoints-emulator

1. Run the emulator

hf-endpoints-emulator --handler handler.py

1. Test the endpoint and send request

curl --request POST \
  --url http://localhost \
  --header 'Content-Type: image/jpg' \
  --data-binary '@example1.jpg'

Run Request

The endpoint expects the image to be served as binary. Below is an curl and python example

cURL

1. get image

wget https://huggingface.co/philschmid/laion-asthetic-endpoint/resolve/main/example1.jpg -O test.jpg

1. send cURL request

curl --request POST \
  --url https://{ENDPOINT}/ \
  --header 'Content-Type: image/jpg' \
  --header 'Authorization: Bearer {HF_TOKEN}' \
  --data-binary '@test.jpg'

1. the expected output

{"aesthetic score": 6.764713287353516}

Python

1. get image

wget https://huggingface.co/philschmid/laion-asthetic-endpoint/resolve/main/example1.jpg -O test.jpg

1. run request

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:
      b = i.read()
    headers= {
        "Authorization": f"Bearer {HF_TOKEN}",
        "Content-Type": "image/jpeg" # content type of image
    }
    response = r.post(ENDPOINT_URL, headers=headers, data=b)
    return response.json()

prediction = predict(path_to_image="test.jpg")

prediction

expected output

{"aesthetic score": 6.764713287353516}