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import spaces
import base64
import requests
import numpy as np
import face_recognition
import gradio as gr
from io import BytesIO
@spaces.GPU
def get_face_embedding(image_input):
# Check if the input is a URL
if isinstance(image_input, str) and (image_input.startswith("http://") or image_input.startswith("https://")):
response = requests.get(image_input)
image = face_recognition.load_image_file(BytesIO(response.content))
else:
# Assume input is a base64 encoded string
if ',' in image_input:
image_input = image_input.split(',')[1] # Remove the prefix
img_data = base64.b64decode(image_input)
image = face_recognition.load_image_file(BytesIO(img_data))
# Get the face encodings for all faces in the image
face_encodings = face_recognition.face_encodings(image)
# If no faces are detected, return an empty list
if not face_encodings:
return []
# Return the first face encoding as a list
return face_encodings[0].tolist()
# Define the Gradio interface
interface = gr.Interface(
fn=get_face_embedding,
inputs="text",
outputs="json",
title="Face Embedding Extractor",
description="Input a base64 encoded image or an image link to get a 128-dimensional face embedding vector. If no face is detected, an empty list is returned."
)
if __name__ == "__main__":
interface.launch(share=True)
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