app.py

import gradio as gr
import cv2
from insightface.app import FaceAnalysis
import torch
import torch.nn as nn
import numpy as np

np.int = np.int32
np.float = np.float64
np.bool = np.bool_

app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
app.prepare(ctx_id=0, det_size=(640, 640))

def calculate(photo1, photo2):
    if photo1 is None or photo2 is None:
        return 0
    image1 = cv2.imread(photo1)
    faces1 = app.get(image1)
    if len(faces1) < 1:
        return 0
    faceid_embeds1 = torch.from_numpy(faces1[0].normed_embedding).unsqueeze(0)
    
    image2 = cv2.imread(photo2)
    faces2 = app.get(image2)
    if len(faces2) < 1:
        return 0
    faceid_embeds2 = torch.from_numpy(faces2[0].normed_embedding).unsqueeze(0)
    
    cos = nn.CosineSimilarity(dim=1, eps=1e-10)
    cos_similarity = cos(faceid_embeds1, faceid_embeds2)
    return cos_similarity
    
with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            face_photo1 = gr.Image(label="Photo of Person 1", type="filepath")            
            face_photo2 = gr.Image(label="Photo of Person 2", type="filepath")
            greet_btn = gr.Button("Calculate")
        with gr.Column():
            output = gr.JSON()
    
    greet_btn.click(fn=calculate, inputs=[face_photo1, face_photo2], outputs=output, api_name="calculate_face_similarity")

if __name__ == "__main__":
    demo.launch()