app.py

import os
import gradio as gr
from gradio_client import Client

hf_token = os.environ.get('HF_TOKEN')

sdxl_client = Client("https://fffiloni-sdxl-dpo.hf.space/")
faceswap_client = Client("https://fffiloni-deepfakeai.hf.space/", hf_token=hf_token)

def infer(portrait_in, prompt_in):
    # Generate Image from SDXL
    gr.Info("Generating SDXL image first ...")
    sdxl_result = sdxl_client.predict(
        prompt_in,
        api_name="/infer"
    )

    unique_id = str(uuid.uuid4())
    
    # Face Swap
    gr.Info("Face swap your face on result ...")
    faceswap_result = faceswap_client.predict(
        portrait_in,	# str (filepath or URL to image) in 'SOURCE IMAGE' Image component
        sdxl_result,	# str (filepath or URL to image) in 'TARGET IMAGE' Image component
        unique_id,	# str in 'parameter_12' Textbox component
        ["face_swapper"],	# List[str] in 'FRAME PROCESSORS' Checkboxgroup component
        "left-right",	# str (Option from: ['left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small']) in 'FACE ANALYSER DIRECTION' Dropdown component
        "none",	# str (Option from: ['none', 'reference', 'many']) in 'FACE RECOGNITION' Dropdown component
        "none",	# str (Option from: ['none', 'male', 'female']) in 'FACE ANALYSER GENDER' Dropdown component
        fn_index=1
    )

    return faceswap_result

css = """
#col-container{
    margin: 0 auto;
    max-width: 840px;
}
"""
with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.HTML("""
        <h2 style="text-align: center;">Portrait Maker</h2>
        """)
        with gr.Row():
            with gr.Column():
                portrait_in = gr.Image(label="Your face portrait", type="filepath")
                prompt_in = gr.Textbox(label="Prompt to desired portrait using your own face")
                submit_btn = gr.Button("Submit")
            with gr.Column():
                result = gr.Image(label="Result")

        submit_btn.click(
            fn = infer, 
            inputs = [
                portrait_in,
                prompt_in
            ],
            outputs = [
                result
            ]  
        )

demo.queue().launch()