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import gradio as gr
import cv2
import numpy as np
from PIL import Image
import os
import warnings
# Suppress warnings
warnings.filterwarnings('ignore')
def create_gui():
# Create the main interface
with gr.Blocks(theme=gr.themes.Base(
primary_hue=gr.themes.colors.red,
font=gr.themes.GoogleFont('Open Sans')
)) as interface:
gr.Markdown("# FaceFusion")
with gr.Tab("Swap"):
with gr.Row():
with gr.Column():
source_image = gr.Image(label="Source Face", type="pil")
target_image = gr.Image(label="Target Image/Video", type="pil")
with gr.Column():
output_image = gr.Image(label="Result")
with gr.Row():
process_button = gr.Button("Process", variant="primary")
clear_button = gr.Button("Clear")
with gr.Tab("Settings"):
with gr.Row():
face_detection_threshold = gr.Slider(
minimum=0.0,
maximum=1.0,
value=0.5,
step=0.01,
label="Face Detection Threshold"
)
output_quality = gr.Slider(
minimum=1,
maximum=100,
value=90,
step=1,
label="Output Quality"
)
with gr.Row():
enable_face_enhancement = gr.Checkbox(
label="Enable Face Enhancement",
value=True
)
preserve_original_resolution = gr.Checkbox(
label="Preserve Original Resolution",
value=True
)
# Placeholder function for processing
def process_images(source, target):
# This is where you'd implement the actual face fusion logic
# For now, just return the target image
return target
def clear_outputs():
return None, None, None
# Connect the interface elements
process_button.click(
fn=process_images,
inputs=[source_image, target_image],
outputs=[output_image]
)
clear_button.click(
fn=clear_outputs,
inputs=[],
outputs=[source_image, target_image, output_image]
)
return interface
# Create and launch the interface
demo = create_gui()
# For deployments
if __name__ == "__main__":
demo.launch() |