Spaces:
Paused
Paused
File size: 2,655 Bytes
9af3c99 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
import gradio as gr
import os
import tempfile
from main import process_face
def enhance_face_gradio(input_image, ref_image):
"""
Wrapper function for process_face that works with Gradio.
Args:
input_image: Input image from Gradio
ref_image: Reference face image from Gradio
Returns:
str: Path to the enhanced image
"""
# Create temporary files for input, reference, and output
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as input_file, \
tempfile.NamedTemporaryFile(suffix=".png", delete=False) as ref_file, \
tempfile.NamedTemporaryFile(suffix=".png", delete=False) as output_file:
input_path = input_file.name
ref_path = ref_file.name
output_path = output_file.name
# Save uploaded images to temporary files
input_image.save(input_path)
ref_image.save(ref_path)
# Process the face
process_face(
input_path=input_path,
ref_path=ref_path,
crop=False,
upscale=False,
output_path=output_path
)
# Clean up temporary input and reference files
os.unlink(input_path)
os.unlink(ref_path)
return output_path
# Create the Gradio interface
with gr.Blocks(title="Face Enhancement Demo") as demo:
gr.Markdown("# Face Enhancement Demo")
gr.Markdown("Upload an input image and a reference face image to enhance the input.")
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Input Image", type="pil")
ref_image = gr.Image(label="Reference Face", type="pil")
enhance_button = gr.Button("Enhance Face")
with gr.Column():
output_image = gr.Image(label="Enhanced Result")
enhance_button.click(
fn=enhance_face_gradio,
inputs=[input_image, ref_image],
outputs=output_image,
queue=True # Enable queue for sequential processing
)
gr.Markdown("""
## Instructions
1. Upload an image you want to enhance
2. Upload a reference face image
3. Click 'Enhance Face' to start the process
4. Processing takes about 60 seconds
""")
# Launch the Gradio app with queue
if __name__ == "__main__":
# Set up queue with max_size=20 and concurrency=1
demo.queue(max_size=20) # Configure queue size
demo.launch(
share=False, # Set to True if you want a public link
server_name="0.0.0.0", # Make available on all network interfaces
server_port=7860, # Default Gradio port
# concurrency_count=1 # Process one job at a time
) |