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import gradio as gr
from pipeline import SmileGen
import torch
from PIL import Image
import numpy as np
import os
def read_samples(path):
# read the samples from the path
samples = []
for filename in os.listdir(path):
if filename.endswith(".jpg") or filename.endswith(".png"):
img = Image.open(os.path.join(path, filename))
samples.append(np.array(img))
return samples
def create_image_generation_demo():
# load sample images
image_list = []
model = SmileGen()
demo = gr.Interface(
fn=model.run,
inputs=[
gr.Image(label="Input Image", type="pil")
],
outputs=[
gr.Image(label="Generated Image")
],
title="Smile!",
description="Upload an image and generate a new image using a custom pipeline.",
examples=image_list
)
return demo
# Launch the demo
if __name__ == "__main__":
demo = create_image_generation_demo()
demo.launch()
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