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import os
import warnings
import gradio as gr
import re
import numpy as np
#HF_TOKEN = os.getenv('HW_TOKEN')
#hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "save_audio")
def sepia(input_img, strength):
sepia_filter = strength * np.array(
[[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]]
) + (1-strength) * np.identity(3)
sepia_img = input_img.dot(sepia_filter.T)
sepia_img /= sepia_img.max()
return sepia_img
callback = gr.CSVLogger()
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
img_input = gr.Image()
strength = gr.Slider(0, 1, 0.5)
img_output = gr.Image()
with gr.Row():
btn = gr.Button("Flag")
# This needs to be called at some point prior to the first call to callback.flag()
callback.setup([img_input, strength, img_output], "flagged_data_points")
img_input.change(sepia, [img_input, strength], img_output)
strength.change(sepia, [img_input, strength], img_output)
# We can choose which components to flag -- in this case, we'll flag all of them
btn.click(lambda *args: callback.flag(args), [img_input, strength, img_output], None, preprocess=False)
demo.launch() |