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import gradio as gr
from models.watermark_faster import watermark_model
import pdb
from options import get_parser_main_model
opts = get_parser_main_model().parse_args()
model = watermark_model(language='English', mode=opts.mode, tau_word=0.8, lamda=0.83)
def watermark_embed_demo(raw):
watermarked_text = model.embed(raw)
return watermarked_text
def watermark_extract(raw):
is_watermark, p_value, n, ones, z_value = model.watermark_detector_fast(raw)
confidence = (1 - p_value) * 100
return f"{confidence:.2f}%"
def precise_watermark_detect(raw):
is_watermark, p_value, n, ones, z_value = model.watermark_detector_precise(raw)
confidence = (1 - p_value) * 100
return f"{confidence:.2f}%"
demo = gr.Blocks()
with demo:
with gr.Column():
gr.Markdown("# Watermarking Text Generated by Black-Box Language Models")
inputs = gr.TextArea(label="Input text", placeholder="Copy your text here...")
output = gr.Textbox(label="Watermarked Text")
analysis_button = gr.Button("Inject Watermark")
inputs_embed = [inputs]
analysis_button.click(fn=watermark_embed_demo, inputs=inputs_embed, outputs=output)
inputs_w = gr.TextArea(label="Text to Analyze", placeholder="Copy your watermarked text here...")
mode = gr.Dropdown(
label="Detection Mode", choices=["Fast", "Precise"], default="Fast"
)
output_detect = gr.Textbox(label="Confidence (the likelihood of the text containing a watermark)")
detect_button = gr.Button("Detect")
def detect_watermark(inputs_w, mode):
if mode == "Fast":
return watermark_extract(inputs_w)
else:
return precise_watermark_detect(inputs_w)
detect_button.click(fn=detect_watermark, inputs=[inputs_w, mode], outputs=output_detect)
if __name__ == "__main__":
gr.close_all()
demo.title = "Watermarking Text Generated by Black-Box Language Models"
demo.launch() |