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import gradio as gr
from utils import *
from transformers import pipeline
css = """
"""
ori_model = None
edit_model = None
with gr.Blocks(css=css,theme=gr.themes.Soft(text_size="sm")) as demo:
gr.HTML("""<div style="text-align: center; margin: 0 auto;"><p><h1> Knowledge Editing</h1></div>""")
with gr.Row():
gr.Markdown("<p align='center'><a href='https://github.com/zjunlp/EasyEdit'>🔧https://github.com/zjunlp/EasyEdit</a></p>")
with gr.Row():
gr.Markdown("### Large Language Models (LLMs) often suffer from knowledge cutoff or fallacy issues, meaning they are unable to recognize unseen events or generate text with incorrect facts due to outdated/noisy data. To address this, many knowledge editing methods for LLMs aim to subtly inject/edit updated knowledge or adjust undesirable behaviors, while minimizing the impact on unrelated inputs.")
with gr.Row():
prompt = gr.Textbox(label="Edit Prompt")
target_new = gr.Textbox(label="Edit Target New")
with gr.Row():
button4clear = gr.Button("Clear")
button4edit = gr.Button("Edit",variant="primary")
with gr.Row():
examples = gr.Examples(
examples=[
["Who is the architect for Toodyay Fire Station?","Wong Tung & Sons"],
["Who is Claire Clairmont\'s sister?","Clairmont-Mayer"],
["Which fictional universe is Chlorophyll Kid part of?","Image Universe"]
],
examples_per_page=3,
inputs=[prompt,target_new],
)
with gr.Row():
input_text = gr.Textbox(label="Status Information",value="Model editing may take about a minute, please be patient.")
with gr.Row():
input = gr.Textbox(label="Input Text",lines=3)
with gr.Row():
with gr.Column():
button4gen_ori=gr.Textbox(label="origin output")
with gr.Column():
button4gen_edit=gr.Textbox(label="edited output")
with gr.Row():
button4gen = gr.Button("Generate",variant="primary")
button4clear.click(lambda: ("", ""), outputs=[prompt,target_new])
button4edit.click(fn=edit, inputs=[prompt,target_new], outputs=input_text)
button4gen.click(fn=generate, inputs=input, outputs=[button4gen_ori,button4gen_edit])
demo.launch()
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