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import gradio as gr
import json
#from llmlingua import PromptCompressor
from utils_llmlingua2_test import PromptCompressor
import tiktoken
compressors = {
"xlm-roberta": PromptCompressor(
#model_name="microsoft/llmlingua-2-xlm-roberta-large-meetingbank",
model_name="qminh369/token-classification-llmlingua2-xlm-roberta-10k_merge_10_epoch_paper",
#model_name='qminh369/token-classification-llmlingua2-xlm-roberta-42k_merge_1_epoch',
use_llmlingua2=True,
device_map="cpu"
)
}
tokenizer = tiktoken.encoding_for_model("gpt-4")
def compress(original_prompt, compression_rate, base_model="xlm-roberta", force_tokens = ['. ', ', '], chunk_end_tokens=['.', '\n']):
if '\\n' in force_tokens:
idx = force_tokens.index('\\n')
force_tokens[idx] = '\n'
compressor = compressors.get(base_model, compressors["xlm-roberta"])
results = compressor.compress_prompt_llmlingua2(
original_prompt,
rate=compression_rate,
force_tokens=force_tokens,
chunk_end_tokens=chunk_end_tokens,
return_word_label=True,
drop_consecutive=True,
force_reserve_digit=True,
)
compressed_prompt = results["compressed_prompt"]
n_word_compressed = len(tokenizer.encode(compressed_prompt))
word_sep = "\t\t|\t\t"
label_sep = " "
lines = results["fn_labeled_original_prompt"].split(word_sep)
preserved_tokens = []
for line in lines:
word, label = line.split(label_sep)
preserved_tokens.append((word, '+') if label == '1' else (word, None))
return compressed_prompt, preserved_tokens, n_word_compressed
title = "LLMLingua-2"
header = """# LLMLingua-2
"""
theme = "soft"
css = """#anno-img .mask {opacity: 0.5; transition: all 0.2s ease-in-out;}
#anno-img .mask.active {opacity: 0.7}"""
original_prompt_text = """"""
with gr.Blocks(title=title, css=css) as app:
gr.Markdown(header)
with gr.Row():
with gr.Column(scale=3):
original_prompt = gr.Textbox(value=original_prompt_text, label="Original Prompt", lines=10, max_lines=10, interactive=True)
compressed_prompt = gr.Textbox(value='', label="Compressed Prompt", lines=10, max_lines=10, interactive=False)
with gr.Column(scale=1):
base_model = gr.Radio(["xlm-roberta"], label="Base Model", value="xlm-roberta", interactive=True)
force_tokens = gr.Dropdown(['\\n', '.', '!', '?', ','],
label="Tokens to Preserve",
value=['\\n', '.', '!', '?', ','],
multiselect=True,
interactive=True)
compression_rate = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, value=0.7, label="Compression rate", info="after compr. / befor compr.", interactive=True)
n_word_original = gr.Textbox(lines=1, label="Original (GPT-4 Tokens)", interactive=False, value=len(tokenizer.encode(original_prompt_text)))
n_word_compressed = gr.Textbox(lines=1, label="Compressed (GPT-4 Tokens)", interactive=False)
button = gr.Button("⚡Click to Compress")
with gr.Accordion(label="Compression Details", open=False):
diff_text = gr.HighlightedText(label="Diff", combine_adjacent=False, show_legend=True, color_map={"+": "green"})
original_prompt.change(lambda x: len(tokenizer.encode(x)), inputs=[original_prompt], outputs=[n_word_original])
original_prompt.change(lambda x: ("", "", []), inputs=[original_prompt], outputs=[compressed_prompt, n_word_compressed, diff_text])
button.click(fn=compress,
inputs=[original_prompt, compression_rate, base_model, force_tokens],
outputs=[compressed_prompt, diff_text, n_word_compressed])
app.queue(max_size=10, api_open=False).launch(show_api=False)
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