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import gradio as gr | |
import re | |
from transformers import ( | |
AutoTokenizer, | |
AutoModelForSeq2SeqLM | |
) | |
def clean_text(text): | |
text = text.encode("ascii", errors="ignore").decode( | |
"ascii" | |
) # remove non-ascii, Chinese characters | |
text = re.sub(r"\n", " ", text) | |
text = re.sub(r"\n\n", " ", text) | |
text = re.sub(r"\t", " ", text) | |
text = text.strip(" ") | |
text = re.sub( | |
" +", " ", text | |
).strip() # get rid of multiple spaces and replace with a single | |
return text | |
modchoice_1 = "chinhon/bart-large-cnn-summarizer_03" | |
def summarizer1(text): | |
input_text = clean_text(text) | |
tokenizer_1 = AutoTokenizer.from_pretrained(modchoice_1) | |
model_1 = AutoModelForSeq2SeqLM.from_pretrained(modchoice_1) | |
with tokenizer_1.as_target_tokenizer(): | |
batch = tokenizer_1( | |
input_text, truncation=True, padding="longest", return_tensors="pt" | |
) | |
raw_1 = model_1.generate(**batch) | |
summary_1 = tokenizer_1.batch_decode(raw_1, skip_special_tokens=True) | |
summed_1 = summary_1[0] | |
lines1 = summed_1.split(". ") | |
for i in range(len(lines1)): | |
lines1[i] = "* " + lines1[i] | |
summ_bullet1 = "\n".join(lines1) | |
return summ_bullet1 | |
summary1 = gr.Interface( | |
fn=summarizer1, inputs=gr.inputs.Textbox(), outputs=gr.outputs.Textbox(label="") | |
) | |
modchoice_2 = ( | |
"chinhon/pegasus-newsroom-summarizer_02" | |
) | |
def summarizer2(text): | |
input_text = clean_text(text) | |
tokenizer_2 = AutoTokenizer.from_pretrained(modchoice_2) | |
model_2 = AutoModelForSeq2SeqLM.from_pretrained(modchoice_2) | |
with tokenizer_2.as_target_tokenizer(): | |
batch = tokenizer_2( | |
input_text, truncation=True, padding="longest", return_tensors="pt" | |
) | |
raw_2 = model_2.generate(**batch) | |
summary_2 = tokenizer_2.batch_decode(raw_2, skip_special_tokens=True) | |
summed_2 = summary_2[0] | |
lines2 = summed_2.split(". ") | |
for i in range(len(lines2)): | |
lines2[i] = "* " + lines2[i] | |
summ_bullet2 = "\n".join(lines2) | |
return summ_bullet2 | |
summary2 = gr.Interface( | |
fn=summarizer2, inputs=gr.inputs.Textbox(), outputs=gr.outputs.Textbox(label="") | |
) | |
gradio_ui = gr.Parallel( | |
summary1, | |
summary2, | |
title="Compare 2 AI Summarizers", | |
inputs=gr.inputs.Textbox( | |
lines=20, | |
label="Paste your news story here, and choose from 2 suggested summaries", | |
), | |
theme="huggingface", | |
) | |
gradio_ui.launch(enable_queue=True) | |