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import gradio as gr
import re
from gradio.mix import Parallel
from transformers import (
AutoTokenizer,
AutoModelForSeq2SeqLM,
)
#define function for text cleaning
def clean_text(text):
text = text.encode("ascii", errors="ignore").decode(
"ascii"
) # remove non-ascii, Chinese characters
text = re.sub(r"http\S+", "", text)
text = re.sub(r"ADVERTISEMENT", " ", text)
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
# define function for headlines generator 1-3
modchoice_1 = "chinhon/pegasus-large-commentaries_hd"
def commentaries_headline1(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"
)
translated = model_1.generate(**batch)
summary_1 = tokenizer_1.batch_decode(translated, skip_special_tokens=True)
return summary_1[0]
headline1 = gr.Interface(
fn=commentaries_headline1,
inputs=gr.inputs.Textbox(),
outputs=gr.outputs.Textbox(label=" | Model: Fine tuned pegasus-large"),
)
modchoice_2 = "chinhon/pegasus-multi_news-commentaries_hdwriter"
def commentaries_headline2(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"
)
translated = model_2.generate(**batch)
summary_2 = tokenizer_2.batch_decode(translated, skip_special_tokens=True)
return summary_2[0]
headline2 = gr.Interface(
fn=commentaries_headline2,
inputs=gr.inputs.Textbox(),
outputs=gr.outputs.Textbox(label=" | Model: Fine tuned pegasus-multi_news"),
)
modchoice_3 = "chinhon/bart-large-commentaries_hdwriter"
def commentaries_headline3(text):
input_text = clean_text(text)
tokenizer_3 = AutoTokenizer.from_pretrained(modchoice_3)
model_3 = AutoModelForSeq2SeqLM.from_pretrained(modchoice_3)
with tokenizer_3.as_target_tokenizer():
batch = tokenizer_3(
input_text, truncation=True, padding="longest", return_tensors="pt"
)
translated = model_3.generate(**batch)
summary_3 = tokenizer_3.batch_decode(
translated, skip_special_tokens=True, max_length=100
)
return summary_3[0]
headline3 = gr.Interface(
fn=commentaries_headline3,
inputs=gr.inputs.Textbox(),
outputs=gr.outputs.Textbox(label=" | Model: Fine tuned bart-large"),
)
#define Gradio interface for 3 parallel apps
Parallel(
headline1,
headline2,
headline3,
title="Commentaries Headlines Generator",
inputs=gr.inputs.Textbox(
lines=20,
label="Paste parts of your commentary here, and choose from 3 suggested headlines",
),
theme="huggingface",
).launch(enable_queue=True)
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