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import numpy as np
import pandas as pd
import re
import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base")
model = AutoModelForSeq2SeqLM.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base")
tokenizer_gen_title = AutoTokenizer.from_pretrained("Ateeqq/news-title-generator")
model_gen_title = AutoModelForSeq2SeqLM.from_pretrained("Ateeqq/news-title-generator")
def generate_title(input_text): #Generate a title for input text with Ateeq model
input_ids = tokenizer_gen_title.encode(input_text, return_tensors="pt") #Tokenize input text
#input_ids = input_ids.to('cuda') #Send tokenized inputs to gpu
output = model_gen_title.generate(input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.8,
top_k = 20
)
decoded_text = tokenizer_gen_title.decode(output[0], skip_special_tokens=True)
return decoded_text
def split_into_sentences(paragraph): #For paraphraser - return a list of sentences from input para
# Split sentences after period. Retains \n if part of the text, but not included in model output
sentence_endings = r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?|\!)\s'
sentences = re.split(sentence_endings, paragraph)
return sentences
def paraphrase(
text,
beam_search,
#num_beams=10,
#num_beam_groups=10,
#num_return_sequences=1,
#repetition_penalty=1.0,
#diversity_penalty=1.0,
#no_repeat_ngram_size=3,
temperature=0.8,
max_length=128
):
if text != "":
sentence_list = split_into_sentences(text) #feed input para into sentence splitter
output = [] #List to hold the individual rephrased sentences obtained from the model
for sentence in sentence_list:
input_ids = tokenizer(
f'paraphrase: {sentence}', #Using paraphrase prompt for T5
return_tensors="pt", padding="longest",
#max_length=max_length,
#truncation=True,
).input_ids
outputs = model.generate(
input_ids,
do_sample=True,
num_beams = 20 if beam_search else 1,
temperature=temperature,
max_length=max_length,
no_repeat_ngram_size=4
)
res = tokenizer.batch_decode(outputs, skip_special_tokens=True)
output.append(res[0]) #Add rephrased sentence to list
paraphrased_text = "" #to hold the combined sentence output made from generated list
titles_list = "" #to hold the three titles
for sentence in output: #Join all new reworded sentences together
paraphrased_text += sentence + " "
for title in range (1,4): #Print 3 titles by calling Ateeq model fn - generate_title
titles_list += (f"Title {title}: {generate_title (paraphrased_text)}<br>")
#titles_list.append ("") #space after each title
return (titles_list, paraphrased_text) # Return paraphrased text after printing three titles above
iface = gr.Interface(fn=paraphrase,
inputs=[gr.Textbox(label="Paste text in the input box and press 'Submit'.", lines=10), "checkbox", gr.Slider(0.1, 2, 0.8)],
outputs=[gr.HTML(label="Titles:"), gr.Textbox(label="Rephrased text:", lines=15)],
title="AI Paraphraser with Title Generator",
description="Sentencet-to-sentence rewording backed with GPT-3.5 training set",
article="<div align=left><h1>AI Paraphraser and Title Generator</h1><li>Each sentence is rephrased separately without context.</li><li>Temperature: Increase value for more creative rewordings. Higher values may corrupt the sentence. Reset value after pressing 'Clear'</li><li>Beam search: Try for safer and conservative rephrasing.</li><p>Models:<br><li>Training set derived by using Chat-GPT3.5. No competition intended.</li><li>Original models: humarin/chatgpt_paraphraser_on_T5_base and Ateeq_news_title_generator. Deployment code modified for long text inputs.</li></p><p>Parameter details:<br><li>For rephraser: Beam search: No. of beams = 20, no_repeat_ngram_size=4, do_sample=True.</li><li>For title generator: do_sample=True, temperature=0.8, top_k = 20 </li></div>",
flagging_mode='never'
)
iface.launch() |