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import gradio as gr
from transformers import pipeline,AutoTokenizer, AutoModelForSeq2SeqLM
from bs4 import BeautifulSoup
from bs4.element import Comment
from urllib.request import urlopen
import urllib.request
from bs4 import BeautifulSoup
def easyterms(text:str)->str:
print("In summerizing function of easyterms")
tokenizer = AutoTokenizer.from_pretrained("EasyTerms/etsummerizer_v2")
model = AutoModelForSeq2SeqLM.from_pretrained("EasyTerms/etsummerizer_v2")
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
summary_ids = model.generate(inputs['input_ids'], attention_mask=inputs['attention_mask'], max_length=128, num_beams=4)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
return summary
def get_paragaph(url:str)-> list:
parser = 'html.parser' # or 'lxml' (preferred) or 'html5lib', if installed
html = urllib.request.urlopen(url)
# parsing the html file
soup = BeautifulSoup(html, parser, from_encoding=html.info().get_param('charset'))
samples = soup.findAll("p")
samples = [item.text for item in samples]
return samples
def summerize(Option:str, Text:str)-> str:
print(Option)
if Option == "text":
return easyterms(Text)
else:
paragraph = get_paragaph(Text)
result = []
for par in paragraph:
result.append(easyterms(par))
res = '\n'.join(data for data in result)
return res
intro = gr.Markdown(
'''
<center><h1>A Legal document summerizer.</h1></span>
If you want to better understand legal text or document, this platform is for you. By choosing the url option you submit a url whose content will in turn be summerized for you.
Otherwhise you can choose the text option and submit your own text to be summerized.
'''
)
interface = gr.Interface(
fn=summerize,
inputs=[gr.Radio(["url", "text"]),"text"],
outputs=["text"]
)
interface.launch()
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