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import torch
import gradio
from transformers import pipeline
from bs4 import BeautifulSoup
import requests
def SUMMARIZE(Url):
summarizer = pipeline("summarization", model="stevhliu/my_awesome_billsum_model")
r = requests.get(Url)
soup = BeautifulSoup(r.text, 'html.parser')
results = soup.find_all(['hl', 'p'])
text = [result.text for result in results]
Article = ''.join(text)
sentences = Article.split(' ')
current_chunk = 0
chunks = []
for sentence in sentences:
if len(chunks) == current_chunk + 1:
if len(chunks[current_chunk]) + len(sentence.split(' ')) <= max_chunk:
chunks[current_chunk].extend(sentence.split(' '))
else:
current_chunk += 1
chunks.append(sentence.split(' '))
else:
#print(current_chunk)
chunks.append(sentence.split(' '))
for chunk_id in range(len(chunks)):
chunks[chunk_id] = ' '.join(chunks[chunk_id])
res = summarizer(chunks, max_length=120, min_length=30, do_sample=False)
for i in range(len(res)):
return res[i].values()
interface = gradio.Interface(fn=SUMMARIZE,
inputs=gradio.TextArea(lines=2, value="https://medium.com/analytics-vidhya/openai-gpt-3-language-models-are-few-shot-learners-82531b3d3122"),
outputs=gradio.TextArea())
interface.launch(share=True) |