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)