import gradio as gr
from transformers import pipeline
from datetime import datetime
import pandas as pd
import requests
from bs4 import BeautifulSoup

import re

benefits = [
  {"benefitName": "Universal Credit", "coreName": "what is this benefit", "link": "https://www.gov.uk/universal-credit/"},
  {"benefitName": "Universal Credit", "coreName": "who can apply", "link": "https://www.gov.uk/universal-credit/eligibility"},
  {"benefitName": "Universal Credit", "coreName": "how much can I get", "link": "https://www.gov.uk/universal-credit/what-youll-get,https://www.gov.uk/universal-credit/how-youre-paid"},
  {"benefitName": "Universal Credit", "coreName": "How to apply", "link": "https://www.gov.uk/universal-credit/how-to-claim"}
]


def requestPage(link):
  page = requests.get(link)
  # print(page.text)

  soup = BeautifulSoup(page.content, "html.parser")

  return soup
  
def scrapeTable(table):
  columns = [col.text.strip() for col in table.thead.tr.find_all()]
  columns

  rows = table.tbody.find_all(recursive=False)
  clean_rows = ""

  for row in rows:
    elements = ["{}: {}".format(columns[index], element.text.strip()) for index, element in enumerate(row.find_all(recursive=False))]
    elements = " ".join(elements)
    # print(elements)
    clean_rows += elements + "\n"

  return clean_rows



def scrapePage(page):
  # Scrape the text
  corpus = ""

  # starting from the main page
  content = page.find('div', {"id":"guide-contents"})

  title = content.find('h1', {"class":"part-title"})
  title = title.text.strip()
  corpus += title +"\n\n"

  print(title)

  content = content.find('div', {"class":"gem-c-govspeak"})

  fragments = content.find_all(recursive=False)
  for frag in fragments:
    text= frag.text.strip()
    if frag.name == 'ul':
      clean = re.sub('\n+', "{;}", text)
      corpus += "{;}" + clean
    elif frag.name == 'table':
      corpus += scrapeTable(frag)
    else:
      corpus += text 

    corpus += "\n"

  # print(corpus)

  return corpus
  
  
for benefit in benefits:
  links = benefit['link'].split(',')
  print(benefit['benefitName'], benefit['coreName'], len(links))

  context = ""
  for link in links:
    page = requestPage(link)
    context += scrapePage(page)

  benefit['context'] = context
  benefit['contextLen'] = len(context)
  print("--------------------------------")


benefitsClasses = list(set(list(map(lambda x: x['benefitName'], benefits))))
core4Classes = list(set(list(map(lambda x: x['coreName'], benefits))))

# contexts
benefitsClasses, core4Classes

question_answerer = pipeline("question-answering")


coreName = 'how much can I get'
def testQA(question):

  predictedBenefit = "Universal Credit"
  predictedCore = coreName

  time = datetime.now()
  context = list(filter(lambda x: x['benefitName']==predictedBenefit and x['coreName']==predictedCore, benefits))[0]
  answer = question_answerer(question = question, context = context['context'])['answer']
  time3 = (datetime.now() - time).total_seconds()

  return answer
  
iface = gr.Interface(fn=testQA, inputs="text", outputs="text")
iface.launch()