import gradio as gr
import tensorflow as tf
import numpy as np
import pickle
# Load model, including its weights and the optimizer
model = tf.keras.models.load_model('core4.h5')
# load tokenizer
with open('tokenizer.pickle', 'rb') as handle:
    tokenize = pickle.load(handle)
text_labels = ['How to apply', 'how much can I get', 'who can apply']
# model.summary() # model architecture
def greet(string):
  tokenizedText = tokenize.texts_to_matrix([string])
  prediction = model.predict(np.array([tokenizedText[0]]))
  predicted_label = text_labels[np.argmax(prediction)]
  print(prediction[0][np.argmax(prediction)])
  print("Predicted label: " + predicted_label + "\n")
  
  ###################
  import requests as rs
  import pandas as pd
  
  spreadsheet_id = '1vjWnYsnGc0J6snT67NVbA-NWSGZ5b0eDBVHmg9lbf9s'  # Please set the Spreadsheet ID.
  csv_url='https://docs.google.com/spreadsheets/d/' + spreadsheet_id + '/export?format=csv&id=' + spreadsheet_id + '&gid=0'
  
  res=rs.get(url=csv_url)
  open('google.csv', 'wb').write(res.content)
  df = pd.read_csv('google.csv')
  
  import json
  import requests
  
  spreadsheet_id = '1vjWnYsnGc0J6snT67NVbA-NWSGZ5b0eDBVHmg9lbf9s'  # Please set the Spreadsheet ID.  
  url = 'https://script.google.com/macros/s/AKfycbwXP5fsDgOXJ9biZQC293o6bTBL3kDOJ07PlmxKjabzdTej6WYdC8Yos6NpDVqAJeVM/exec?spreadsheetId=' + spreadsheet_id
  body = {
      "arguments": {"range": "Sheet1!A"+str(len(df)+2), "valueInputOption": "USER_ENTERED"},
      "body": {"values": [[string]]}
  }
  res = requests.post(url, json.dumps(body), headers={'Content-Type': 'application/json'})
  
  body = {
      "arguments": {"range": "Sheet1!B"+str(len(df)+2), "valueInputOption": "USER_ENTERED"},
      "body": {"values": [[predicted_label]]}
  }
  res = requests.post(url, json.dumps(body), headers={'Content-Type': 'application/json'})
  
  import datetime
  current_time = datetime.datetime.now()
  body = {
      "arguments": {"range": "Sheet1!C"+str(len(df)+2), "valueInputOption": "USER_ENTERED"},
      "body": {"values": [[str(current_time)]]}
  }
  res = requests.post(url, json.dumps(body), headers={'Content-Type': 'application/json'})
  #print(res.text)
  #######################
  return predicted_label
#One testing case

###################################################
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")


def testQA(question):
  predictedBenefit = "Universal Credit"
  coreName = greet(string)
  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 coreName + ': ' + answer
  
iface = gr.Interface(fn=testQA, inputs="text", outputs="text")
iface.launch()