from flask import Flask,render_template,request
import torch
import PyPDF4
#from rank_bm25 import BM25L
from autocorrect import Speller
from transformers import BertForQuestionAnswering  #pretrained model
model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')
from transformers import BertTokenizer  #loading tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')
import pickle



app= Flask(__name__)

@app.route('/',methods = ['POST', 'GET'])
def hello():
    return render_template("after1.html")

@app.route('/predict',methods = ['POST', 'GET'])
def home():
    pdfFileObj = open('Water.pdf','rb')

    pdfReader = PyPDF4.PdfFileReader(pdfFileObj)

    pages = pdfReader.numPages

    for i in range(pages):
            pageObj = pdfReader.getPage(i)
            text = pageObj.extractText().split("  ")

    pdfFileObj.close()


    text1=list()
    for line in text:
        line = line.replace('\n','')
        text1.append(line)


    list1_joined1 = ",".join(text1)



    pdfFileObj = open('Water1.pdf','rb')

    pdfReader = PyPDF4.PdfFileReader(pdfFileObj)

    pages = pdfReader.numPages

    for i in range(pages):
        pageObj = pdfReader.getPage(i)
        text = pageObj.extractText().split("  ")

    pdfFileObj.close()


    text1=list()
    for line in text:
        line = line.replace('\n','')
        text1.append(line)


    list1_joined3 = ",".join(text1)
    #
    #
    #
    #
    # ########################
    #
    # pdfFileObj = open('Sandra.pdf','rb')
    #
    # pdfReader = PyPDF4.PdfFileReader(pdfFileObj)
    #
    # pages = pdfReader.numPages
    #
    # for i in range(pages):
    #     pageObj = pdfReader.getPage(i)
    #     text = pageObj.extractText().split("  ")
    #
    # pdfFileObj.close()
    #
    #
    # text1=list()
    # for line in text:
    #     line = line.replace('\n','')
    #     text1.append(line)
    #
    #
    # list1_joined2 = ",".join(text1)



    corpus=[]
    corpus.append(list1_joined1)
    corpus.append(list1_joined3)




    ##########################


    #tokenized_corpus = [doc.split(" ") for doc in corpus]
    # bm25 = BM25L(tokenized_corpus)
    question=request.form.get('a')
    spell = Speller(lang='en')
    question=spell(question)
   # Correcting the text
    word=''
    if 'Ohio' in question or 'ohio' in question:
        word='Ohio'
    elif 'Nevada' in question or 'nevada' in question:
        word='Nevada'
    elif 'Iowa' in question or 'iowa' in question:
        word='Iowa'
    elif 'Missouri' in question or 'missouri' in question:
        word='Missouri'
    elif 'South Carolina' in question or 'south carolina' in question:
        word='South Carolina'
    elif 'North Carolina' in question or 'north carolina' in question:
        word='North Carolina'
    elif 'New York' in question or 'new york' in question:
        word='New York'
    elif 'Environmental Protection Agency' in question or 'environmental protection agency' in question or 'EPA' in question or 'epa' in question:
        word='Environmental Protection Agency'
    elif 'World Health Organization' in question or 'world health organization' in question or 'WHO' in question or 'who' in question:
        word='World Health Organization'
    elif 'Michigan' in question or 'michigan' in question:
        word='Michigan'
    elif 'California' in question or 'california' in question:
        word='California'
    elif 'Illinois' in question or 'illinois' in question:
        word='Illinois'

    index = [idx for idx, s in enumerate(corpus) if word in s][0]

    answer_text=corpus[index]



    # tokenized_query = question.split(" ")
    #
    # doc=bm25.get_top_n(tokenized_query, corpus, n=1)
    # answer_text=doc[0]

    ##############################

    input_ids = tokenizer.encode(question, answer_text)  #applying tokenizer to get total tokens



    tokens = tokenizer.convert_ids_to_tokens(input_ids)

    # See where the SEP token is
    sep_index = input_ids.index(tokenizer.sep_token_id)


    SEG_A_num = sep_index + 1


    SEG_B_num = len(input_ids) - SEG_A_num

    #list of 0 and 1
    segment_ids = [0]*SEG_A_num + [1]*SEG_B_num

    #Ensure for each input id we have segment id and of same length
    assert len(segment_ids) == len(input_ids)





    start, end = model(torch.tensor([input_ids]), # The tokens representing our input text.
                                     token_type_ids=torch.tensor([segment_ids]),return_dict=False) # The segment IDs to differentiate question from answer_text



    beginAnswer = torch.argmax(start)
    endAnswer = torch.argmax(end)
    answer = tokens[beginAnswer]

    # Select the remaining answer tokens and join them with whitespace.
    for i in range(beginAnswer + 1, endAnswer + 1):

        # If it's a subword token, then recombine it with the previous token.
        if tokens[i][0:2] == '##':
            answer += tokens[i][2:]

        # Otherwise, add a space then the token.
        else:
            answer += ' ' + tokens[i]




    return render_template("answer1.html",data=answer)



if __name__ == "__main__":
    app.run(debug=True)

from flask import Flask,render_template,request
import torch
import PyPDF4
#from rank_bm25 import BM25L
from autocorrect import Speller
from transformers import BertForQuestionAnswering  #pretrained model
model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')
from transformers import BertTokenizer  #loading tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')
import pickle



app= Flask(__name__)

@app.route('/',methods = ['POST', 'GET'])
def hello():
    return render_template("after1.html")

@app.route('/predict',methods = ['POST', 'GET'])
def home():
    pdfFileObj = open('Water.pdf','rb')

    pdfReader = PyPDF4.PdfFileReader(pdfFileObj)

    pages = pdfReader.numPages

    for i in range(pages):
            pageObj = pdfReader.getPage(i)
            text = pageObj.extractText().split("  ")

    pdfFileObj.close()


    text1=list()
    for line in text:
        line = line.replace('\n','')
        text1.append(line)


    list1_joined1 = ",".join(text1)



    pdfFileObj = open('Water1.pdf','rb')

    pdfReader = PyPDF4.PdfFileReader(pdfFileObj)

    pages = pdfReader.numPages

    for i in range(pages):
        pageObj = pdfReader.getPage(i)
        text = pageObj.extractText().split("  ")

    pdfFileObj.close()


    text1=list()
    for line in text:
        line = line.replace('\n','')
        text1.append(line)


    list1_joined3 = ",".join(text1)
    #
    #
    #
    #
    # ########################
    #
    # pdfFileObj = open('Sandra.pdf','rb')
    #
    # pdfReader = PyPDF4.PdfFileReader(pdfFileObj)
    #
    # pages = pdfReader.numPages
    #
    # for i in range(pages):
    #     pageObj = pdfReader.getPage(i)
    #     text = pageObj.extractText().split("  ")
    #
    # pdfFileObj.close()
    #
    #
    # text1=list()
    # for line in text:
    #     line = line.replace('\n','')
    #     text1.append(line)
    #
    #
    # list1_joined2 = ",".join(text1)



    corpus=[]
    corpus.append(list1_joined1)
    corpus.append(list1_joined3)




    ##########################


    #tokenized_corpus = [doc.split(" ") for doc in corpus]
    # bm25 = BM25L(tokenized_corpus)
    question=request.form.get('a')
    spell = Speller(lang='en')
    question=spell(question)
   # Correcting the text
    word=''
    if 'Ohio' in question or 'ohio' in question:
        word='Ohio'
    elif 'Nevada' in question or 'nevada' in question:
        word='Nevada'
    elif 'Iowa' in question or 'iowa' in question:
        word='Iowa'
    elif 'Missouri' in question or 'missouri' in question:
        word='Missouri'
    elif 'South Carolina' in question or 'south carolina' in question:
        word='South Carolina'
    elif 'North Carolina' in question or 'north carolina' in question:
        word='North Carolina'
    elif 'New York' in question or 'new york' in question:
        word='New York'
    elif 'Environmental Protection Agency' in question or 'environmental protection agency' in question or 'EPA' in question or 'epa' in question:
        word='Environmental Protection Agency'
    elif 'World Health Organization' in question or 'world health organization' in question or 'WHO' in question or 'who' in question:
        word='World Health Organization'
    elif 'Michigan' in question or 'michigan' in question:
        word='Michigan'
    elif 'California' in question or 'california' in question:
        word='California'
    elif 'Illinois' in question or 'illinois' in question:
        word='Illinois'

    index = [idx for idx, s in enumerate(corpus) if word in s][0]

    answer_text=corpus[index]



    # tokenized_query = question.split(" ")
    #
    # doc=bm25.get_top_n(tokenized_query, corpus, n=1)
    # answer_text=doc[0]

    ##############################

    input_ids = tokenizer.encode(question, answer_text)  #applying tokenizer to get total tokens



    tokens = tokenizer.convert_ids_to_tokens(input_ids)

    # See where the SEP token is
    sep_index = input_ids.index(tokenizer.sep_token_id)


    SEG_A_num = sep_index + 1


    SEG_B_num = len(input_ids) - SEG_A_num

    #list of 0 and 1
    segment_ids = [0]*SEG_A_num + [1]*SEG_B_num

    #Ensure for each input id we have segment id and of same length
    assert len(segment_ids) == len(input_ids)





    start, end = model(torch.tensor([input_ids]), # The tokens representing our input text.
                                     token_type_ids=torch.tensor([segment_ids]),return_dict=False) # The segment IDs to differentiate question from answer_text



    beginAnswer = torch.argmax(start)
    endAnswer = torch.argmax(end)
    answer = tokens[beginAnswer]

    # Select the remaining answer tokens and join them with whitespace.
    for i in range(beginAnswer + 1, endAnswer + 1):

        # If it's a subword token, then recombine it with the previous token.
        if tokens[i][0:2] == '##':
            answer += tokens[i][2:]

        # Otherwise, add a space then the token.
        else:
            answer += ' ' + tokens[i]




    return render_template("answer1.html",data=answer)



if __name__ == "__main__":
    app.run(debug=True)

#from rank_bm25 import BM25L #pretrained model #loading tokenizer # # # # # ######################## # # pdfFileObj = open('Sandra.pdf','rb') # # pdfReader = PyPDF4.PdfFileReader(pdfFileObj) # # pages = pdfReader.numPages # # for i in range(pages): #     pageObj = pdfReader.getPage(i) #     text = pageObj.extractText().split("  ") # # pdfFileObj.close() # # # text1=list() # for line in text: #     line = line.replace('\n','') #     text1.append(line) # # # list1_joined2 = ",".join(text1) ########################## #tokenized_corpus = [doc.split(" ") for doc in corpus] # bm25 = BM25L(tokenized_corpus) # Correcting the text # tokenized_query = question.split(" ") # # doc=bm25.get_top_n(tokenized_query, corpus, n=1) # answer_text=doc[0] ############################## #applying tokenizer to get total tokens # See where the SEP token is #list of 0 and 1 #Ensure for each input id we have segment id and of same length # The tokens representing our input text. # The segment IDs to differentiate question from answer_text # Select the remaining answer tokens and join them with whitespace. # If it's a subword token, then recombine it with the previous token. #': # Otherwise, add a space then the token.