from text_extractor import TextExtractor
from and_other_pattern_matcher import AndOtherPatternMatcher
from such_as_pattern_matcher import SuchAsPatternMatcher
from or_other_pattern_matcher import OrOtherPatternMatcher
from including_pattern_matcher import IncludingPatternMatcher
from especially_pattern_matcher import EspeciallyPatternMatcher
from text_extractor_pipe import TextExtractorPipe
from knowledge_graph import KnowledgeGraph
from matcher_pipe import MatcherPipe
import spacy
textExtractor1 = TextExtractor("WWII", "Q362")
textExtractor1.extract()
textExtractor2 = TextExtractor("London", "Q84")
textExtractor2.extract()
textExtractor3 = TextExtractor("Paris", "Q90")
textExtractor3.extract()
textExtractor4 = TextExtractor("World War I", "Q361")
textExtractor4.extract()
textExtractorPipe = TextExtractorPipe()
textExtractorPipe.addTextExtractor(textExtractor1)
textExtractorPipe.addTextExtractor(textExtractor2)
textExtractorPipe.addTextExtractor(textExtractor3)
textExtractorPipe.addTextExtractor(textExtractor4)
nlp = spacy.load('en_core_web_sm')
nlp.add_pipe(nlp.create_pipe('sentencizer')) # updated
doc = nlp(textExtractorPipe.extract())
andOtherPatternMatcher = AndOtherPatternMatcher(nlp)
suchAsMatcher = SuchAsPatternMatcher(nlp)
from question_processor import QuestionProcessor
from text_extractor import TextExtractor
from text_extractor_pipe import TextExtractorPipe
from context_retriever import ContextRetriever
from answer_retriever import AnswerRetriever
from find_keywords import FindKeywords
# STEP 1: Extract keywords from the question
print(fg.green + "Please enter your question here: " + fg.rs)
question = input()
getKeywords = FindKeywords(question)
key_word = getKeywords.distill()
# STEP 2: Download text from wikipedia
textExtractor = TextExtractor(key_word, "1")
textExtractor.extract()
textExtractorPipe = TextExtractorPipe()
textExtractorPipe.addTextExtractor(textExtractor)
# STEP 3: Retrieve corpus from the text.
nlp = spacy.load('en_core_web_sm')
nlp.add_pipe('sentencizer')
doc = nlp(textExtractorPipe.extract())
sentences = [sent.text.strip() for sent in doc.sents]
questionProcessor = QuestionProcessor(nlp)
contextRetriever = ContextRetriever(nlp, 3)
questionContext = contextRetriever.getContext(sentences, questionProcessor.process(question))
# STEP 4: Retrieve answer from the corpus.
answerRetriever = AnswerRetriever()
answerRetriever.getAnswer(question, questionContext)
