# Author: Omkar Pathak

import io
import os
import re
import nltk
import spacy
import pandas as pd
import docx2txt
from . import constants as cs
from spacy.matcher import Matcher
from pdfminer.converter import TextConverter
from pdfminer.pdfinterp import PDFPageInterpreter
from pdfminer.pdfinterp import PDFResourceManager
from pdfminer.layout import LAParams
from pdfminer.pdfpage import PDFPage
from nltk.stem import WordNetLemmatizer
from nltk.corpus import stopwords 

def extract_text_from_pdf(pdf_path):
    Helper function to extract the plain text from .pdf files

    :param pdf_path: path to PDF file to be extracted
    :return: iterator of string of extracted text
    # https://www.blog.pythonlibrary.org/2018/05/03/exporting-data-from-pdfs-with-python/
    with open(pdf_path, 'rb') as fh:
        for page in PDFPage.get_pages(fh, 
            resource_manager = PDFResourceManager()
            fake_file_handle = io.StringIO()
            converter = TextConverter(resource_manager, fake_file_handle, codec='utf-8', laparams=LAParams())
            page_interpreter = PDFPageInterpreter(resource_manager, converter)
            text = fake_file_handle.getvalue()
            yield text
            # close open handles

def extract_text_from_doc(doc_path):
    Helper function to extract plain text from .doc or .docx files

    :param doc_path: path to .doc or .docx file to be extracted
    :return: string of extracted text
    temp = docx2txt.process(doc_path)
    text = [line.replace('\t', ' ') for line in temp.split('\n') if line]
    return ' '.join(text)

def extract_text(file_path, extension):
    Wrapper function to detect the file extension and call text extraction function accordingly

    :param file_path: path of file of which text is to be extracted
    :param extension: extension of file `file_name`
    text = ''
    if extension == '.pdf':
        for page in extract_text_from_pdf(file_path):
            text += ' ' + page
    elif extension == '.docx' or extension == '.doc':
        text = extract_text_from_doc(file_path)
    return text

def extract_entity_sections(text):
    Helper function to extract all the raw text from sections of resume

    :param text: Raw text of resume
    :return: dictionary of entities
    text_split = [i.strip() for i in text.split('\n')]
    # sections_in_resume = [i for i in text_split if i.lower() in sections]
    entities = {}
    key = False
    for phrase in text_split:
        if len(phrase) == 1:
            p_key = phrase
            p_key = set(phrase.lower().split()) & set(cs.RESUME_SECTIONS)
            p_key = list(p_key)[0]
        except IndexError:
        if p_key in cs.RESUME_SECTIONS:
            entities[p_key] = []
            key = p_key
        elif key and phrase.strip():
    # entity_key = False
    # for entity in entities.keys():
    #     sub_entities = {}
    #     for entry in entities[entity]:
    #         if u'\u2022' not in entry:
    #             sub_entities[entry] = []
    #             entity_key = entry
    #         elif entity_key:
    #             sub_entities[entity_key].append(entry)
    #     entities[entity] = sub_entities

    # pprint.pprint(entities)

    # make entities that are not found None
    # for entity in cs.RESUME_SECTIONS:
    #     if entity not in entities.keys():
    #         entities[entity] = None 
    return entities

def extract_email(text):
    Helper function to extract email id from text

    :param text: plain text extracted from resume file
    email = re.findall("([^@|\s]+@[^@]+\.[^@|\s]+)", text)
    if email:
            return email[0].split()[0].strip(';')
        except IndexError:
            return None

def extract_name(nlp_text, matcher):
    Helper function to extract name from spacy nlp text

    :param nlp_text: object of `spacy.tokens.doc.Doc`
    :param matcher: object of `spacy.matcher.Matcher`
    :return: string of full name
    pattern = [cs.NAME_PATTERN]
    matcher.add('NAME', None, *pattern)
    matches = matcher(nlp_text)
    for match_id, start, end in matches:
        span = nlp_text[start:end]
        return span.text

def extract_mobile_number(text):
    Helper function to extract mobile number from text

    :param text: plain text extracted from resume file
    :return: string of extracted mobile numbers
    # Found this complicated regex on : https://zapier.com/blog/extract-links-email-phone-regex/
    phone = re.findall(re.compile(r'(?:(?:\+?([1-9]|[0-9][0-9]|[0-9][0-9][0-9])\s*(?:[.-]\s*)?)?(?:\(\s*([2-9]1[02-9]|[2-9][02-8]1|[2-9][02-8][02-9])\s*\)|([0-9][1-9]|[0-9]1[02-9]|[2-9][02-8]1|[2-9][02-8][02-9]))\s*(?:[.-]\s*)?)?([2-9]1[02-9]|[2-9][02-9]1|[2-9][02-9]{2})\s*(?:[.-]\s*)?([0-9]{4})(?:\s*(?:#|x\.?|ext\.?|extension)\s*(\d+))?'), text)
    if phone:
        number = ''.join(phone[0])
        if len(number) > 10:
            return '+' + number
            return number

def extract_skills(nlp_text, noun_chunks):
    Helper function to extract skills from spacy nlp text

    :param nlp_text: object of `spacy.tokens.doc.Doc`
    :param noun_chunks: noun chunks extracted from nlp text
    :return: list of skills extracted
    tokens = [token.text for token in nlp_text if not token.is_stop]
    data = pd.read_csv(os.path.join(os.path.dirname(__file__), 'skills.csv')) 
    skills = list(data.columns.values)
    skillset = []
    # check for one-grams
    for token in tokens:
        if token.lower() in skills:
    # check for bi-grams and tri-grams
    for token in noun_chunks:
        token = token.text.lower().strip()
        if token in skills:
    return [i.capitalize() for i in set([i.lower() for i in skillset])]

def cleanup(token, lower = True):
    if lower:
       token = token.lower()
    return token.strip()

def extract_education(nlp_text):
    Helper function to extract education from spacy nlp text

    :param nlp_text: object of `spacy.tokens.doc.Doc`
    :return: tuple of education degree and year if year if found else only returns education degree
    edu = {}
    # Extract education degree
    for index, text in enumerate(nlp_text):
        for tex in text.split():
            tex = re.sub(r'[?|$|.|!|,]', r'', tex)
            if tex.upper() in cs.EDUCATION and tex not in cs.STOPWORDS:
                edu[tex] = text + nlp_text[index + 1]

    # Extract year
    education = []
    for key in edu.keys():
        year = re.search(re.compile(cs.YEAR), edu[key])
        if year:
            education.append((key, ''.join(year.group(0))))
    return education

def extract_experience(resume_text):
    Helper function to extract experience from resume text

    :param resume_text: Plain resume text
    :return: list of experience
    wordnet_lemmatizer = WordNetLemmatizer()
    stop_words = set(stopwords.words('english'))

    # word tokenization 
    word_tokens = nltk.word_tokenize(resume_text)

    # remove stop words and lemmatize  
    filtered_sentence = [w for w in word_tokens if not w in stop_words and wordnet_lemmatizer.lemmatize(w) not in stop_words] 
    sent = nltk.pos_tag(filtered_sentence)

    # parse regex
    cp = nltk.RegexpParser('P: {<NNP>+}')
    cs = cp.parse(sent)
    # for i in cs.subtrees(filter=lambda x: x.label() == 'P'):
    #     print(i)
    test = []
    for vp in list(cs.subtrees(filter=lambda x: x.label()=='P')):
        test.append(" ".join([i[0] for i in vp.leaves() if len(vp.leaves()) >= 2]))

    # Search the word 'experience' in the chunk and then print out the text after it
    x = [x[x.lower().index('experience') + 10:] for i, x in enumerate(test) if x and 'experience' in x.lower()]
    return x

def extract_competencies(text, experience_list):
    Helper function to extract competencies from resume text

    :param resume_text: Plain resume text
    :return: dictionary of competencies
    experience_text = ' '.join(experience_list)
    competency_dict = {}

    for competency in cs.COMPETENCIES.keys():
        for item in cs.COMPETENCIES[competency]:
            if string_found(item, experience_text):
                if competency not in competency_dict.keys():
                    competency_dict[competency] = [item]
    return competency_dict

def extract_measurable_results(text, experience_list):
    Helper function to extract measurable results from resume text

    :param resume_text: Plain resume text
    :return: dictionary of measurable results

    # we scan for measurable results only in first half of each sentence
    experience_text = ' '.join([text[:len(text) // 2 - 1] for text in experience_list])
    mr_dict = {}

    for mr in cs.MEASURABLE_RESULTS.keys():
        for item in cs.MEASURABLE_RESULTS[mr]:
            if string_found(item, experience_text):
                if mr not in mr_dict.keys():
                    mr_dict[mr] = [item]
    return mr_dict

def string_found(string1, string2):
    if re.search(r"\b" + re.escape(string1) + r"\b", string2):
        return True
    return False