Venkatstatistics/Resume Phrase Matcher code

## Resume Phrase Matcher code
#Resume Phrase Matcher code


#importing all required libraries

import PyPDF2
import os
from os import listdir
from os.path import isfile, join
from io import StringIO
import pandas as pd
from collections import Counter
import en_core_web_sm
nlp = en_core_web_sm.load()
from spacy.matcher import PhraseMatcher

#Function to read resumes from the folder one by one
mypath='D:/NLP_Resume/Candidate Resume' #enter your path here where you saved the resumes
onlyfiles = [os.path.join(mypath, f) for f in os.listdir(mypath) if os.path.isfile(os.path.join(mypath, f))]

def pdfextract(file):
    fileReader = PyPDF2.PdfFileReader(open(file,'rb'))
    countpage = fileReader.getNumPages()
    count = 0
    text = []
    while count < countpage:
        pageObj = fileReader.getPage(count)
        count +=1
        t = pageObj.extractText()
        print (t)
        text.append(t)
    return text

#function to read resume ends


#function that does phrase matching and builds a candidate profile
def create_profile(file):
    text = pdfextract(file)
    text = str(text)
    text = text.replace("\\n", "")
    text = text.lower()
    #below is the csv where we have all the keywords, you can customize your own
    keyword_dict = pd.read_csv('D:/NLP_Resume/resume/template_new.csv')
    stats_words = [nlp(text) for text in keyword_dict['Statistics'].dropna(axis = 0)]
    NLP_words = [nlp(text) for text in keyword_dict['NLP'].dropna(axis = 0)]
    ML_words = [nlp(text) for text in keyword_dict['Machine Learning'].dropna(axis = 0)]
    DL_words = [nlp(text) for text in keyword_dict['Deep Learning'].dropna(axis = 0)]
    R_words = [nlp(text) for text in keyword_dict['R Language'].dropna(axis = 0)]
    python_words = [nlp(text) for text in keyword_dict['Python Language'].dropna(axis = 0)]
    Data_Engineering_words = [nlp(text) for text in keyword_dict['Data Engineering'].dropna(axis = 0)]

    matcher = PhraseMatcher(nlp.vocab)
    matcher.add('Stats', None, *stats_words)
    matcher.add('NLP', None, *NLP_words)
    matcher.add('ML', None, *ML_words)
    matcher.add('DL', None, *DL_words)
    matcher.add('R', None, *R_words)
    matcher.add('Python', None, *python_words)
    matcher.add('DE', None, *Data_Engineering_words)
    doc = nlp(text)

    d = []
    matches = matcher(doc)
    for match_id, start, end in matches:
        rule_id = nlp.vocab.strings[match_id]  # get the unicode ID, i.e. 'COLOR'
        span = doc[start : end]  # get the matched slice of the doc
        d.append((rule_id, span.text))
    keywords = "\n".join(f'{i[0]} {i[1]} ({j})' for i,j in Counter(d).items())

    ## convertimg string of keywords to dataframe
    df = pd.read_csv(StringIO(keywords),names = ['Keywords_List'])
    df1 = pd.DataFrame(df.Keywords_List.str.split(' ',1).tolist(),columns = ['Subject','Keyword'])
    df2 = pd.DataFrame(df1.Keyword.str.split('(',1).tolist(),columns = ['Keyword', 'Count'])
    df3 = pd.concat([df1['Subject'],df2['Keyword'], df2['Count']], axis =1)
    df3['Count'] = df3['Count'].apply(lambda x: x.rstrip(")"))

    base = os.path.basename(file)
    filename = os.path.splitext(base)[0]

    name = filename.split('_')
    name2 = name[0]
    name2 = name2.lower()
    ## converting str to dataframe
    name3 = pd.read_csv(StringIO(name2),names = ['Candidate Name'])

    dataf = pd.concat([name3['Candidate Name'], df3['Subject'], df3['Keyword'], df3['Count']], axis = 1)
    dataf['Candidate Name'].fillna(dataf['Candidate Name'].iloc[0], inplace = True)

    return(dataf)

#function ends

#code to execute/call the above functions

final_database=pd.DataFrame()
i = 0
while i < len(onlyfiles):
    file = onlyfiles[i]
    dat = create_profile(file)
    final_database = final_database.append(dat)
    i +=1
    print(final_database)


#code to count words under each category and visulaize it through Matplotlib

final_database2 = final_database['Keyword'].groupby([final_database['Candidate Name'], final_database['Subject']]).count().unstack()
final_database2.reset_index(inplace = True)
final_database2.fillna(0,inplace=True)
new_data = final_database2.iloc[:,1:]
new_data.index = final_database2['Candidate Name']
#execute the below line if you want to see the candidate profile in a csv format
#sample2=new_data.to_csv('sample.csv')
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 10})
ax = new_data.plot.barh(title="Resume keywords by category", legend=False, figsize=(25,7), stacked=True)
labels = []
for j in new_data.columns:
    for i in new_data.index:
        label = str(j)+": " + str(new_data.loc[i][j])
        labels.append(label)
patches = ax.patches
for label, rect in zip(labels, patches):
    width = rect.get_width()
    if width > 0:
        x = rect.get_x()
        y = rect.get_y()
        height = rect.get_height()
        ax.text(x + width/2., y + height/2., label, ha='center', va='center')
plt.show()
	#Resume Phrase Matcher code


	#importing all required libraries

	import PyPDF2
	import os
	from os import listdir
	from os.path import isfile, join
	from io import StringIO
	import pandas as pd
	from collections import Counter
	import en_core_web_sm
	nlp = en_core_web_sm.load()
	from spacy.matcher import PhraseMatcher

	#Function to read resumes from the folder one by one
	mypath='D:/NLP_Resume/Candidate Resume' #enter your path here where you saved the resumes
	onlyfiles = [os.path.join(mypath, f) for f in os.listdir(mypath) if os.path.isfile(os.path.join(mypath, f))]

	def pdfextract(file):
	fileReader = PyPDF2.PdfFileReader(open(file,'rb'))
	countpage = fileReader.getNumPages()
	count = 0
	text = []
	while count < countpage:
	pageObj = fileReader.getPage(count)
	count +=1
	t = pageObj.extractText()
	print (t)
	text.append(t)
	return text

	#function to read resume ends


	#function that does phrase matching and builds a candidate profile
	def create_profile(file):
	text = pdfextract(file)
	text = str(text)
	text = text.replace("\\n", "")
	text = text.lower()
	#below is the csv where we have all the keywords, you can customize your own
	keyword_dict = pd.read_csv('D:/NLP_Resume/resume/template_new.csv')
	stats_words = [nlp(text) for text in keyword_dict['Statistics'].dropna(axis = 0)]
	NLP_words = [nlp(text) for text in keyword_dict['NLP'].dropna(axis = 0)]
	ML_words = [nlp(text) for text in keyword_dict['Machine Learning'].dropna(axis = 0)]
	DL_words = [nlp(text) for text in keyword_dict['Deep Learning'].dropna(axis = 0)]
	R_words = [nlp(text) for text in keyword_dict['R Language'].dropna(axis = 0)]
	python_words = [nlp(text) for text in keyword_dict['Python Language'].dropna(axis = 0)]
	Data_Engineering_words = [nlp(text) for text in keyword_dict['Data Engineering'].dropna(axis = 0)]

	matcher = PhraseMatcher(nlp.vocab)
	matcher.add('Stats', None, *stats_words)
	matcher.add('NLP', None, *NLP_words)
	matcher.add('ML', None, *ML_words)
	matcher.add('DL', None, *DL_words)
	matcher.add('R', None, *R_words)
	matcher.add('Python', None, *python_words)
	matcher.add('DE', None, *Data_Engineering_words)
	doc = nlp(text)

	d = []
	matches = matcher(doc)
	for match_id, start, end in matches:
	rule_id = nlp.vocab.strings[match_id] # get the unicode ID, i.e. 'COLOR'
	span = doc[start : end] # get the matched slice of the doc
	d.append((rule_id, span.text))
	keywords = "\n".join(f'{i[0]} {i[1]} ({j})' for i,j in Counter(d).items())

	## convertimg string of keywords to dataframe
	df = pd.read_csv(StringIO(keywords),names = ['Keywords_List'])
	df1 = pd.DataFrame(df.Keywords_List.str.split(' ',1).tolist(),columns = ['Subject','Keyword'])
	df2 = pd.DataFrame(df1.Keyword.str.split('(',1).tolist(),columns = ['Keyword', 'Count'])
	df3 = pd.concat([df1['Subject'],df2['Keyword'], df2['Count']], axis =1)
	df3['Count'] = df3['Count'].apply(lambda x: x.rstrip(")"))

	base = os.path.basename(file)
	filename = os.path.splitext(base)[0]

	name = filename.split('_')
	name2 = name[0]
	name2 = name2.lower()
	## converting str to dataframe
	name3 = pd.read_csv(StringIO(name2),names = ['Candidate Name'])

	dataf = pd.concat([name3['Candidate Name'], df3['Subject'], df3['Keyword'], df3['Count']], axis = 1)
	dataf['Candidate Name'].fillna(dataf['Candidate Name'].iloc[0], inplace = True)

	return(dataf)

	#function ends

	#code to execute/call the above functions

	final_database=pd.DataFrame()
	i = 0
	while i < len(onlyfiles):
	file = onlyfiles[i]
	dat = create_profile(file)
	final_database = final_database.append(dat)
	i +=1
	print(final_database)


	#code to count words under each category and visulaize it through Matplotlib

	final_database2 = final_database['Keyword'].groupby([final_database['Candidate Name'], final_database['Subject']]).count().unstack()
	final_database2.reset_index(inplace = True)
	final_database2.fillna(0,inplace=True)
	new_data = final_database2.iloc[:,1:]
	new_data.index = final_database2['Candidate Name']
	#execute the below line if you want to see the candidate profile in a csv format
	#sample2=new_data.to_csv('sample.csv')
	import matplotlib.pyplot as plt
	plt.rcParams.update({'font.size': 10})
	ax = new_data.plot.barh(title="Resume keywords by category", legend=False, figsize=(25,7), stacked=True)
	labels = []
	for j in new_data.columns:
	for i in new_data.index:
	label = str(j)+": " + str(new_data.loc[i][j])
	labels.append(label)
	patches = ax.patches
	for label, rect in zip(labels, patches):
	width = rect.get_width()
	if width > 0:
	x = rect.get_x()
	y = rect.get_y()
	height = rect.get_height()
	ax.text(x + width/2., y + height/2., label, ha='center', va='center')
	plt.show()