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()

OSError: Expected file path name or file-like object, got <class 'bytes'> type
I'm getting this error while reading csv file:(

hi Mili do you manage to solve it? I got the same error as well and get stucked TT

Hey! I'm sorry...you might be using windows! I heard to use utf-8 encoding if it is an encoding issue, bt didn't work for me though you can try!
Good luck:)

utf-8 encoding

OSError: Expected file path name or file-like object, got <class 'bytes'> type
I'm getting this error while reading csv file:(

hi Mili do you manage to solve it? I got the same error as well and get stucked TT

Hey! I'm sorry...you might be using windows! I heard to use utf-8 encoding if it is an encoding issue, bt didn't work for me though you can try!
Good luck:)

Nope, I'm using mac. Anyways, thanks for replying! You too! :]

utf-8 encoding

OSError: Expected file path name or file-like object, got <class 'bytes'> type
I'm getting this error while reading csv file:(

hi Mili do you manage to solve it? I got the same error as well and get stucked TT

Hey! I'm sorry...you might be using windows! I heard to use utf-8 encoding if it is an encoding issue, bt didn't work for me though you can try!
Good luck:)

Nope, I'm using mac. Anyways, thanks for replying! You too! :]

Hi Mili and hueyyi

Do you Manage to solve it?
I am getting utf-8 encoding issue.
Please do let me know the solution.

Many thanks in advance

Working perfectly for me

PDF starts with '{
"c', but '%PDF-' expected

Any idea how to solve this