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upload_containter_resume_classification.py
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upload_containter_resume_classification.py
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import os
import re
import sys
import time
import nltk
import spacy
import sklearn
import datetime
import requests
import docx2txt
import numpy as np
import pandas as pd
import importlib.util
from io import BytesIO
import constants as cs
from glob import glob
import streamlit as st
from pickle import load
from datetime import datetime
from nltk import word_tokenize
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import hydralit_components as hc
from nltk.corpus import stopwords
from spacy.matcher import Matcher
from streamlit_tags import st_tags
from sklearn.ensemble import RandomForestClassifier
from nltk.stem import WordNetLemmatizer, PorterStemmer
from nltk.tokenize import RegexpTokenizer, word_tokenize
sys.coinit_flags = 0
# load pre-trained model
import en_core_web_sm
nlp = en_core_web_sm.load()
nltk.download('stopwords')
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
nltk.download('maxent_ne_chunker')
nltk.download('words')
nltk.download('wordnet')
nltk.download('omw-1.4')
stop=set(stopwords.words('english'))
lemmatizer = WordNetLemmatizer()
stemmer = PorterStemmer()
st.set_option('deprecation.showPyplotGlobalUse', False)
mfile = BytesIO(requests.get('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/model_rc.pkl?raw=true').content)
model = load(mfile)
mfile1 = BytesIO(requests.get('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/model_id.pkl?raw=true').content)
model1 = load(mfile1)
#make it look nice from the start
st.set_page_config(layout='wide',initial_sidebar_state='collapsed')
# specify the primary menu definition
menu_data = [
{'icon': "fa fa-address-card", 'label':"Resume Parser"},
{'icon': "far fa-file-word", 'label':"Resume Classification"},
{'icon':"fa fa-check-square",'label':"Model Evaluation"},
{'icon': "far fa-chart-bar", 'label':"Data Analysis"},#no tooltip message
]
over_theme = {'txc_inactive': '#FFFFFF'}
menu_id = hc.nav_bar(
menu_definition=menu_data,
override_theme=over_theme,
home_name='Home',
login_name=None,
hide_streamlit_markers=False, #will show the st hamburger as well as the navbar now!
sticky_nav=True, #at the top or not
sticky_mode='pinned', #jumpy or not-jumpy, but sticky or pinned
)
if menu_id == 'Home':
my_bar = st.progress(0)
for percent_complete in range(100):
time.sleep(0.001)
my_bar.progress(percent_complete + 1)
tab1, tab2 = st.tabs(["💾 About Project","🦸♂️ About Me"])
with tab1:
col1, col2 = st.columns(2)
with col1:
st.title('About Project')
st.subheader('Resume Classificaiton')
st.header(f"Business Objective: The document classification solution should significantly reduce the manual human effort in the HRM. It should achieve a higher level of accuracy and automation with minimal human intervention.")
st.image("https://res.cloudinary.com/people-matters/image/upload/q_auto,f_auto/v1513589400/1513589398.jpg")
st.markdown("### **Abstract:**\n\
#### A resume is a brief summary of your skills and experience. Companies recruiters and HR teams have a tough time scanning thousands of qualified resumes. Spending too many labor hours segregating candidates resume's manually is a waste of a company's time, money, and productivity. Recruiters, therefore, use resume classification in order to streamline the resume and applicant screening process. NLP technology allows recruiters to electronically gather, store, and organize large quantities of resumes. Once acquired, the resume data can be easily searched through and analyzed.\
\n\
#### Resumes are an ideal example of unstructured data. Since there is no widely accepted resume layout, each resume may have its own style of formatting, different text blocks and different category titles. Building a resume classification and gathering text from it is no easy task as there are so many kinds of layouts of resumes that you could imagine.\n\
\
### Introduction:\n\
\
#### In this project we dive into building a Machine learning model for Resume Classification using Python and basic Natural language processing techniques. We would be using Python's libraries to implement various NLP (natural language processing) techniques like tokenization, lemmatization, parts of speech tagging, etc.\n\
\
#### A resume classification technology needs to be implemented in order to make it easy for the companies to process the huge number of resumes that are received by the organizations. This technology converts an unstructured form of resume data into a structured data format. The resumes received are in the form of documents from which the data needs to be extracted first such that the text can be classified or predicted based on the requirements. A resume classification analyzes resume data and extracts the information into the machine readable output. It helps automatically store, organize, and analyze the resume data to find out the candidate for the particular job position and requirements. This thus helps the organizations eliminate the error-prone and time-consuming process of going through thousands of resumes manually and aids in improving the recruiters’ efficiency.\n\
\
#### The basic data analysis process is performed such as data collection, data cleaning, exploratory data analysis, data visualization, and model building. The dataset consists of two columns, namely, Role Applied and Resume, where ‘role applied’ column is the domain field of the industry and ‘resume’ column consists of the text extracted from the resume document for each domain and industry.\n\
\
#### The aim of this project is achieved by performing the various data analytical methods and using the Machine Learning models and Natural Language Processing which will help in classifying the categories of the resume and building the Resume Classification Model.")
st.markdown('<img align="right" alt="code" height="200" width="200" src = "https://static.wixstatic.com/media/15e6c3_8f8cac375de143dc9d1d552090d975cf~mv2.gif">', unsafe_allow_html=True)
with col2:
st.markdown('<img align="right" alt="code" height="500" width="300" src = "https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/Images/Scroll_down.gif?raw=true">', unsafe_allow_html=True)
with tab2:
col1, col2 = st.columns(2)
with col1:
st.title('About Me')
st.markdown('<img align="center" alt="code" height="180" width="720" src = "https://user-images.githubusercontent.com/99672298/186088252-ef41cefc-cda7-49e8-960d-efbd59d3696c.jpg">\
\
<table>\
<tr>\
<td>\
<img src="https://avatars.githubusercontent.com/u/99672298?v=4" width="180"/>\
\
moindalvs@gmail.com\
\
<p align="center">\
<a href = "https://github.com/MoinDalvs"><img src = "http://www.iconninja.com/files/241/825/211/round-collaboration-social-github-code-circle-network-icon.svg" width="36" height = "36"/></a>\
<a href = "https://twitter.com/DalvsHubot"><img src = "https://www.shareicon.net/download/2016/07/06/107115_media.svg" width="36" height="36"/></a>\
<a href = "https://www.linkedin.com/in/moin-dalvi-277b0214a//"><img src = "http://www.iconninja.com/files/863/607/751/network-linkedin-social-connection-circular-circle-media-icon.svg" width="36" height="36"/></a>\
</p>\
</td>\
</tr> \
</table>\
\n\
<h1 align="center">Hi 👋, I"m Dalvi Moin</h1>\n\
<h3 align="center">A passionate Data Science Enthusiast</h3>\n\
\n\
- 🌱 I love learning as a process and mostly about new things...\n\
\n\
- 🔭 I’m currently working as an Intern at <a href="https://aivariant.com/" target="blank"><img align="center" src="https://www.aivariant.com/wp-content/uploads/2021/01/ai_logo5_2-1.png" alt="MoinDalvs" height="25" width="70" /></a>\n\
\n\
- 🌱 I’m currently learning **Machine Learning and Deep Learning**\n\
\n\
- 💞️ I’m looking to collaborate on all topics related to Data Science, Machine Learning and Artificial Intellegence.\n\
\n\
- 👀 I’m interested in Data Science, Machine Learning and Artificial Intelligence\n\
\n\
- 🌱 I’m currently mastering Python, Tableau, R, MySQL, Azure, Apache Spark, Hadoop, SAS, Artificial intellegence and Deep learning\n\
\n\
- 📫 You can reach me on my email id moindalvs@gmail.com\n\
\n\
## Projects:\
\n\
### Forecasting $CO_2$ Emissions Deployment [](https://moindalvs-co2-emission-forecasting-final-github-backup-vrz2x6.streamlitapp.com/)\
\n\
<img align="right" alt="code" height="400" width="400" src = "https://user-images.githubusercontent.com/94888819/179503858-d2f6d197-7a3f-495b-888c-5a60679bed94.gif">\n\
\
<img align="left" src="https://user-images.githubusercontent.com/94888819/179538709-781ca826-4b36-42e7-aeda-ad6b07e719ea.png" alt="pp" width="50" height="50" /> </a>\
<img align="left" alt="SQL" height="40" src="https://raw.githubusercontent.com/habc0d3r/0th-project/master/icons8-sql-96.png" /> <b>\
<img align="left" alt="excel" height="40" src="https://d3j0t7vrtr92dk.cloudfront.net/stembakuniversity/1616519913_png-clipart-microsoft-excel-computer-icons-microsoft-template-angle-removebg-preview.png" />\
<img src="https://user-images.githubusercontent.com/94888819/179532814-fa9beb8f-0fd6-4160-8d47-650af59c58a1.png" alt="GC" width="40" height="40"/> </a> \
<img src="https://www.vectorlogo.zone/logos/tensorflow/tensorflow-icon.svg" alt="tensorflow" width="40" height="40"/> </a> <a href="https://unity.com/" target="_blank" rel="noreferrer"> </p> \
<img src="https://user-images.githubusercontent.com/94888819/179531328-610ccc8c-11cc-40cf-82dc-5902d473b7e1.png" alt="tab" width="40" height="40"/> </a> \
<img align="left" alt="teminal" height="50" \
src="https://raw.githubusercontent.com/github/explore/80688e429a7d4ef2fca1e82350fe8e3517d3494d/topics/terminal/terminal.png" /> </a>\
<a href="https://code.visualstudio.com" target="_blank" rel="noreferrer"> <img src="https://user-images.githubusercontent.com/94888819/179420219-9ce785fd-b1eb-4caa-aebd-086c088d05f2.png" alt="vsc" width="60" height="40"/> </a>\
<a href="https://github.com" target="_blank" rel="noreferrer"> <img src="https://raw.githubusercontent.com/habc0d3r/0th-project/master/icons8-github.svg" alt="github" width="50" height="50"/> </a> \
<a href="https://www.mysql.com/" target="_blank" rel="noreferrer"> <img src="https://raw.githubusercontent.com/devicons/devicon/master/icons/mysql/mysql-original-wordmark.svg" alt="mysql" width="40" height="40"/> </a>\
<a href="https://opencv.org/" target="_blank" rel="noreferrer"> <img src="https://www.vectorlogo.zone/logos/opencv/opencv-icon.svg" alt="opencv" width="40" height="40"/> </a> </p>\
<img align="left" alt="pycharm" height="40" src="https://user-images.githubusercontent.com/94888819/179536543-d438fb65-8501-475d-9f8b-5bbc18bab86a.png" />\
<img align="left" alt="jn" height="50" src="https://user-images.githubusercontent.com/94888819/179537708-2241ab23-8c86-40c6-8fa9-f3979be75ade.png" /> \
<img src="https://user-images.githubusercontent.com/94888819/179422108-0108ecc4-96d4-4a9f-93d2-f4f2ea98688e.png" alt="stream" width="40" height="50"/> </a>\
<a href="https://aws.amazon.com" target="_blank" rel="noreferrer"> <img src="https://raw.githubusercontent.com/devicons/devicon/master/icons/amazonwebservices/amazonwebservices-original-wordmark.svg" alt="aws" width="40" height="40"/> </a>\
<a href="https://azure.microsoft.com/en-in/" target="_blank" rel="noreferrer"> <img src="https://www.vectorlogo.zone/logos/microsoft_azure/microsoft_azure-icon.svg" alt="azure" width="40" height="40"/> </a>\
<a href="https://www.python.org" target="_blank" rel="noreferrer"> <img src="https://raw.githubusercontent.com/devicons/devicon/master/icons/python/python-original.svg" alt="python" width="50" height="50"/> </a> </p>\
\
Connect with me:\
<p align="left">\
<a href="https://www.linkedin.com/in/moin-dalvi-277b0214a" target="blank"><img align="center" src="https://img.icons8.com/color/344/linkedin-circled--v1.png" alt="MoinDalvs" height="50" width="50" /></a>\
<a href="https://www.kaggle.com/moindalvi" target="blank"><img align="center" src="https://img.icons8.com/external-tal-revivo-color-tal-revivo/344/external-kaggle-an-online-community-of-data-scientists-and-machine-learners-owned-by-google-logo-color-tal-revivo.png" alt="MoinDalvs" height="40" width="40" /></a>\
<a href="https://www.youtube.com/channel/UCN7xdRsVxEaaH5H0f3eNNmg" target="blank"><img align="center" src="https://img.icons8.com/color/344/youtube-play.png" alt="MoinDalvs" height="50" width="50" /></a>\
</p>\
\
<img align="center" alt="code" height="300" width="600" src = "https://static.wixstatic.com/media/3e99b9_f53a1cab95ae4dfd938a1bf6a1a62f49~mv2.gif">\n\
\n\
\
Github Status: \n\
<p align="center"> <img width="50%" src="https://github-readme-stats.vercel.app/api?username=MoinDalvs&show_icons=true&theme=tokyonight"/> </p>\
<p align="center"> <img src="http://github-readme-streak-stats.herokuapp.com?user=MoinDalvs&theme=neon-palenight" width="50%" alt="streak"> </p>\
<p align="center"> <a href="https://github.com/ryo-ma/github-profile-trophy"><img src="https://github-profile-trophy.vercel.app/?username=MoinDalvs&show_icons=true&theme=discord" alt="MoinDalvs" />\
</a> </p>\
\
<div style="align:center;\
display:fill;\
border-radius: false;\
border-style: solid;\
border-color:#000000;\
border-style: false;\
border-width: 2px;\
color:#CF673A;\
font-size:15px;\
font-family: Georgia;\
background-color:#E8DCCC;\
text-align:center;\
letter-spacing:0.1px;\
padding: 0.1em;">\
\
**<h2>♡ Thank you for taking the time ♡**', unsafe_allow_html=True)
st.markdown('<img align="right" alt="code" height="200" width="200" src = "https://static.wixstatic.com/media/15e6c3_8f8cac375de143dc9d1d552090d975cf~mv2.gif">', unsafe_allow_html=True)
with col2:
st.markdown('<img align="right" alt="code" height="500" width="300" src = "https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/Images/Scroll_down.gif?raw=true">', unsafe_allow_html=True)
def extract_text_from_docx(docx_path):
if docx_path.type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document":
txt = docx2txt.process(docx_path)
if txt:
return txt.replace('\t', ' ')
else :
st.warning(body="Not Supported file Format Found")
def display(doc_file):
resume = []
if doc_file.type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document":
resume.append(docx2txt.process(doc_file))
else :
st.warning(body="Not Supported file Format Found")
return resume
def preprocess(sentence):
sentence=str(sentence)
sentence = sentence.lower()
sentence=sentence.replace('{html}',"")
cleanr = re.compile('<.*?>')
cleantext = re.sub(cleanr, '', sentence)
rem_url=re.sub(r'http\S+', '',cleantext)
rem_num = re.sub('[0-9]+', '', rem_url)
tokenizer = RegexpTokenizer(r'\w+')
tokens = tokenizer.tokenize(rem_num)
filtered_words = [w for w in tokens if len(w) > 2 if not w in stopwords.words('english')]
lemmatizer = WordNetLemmatizer()
lemma_words=[lemmatizer.lemmatize(w) for w in filtered_words]
return " ".join(lemma_words)
# Function to extract experience details
def expDetails(Text):
global sent
Text = Text.split()
for i in range(len(Text)-2):
Text[i].lower()
if Text[i] == 'years':
sent = Text[i-2] + ' ' + Text[i-1] +' ' + Text[i] +' '+ Text[i+1] +' ' + Text[i+2]
l = re.findall(r'\d*\.?\d+',sent)
for i in l:
a = float(i)
return(a)
return (sent)
def extract_skills(resume_text):
nlp_text = nlp(resume_text)
noun_chunks = nlp_text.noun_chunks
# removing stop words and implementing word tokenization
tokens = [token.text for token in nlp_text if not token.is_stop]
# reading the csv file
data = pd.read_csv("https://raw.githubusercontent.com/MoinDalvs/Resume_Parser_and_Classification/main/Files/skills.csv")
# extract values
skills = list(data.columns.values)
skillset = []
# check for one-grams (example: python)
for token in tokens:
if token.lower() in skills:
skillset.append(token)
# check for bi-grams and tri-grams (example: machine learning)
for token in noun_chunks:
token = token.text.lower().strip()
if token in skills:
skillset.append(token)
return [i.capitalize() for i in set([i.lower() for i in skillset])]
if menu_id == 'Resume Classification':
def add_bg_image():
st.markdown(
f"""
<style>
.stApp {{
background-image: url("https://img.freepik.com/free-vector/abstract-background-with-squares_23-2148995948.jpg?w=996&t=st=1663219978~exp=1663220578~hmac=aee3da925492e169a7f9fb7d1aa1577c58a7db3849d8be3f448114080d42a7a7");
background-attachment: fixed;
background-size: cover
}}
</style>
""",
unsafe_allow_html=True)
add_bg_image()
with hc.HyLoader('Please Wait!',hc.Loaders.standard_loaders,index=5):
time.sleep(0.8)
st.title("RESUME CLASSIFICATION")
st.subheader('Upload Resumes')
st.write(r'Note: Classifies only Peoplesoft, Workday, SQL Developer and ReactJS Developer Resumes')
tab1, tab2 = st.tabs(["💾 Single File","📁 Multiple Files"])
with tab1:
upload_file1 = st.file_uploader('', type= ['docx'], accept_multiple_files=False)
st.write('*Note: For different Resumes Results Reupload')
if upload_file1 is not None:
displayed=extract_text_from_docx(upload_file1)
cleaned=preprocess(display(upload_file1))
predicted= model.predict(model1.transform([cleaned]))
st.header("The "+ upload_file1.name +" is Applied for"+ " " + predicted + " " + "Profile")
expander = st.expander("See Resume")
expander.write(displayed)
if predicted == 'Workday':
st.image("https://www.workday.com/content/dam/web/en-us/images/social/workday-og-theme.png",width=480)
elif predicted == 'SQL Developer':
st.image("https://wallpaperaccess.com/full/2138094.jpg",width=480)
elif predicted == 'React Developer':
st.image("https://i0.wp.com/www.electrumitsolutions.com/wp-content/uploads/2020/12/wp4923992-react-js-wallpapers.png",width=480)
elif predicted == 'Peoplesoft':
st.image("https://s3.amazonaws.com/questoracle-staging/wordpress/uploads/2019/07/25164143/PeopleSoft-Now.jpg",width=480)
with tab2:
st.write('Upload Folder Containing Multiple .docx Files')
file_type=pd.DataFrame(columns=['Uploaded File', 'Experience', 'Skills', 'Predicted Profile'], dtype=object)
filename = []
predicted = []
experience = []
skills = []
upload_file2 = st.file_uploader('', type= ['docx'], accept_multiple_files=True)
for doc_file in upload_file2:
if doc_file is not None:
filename.append(doc_file.name)
cleaned=preprocess(extract_text_from_docx(doc_file))
predicted.append(model.predict(model1.transform([cleaned])))
extText = extract_text_from_docx(doc_file)
exp = expDetails(extText)
experience.append(exp)
skills.append(extract_skills(extText))
if len(predicted) > 0:
file_type['Uploaded File'] = filename
file_type['Experience'] = experience
file_type['Skills'] = skills
file_type['Predicted Profile'] = predicted
# file_type
st.table(file_type.style.format({'Experience': '{:.1f}'}))
# opt = st.radio("Choose candidate with prospective of :",["Skills","Experience(years)"])
# if opt == "Skills":
# Skill_option = file_type["Skills"].unique().tolist()
# Skill = st.selectbox("Choose the candidate by selecting skills",Skill_option, 0)
if menu_id == 'Data Analysis':
tab1, tab2, tab3, tab4 = st.tabs(["💾 File Types","📁 Multiple Profiles","🔍 Text Analysis","🔤 WordCloud"])
with tab1:
col1, col2 = st.columns(2)
with col1:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/file%20types1_1.png?raw=true')
with col2:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/file_types1_2.png?raw=true')
with tab2:
col1, col2 = st.columns(2)
with col1:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/multiple_profiles1_1.png?raw=true')
with col2:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/multiple_profiles1_2.png?raw=true')
with tab3:
col1, col2, col3, col4, col5, col6, col7 = st.columns(7)
with col1:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/text_analysis1_1.png?raw=true')
with col2:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/text_analysis1_2.png?raw=true')
with col3:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/text_analysis1_3.png?raw=true')
with col4:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/text_analysis1_4.png?raw=true')
with col5:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/text_analysis1_5.png?raw=true')
with col6:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/text_analysis1_6.png?raw=true')
with col7:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/text_analysis1_7.png?raw=true')
with tab4:
ab1, ab2 = st.tabs(["📊 Most Word Used and 🔤 WordCloud","🔤 Profiles WordCloud and 📊 Word BarChart"])
with ab1:
col1, col2 = st.columns(2)
with col1:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/mostwordsused.png?raw=true')
with col2:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/worldcloudall.png?raw=true')
with ab2:
col1, col2 = st.columns(2)
with col1:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/peoplesoftwordcloud.png?raw=true')
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/reactdeveloperwordcloud.png?raw=true')
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/sqldeveloperwordcloud.png?raw=true')
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/workdaywordcloud.png?raw=true')
with col2:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/peoplesoftbarplot.png?raw=true')
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/reactdeveloperbarplot.png?raw=true')
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/sqldeveloperbarplot.png?raw=true')
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/workdaybarplot.png?raw=true')
if menu_id == 'Model Evaluation':
tab1, tab2, tab3= st.tabs(["➗ Train Test Split", '💯 K-Fold Cross Validation', "♻️ Model Evaluation"])
with tab1:
col1, col2, col3, col4 = st.columns(4)
with col1:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/traintestsplit1_1.png?raw=true')
with col2:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/traintestsplit1_2.png?raw=true')
with col3:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/traintestsplit1_3.png?raw=true')
with col4:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/train_test_split1_4.png?raw=true')
with tab2:
col1, col2 = st.columns(2)
with col1:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/cross_validation1.png?raw=true')
with col2:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/cross_validation1_2.png?raw=true')
with tab3:
col1, col2, col3 = st.columns(3)
with col1:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/modelevaluation2_1.png?raw=true')
with col2:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/modelevaluation2_2.png?raw=true')
with col3:
st.image('https://github.com/MoinDalvs/Resume_Parser_and_Classification/blob/main/GIthub%20Requirements/model_evaluatoin2_3.png?raw=true')
if menu_id == 'Resume Parser':
with hc.HyLoader('Please Wait!',hc.Loaders.standard_loaders,index=5):
time.sleep(2)
# FOR INDIAN RESUME RUN THE BELOW FUNCTION TO EXTRACT MOBILE NUMBER
def extract_mobile_number(text):
phone= re.findall(r'[8-9]{1}[0-9]{9}',text)
if len(phone) > 10:
return '+' + phone
else:
return phone
def extract_email(text):
email = re.findall(r"([^@|\s]+@[^@]+\.[^@|\s]+)", text)
if email:
try:
return email[0].split()[0].strip(';')
except IndexError:
return None
# Function to remove punctuation and tokenize the text
def tokenText(extText):
# Remove punctuation marks
punc = r'''!()-[]{};:'"\,.<>/?@#$%^&*_~'''
for ele in extText:
if ele in punc:
puncText = extText.replace(ele, "")
# Tokenize the text and remove stop words
stop_words = set(stopwords.words('english'))
puncText.split()
word_tokens = word_tokenize(puncText)
TokenizedText = [w for w in word_tokens if not w.lower() in stop_words]
TokenizedText = []
for w in word_tokens:
if w not in stop_words:
TokenizedText.append(w)
return(TokenizedText)
# Function to extract Name and contact details
def extract_name(Text):
name = ''
for i in range(0,3):
name = " ".join([name, Text[i]])
return(name)
# Grad all general stop words
STOPWORDS = set(stopwords.words('english'))
# Education Degrees
EDUCATION = ['BE','B.E.', 'B.E', 'BS','B.S','B.Com','BCA','ME','M.E', 'M.E.', 'M.S','B.com','10','10+2','BTECH', 'B.TECH', 'M.TECH', 'MTECH', 'SSC', 'HSC', 'C.B.S.E','CBSE','ICSE', 'X', 'XII','10th','12th',' 10th',' 12th','Bachelor of Arts in Mathematics','Master of Science in Analytics','Bachelor of Business Administration','Major: Business Management']
def extract_education(text):
nlp_text = nlp(text)
# Sentence Tokenizer
nlp_text = [sent.text.strip() for sent in nlp_text.sents]
edu = {}
# Extract education degree
for index, t in enumerate(nlp_text):
for tex in t.split():
# Replace all special symbols
tex = re.sub(r'[?|$|.|!|,]', r'', tex)
if tex in EDUCATION and tex not in STOPWORDS:
edu[tex] = t + nlp_text[index + 1]
# Extract year
education = []
for key in edu.keys():
year = re.search(re.compile(r'(((20|19)(\d{2})))'), edu[key])
if year:
education.append((key, ''.join(year[0])))
else:
education.append(key)
return education
def extract_skills(resume_text):
nlp_text = nlp(resume_text)
noun_chunks = nlp_text.noun_chunks
# removing stop words and implementing word tokenization
tokens = [token.text for token in nlp_text if not token.is_stop]
# reading the csv file
data = pd.read_csv("https://raw.githubusercontent.com/MoinDalvs/Resume_Parser_and_Classification/main/Files/skills.csv")
# extract values
skills = list(data.columns.values)
skillset = []
# check for one-grams (example: python)
for token in tokens:
if token.lower() in skills:
skillset.append(token)
# check for bi-grams and tri-grams (example: machine learning)
for token in noun_chunks:
token = token.text.lower().strip()
if token in skills:
skillset.append(token)
return [i.capitalize() for i in set([i.lower() for i in skillset])]
def string_found(string1, string2):
if re.search(r"\b" + re.escape(string1) + r"\b", string2):
return True
return False
def extract_entity_sections_grad(text):
'''
Helper function to extract all the raw text from sections of resume specifically for
graduates and undergraduates
:param text: Raw text of resume
:return: dictionary of entities
'''
text_split = [i.strip() for i in text.split('\n')]
entities = {}
key = False
for phrase in text_split:
if len(phrase) == 1:
p_key = phrase
else:
p_key = set(phrase.lower().split()) & set(cs.RESUME_SECTIONS_GRAD)
try:
p_key = list(p_key)[0]
except IndexError:
pass
if p_key in cs.RESUME_SECTIONS_GRAD:
entities[p_key] = []
key = p_key
elif key and phrase.strip():
entities[key].append(phrase)
return entities
# Function to extract experience details
def expDetails(Text):
global sent
Text = Text.split()
for i in range(len(Text)-2):
Text[i].lower()
if Text[i] == 'years':
sent = Text[i-2] + ' ' + Text[i-1] +' ' + Text[i] +' '+ Text[i+1] +' ' + Text[i+2]
l = re.findall(r'\d*\.?\d+',sent)
for i in l:
a = float(i)
return(a)
return (sent)
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 string_found(string1, string2):
if re.search(r"\b" + re.escape(string1) + r"\b", string2):
return True
return False
def get_score(_dict):
_len = len(_dict)
if _len >= 5:
return 1
elif _len < 5 and _len > 2:
return 0.5
elif _len == 1:
return 0.2
else:
return 0
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 = {}
score = 0
percentage = (100 // len(cs.COMPETENCIES.keys()))
for competency in cs.COMPETENCIES.keys():
matches = {}
for item in cs.COMPETENCIES[competency]:
if string_found(item, experience_text):
if competency not in competency_dict.keys():
match = re.search(r'([^.|,]*' + item + '[^.|,]*)', experience_text)
if item not in matches.keys():
matches[item] = [match.group(0)]
else:
for i in match.groups():
matches[item].append(i)
competency_dict[competency] = matches
else:
match = re.search(r'([^.|,]*' + item + '[^.|,]*)', experience_text)
if item not in matches.keys():
matches[item] = [match.group(0)]
else:
for i in match.groups():
matches[item].append(i)
competency_dict[competency] = matches
score += get_score(competency_dict[competency]) * percentage
competency_dict['score'] = score
lists=list(competency_dict.keys())
return(lists)
def extract_competencies_score(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 = {}
score = 0
percentage = (100 // len(cs.COMPETENCIES.keys()))
for competency in cs.COMPETENCIES.keys():
matches = {}
for item in cs.COMPETENCIES[competency]:
if string_found(item, experience_text):
if competency not in competency_dict.keys():
match = re.search(r'([^.|,]*' + item + '[^.|,]*)', experience_text)
if item not in matches.keys():
matches[item] = [match.group(0)]
else:
for i in match.groups():
matches[item].append(i)
competency_dict[competency] = matches
else:
match = re.search(r'([^.|,]*' + item + '[^.|,]*)', experience_text)
if item not in matches.keys():
matches[item] = [match.group(0)]
else:
for i in match.groups():
matches[item].append(i)
competency_dict[competency] = matches
score += get_score(competency_dict[competency]) * percentage
competency_dict['score'] = score
return(competency_dict['score'])
def extract_dob(text):
result1=re.findall(r"[\d]{1,2}/[\d]{1,2}/[\d]{4}",text)
result2=re.findall(r"[\d]{1,2}-[\d]{1,2}-[\d]{4}",text)
result3= re.findall(r"[\d]{1,2} [ADFJMNOSadfjmnos]\w* [\d]{4}",text)
result4=re.findall(r"([\d]{1,2})\.([\d]{1,2})\.([\d]{4})",text)
l=[result1,result2,result3,result4]
for i in l:
if i==[]:
continue
else:
return i
def extract_text_from_docx(path):
'''
Helper function to extract plain text from .docx files
:param doc_path: path to .docx file to be extracted
:return: string of extracted text
'''
if path.type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document":
temp = docx2txt.process(path)
return temp
def display(docx_path):
txt = docx2txt.process(docx_path)
if txt:
return txt.replace('\t', ' ')
df = pd.DataFrame(columns=['Name','Mobile No.', 'Email','DOB','Education Qualifications','Skills','Experience (Years)','Last Position','Competence','competence score'], dtype=object)
st.title("RESUME PARSER")
st.subheader('Upload Resume (Single File Accepted) 👇')
upload_file3 = st.file_uploader('', type= ['docx'], accept_multiple_files=False)
st.write('*Note: For different Resumes Results Reupload')
if upload_file3 is not None:
displayed=display(upload_file3)
i=0
text = extract_text_from_docx(upload_file3)
tokText = tokenText(text)
df.loc[i,'Name']=extract_name(tokText)
df.loc[i,'Mobile No.']=extract_mobile_number(text)
df.loc[i,'Email']=extract_email(text)
df.loc[i,'DOB']=extract_dob(text)
df.loc[i,'Education Qualifications']=extract_education(text)
df.loc[i,'Skills']=extract_skills(text)
df.loc[i,'Experience (Years)']=expDetails(text)
experience_list1=extract_entity_sections_grad(text)
if 'experience' in experience_list1:
experience_list=experience_list1['experience']
df.loc[i,'Last Position']=extract_experience(text)
df.loc[i,'Competence']=extract_competencies(text,experience_list)
df.loc[i,'competence score']=extract_competencies_score(text,experience_list)
else:
df.loc[i,'Last Position']='NA'
df.loc[i,'Competence']='NA'
df.loc[i,'competence score']='NA'
st.header("**Resume Analysis**")
st.success("Hello "+ df['Name'][0])
col1, col2 = st.columns(2)
with col1:
st.header("Basic info")
try:
st.subheader('Name: '+ df['Name'][0])
st.subheader('Experience (Years): ' + str(df['Experience (Years)'][0]))
st.subheader('Last Position: ' + str(df['Last Position'][0]))
st.subheader('Competence: ' + str(df['Competence'][0]))
st.subheader('Education: ' + str(df['Education Qualifications'][0]))
st.subheader('Email: ' + str(df['Email'][0]))
st.subheader('Contact: ' + str(df['Mobile No.'][0]))
st.subheader('Date of Birth: ' + str(df['DOB'][0]))
except:
pass
expander = st.expander("See Resume")
expander.write(displayed)
with col2:
st.header("**Skills Analysis💡**")
## shows skill
keywords = st_tags(label='### Skills that'+ df['Name'][0] + ' have',
text=' -- Skills',value=df['Skills'][0],key = '1')
st.subheader("**Competence Score📝**")
st.markdown(
"""
<style>
.stProgress > div > div > div > div {
background-color: #d73b5c;
}
</style>""",
unsafe_allow_html=True,
)
my_bar = st.progress(0)
score = 0
if df['competence score'][0] != 'NA':
for percent_complete in range(int(df['competence score'][0])):
score +=1
time.sleep(0.1)
my_bar.progress(percent_complete + 1)
st.success(df['Name'][0] + "'s Competence Score: " + str(score))
df.T