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Commit f0a5f6e

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Merge pull request avinashkranjan#2053 from mkswagger/master
[GSSOC'23] Added resume parser using python and pdfminer
2 parents dfe0df2 + 7fff95c commit f0a5f6e

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‎Resume_parser/README.md

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# Resume Parser
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The Resume Parser is a Python script that extracts relevant information such as educational background, work experience, and skills from a resume in PDF format.
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## How It Works
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The Resume Parser follows these steps to extract information from a resume:
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1. The PDF file is opened and processed using the `pdfminer` library, which extracts the text content from each page of the PDF.
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2. The extracted text is stored as a string.
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3. Regular expressions are used to search for patterns and extract the educational background and work experience sections from the resume text. These regular expressions can be customized in the `extract_education` and `extract_experience` functions of the `newparser.py` file.
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4. If a `skills_list.csv` file is provided, the script reads the file and creates a list of skills to search for in the resume. Each skill should be placed on a separate line in the CSV file.
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5. The script searches for each skill in the resume text using case-insensitive matching. If a skill is found, it is added to the list of extracted skills.
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6. The extracted educational background, work experience, and skills are displayed in the console output.
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<img width="205" alt="image" src="https://github.com/mkswagger/Amazing-Python-Scripts/assets/34826479/3600c2f8-2fea-436d-8679-327e2ecdea81">
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## Usage
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1. Clone the repository to your local machine:
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```shell
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git clone https://github.com/mkswagger/Amazing-Python-Scripts/tree/master/Resume_parser
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2. Place the resume PDF file you want to parse in the project directory.
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3. Modify the `file_name` variable in the `newparser.py` file to match the name of your resume file.
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4. Optionally, if you have a wide range of skills to extract, create a CSV file named `skills_list.csv` in the project directory. Each skill should be placed on a separate line.
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5. Run the script:
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```shell
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python newparser.py
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The script will extract the educational background, work experience, and skills from the resume and display them in the console.
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Feel free to customize the regular expressions and add additional extraction logic based on your specific requirements.
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‎Resume_parser/requirements.txt

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altair==5.0.1
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asgiref==3.6.0
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async-generator==1.10
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attrs==23.1.0
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beautifulsoup4==4.12.2
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bitarray==2.7.3
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blinker==1.6.2
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blis==0.7.9
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breadability==0.1.20
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bs4==0.0.1
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cachetools==5.3.1
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catalogue==2.0.8
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certifi==2022年12月7日
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cffi==1.15.1
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chardet==5.1.0
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charset-normalizer==3.1.0
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click==8.1.3
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colorama==0.4.6
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confection==0.0.4
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cssselect==1.2.0
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cymem==2.0.7
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decorator==5.1.1
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Django==4.1.7
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doc2text==0.2.4
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docopt==0.6.2
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docx==0.2.4
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et-xmlfile==1.1.0
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exceptiongroup==1.1.1
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feedfinder2==0.0.4
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feedparser==6.0.10
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filelock==3.12.2
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fsspec==202360
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future==0.18.3
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gensim==4.3.1
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gitdb==4.0.10
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GitPython==3.1.31
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greenlet==2.0.2
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h11==0.14.0
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huggingface-hub==0.15.1
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idna==3.4
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importlib-metadata==6.7.0
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jieba3k==0.35.1
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Jinja2==3.1.2
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joblib==1.2.0
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jsonschema==4.17.3
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langcodes==3.3.0
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lxml==4.9.2
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markdown-it-py==3.0.0
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MarkupSafe==2.1.2
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mdurl==0.1.2
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mime==0.1.0
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murmurhash==1.0.9
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newspaper3k==0.2.8
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nltk==3.8.1
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numpy==1.24.2
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openpyxl==3.1.2
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outcome==1.2.0
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packaging==23.0
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pandas==1.5.3
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parse==1.19.0
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pathy==0.10.1
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pdfminer==20191125
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pdfreader==0.1.12
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Pillow==9.4.0
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playwright==1.34.0
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preshed==3.0.8
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protobuf==4.23.3
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pyarrow==12.0.1
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pycountry==22.3.5
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pycparser==2.21
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pycryptodome==3.17
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pydantic==1.10.6
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pydeck==0.8.1b0
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pyee==9.0.4
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Pygments==2.15.1
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Pympler==1.0.1
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PyPDF2==3.0.1
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pyrsistent==0.19.3
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PySocks==1.7.1
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pytesseract==0.3.10
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python-dateutil==2.8.2
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python-docx==0.8.11
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pytz==2022年7月1日
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pytz-deprecation-shim==0.1.0.post0
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PyYAML==6.0
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regex==2023年6月3日
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requests==2.28.2
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requests-file==1.5.1
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rich==13.4.2
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safetensors==0.3.1
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scikit-learn==1.2.2
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scipy==1.10.1
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selenium==4.10.0
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sgmllib3k==1.0.0
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six==1.16.0
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sklearn==0.0.post1
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smart-open==6.3.0
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smmap==5.0.0
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sniffio==1.3.0
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sortedcontainers==2.4.0
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soupsieve==2.4.1
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spacy==3.5.1
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spacy-legacy==3.0.12
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spacy-loggers==1.0.4
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sqlparse==0.4.3
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srsly==2.4.6
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streamlit==1.23.1
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sumy==0.11.0
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tenacity==8.2.2
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thinc==8.1.9
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threadpoolctl==3.1.0
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tika==2.6.0
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tinysegmenter==0.3
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tldextract==3.4.4
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tokenizers==0.13.3
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toml==0.10.2
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toolz==0.12.0
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tornado==6.3.2
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tqdm==4.65.0
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transformers==4.30.2
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trio==0.22.0
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trio-websocket==0.10.3
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typer==0.7.0
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typing_extensions==4.5.0
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tzdata==2022.7
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tzlocal==4.3
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urllib3==1.26.15
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validators==0.20.0
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wasabi==1.1.1
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watchdog==3.0.0
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wsproto==1.2.0
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zipp==3.15.0

‎Resume_parser/resumeparser.py

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import re
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import csv
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from pdfminer.pdfpage import PDFPage
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from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
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from pdfminer.converter import TextConverter
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from pdfminer.layout import LAParams
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import io
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def extract_education(resume_text):
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education_pattern = r"((?:Bachelor|Master|Ph\.?D|Diploma)[^.,]*\b(?:\.\b)?(?:[^.,\n]*\b(?:University|College|School|Institute)\b[^.,\n]*)?)"
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education_matches = re.findall(education_pattern, resume_text, re.IGNORECASE)
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return education_matches
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def extract_experience(resume_text):
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experience_pattern = r"(?:(?:[A-Z][a-z]+\s+){1,3})?(?:(?:\d{4}\s?-\s?\d{4}|\d{4})\s)?(?:(?:Present|Jan(?:uary)?|Feb(?:ruary)?|Mar(?:ch)?|Apr(?:il)?|May|Jun(?:e)?|Jul(?:y)?|Aug(?:ust)?|Sep(?:tember)?|Oct(?:ober)?|Nov(?:ember)?|Dec(?:ember)?)[A-Za-z\s]+\d{4})"
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experience_matches = re.findall(experience_pattern, resume_text, re.IGNORECASE)
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return experience_matches
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def extract_skills(resume_text, skills_list):
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skills_found = []
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for skill in skills_list:
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escaped_skill = re.escape(skill)
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if re.search(r'\b{}\b'.format(escaped_skill), resume_text, re.IGNORECASE):
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skills_found.append(skill)
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return skills_found
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file_name = "resumes\Resume_12.pdf"
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skills_file = "skills_list.csv" # Path to the CSV file containing skills
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i_f = open(file_name, 'rb')
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res_mgr = PDFResourceManager()
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ret_data = io.StringIO()
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txt_converter = TextConverter(res_mgr, ret_data, laparams=LAParams())
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interpreter = PDFPageInterpreter(res_mgr, txt_converter)
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for page in PDFPage.get_pages(i_f):
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interpreter.process_page(page)
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resume_text = ret_data.getvalue()
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# Extract educational and work experience
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education = extract_education(resume_text)
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experience = extract_experience(resume_text)
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# Extract skills from CSV file
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skills_list = []
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with open(skills_file, 'r') as csv_file:
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reader = csv.reader(csv_file)
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for row in reader:
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skills_list.extend(row)
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# Extract skills
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skills = extract_skills(resume_text, skills_list)
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# Print the extracted information
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print("Educational Background:")
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for edu in education:
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print(edu)
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print("\nWork Experience:")
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for exp in experience:
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print(exp)
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print("\nSkills:")
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for skill in skills:
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print(skill)
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# Close the file and converter
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i_f.close()
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txt_converter.close()

‎Resume_parser/resumes/Resume_12.pdf

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‎Resume_parser/resumes/resume_1.pdf

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‎Resume_parser/resumes/resume_10.pdf

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‎Resume_parser/resumes/resume_11.pdf

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‎Resume_parser/resumes/resume_13.pdf

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‎Resume_parser/resumes/resume_14.pdf

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‎Resume_parser/resumes/resume_15.pdf

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