├── .github
└── FUNDING.yml
├── .gitignore
├── .travis.yml
├── LICENSE
├── MANIFEST.in
├── OmkarResume.pdf
├── README.md
├── README.rst
├── docs
├── cli.md
└── index.md
├── export_to_csv.py
├── mkdocs.yml
├── pre_requisites.py
├── pyresparser
├── CHANGELOG.md
├── __init__.py
├── command_line.py
├── constants.py
├── custom_t.py
├── custom_train.py
├── meta.json
├── ner
│ ├── cfg
│ ├── model
│ └── moves
├── requirements.txt
├── resume_parser.py
├── skills.csv
├── tokenizer
├── traindata.json
├── utils.py
└── vocab
│ ├── key2row
│ ├── lexemes.bin
│ ├── strings.json
│ └── vectors
├── rank_candidate.py
├── requirements.txt
├── setup.py
└── test_name.py
/.github/FUNDING.yml:
--------------------------------------------------------------------------------
1 | # These are supported funding model platforms
2 |
3 | github: [pyresparser]
4 |
--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | *.egg-info/
24 | .installed.cfg
25 | *.egg
26 | MANIFEST
27 |
28 | # PyInstaller
29 | # Usually these files are written by a python script from a template
30 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
31 | *.manifest
32 | *.spec
33 |
34 | # Installer logs
35 | pip-log.txt
36 | pip-delete-this-directory.txt
37 |
38 | # Unit test / coverage reports
39 | htmlcov/
40 | .tox/
41 | .coverage
42 | .coverage.*
43 | .cache
44 | nosetests.xml
45 | coverage.xml
46 | *.cover
47 | .hypothesis/
48 | .pytest_cache/
49 |
50 | # Translations
51 | *.mo
52 | *.pot
53 |
54 | # Django stuff:
55 | *.log
56 | local_settings.py
57 | db.sqlite3
58 |
59 | # Flask stuff:
60 | instance/
61 | .webassets-cache
62 |
63 | # Scrapy stuff:
64 | .scrapy
65 |
66 | # Sphinx documentation
67 | docs/_build/
68 |
69 | # PyBuilder
70 | target/
71 |
72 | # Jupyter Notebook
73 | .ipynb_checkpoints
74 |
75 | # pyenv
76 | .python-version
77 |
78 | # celery beat schedule file
79 | celerybeat-schedule
80 |
81 | # SageMath parsed files
82 | *.sage.py
83 |
84 | # Environments
85 | .env
86 | .venv
87 | env/
88 | venv/
89 | ENV/
90 | env.bak/
91 | venv.bak/
92 |
93 | # Spyder project settings
94 | .spyderproject
95 | .spyproject
96 |
97 | # Rope project settings
98 | .ropeproject
99 |
100 | # mkdocs documentation
101 | /site
102 |
103 | # mypy
104 | .mypy_cache/
105 |
--------------------------------------------------------------------------------
/.travis.yml:
--------------------------------------------------------------------------------
1 | language: python
2 | cache: pip
3 | matrix:
4 | include:
5 | - python: 3.5
6 | - python: 3.6
7 | - python: 3.7
8 | install:
9 | - pip install -r requirements.txt
10 | - python -m spacy download en_core_web_sm
11 | - python -m nltk.downloader words
12 | - python -m nltk.downloader stopwords
13 | - pip install pytest
14 | - pip install coverage
15 | - pip install codecov
16 | - pip install flake8
17 | before_script:
18 | - "flake8 pyresparser"
19 | script:
20 | - "coverage run -m pytest"
21 | after_success:
22 | - codecov
23 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/MANIFEST.in:
--------------------------------------------------------------------------------
1 | include README.md
2 | include bin/pyresparser.py
3 |
4 | recursive-include pyresparser *
5 |
6 | exclude pyresparser/traindata.json
7 | exclude pyresparser/custom_train.py
8 | exclude pyresparser/custom_test.py
--------------------------------------------------------------------------------
/OmkarResume.pdf:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/OmkarPathak/pyresparser/a66f25b583f2dd8dbd18f419321eed57b04a006e/OmkarResume.pdf
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # pyresparser
2 |
3 | ```
4 | A simple resume parser used for extracting information from resumes
5 | ```
6 |
7 | Built with ❤︎ and :coffee: by [Omkar Pathak](https://github.com/OmkarPathak)
8 |
9 | ---
10 |
11 | [](https://github.com/OmkarPathak/pyresparser/stargazers)
12 | [](https://pypi.org/project/pyresparser/)
13 | [](https://pepy.tech/project/pyresparser)
14 | [](https://github.com/OmkarPathak/pyresparser/blob/master/LICENSE)  [](https://saythanks.io/to/omkarpathak27@gmail.com)
15 | [](https://travis-ci.com/OmkarPathak/pyresparser)
16 | [](https://codecov.io/gh/OmkarPathak/pyresparser)
17 |
18 | # Features
19 |
20 | - Extract name
21 | - Extract email
22 | - Extract mobile numbers
23 | - Extract skills
24 | - Extract total experience
25 | - Extract college name
26 | - Extract degree
27 | - Extract designation
28 | - Extract company names
29 |
30 | # Installation
31 |
32 | - You can install this package using
33 |
34 | ```bash
35 | pip install pyresparser
36 | ```
37 |
38 | - For NLP operations we use spacy and nltk. Install them using below commands:
39 |
40 | ```bash
41 | # spaCy
42 | python -m spacy download en_core_web_sm
43 |
44 | # nltk
45 | python -m nltk.downloader words
46 | python -m nltk.downloader stopwords
47 | ```
48 |
49 | # Documentation
50 |
51 | Official documentation is available at: https://www.omkarpathak.in/pyresparser/
52 |
53 | # Supported File Formats
54 |
55 | - PDF and DOCx files are supported on all Operating Systems
56 | - If you want to extract DOC files you can install [textract](https://textract.readthedocs.io/en/stable/installation.html) for your OS (Linux, MacOS)
57 | - Note: You just have to install textract (and nothing else) and doc files will get parsed easily
58 |
59 | # Usage
60 |
61 | - Import it in your Python project
62 |
63 | ```python
64 | from pyresparser import ResumeParser
65 | data = ResumeParser('/path/to/resume/file').get_extracted_data()
66 | ```
67 |
68 | # CLI
69 |
70 | For running the resume extractor you can also use the `cli` provided
71 |
72 | ```bash
73 | usage: pyresparser [-h] [-f FILE] [-d DIRECTORY] [-r REMOTEFILE]
74 | [-re CUSTOM_REGEX] [-sf SKILLSFILE] [-e EXPORT_FORMAT]
75 |
76 | optional arguments:
77 | -h, --help show this help message and exit
78 | -f FILE, --file FILE resume file to be extracted
79 | -d DIRECTORY, --directory DIRECTORY
80 | directory containing all the resumes to be extracted
81 | -r REMOTEFILE, --remotefile REMOTEFILE
82 | remote path for resume file to be extracted
83 | -re CUSTOM_REGEX, --custom-regex CUSTOM_REGEX
84 | custom regex for parsing mobile numbers
85 | -sf SKILLSFILE, --skillsfile SKILLSFILE
86 | custom skills CSV file against which skills are
87 | searched for
88 | -e EXPORT_FORMAT, --export-format EXPORT_FORMAT
89 | the information export format (json)
90 | ```
91 |
92 | # Notes:
93 |
94 | - If you are running the app on windows, then you can only extract .docs and .pdf files
95 |
96 | # Result
97 |
98 | The module would return a list of dictionary objects with result as follows:
99 |
100 | ```
101 | [
102 | {
103 | 'college_name': ['Marathwada Mitra Mandal’s College of Engineering'],
104 | 'company_names': None,
105 | 'degree': ['B.E. IN COMPUTER ENGINEERING'],
106 | 'designation': ['Manager',
107 | 'TECHNICAL CONTENT WRITER',
108 | 'DATA ENGINEER'],
109 | 'email': 'omkarpathak27@gmail.com',
110 | 'mobile_number': '8087996634',
111 | 'name': 'Omkar Pathak',
112 | 'no_of_pages': 3,
113 | 'skills': ['Operating systems',
114 | 'Linux',
115 | 'Github',
116 | 'Testing',
117 | 'Content',
118 | 'Automation',
119 | 'Python',
120 | 'Css',
121 | 'Website',
122 | 'Django',
123 | 'Opencv',
124 | 'Programming',
125 | 'C',
126 | ...],
127 | 'total_experience': 1.83
128 | }
129 | ]
130 | ```
131 |
132 | # References that helped me get here
133 |
134 | - Some of the core concepts behind the algorithm have been taken from [https://github.com/divapriya/Language_Processing](https://github.com/divapriya/Language_Processing) which has been summed up in this blog [https://medium.com/@divalicious.priya/information-extraction-from-cv-acec216c3f48](https://medium.com/@divalicious.priya/information-extraction-from-cv-acec216c3f48). Thanks to Priya for sharing this concept
135 |
136 | - [https://www.kaggle.com/nirant/hitchhiker-s-guide-to-nlp-in-spacy](https://www.kaggle.com/nirant/hitchhiker-s-guide-to-nlp-in-spacy)
137 |
138 | - [https://www.analyticsvidhya.com/blog/2017/04/natural-language-processing-made-easy-using-spacy-%E2%80%8Bin-python/](https://www.analyticsvidhya.com/blog/2017/04/natural-language-processing-made-easy-using-spacy-%E2%80%8Bin-python/)
139 |
140 | - **Special thanks** to dataturks for their [annotated dataset](https://dataturks.com/blog/named-entity-recognition-in-resumes.php)
141 |
142 | # Donation
143 |
144 | If you have found my softwares to be of any use to you, do consider helping me pay my internet bills. This would encourage me to create many such softwares :smile:
145 |
146 | | PayPal |
|
147 | |:-------------------------------------------:|:-------------------------------------------------------------:|
148 | | ₹ (INR) |
|
149 |
150 | # Stargazer over time
151 | [](https://starchart.cc/OmkarPathak/pyresparser)
152 |
--------------------------------------------------------------------------------
/README.rst:
--------------------------------------------------------------------------------
1 | pyresparser
2 | ===========
3 |
4 | ::
5 |
6 | A simple resume parser used for extracting information from resumes
7 |
8 | Built with ❤︎ and :coffee: by `Omkar
9 | Pathak `__
10 |
11 | --------------
12 |
13 | |GitHub stars| |PyPI| |Downloads| |GitHub| |PyPI - Python Version| |Say
14 | Thanks!| |Build Status| |codecov|
15 |
16 | Features
17 | ========
18 |
19 | - Extract name
20 | - Extract email
21 | - Extract mobile numbers
22 | - Extract skills
23 | - Extract total experience
24 | - Extract college name
25 | - Extract degree
26 | - Extract designation
27 | - Extract company names
28 |
29 | Installation
30 | ============
31 |
32 | - You can install this package using
33 |
34 | .. code:: bash
35 |
36 | pip install pyresparser
37 |
38 | - For NLP operations we use spacy and nltk. Install them using below
39 | commands:
40 |
41 | .. code:: bash
42 |
43 | # spaCy
44 | python -m spacy download en_core_web_sm
45 |
46 | # nltk
47 | python -m nltk.downloader words
48 |
49 | Documentation
50 | =============
51 |
52 | Official documentation is available at:
53 | https://www.omkarpathak.in/pyresparser/
54 |
55 | Supported File Formats
56 | ======================
57 |
58 | - PDF and DOCx files are supported on all Operating Systems
59 | - If you want to extract DOC files you can install
60 | `textract `__
61 | for your OS (Linux, MacOS)
62 | - Note: You just have to install textract (and nothing else) and doc
63 | files will get parsed easily
64 |
65 | Usage
66 | =====
67 |
68 | - Import it in your Python project
69 |
70 | .. code:: python
71 |
72 | from pyresparser import ResumeParser
73 | data = ResumeParser('/path/to/resume/file').get_extracted_data()
74 |
75 | CLI
76 | ===
77 |
78 | For running the resume extractor you can also use the ``cli`` provided
79 |
80 | .. code:: bash
81 |
82 | usage: pyresparser [-h] [-f FILE] [-d DIRECTORY] [-r REMOTEFILE]
83 | [-re CUSTOM_REGEX] [-sf SKILLSFILE] [-e EXPORT_FORMAT]
84 |
85 | optional arguments:
86 | -h, --help show this help message and exit
87 | -f FILE, --file FILE resume file to be extracted
88 | -d DIRECTORY, --directory DIRECTORY
89 | directory containing all the resumes to be extracted
90 | -r REMOTEFILE, --remotefile REMOTEFILE
91 | remote path for resume file to be extracted
92 | -re CUSTOM_REGEX, --custom-regex CUSTOM_REGEX
93 | custom regex for parsing mobile numbers
94 | -sf SKILLSFILE, --skillsfile SKILLSFILE
95 | custom skills CSV file against which skills are
96 | searched for
97 | -e EXPORT_FORMAT, --export-format EXPORT_FORMAT
98 | the information export format (json)
99 |
100 | Notes:
101 | ======
102 |
103 | - If you are running the app on windows, then you can only extract
104 | .docs and .pdf files
105 |
106 | Result
107 | ======
108 |
109 | The module would return a list of dictionary objects with result as
110 | follows:
111 |
112 | ::
113 |
114 | [
115 | {
116 | 'college_name': ['Marathwada Mitra Mandal’s College of Engineering'],
117 | 'company_names': None,
118 | 'degree': ['B.E. IN COMPUTER ENGINEERING'],
119 | 'designation': ['Manager',
120 | 'TECHNICAL CONTENT WRITER',
121 | 'DATA ENGINEER'],
122 | 'email': 'omkarpathak27@gmail.com',
123 | 'mobile_number': '8087996634',
124 | 'name': 'Omkar Pathak',
125 | 'no_of_pages': 3,
126 | 'skills': ['Operating systems',
127 | 'Linux',
128 | 'Github',
129 | 'Testing',
130 | 'Content',
131 | 'Automation',
132 | 'Python',
133 | 'Css',
134 | 'Website',
135 | 'Django',
136 | 'Opencv',
137 | 'Programming',
138 | 'C',
139 | ...],
140 | 'total_experience': 1.83
141 | }
142 | ]
143 |
144 | References that helped me get here
145 | ==================================
146 |
147 | - https://www.kaggle.com/nirant/hitchhiker-s-guide-to-nlp-in-spacy
148 |
149 | - https://www.analyticsvidhya.com/blog/2017/04/natural-language-processing-made-easy-using-spacy-%E2%80%8Bin-python/
150 |
151 | - [https://medium.com/@divalicious.priya/information-extraction-from-cv-acec216c3f48](https://medium.com/@divalicious.priya/information-extraction-from-cv-acec216c3f48)
152 |
153 | - **Special thanks** to dataturks for their `annotated
154 | dataset `__
155 |
156 | Donation
157 | ========
158 |
159 | If you have found my softwares to be of any use to you, do consider
160 | helping me pay my internet bills. This would encourage me to create many
161 | such softwares :smile:
162 |
163 | +-----------+----+
164 | | PayPal | |
165 | +===========+====+
166 | | ₹ (INR) | |
167 | +-----------+----+
168 |
169 | Stargazer over time
170 | ===================
171 |
172 | |Stargazers over time|
173 |
174 | .. |GitHub stars| image:: https://img.shields.io/github/stars/OmkarPathak/pyresparser.svg
175 | :target: https://github.com/OmkarPathak/pyresparser/stargazers
176 | .. |PyPI| image:: https://img.shields.io/pypi/v/pyresparser.svg
177 | :target: https://pypi.org/project/pyresparser/
178 | .. |Downloads| image:: https://pepy.tech/badge/pyresparser
179 | :target: https://pepy.tech/project/pyresparser
180 | .. |GitHub| image:: https://img.shields.io/github/license/omkarpathak/pyresparser.svg
181 | :target: https://github.com/OmkarPathak/pyresparser/blob/master/LICENSE
182 | .. |PyPI - Python Version| image:: https://img.shields.io/pypi/pyversions/Django.svg
183 | .. |Say Thanks!| image:: https://img.shields.io/badge/Say%20Thanks-:D-1EAEDB.svg
184 | :target: https://saythanks.io/to/OmkarPathak
185 | .. |Build Status| image:: https://travis-ci.com/OmkarPathak/pyresparser.svg?branch=master
186 | :target: https://travis-ci.com/OmkarPathak/pyresparser
187 | .. |codecov| image:: https://codecov.io/gh/OmkarPathak/pyresparser/branch/master/graph/badge.svg
188 | :target: https://codecov.io/gh/OmkarPathak/pyresparser
189 | .. |Stargazers over time| image:: https://starchart.cc/OmkarPathak/pyresparser.svg
190 | :target: https://starchart.cc/OmkarPathak/pyresparser
191 |
--------------------------------------------------------------------------------
/docs/cli.md:
--------------------------------------------------------------------------------
1 | # CLI
2 |
3 | `pyresparser` comes with a **cli** option which you can use right away in your terminal
4 |
5 | ```bash
6 | usage: pyresparser [-h] [-f FILE] [-d DIRECTORY] [-r REMOTEFILE]
7 | [-sf SKILLSFILE]
8 |
9 | optional arguments:
10 | -h, --help show this help message and exit
11 | -f FILE, --file FILE resume file to be extracted
12 | -d DIRECTORY, --directory DIRECTORY directory containing all the resumes to be extracted
13 | -r REMOTEFILE, --remotefile REMOTEFILE remote path for resume file to be extracted
14 | -sf SKILLSFILE, --skillsfile SKILLSFILE custom skills CSV file against which skills are searched for
15 | ```
16 |
17 | ## Parsing single resume
18 |
19 | For extracting data from a **single resume** file, use
20 |
21 | ```bash
22 | pyresparser -f /path/to/resume/file
23 | ```
24 |
25 | ## Parsing mutliple resumes
26 |
27 | For extracting data from several resumes, place them in a **directory** and then execute
28 |
29 | ```bash
30 | pyresparser -d /path/to/resume/directory/
31 | ```
32 |
33 | ## Parsing hosted resumes
34 |
35 | For extracting data from **remote resumes**, execute
36 |
37 | ```bash
38 | pyresparser -r https://www.example.com/path/to/resume/file
39 | ```
40 |
41 | ## Specifying skills explicitly
42 |
43 | Pyresparser comes with built-in skills file that defaults to many technical skills. You can find the default skills file [here](https://github.com/OmkarPathak/pyresparser/blob/master/pyresparser/skills.csv).
44 |
45 | For extracting data against your specified skills, create a CSV file with no headers and execute
46 |
47 | ```bash
48 | pyresparser -sf /path/to/resume/file.csv -f /path/to/resume/file
49 | ```
50 |
51 | ## Specifying export format
52 |
53 | For specifying the export format you can use the following option:
54 |
55 | ```bash
56 | pyresparser -e json -f /path/to/resume/file
57 | ```
58 |
59 | Note: Currently only JSON export is supported
60 |
61 | ## Custom regex for parsing phone numbers
62 |
63 | While pyresparser parses most of the phone numbers correctly, there is a possibility of new patterns being added in near future. Hence, we can explicitly provide the regex required to parse the desired phone numbers. This can be done using
64 |
65 | ```bash
66 | pyresparser -re '' -f /path/to/resume/file
67 | ```
--------------------------------------------------------------------------------
/docs/index.md:
--------------------------------------------------------------------------------
1 | # Pyresparser
2 |
3 | A simple resume parser used for extracting information from resumes
4 |
5 | # Features
6 |
7 | - Extract name
8 | - Extract email
9 | - Extract mobile numbers
10 | - Extract skills
11 | - Extract total experience
12 | - Extract college name
13 | - Extract degree
14 | - Extract designation
15 | - Extract company names
16 |
17 | # Getting Started
18 |
19 | ## Installation
20 |
21 | - You can install this package using
22 |
23 | ```bash
24 | pip install pyresparser
25 | ```
26 |
27 | - For NLP operations we use spacy and nltk. Install them using below commands:
28 |
29 | ```bash
30 | # spaCy
31 | python -m spacy download en_core_web_sm
32 |
33 | # nltk
34 | python -m nltk.downloader words
35 | python -m nltk.downloader stopwords
36 | ```
37 |
38 | ## Usage
39 |
40 | - Import it in your Python project
41 |
42 | ```python
43 | from pyresparser import ResumeParser
44 | data = ResumeParser('/path/to/resume/file').get_extracted_data()
45 | ```
46 |
47 | ## Result
48 |
49 | The module would return a list of dictionary objects with result as follows:
50 |
51 | ```
52 | [
53 | {
54 | 'college_name': ['Marathwada Mitra Mandal’s College of Engineering'],
55 | 'company_names': None,
56 | 'degree': ['B.E. IN COMPUTER ENGINEERING'],
57 | 'designation': ['Manager',
58 | 'TECHNICAL CONTENT WRITER',
59 | 'DATA ENGINEER'],
60 | 'email': 'omkarpathak27@gmail.com',
61 | 'mobile_number': '8087996634',
62 | 'name': 'Omkar Pathak',
63 | 'no_of_pages': 3,
64 | 'skills': ['Operating systems',
65 | 'Linux',
66 | 'Github',
67 | 'Testing',
68 | 'Content',
69 | 'Automation',
70 | 'Python',
71 | 'Css',
72 | 'Website',
73 | 'Django',
74 | 'Opencv',
75 | 'Programming',
76 | 'C',
77 | ...],
78 | 'total_experience': 1.83
79 | }
80 | ]
81 | ```
82 |
83 | ## Supported Resume File Formats
84 |
85 | - Parsing of PDF and DOCx files are supported on all Operating Systems
86 | - If you want to parse DOC files you can install [textract](https://textract.readthedocs.io/en/stable/installation.html) for your OS (Linux, MacOS)
87 | - Note: You just have to install textract (and nothing else) and doc files will get parsed easily
88 |
89 | # Advanced Options
90 |
91 | ## Explicitly specifying skills file
92 |
93 | Pyresparser comes with built-in skills file that defaults to many technical skills. You can find the default skills file [here](https://github.com/OmkarPathak/pyresparser/blob/master/pyresparser/skills.csv).
94 |
95 | For extracting data against your specified skills, create a CSV file with no headers.
96 |
97 | ```python
98 | from pyresparser import ResumeParser
99 | data = ResumeParser('/path/to/resume/file', skills_file='/path/to/skills.csv').get_extracted_data()
100 | ```
101 |
102 | ## Explicitly providing regex to parse phone numbers
103 |
104 | While pyresparser parses most of the phone numbers correctly, there is a possibility of new patterns being added in near future. Hence, we can explicitly provide the regex required to parse the desired phone numbers. This can be done using
105 |
106 | ```python
107 | from pyresparser import ResumeParser
108 | data = ResumeParser('/path/to/resume/file', custom_regex='pattern').get_extracted_data()
109 | ```
--------------------------------------------------------------------------------
/export_to_csv.py:
--------------------------------------------------------------------------------
1 | from pyresparser.resume_parser import ResumeParser
2 | from rank_candidate import sort_candidates
3 | from datetime import datetime
4 | import pandas as pd
5 | import sys
6 | import csv
7 | import os
8 |
9 | result = []
10 | fields = ['Date', 'Skills', 'Name', 'Contact Number', 'Email ID', 'Current Company', 'Experience', 'College Name', 'Designation', 'Filename']
11 |
12 | for root, directories, filenames in os.walk(sys.argv[1]):
13 | for filename in filenames:
14 | try:
15 | file_name = os.path.join(root, filename)
16 | print('Extracting data from ' + file_name)
17 | parser = ResumeParser(file_name)
18 | data = parser.get_extracted_data()
19 | name = data.get('name')
20 | email = data.get('email')
21 | mobile_number = data.get('mobile_number')
22 | skills = ', '.join(data.get('skills')) if data.get('skills') else ''
23 | total_experience = str(data.get('total_experience'))
24 | experience = ' '.join(data.get('experience')) if data.get('experience') else ''
25 | company_names = ', '.join(data.get('company_names')) if data.get('company_names') else ''
26 | college_name = data.get('college_name')
27 | designation = ', '.join(data.get('designation')) if data.get('designation') else ''
28 |
29 | result.append(
30 | [
31 | datetime.today().strftime('%d-%B-%y'),
32 | skills,
33 | name,
34 | mobile_number,
35 | email,
36 | company_names,
37 | experience,
38 | college_name,
39 | designation,
40 | file_name
41 | ]
42 | )
43 | except:
44 | continue
45 |
46 | # writing to csv file
47 | df = pd.DataFrame(result, columns=fields)
48 |
49 | try:
50 | ranked_df = sort_candidates(sys.argv[2], df)
51 |
52 | # Sort candidates in descending order of score
53 | ranked_df.sort_values(by="Score", ascending=False, inplace=True)
54 | ranked_df.to_csv(os.path.join(root, (datetime.today().strftime('Extracted-Resumes-%d-%m-%y.csv'))), index=False)
55 | except IndexError:
56 | df.to_csv(os.path.join(root, (datetime.today().strftime('Extracted-Resumes-%d-%m-%y.csv'))), index=False)
57 |
58 | # with open(os.path.join(root, (datetime.today().strftime('%d-%m-%y.csv'))), 'w', encoding="utf-8") as csvfile:
59 | # try:
60 | # # creating a csv writer object
61 | # csvwriter = csv.writer(csvfile)
62 |
63 | # # writing the fields
64 | # csvwriter.writerow(fields)
65 |
66 | # # writing the data rows
67 | # csvwriter.writerows(result)
68 | # except:
69 | # print('Some of the file might be corrupted or is not supported by parser')
70 | # print(ranked_df)
71 |
--------------------------------------------------------------------------------
/mkdocs.yml:
--------------------------------------------------------------------------------
1 | site_name: pyresparser
2 | nav:
3 | - Home: index.md
4 | - CLI: cli.md
5 | repo_url: https://github.com/OmkarPathak/pyresparser
6 | site_author: Omkar Pathak
7 | google_analytics: ['UA-111548790-1', 'omkarpathak.in']
8 | markdown_extensions:
9 | - toc:
10 | permalink: True
11 | separator: "_"
--------------------------------------------------------------------------------
/pre_requisites.py:
--------------------------------------------------------------------------------
1 | import os
2 | import nltk
3 |
4 | # Install SpaCy Dependencies
5 | os.system('python -m pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz')
6 |
7 | # Install nltk Dependencies
8 | nltk.download('maxent_ne_chunker')
9 | nltk.download('words')
10 | nltk.download('stopwords')
11 | nltk.download('punkt')
12 | nltk.download('wordnet')
13 | nltk.download('averaged_perceptron_tagger')
--------------------------------------------------------------------------------
/pyresparser/CHANGELOG.md:
--------------------------------------------------------------------------------
1 | ## What will be available in 1.0.6
2 |
3 | - Exporting data in JSON
4 | - More robust phone number parsing (earlier it was only for Indian numbers, now internationals are supported as well)
5 | - Custom regex option for parsing phone numbers
6 | - Added banner for CLI
7 | - Address parsing will be available
8 | - .doc parsing bug resolved
9 | - Tests are made available for better code coverage
--------------------------------------------------------------------------------
/pyresparser/__init__.py:
--------------------------------------------------------------------------------
1 | from . import utils
2 | from . import constants
3 | from .resume_parser import ResumeParser
4 |
5 | __all__ = [
6 | 'utils',
7 | 'constants',
8 | 'ResumeParser'
9 | ]
10 |
--------------------------------------------------------------------------------
/pyresparser/command_line.py:
--------------------------------------------------------------------------------
1 | # Author: Omkar Pathak
2 |
3 | import os
4 | import json
5 | import argparse
6 | from pprint import pprint
7 | import io
8 | import sys
9 | import multiprocessing as mp
10 | import urllib
11 | from urllib.request import Request, urlopen
12 | from pyresparser import ResumeParser
13 |
14 |
15 | def print_cyan(text):
16 | print("\033[96m {}\033[00m" .format(text))
17 |
18 |
19 | class ResumeParserCli(object):
20 |
21 | def __init__(self):
22 | self.__parser = argparse.ArgumentParser()
23 | self.__parser.add_argument(
24 | '-f',
25 | '--file',
26 | help="resume file to be extracted")
27 | self.__parser.add_argument(
28 | '-d',
29 | '--directory',
30 | help="directory containing all the resumes to be extracted")
31 | self.__parser.add_argument(
32 | '-r',
33 | '--remotefile',
34 | help="remote path for resume file to be extracted")
35 | self.__parser.add_argument(
36 | '-re',
37 | '--custom-regex',
38 | help="custom regex for parsing mobile numbers")
39 | self.__parser.add_argument(
40 | '-sf',
41 | '--skillsfile',
42 | help="custom skills CSV file against \
43 | which skills are searched for")
44 | self.__parser.add_argument(
45 | '-e',
46 | '--export-format',
47 | help="the information export format (json)")
48 | self.__parser.add_argument(
49 | '-o',
50 | '--export-filepath',
51 | help="the export file path")
52 |
53 | def __banner(self):
54 | banner_string = r'''
55 | ____ __ __________ _________ ____ _____________ _____
56 | / __ \/ / / / ___/ _ \/ ___/ __ \/ __ `/ ___/ ___/ _ \/ ___/
57 | / /_/ / /_/ / / / __(__ ) /_/ / /_/ / / (__ ) __/ /
58 | / .___/\__, /_/ \___/____/ .___/\__,_/_/ /____/\___/_/
59 | /_/ /____/ /_/
60 |
61 | - By Omkar Pathak (omkarpathak27@gmail.com)
62 | '''
63 | print(banner_string)
64 |
65 | def export_data(self, exported_data, args):
66 | '''function to export resume data in specified format
67 | '''
68 | if args.export_format:
69 | if args.export_format == 'json':
70 | with open(args.export_filepath, 'w') as fd:
71 | json.dump(exported_data, fd, sort_keys=True, indent=4)
72 | abs_path = os.path.abspath(args.export_filepath)
73 | print('Data exported successfully at: ' + abs_path)
74 | sys.exit(0)
75 | else:
76 | return exported_data
77 |
78 | def extract_resume_data(self):
79 | args = self.__parser.parse_args()
80 |
81 | if args.export_format and not args.export_filepath:
82 | print('Please specify output file path using -o option')
83 | sys.exit(1)
84 |
85 | if args.remotefile:
86 | return self.export_data(
87 | self.__extract_from_remote_file(
88 | args.remotefile,
89 | args.skillsfile,
90 | args.custom_regex
91 | ),
92 | args
93 | )
94 |
95 | if args.file and not args.directory:
96 | return self.export_data(
97 | self.__extract_from_file(
98 | args.file,
99 | args.skillsfile,
100 | args.custom_regex
101 | ),
102 | args
103 | )
104 | elif args.directory and not args.file:
105 | return self.export_data(
106 | self.__extract_from_directory(
107 | args.directory,
108 | args.skillsfile,
109 | args.custom_regex
110 | ),
111 | args
112 | )
113 | else:
114 | self.__parser.print_help()
115 |
116 | def __extract_from_file(self, file, skills_file=None, custom_regex=None):
117 | if os.path.exists(file):
118 | print_cyan('Extracting data from: {}'.format(file))
119 | resume_parser = ResumeParser(file, skills_file, custom_regex)
120 | return [resume_parser.get_extracted_data()]
121 | else:
122 | print('File not found. Please provide a valid file name')
123 | sys.exit(1)
124 |
125 | def __extract_from_directory(
126 | self,
127 | directory,
128 | skills_file=None,
129 | custom_regex=None
130 | ):
131 | if os.path.exists(directory):
132 | pool = mp.Pool(mp.cpu_count())
133 |
134 | resumes = []
135 | for root, _, filenames in os.walk(directory):
136 | for filename in filenames:
137 | file = os.path.join(root, filename)
138 | resumes.append([file, skills_file, custom_regex])
139 | results = pool.map(resume_result_wrapper, resumes)
140 | pool.close()
141 | pool.join()
142 |
143 | return results
144 | else:
145 | print('Directory not found. Please provide a valid directory')
146 | sys.exit(1)
147 |
148 | def __extract_from_remote_file(
149 | self,
150 | remote_file,
151 | skills_file,
152 | custom_regex
153 | ):
154 | try:
155 | print_cyan('Extracting data from: {}'.format(remote_file))
156 | req = Request(remote_file, headers={'User-Agent': 'Mozilla/5.0'})
157 | webpage = urlopen(req).read()
158 | _file = io.BytesIO(webpage)
159 | _file.name = remote_file.split('/')[-1]
160 | resume_parser = ResumeParser(_file, skills_file, custom_regex)
161 | return [resume_parser.get_extracted_data()]
162 | except urllib.error.HTTPError:
163 | print('File not found. Please provide correct URL for resume file')
164 | sys.exit(1)
165 |
166 |
167 | def resume_result_wrapper(args):
168 | print_cyan('Extracting data from: {}'.format(args[0]))
169 | parser = ResumeParser(args[0], args[1], args[2])
170 | return parser.get_extracted_data()
171 |
172 |
173 | def main():
174 | cli_obj = ResumeParserCli()
175 | pprint(cli_obj.extract_resume_data())
176 |
--------------------------------------------------------------------------------
/pyresparser/constants.py:
--------------------------------------------------------------------------------
1 | from nltk.corpus import stopwords
2 |
3 | # Omkar Pathak
4 | NAME_PATTERN = [{'POS': 'PROPN'}, {'POS': 'PROPN'}]
5 |
6 | # Education (Upper Case Mandatory)
7 | EDUCATION = [
8 | 'BE', 'B.E.', 'B.E', 'BS', 'B.S', 'ME', 'M.E',
9 | 'M.E.', 'MS', 'M.S', 'BTECH', 'MTECH',
10 | 'SSC', 'HSC', 'CBSE', 'ICSE', 'X', 'XII'
11 | ]
12 |
13 | NOT_ALPHA_NUMERIC = r'[^a-zA-Z\d]'
14 |
15 | NUMBER = r'\d+'
16 |
17 | # For finding date ranges
18 | MONTHS_SHORT = r'''(jan)|(feb)|(mar)|(apr)|(may)|(jun)|(jul)
19 | |(aug)|(sep)|(oct)|(nov)|(dec)'''
20 | MONTHS_LONG = r'''(january)|(february)|(march)|(april)|(may)|(june)|(july)|
21 | (august)|(september)|(october)|(november)|(december)'''
22 | MONTH = r'(' + MONTHS_SHORT + r'|' + MONTHS_LONG + r')'
23 | YEAR = r'(((20|19)(\d{2})))'
24 |
25 | STOPWORDS = set(stopwords.words('english'))
26 |
27 | RESUME_SECTIONS_PROFESSIONAL = [
28 | 'experience',
29 | 'education',
30 | 'interests',
31 | 'professional experience',
32 | 'publications',
33 | 'skills',
34 | 'certifications',
35 | 'objective',
36 | 'career objective',
37 | 'summary',
38 | 'leadership'
39 | ]
40 |
41 | RESUME_SECTIONS_GRAD = [
42 | 'accomplishments',
43 | 'experience',
44 | 'education',
45 | 'interests',
46 | 'projects',
47 | 'professional experience',
48 | 'publications',
49 | 'skills',
50 | 'certifications',
51 | 'objective',
52 | 'career objective',
53 | 'summary',
54 | 'leadership'
55 | ]
56 |
--------------------------------------------------------------------------------
/pyresparser/custom_t.py:
--------------------------------------------------------------------------------
1 | import os
2 | import io
3 | import spacy
4 | import docx2txt
5 | import constants as cs
6 | from pdfminer.converter import TextConverter
7 | from pdfminer.pdfinterp import PDFPageInterpreter
8 | from pdfminer.pdfinterp import PDFResourceManager
9 | from pdfminer.layout import LAParams
10 | from pdfminer.pdfpage import PDFPage
11 | from pdfminer.pdfparser import PDFSyntaxError
12 |
13 |
14 | def extract_text_from_pdf(pdf_path):
15 | '''
16 | Helper function to extract the plain text from .pdf files
17 |
18 | :param pdf_path: path to PDF file to be extracted (remote or local)
19 | :return: iterator of string of extracted text
20 | '''
21 | # https://www.blog.pythonlibrary.org/2018/05/03/exporting-data-from-pdfs-with-python/
22 | if not isinstance(pdf_path, io.BytesIO):
23 | # extract text from local pdf file
24 | with open(pdf_path, 'rb') as fh:
25 | try:
26 | for page in PDFPage.get_pages(
27 | fh,
28 | caching=True,
29 | check_extractable=True
30 | ):
31 | resource_manager = PDFResourceManager()
32 | fake_file_handle = io.StringIO()
33 | converter = TextConverter(
34 | resource_manager,
35 | fake_file_handle,
36 | codec='utf-8',
37 | laparams=LAParams()
38 | )
39 | page_interpreter = PDFPageInterpreter(
40 | resource_manager,
41 | converter
42 | )
43 | page_interpreter.process_page(page)
44 |
45 | text = fake_file_handle.getvalue()
46 | yield text
47 |
48 | # close open handles
49 | converter.close()
50 | fake_file_handle.close()
51 | except PDFSyntaxError:
52 | return
53 | else:
54 | # extract text from remote pdf file
55 | try:
56 | for page in PDFPage.get_pages(
57 | pdf_path,
58 | caching=True,
59 | check_extractable=True
60 | ):
61 | resource_manager = PDFResourceManager()
62 | fake_file_handle = io.StringIO()
63 | converter = TextConverter(
64 | resource_manager,
65 | fake_file_handle,
66 | codec='utf-8',
67 | laparams=LAParams()
68 | )
69 | page_interpreter = PDFPageInterpreter(
70 | resource_manager,
71 | converter
72 | )
73 | page_interpreter.process_page(page)
74 |
75 | text = fake_file_handle.getvalue()
76 | yield text
77 |
78 | # close open handles
79 | converter.close()
80 | fake_file_handle.close()
81 | except PDFSyntaxError:
82 | return
83 |
84 |
85 | def get_number_of_pages(file_name):
86 | try:
87 | if isinstance(file_name, io.BytesIO):
88 | # for remote pdf file
89 | count = 0
90 | for page in PDFPage.get_pages(
91 | file_name,
92 | caching=True,
93 | check_extractable=True
94 | ):
95 | count += 1
96 | return count
97 | else:
98 | # for local pdf file
99 | if file_name.endswith('.pdf'):
100 | count = 0
101 | with open(file_name, 'rb') as fh:
102 | for page in PDFPage.get_pages(
103 | fh,
104 | caching=True,
105 | check_extractable=True
106 | ):
107 | count += 1
108 | return count
109 | else:
110 | return None
111 | except PDFSyntaxError:
112 | return None
113 |
114 |
115 | def extract_text_from_docx(doc_path):
116 | '''
117 | Helper function to extract plain text from .docx files
118 |
119 | :param doc_path: path to .docx file to be extracted
120 | :return: string of extracted text
121 | '''
122 | try:
123 | temp = docx2txt.process(doc_path)
124 | text = [line.replace('\t', ' ') for line in temp.split('\n') if line]
125 | return ' '.join(text)
126 | except KeyError:
127 | return ' '
128 |
129 |
130 | def extract_text_from_doc(doc_path):
131 | '''
132 | Helper function to extract plain text from .doc files
133 |
134 | :param doc_path: path to .doc file to be extracted
135 | :return: string of extracted text
136 | '''
137 | try:
138 | try:
139 | import textract
140 | except ImportError:
141 | return ' '
142 | temp = textract.process(doc_path).decode('utf-8')
143 | text = [line.replace('\t', ' ') for line in temp.split('\n') if line]
144 | return ' '.join(text)
145 | except KeyError:
146 | return ' '
147 |
148 |
149 | def extract_text(file_path, extension):
150 | '''
151 | Wrapper function to detect the file extension and call text
152 | extraction function accordingly
153 |
154 | :param file_path: path of file of which text is to be extracted
155 | :param extension: extension of file `file_name`
156 | '''
157 | text = ''
158 | if extension == '.pdf':
159 | for page in extract_text_from_pdf(file_path):
160 | text += ' ' + page
161 | elif extension == '.docx':
162 | text = extract_text_from_docx(file_path)
163 | elif extension == '.doc':
164 | text = extract_text_from_doc(file_path)
165 | return text
166 |
167 |
168 | def extract_entity_sections_grad(text):
169 | '''
170 | Helper function to extract all the raw text from sections of resume
171 | specifically for graduates and undergraduates
172 |
173 | :param text: Raw text of resume
174 | :return: dictionary of entities
175 | '''
176 | text_split = [i.strip() for i in text.split('\n')]
177 | # sections_in_resume = [i for i in text_split if i.lower() in sections]
178 | entities = {}
179 | key = False
180 | for phrase in text_split:
181 | if len(phrase) == 1:
182 | p_key = phrase
183 | else:
184 | p_key = set(phrase.lower().split()) & set(cs.RESUME_SECTIONS_GRAD)
185 | try:
186 | p_key = list(p_key)[0]
187 | except IndexError:
188 | pass
189 | if p_key in cs.RESUME_SECTIONS_GRAD:
190 | entities[p_key] = []
191 | key = p_key
192 | elif key and phrase.strip():
193 | entities[key].append(phrase)
194 | return entities
195 |
196 |
197 | nlp = spacy.load(os.path.dirname(os.path.abspath(__file__)))
198 | # resumes = '/home/omkarpathak27/Documents/GITS/resumeparser/resumes/'
199 | # text_raw = extract_text(resume, '.pdf')
200 | # text = ' '.join(text_raw.split())
201 | # print(text)
202 | # for resume in os.listdir(resumes):
203 | text_raw = extract_text(
204 | '/home/omkarpathak27/Downloads/OmkarResume.pdf',
205 | '.pdf'
206 | )
207 | # entity = extract_entity_sections_grad(text_raw)
208 | # if 'experience' in entity.keys():
209 | doc2 = nlp(text_raw)
210 | entities = {}
211 | for ent in doc2.ents:
212 | if ent.label_ not in entities.keys():
213 | entities[ent.label_] = [ent.text]
214 | else:
215 | entities[ent.label_].append(ent.text)
216 | for key in entities.keys():
217 | entities[key] = list(set(entities[key]))
218 | print(entities)
219 | # print(doc2.ents)
220 |
--------------------------------------------------------------------------------
/pyresparser/custom_train.py:
--------------------------------------------------------------------------------
1 | #!/usr/bin/env python
2 | # coding: utf8
3 | """Example of training an additional entity type
4 |
5 | This script shows how to add a new entity type to an existing pre-trained NER
6 | model. To keep the example short and simple, only four sentences are provided
7 | as examples. In practice, you'll need many more — a few hundred would be a
8 | good start. You will also likely need to mix in examples of other entity
9 | types, which might be obtained by running the entity recognizer over unlabelled
10 | sentences, and adding their annotations to the training set.
11 |
12 | The actual training is performed by looping over the examples, and calling
13 | `nlp.entity.update()`. The `update()` method steps through the words of the
14 | input. At each word, it makes a prediction. It then consults the annotations
15 | provided on the GoldParse instance, to see whether it was right. If it was
16 | wrong, it adjusts its weights so that the correct action will score higher
17 | next time.
18 |
19 | After training your model, you can save it to a directory. We recommend
20 | wrapping models as Python packages, for ease of deployment.
21 |
22 | For more details, see the documentation:
23 | * Training: https://spacy.io/usage/training
24 | * NER: https://spacy.io/usage/linguistic-features#named-entities
25 |
26 | Compatible with: spaCy v2.1.0+
27 | Last tested with: v2.1.0
28 | """
29 | from __future__ import unicode_literals
30 | from __future__ import print_function
31 | import re
32 | import plac
33 | import random
34 | from pathlib import Path
35 | import spacy
36 | import json
37 | import logging
38 |
39 |
40 | # new entity label
41 | LABEL = "COL_NAME"
42 |
43 | # training data
44 | # Note: If you're using an existing model, make sure to mix in examples of
45 | # other entity types that spaCy correctly recognized before. Otherwise, your
46 | # model might learn the new type, but "forget" what it previously knew.
47 | # https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting
48 |
49 | # training data
50 | # TRAIN_DATA = [
51 | # ("i study in maria college", {"entities": [(11, 24, LABEL)]}),
52 | # ("completed graduation from napier university (edinburgh,
53 | # united kingdom)", {"entities": [(26, 43, LABEL)]}),
54 | # ("studied in school of continuing and professional studies",
55 | # {"entities": [(11, 16, LABEL)]}),
56 | # ("studied at chinese university of hong kong", {"entities":
57 | # [(11, 29, LABEL)]}),
58 | # ("studied in University of Strathclyde", {"entities":
59 | # [(11, 37, LABEL)]}),
60 | # ]
61 |
62 |
63 | def trim_entity_spans(data: list) -> list:
64 | """Removes leading and trailing white spaces from entity spans.
65 |
66 | Args:
67 | data (list): The data to be cleaned in spaCy JSON format.
68 |
69 | Returns:
70 | list: The cleaned data.
71 | """
72 | invalid_span_tokens = re.compile(r'\s')
73 |
74 | cleaned_data = []
75 | for text, annotations in data:
76 | entities = annotations['entities']
77 | valid_entities = []
78 | for start, end, label in entities:
79 | valid_start = start
80 | valid_end = end
81 | while valid_start < len(text) and invalid_span_tokens.match(
82 | text[valid_start]):
83 | valid_start += 1
84 | while valid_end > 1 and invalid_span_tokens.match(
85 | text[valid_end - 1]):
86 | valid_end -= 1
87 | valid_entities.append([valid_start, valid_end, label])
88 | cleaned_data.append([text, {'entities': valid_entities}])
89 |
90 | return cleaned_data
91 |
92 |
93 | def convert_dataturks_to_spacy(dataturks_JSON_FilePath):
94 | try:
95 | training_data = []
96 | lines = []
97 | with open(dataturks_JSON_FilePath, 'r', encoding="utf8") as f:
98 | lines = f.readlines()
99 |
100 | for line in lines:
101 | data = json.loads(line)
102 | text = data['content']
103 | entities = []
104 | if data['annotation'] is not None:
105 | for annotation in data['annotation']:
106 | # only a single point in text annotation.
107 | point = annotation['points'][0]
108 | labels = annotation['label']
109 | # handle both list of labels or a single label.
110 | if not isinstance(labels, list):
111 | labels = [labels]
112 |
113 | for label in labels:
114 | # dataturks indices are both inclusive [start, end]
115 | # but spacy is not [start, end)
116 | entities.append((
117 | point['start'],
118 | point['end'] + 1,
119 | label
120 | ))
121 |
122 | training_data.append((text, {"entities": entities}))
123 | return training_data
124 | except Exception:
125 | logging.exception("Unable to process " + dataturks_JSON_FilePath)
126 | return None
127 |
128 |
129 | TRAIN_DATA = trim_entity_spans(convert_dataturks_to_spacy("traindata.json"))
130 |
131 |
132 | @plac.annotations(
133 | model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
134 | new_model_name=("New model name for model meta.", "option", "nm", str),
135 | output_dir=("Optional output directory", "option", "o", Path),
136 | n_iter=("Number of training iterations", "option", "n", int),
137 | )
138 | def main(
139 | model=None,
140 | new_model_name="training",
141 | output_dir='/home/omkarpathak27/Downloads/zipped/pyresparser/pyresparser',
142 | n_iter=30
143 | ):
144 | """Set up the pipeline and entity recognizer, and train the new entity."""
145 | random.seed(0)
146 | if model is not None:
147 | nlp = spacy.load(model) # load existing spaCy model
148 | print("Loaded model '%s'" % model)
149 | else:
150 | nlp = spacy.blank("en") # create blank Language class
151 | print("Created blank 'en' model")
152 | # Add entity recognizer to model if it's not in the pipeline
153 | # nlp.create_pipe works for built-ins that are registered with spaCy
154 |
155 | if "ner" not in nlp.pipe_names:
156 | print("Creating new pipe")
157 | ner = nlp.create_pipe("ner")
158 | nlp.add_pipe(ner, last=True)
159 |
160 | # otherwise, get it, so we can add labels to it
161 | else:
162 | ner = nlp.get_pipe("ner")
163 |
164 | # add labels
165 | for _, annotations in TRAIN_DATA:
166 | for ent in annotations.get('entities'):
167 | ner.add_label(ent[2])
168 |
169 | # if model is None or reset_weights:
170 | # optimizer = nlp.begin_training()
171 | # else:
172 | # optimizer = nlp.resume_training()
173 | move_names = list(ner.move_names)
174 | # get names of other pipes to disable them during training
175 | other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "ner"]
176 | with nlp.disable_pipes(*other_pipes): # only train NER
177 | optimizer = nlp.begin_training()
178 | # batch up the examples using spaCy's minibatch
179 | for itn in range(n_iter):
180 | print("Starting iteration " + str(itn))
181 | random.shuffle(TRAIN_DATA)
182 | losses = {}
183 | for text, annotations in TRAIN_DATA:
184 | nlp.update(
185 | [text], # batch of texts
186 | [annotations], # batch of annotations
187 | drop=0.2, # dropout - make it harder to memorise data
188 | sgd=optimizer, # callable to update weights
189 | losses=losses)
190 | print("Losses", losses)
191 |
192 | # test the trained model
193 | test_text = "Marathwada Mitra Mandals College of Engineering"
194 | doc = nlp(test_text)
195 | print("Entities in '%s'" % test_text)
196 | for ent in doc.ents:
197 | print(ent.label_, ent.text)
198 |
199 | # save model to output directory
200 | if output_dir is not None:
201 | output_dir = Path(output_dir)
202 | if not output_dir.exists():
203 | output_dir.mkdir()
204 | nlp.meta["name"] = new_model_name # rename model
205 | nlp.to_disk(output_dir)
206 | print("Saved model to", output_dir)
207 |
208 | # test the saved model
209 | print("Loading from", output_dir)
210 | nlp2 = spacy.load(output_dir)
211 | # Check the classes have loaded back consistently
212 | assert nlp2.get_pipe("ner").move_names == move_names
213 | doc2 = nlp2(test_text)
214 | for ent in doc2.ents:
215 | print(ent.label_, ent.text)
216 |
217 |
218 | if __name__ == "__main__":
219 | plac.call(main)
220 |
--------------------------------------------------------------------------------
/pyresparser/meta.json:
--------------------------------------------------------------------------------
1 | {"lang":"en","name":"training","version":"0.0.0","spacy_version":">=2.1.4","description":"","author":"","email":"","url":"","license":"","vectors":{"width":0,"vectors":0,"keys":0,"name":"spacy_pretrained_vectors"},"pipeline":["ner"]}
--------------------------------------------------------------------------------
/pyresparser/ner/cfg:
--------------------------------------------------------------------------------
1 | {
2 | "beam_width":1,
3 | "beam_density":0.0,
4 | "beam_update_prob":1.0,
5 | "cnn_maxout_pieces":3,
6 | "nr_class":101,
7 | "hidden_depth":1,
8 | "token_vector_width":96,
9 | "hidden_width":64,
10 | "maxout_pieces":2,
11 | "pretrained_vectors":null,
12 | "bilstm_depth":0
13 | }
--------------------------------------------------------------------------------
/pyresparser/ner/model:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/OmkarPathak/pyresparser/a66f25b583f2dd8dbd18f419321eed57b04a006e/pyresparser/ner/model
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/pyresparser/ner/moves:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/OmkarPathak/pyresparser/a66f25b583f2dd8dbd18f419321eed57b04a006e/pyresparser/ner/moves
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/pyresparser/requirements.txt:
--------------------------------------------------------------------------------
1 | attrs==19.1.0
2 | blis==0.2.4
3 | certifi==2019.6.16
4 | chardet==3.0.4
5 | cymem==2.0.2
6 | docx2txt==0.7
7 | idna==2.8
8 | jsonschema==3.0.1
9 | murmurhash==1.0.2
10 | nltk==3.4.5
11 | numpy==1.16.4
12 | pandas==0.24.2
13 | pdfminer.six==20181108
14 | plac==0.9.6
15 | preshed==2.0.1
16 | pycryptodome==3.8.2
17 | pyrsistent==0.15.2
18 | python-dateutil==2.8.0
19 | pytz==2019.1
20 | requests==2.22.0
21 | six==1.12.0
22 | sortedcontainers==2.1.0
23 | spacy==2.1.4
24 | srsly==0.0.7
25 | textract==1.6.1
26 | thinc==7.0.4
27 | tqdm==4.32.2
28 | urllib3==1.25.3
29 | wasabi==0.2.2
--------------------------------------------------------------------------------
/pyresparser/resume_parser.py:
--------------------------------------------------------------------------------
1 | # Author: Omkar Pathak
2 |
3 | import os
4 | import multiprocessing as mp
5 | import io
6 | import spacy
7 | import pprint
8 | from spacy.matcher import Matcher
9 | from . import utils
10 |
11 |
12 | class ResumeParser(object):
13 |
14 | def __init__(
15 | self,
16 | resume,
17 | skills_file=None,
18 | custom_regex=None
19 | ):
20 | nlp = spacy.load('en_core_web_sm')
21 | custom_nlp = spacy.load(os.path.dirname(os.path.abspath(__file__)))
22 | self.__skills_file = skills_file
23 | self.__custom_regex = custom_regex
24 | self.__matcher = Matcher(nlp.vocab)
25 | self.__details = {
26 | 'name': None,
27 | 'email': None,
28 | 'mobile_number': None,
29 | 'skills': None,
30 | 'college_name': None,
31 | 'degree': None,
32 | 'designation': None,
33 | 'experience': None,
34 | 'company_names': None,
35 | 'no_of_pages': None,
36 | 'total_experience': None,
37 | }
38 | self.__resume = resume
39 | if not isinstance(self.__resume, io.BytesIO):
40 | ext = os.path.splitext(self.__resume)[1].split('.')[1]
41 | else:
42 | ext = self.__resume.name.split('.')[1]
43 | self.__text_raw = utils.extract_text(self.__resume, '.' + ext)
44 | self.__text = ' '.join(self.__text_raw.split())
45 | self.__nlp = nlp(self.__text)
46 | self.__custom_nlp = custom_nlp(self.__text_raw)
47 | self.__noun_chunks = list(self.__nlp.noun_chunks)
48 | self.__get_basic_details()
49 |
50 | def get_extracted_data(self):
51 | return self.__details
52 |
53 | def __get_basic_details(self):
54 | cust_ent = utils.extract_entities_wih_custom_model(
55 | self.__custom_nlp
56 | )
57 | name = utils.extract_name(self.__nlp, matcher=self.__matcher)
58 | email = utils.extract_email(self.__text)
59 | mobile = utils.extract_mobile_number(self.__text, self.__custom_regex)
60 | skills = utils.extract_skills(
61 | self.__nlp,
62 | self.__noun_chunks,
63 | self.__skills_file
64 | )
65 | # edu = utils.extract_education(
66 | # [sent.string.strip() for sent in self.__nlp.sents]
67 | # )
68 | entities = utils.extract_entity_sections_grad(self.__text_raw)
69 |
70 | # extract name
71 | try:
72 | self.__details['name'] = cust_ent['Name'][0]
73 | except (IndexError, KeyError):
74 | self.__details['name'] = name
75 |
76 | # extract email
77 | self.__details['email'] = email
78 |
79 | # extract mobile number
80 | self.__details['mobile_number'] = mobile
81 |
82 | # extract skills
83 | self.__details['skills'] = skills
84 |
85 | # extract college name
86 | try:
87 | self.__details['college_name'] = entities['College Name']
88 | except KeyError:
89 | pass
90 |
91 | # extract education Degree
92 | try:
93 | self.__details['degree'] = cust_ent['Degree']
94 | except KeyError:
95 | pass
96 |
97 | # extract designation
98 | try:
99 | self.__details['designation'] = cust_ent['Designation']
100 | except KeyError:
101 | pass
102 |
103 | # extract company names
104 | try:
105 | self.__details['company_names'] = cust_ent['Companies worked at']
106 | except KeyError:
107 | pass
108 |
109 | try:
110 | self.__details['experience'] = entities['experience']
111 | try:
112 | exp = round(
113 | utils.get_total_experience(entities['experience']) / 12,
114 | 2
115 | )
116 | self.__details['total_experience'] = exp
117 | except KeyError:
118 | self.__details['total_experience'] = 0
119 | except KeyError:
120 | self.__details['total_experience'] = 0
121 | self.__details['no_of_pages'] = utils.get_number_of_pages(
122 | self.__resume
123 | )
124 | return
125 |
126 |
127 | def resume_result_wrapper(resume):
128 | parser = ResumeParser(resume)
129 | return parser.get_extracted_data()
130 |
131 |
132 | if __name__ == '__main__':
133 | pool = mp.Pool(mp.cpu_count())
134 |
135 | resumes = []
136 | data = []
137 | for root, directories, filenames in os.walk('resumes/'):
138 | for filename in filenames:
139 | file = os.path.join(root, filename)
140 | resumes.append(file)
141 |
142 | results = [
143 | pool.apply_async(
144 | resume_result_wrapper,
145 | args=(x,)
146 | ) for x in resumes
147 | ]
148 |
149 | results = [p.get() for p in results]
150 |
151 | pprint.pprint(results)
152 |
--------------------------------------------------------------------------------
/pyresparser/skills.csv:
--------------------------------------------------------------------------------
1 | technical 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learning,data analytics,predictive analytics,html,js,accounts payable,receivables,inventory controls,payroll,deposits,bank reconciliation,planning and enacting cash-flows,report preparation,financial models,financial controls,documentation,time management,schedules,benchmarking,future state assessment,business process re-engineering,as-is analysis,defining solutions and scope,gap analysis,role change,wireframing,prototyping,user stories,financial analysis/modeling,swot analysis,quickbooks,quicken,erp,enterprise resource planning,spanish,german,rest,soap,json,website,ui,ux,design,crm,cms,communication,coding,windows,servers,unix,linux,redhat,solaris,java,perl,vb script,xml,database,oracle,microsoft sql,sql,microsoft word,microsoft powerpoint,powerpoint,word,excel,visio,microsoft visio,microsoft excel,adobe,photoshop,hadoop,hbase,hive,zookeeper,openserver,auto cad,pl/sql,ruby on 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drivers license,electronics,pr,public relations,assembly,facebook,spreadsheets,recruit,proposal,data entry,hotel,ordering,branding,life cycle,real estate,relationship management,researching,process improvements,chemistry,saas,cad,sales experience,mathematics,customer-facing,audio,project management skills,six sigma,hospitality,mechanical engineering,auditing,employee relations,android,security clearance,licensing,fundraising,repairs,iso,market research,business strategy,pmp,data management,quality control,reconciliation,conversion,business analysis,financial analysis,ecommerce,client service,publishing,supervising,complex projects,key performance indicators,scrum,sports,e-commerce,journalism,d (programming language),data collection,higher education,marketing programs,financial management,business plans,user experience,client relationships,cloud,analytical skills,cisco,internal stakeholders,product marketing,regulatory requirements,itil,information security,aviation,supply chain management,industry experience,autocad,purchase orders,acquisitions,tv,instrumentation,strategic direction,law enforcement,call center,experiments,technical skills,human resource,business cases,build relationships,invoicing,support services,marketing strategy,operating systems,biology,start-up,electrical engineering,workflows,routing,non-profit,marketing plans,due diligence,business management,iphone,architectures,reconcile,dynamic environment,external partners,asset management,emea,intranet,sops,sas,digital media,prospecting,financial reporting,project delivery,operational excellence,standard operating procedures,technical knowledge,on-call,talent management,stakeholder management,tablets,analyze data,financial statements,microsoft office suite,fitness,case management,value proposition,industry trends,rfp,broadcast,portfolio management,fabrication,financial performance,customer requirements,psychology,marketing materials,resource management,physics,mortgage,development activities,end user,business planning,root cause,analysis,leadership development,relationship building,sdlc,on-boarding,quality standards,regulatory compliance,aws,kpi,status reports,product line,drafting,phone calls,product knowledge,business stakeholders,technical issues,admissions,supervisory experience,usability,pharmacy,commissioning,project plan,ms excel,fda,test plans,variances,financing,travel arrangements,internal customers,medical device,counsel,inventory management,performance metrics,lighting,outsourcing,performance improvement,management consulting,graphic design,transport,information management,.net,startup,matrix,front-end,project planning,business systems,accounts receivable,public health,hris,instructional design,in-store,employee engagement,cost effective,sales management,api,adobe creative suite,twitter,program development,event planning,cash flow,strategic plans,vendor management,trade shows,hotels,segmentation,contract management,gis,talent acquisition,photography,internal communications,client services,ibm,financial reports,product quality,beverage,strong analytical skills,underwriting,cpr,mining,sales goals,chemicals,scripting,migration,software engineering,mis,therapeutic,general ledger,ms project,standardization,retention,spelling,media relations,os,daily operations,immigration,product design,etl,field sales,driving record,peoplesoft,benchmark,quality management,apis,test cases,internal controls,telecom,business issues,research projects,data quality,strategic initiatives,office software,cfa,co-op,big data,journal entries,vmware,help desk,statistical analysis,datasets,alliances,solidworks,prototype,lan,sci,budget management,rfps,flex,gaap,experimental,cpg,information system,customer facing,process development,web services,international,travel,revenue growth,software development life cycle,operations management,computer applications,risk assessments,sales operations,raw materials,internal audit,physical security,sql server,affiliate,computer software,manage projects,business continuity,litigation,it infrastructure,cost reduction,small business,annual budget,ios,html5,real-time,consulting experience,circuits,risk assessment,cross-functional team,public policy,analyzing data,consulting services,google drive,ad words,pay per click,email,db2,expense tracking,reports,wordpress,yoast,ghostwriting,corel draw,automated billing,system,customer management,debugging,system administration,network configuration,software installation,security,tech support,updates,tci/ip,dhcp,wan/lan,ubuntu,virtualized networks,network automation,cloud management,ai,salesforce,mango db,math,calculus,product launch,mvp
2 |
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/pyresparser/tokenizer:
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/pyresparser/utils.py:
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1 | # Author: Omkar Pathak
2 |
3 | import io
4 | import os
5 | import re
6 | import nltk
7 | import pandas as pd
8 | import docx2txt
9 | from datetime import datetime
10 | from dateutil import relativedelta
11 | from . import constants as cs
12 | from pdfminer.converter import TextConverter
13 | from pdfminer.pdfinterp import PDFPageInterpreter
14 | from pdfminer.pdfinterp import PDFResourceManager
15 | from pdfminer.layout import LAParams
16 | from pdfminer.pdfpage import PDFPage
17 | from pdfminer.pdfparser import PDFSyntaxError
18 | from nltk.stem import WordNetLemmatizer
19 | from nltk.corpus import stopwords
20 |
21 |
22 | def extract_text_from_pdf(pdf_path):
23 | '''
24 | Helper function to extract the plain text from .pdf files
25 |
26 | :param pdf_path: path to PDF file to be extracted (remote or local)
27 | :return: iterator of string of extracted text
28 | '''
29 | # https://www.blog.pythonlibrary.org/2018/05/03/exporting-data-from-pdfs-with-python/
30 | if not isinstance(pdf_path, io.BytesIO):
31 | # extract text from local pdf file
32 | with open(pdf_path, 'rb') as fh:
33 | try:
34 | for page in PDFPage.get_pages(
35 | fh,
36 | caching=True,
37 | check_extractable=True
38 | ):
39 | resource_manager = PDFResourceManager()
40 | fake_file_handle = io.StringIO()
41 | converter = TextConverter(
42 | resource_manager,
43 | fake_file_handle,
44 | codec='utf-8',
45 | laparams=LAParams()
46 | )
47 | page_interpreter = PDFPageInterpreter(
48 | resource_manager,
49 | converter
50 | )
51 | page_interpreter.process_page(page)
52 |
53 | text = fake_file_handle.getvalue()
54 | yield text
55 |
56 | # close open handles
57 | converter.close()
58 | fake_file_handle.close()
59 | except PDFSyntaxError:
60 | return
61 | else:
62 | # extract text from remote pdf file
63 | try:
64 | for page in PDFPage.get_pages(
65 | pdf_path,
66 | caching=True,
67 | check_extractable=True
68 | ):
69 | resource_manager = PDFResourceManager()
70 | fake_file_handle = io.StringIO()
71 | converter = TextConverter(
72 | resource_manager,
73 | fake_file_handle,
74 | codec='utf-8',
75 | laparams=LAParams()
76 | )
77 | page_interpreter = PDFPageInterpreter(
78 | resource_manager,
79 | converter
80 | )
81 | page_interpreter.process_page(page)
82 |
83 | text = fake_file_handle.getvalue()
84 | yield text
85 |
86 | # close open handles
87 | converter.close()
88 | fake_file_handle.close()
89 | except PDFSyntaxError:
90 | return
91 |
92 |
93 | def get_number_of_pages(file_name):
94 | try:
95 | if isinstance(file_name, io.BytesIO):
96 | # for remote pdf file
97 | count = 0
98 | for page in PDFPage.get_pages(
99 | file_name,
100 | caching=True,
101 | check_extractable=True
102 | ):
103 | count += 1
104 | return count
105 | else:
106 | # for local pdf file
107 | if file_name.endswith('.pdf'):
108 | count = 0
109 | with open(file_name, 'rb') as fh:
110 | for page in PDFPage.get_pages(
111 | fh,
112 | caching=True,
113 | check_extractable=True
114 | ):
115 | count += 1
116 | return count
117 | else:
118 | return None
119 | except PDFSyntaxError:
120 | return None
121 |
122 |
123 | def extract_text_from_docx(doc_path):
124 | '''
125 | Helper function to extract plain text from .docx files
126 |
127 | :param doc_path: path to .docx file to be extracted
128 | :return: string of extracted text
129 | '''
130 | try:
131 | temp = docx2txt.process(doc_path)
132 | text = [line.replace('\t', ' ') for line in temp.split('\n') if line]
133 | return ' '.join(text)
134 | except KeyError:
135 | return ' '
136 |
137 |
138 | def extract_text_from_doc(doc_path):
139 | '''
140 | Helper function to extract plain text from .doc files
141 |
142 | :param doc_path: path to .doc file to be extracted
143 | :return: string of extracted text
144 | '''
145 | try:
146 | try:
147 | import textract
148 | except ImportError:
149 | return ' '
150 | text = textract.process(doc_path).decode('utf-8')
151 | return text
152 | except KeyError:
153 | return ' '
154 |
155 |
156 | def extract_text(file_path, extension):
157 | '''
158 | Wrapper function to detect the file extension and call text
159 | extraction function accordingly
160 |
161 | :param file_path: path of file of which text is to be extracted
162 | :param extension: extension of file `file_name`
163 | '''
164 | text = ''
165 | if extension == '.pdf':
166 | for page in extract_text_from_pdf(file_path):
167 | text += ' ' + page
168 | elif extension == '.docx':
169 | text = extract_text_from_docx(file_path)
170 | elif extension == '.doc':
171 | text = extract_text_from_doc(file_path)
172 | return text
173 |
174 |
175 | def extract_entity_sections_grad(text):
176 | '''
177 | Helper function to extract all the raw text from sections of
178 | resume specifically for graduates and undergraduates
179 |
180 | :param text: Raw text of resume
181 | :return: dictionary of entities
182 | '''
183 | text_split = [i.strip() for i in text.split('\n')]
184 | # sections_in_resume = [i for i in text_split if i.lower() in sections]
185 | entities = {}
186 | key = False
187 | for phrase in text_split:
188 | if len(phrase) == 1:
189 | p_key = phrase
190 | else:
191 | p_key = set(phrase.lower().split()) & set(cs.RESUME_SECTIONS_GRAD)
192 | try:
193 | p_key = list(p_key)[0]
194 | except IndexError:
195 | pass
196 | if p_key in cs.RESUME_SECTIONS_GRAD:
197 | entities[p_key] = []
198 | key = p_key
199 | elif key and phrase.strip():
200 | entities[key].append(phrase)
201 |
202 | # entity_key = False
203 | # for entity in entities.keys():
204 | # sub_entities = {}
205 | # for entry in entities[entity]:
206 | # if u'\u2022' not in entry:
207 | # sub_entities[entry] = []
208 | # entity_key = entry
209 | # elif entity_key:
210 | # sub_entities[entity_key].append(entry)
211 | # entities[entity] = sub_entities
212 |
213 | # pprint.pprint(entities)
214 |
215 | # make entities that are not found None
216 | # for entity in cs.RESUME_SECTIONS:
217 | # if entity not in entities.keys():
218 | # entities[entity] = None
219 | return entities
220 |
221 |
222 | def extract_entities_wih_custom_model(custom_nlp_text):
223 | '''
224 | Helper function to extract different entities with custom
225 | trained model using SpaCy's NER
226 |
227 | :param custom_nlp_text: object of `spacy.tokens.doc.Doc`
228 | :return: dictionary of entities
229 | '''
230 | entities = {}
231 | for ent in custom_nlp_text.ents:
232 | if ent.label_ not in entities.keys():
233 | entities[ent.label_] = [ent.text]
234 | else:
235 | entities[ent.label_].append(ent.text)
236 | for key in entities.keys():
237 | entities[key] = list(set(entities[key]))
238 | return entities
239 |
240 |
241 | def get_total_experience(experience_list):
242 | '''
243 | Wrapper function to extract total months of experience from a resume
244 |
245 | :param experience_list: list of experience text extracted
246 | :return: total months of experience
247 | '''
248 | exp_ = []
249 | for line in experience_list:
250 | experience = re.search(
251 | r'(?P\w+.\d+)\s*(\D|to)\s*(?P\w+.\d+|present)',
252 | line,
253 | re.I
254 | )
255 | if experience:
256 | exp_.append(experience.groups())
257 | total_exp = sum(
258 | [get_number_of_months_from_dates(i[0], i[2]) for i in exp_]
259 | )
260 | total_experience_in_months = total_exp
261 | return total_experience_in_months
262 |
263 |
264 | def get_number_of_months_from_dates(date1, date2):
265 | '''
266 | Helper function to extract total months of experience from a resume
267 |
268 | :param date1: Starting date
269 | :param date2: Ending date
270 | :return: months of experience from date1 to date2
271 | '''
272 | if date2.lower() == 'present':
273 | date2 = datetime.now().strftime('%b %Y')
274 | try:
275 | if len(date1.split()[0]) > 3:
276 | date1 = date1.split()
277 | date1 = date1[0][:3] + ' ' + date1[1]
278 | if len(date2.split()[0]) > 3:
279 | date2 = date2.split()
280 | date2 = date2[0][:3] + ' ' + date2[1]
281 | except IndexError:
282 | return 0
283 | try:
284 | date1 = datetime.strptime(str(date1), '%b %Y')
285 | date2 = datetime.strptime(str(date2), '%b %Y')
286 | months_of_experience = relativedelta.relativedelta(date2, date1)
287 | months_of_experience = (months_of_experience.years
288 | * 12 + months_of_experience.months)
289 | except ValueError:
290 | return 0
291 | return months_of_experience
292 |
293 |
294 | def extract_entity_sections_professional(text):
295 | '''
296 | Helper function to extract all the raw text from sections of
297 | resume specifically for professionals
298 |
299 | :param text: Raw text of resume
300 | :return: dictionary of entities
301 | '''
302 | text_split = [i.strip() for i in text.split('\n')]
303 | entities = {}
304 | key = False
305 | for phrase in text_split:
306 | if len(phrase) == 1:
307 | p_key = phrase
308 | else:
309 | p_key = set(phrase.lower().split()) \
310 | & set(cs.RESUME_SECTIONS_PROFESSIONAL)
311 | try:
312 | p_key = list(p_key)[0]
313 | except IndexError:
314 | pass
315 | if p_key in cs.RESUME_SECTIONS_PROFESSIONAL:
316 | entities[p_key] = []
317 | key = p_key
318 | elif key and phrase.strip():
319 | entities[key].append(phrase)
320 | return entities
321 |
322 |
323 | def extract_email(text):
324 | '''
325 | Helper function to extract email id from text
326 |
327 | :param text: plain text extracted from resume file
328 | '''
329 | email = re.findall(r"([^@|\s]+@[^@]+\.[^@|\s]+)", text)
330 | if email:
331 | try:
332 | return email[0].split()[0].strip(';')
333 | except IndexError:
334 | return None
335 |
336 |
337 | def extract_name(nlp_text, matcher):
338 | '''
339 | Helper function to extract name from spacy nlp text
340 |
341 | :param nlp_text: object of `spacy.tokens.doc.Doc`
342 | :param matcher: object of `spacy.matcher.Matcher`
343 | :return: string of full name
344 | '''
345 | pattern = [cs.NAME_PATTERN]
346 |
347 | matcher.add('NAME', None, *pattern)
348 |
349 | matches = matcher(nlp_text)
350 |
351 | for _, start, end in matches:
352 | span = nlp_text[start:end]
353 | if 'name' not in span.text.lower():
354 | return span.text
355 |
356 |
357 | def extract_mobile_number(text, custom_regex=None):
358 | '''
359 | Helper function to extract mobile number from text
360 |
361 | :param text: plain text extracted from resume file
362 | :return: string of extracted mobile numbers
363 | '''
364 | # Found this complicated regex on :
365 | # https://zapier.com/blog/extract-links-email-phone-regex/
366 | # mob_num_regex = r'''(?:(?:\+?([1-9]|[0-9][0-9]|
367 | # [0-9][0-9][0-9])\s*(?:[.-]\s*)?)?(?:\(\s*([2-9]1[02-9]|
368 | # [2-9][02-8]1|[2-9][02-8][02-9])\s*\)|([0-9][1-9]|
369 | # [0-9]1[02-9]|[2-9][02-8]1|
370 | # [2-9][02-8][02-9]))\s*(?:[.-]\s*)?)?([2-9]1[02-9]|
371 | # [2-9][02-9]1|[2-9][02-9]{2})\s*(?:[.-]\s*)?([0-9]{7})
372 | # (?:\s*(?:#|x\.?|ext\.?|
373 | # extension)\s*(\d+))?'''
374 | if not custom_regex:
375 | mob_num_regex = r'''(\d{3}[-\.\s]??\d{3}[-\.\s]??\d{4}|\(\d{3}\)
376 | [-\.\s]*\d{3}[-\.\s]??\d{4}|\d{3}[-\.\s]??\d{4})'''
377 | phone = re.findall(re.compile(mob_num_regex), text)
378 | else:
379 | phone = re.findall(re.compile(custom_regex), text)
380 | if phone:
381 | number = ''.join(phone[0])
382 | return number
383 |
384 |
385 | def extract_skills(nlp_text, noun_chunks, skills_file=None):
386 | '''
387 | Helper function to extract skills from spacy nlp text
388 |
389 | :param nlp_text: object of `spacy.tokens.doc.Doc`
390 | :param noun_chunks: noun chunks extracted from nlp text
391 | :return: list of skills extracted
392 | '''
393 | tokens = [token.text for token in nlp_text if not token.is_stop]
394 | if not skills_file:
395 | data = pd.read_csv(
396 | os.path.join(os.path.dirname(__file__), 'skills.csv')
397 | )
398 | else:
399 | data = pd.read_csv(skills_file)
400 | skills = list(data.columns.values)
401 | skillset = []
402 | # check for one-grams
403 | for token in tokens:
404 | if token.lower() in skills:
405 | skillset.append(token)
406 |
407 | # check for bi-grams and tri-grams
408 | for token in noun_chunks:
409 | token = token.text.lower().strip()
410 | if token in skills:
411 | skillset.append(token)
412 | return [i.capitalize() for i in set([i.lower() for i in skillset])]
413 |
414 |
415 | def cleanup(token, lower=True):
416 | if lower:
417 | token = token.lower()
418 | return token.strip()
419 |
420 |
421 | def extract_education(nlp_text):
422 | '''
423 | Helper function to extract education from spacy nlp text
424 |
425 | :param nlp_text: object of `spacy.tokens.doc.Doc`
426 | :return: tuple of education degree and year if year if found
427 | else only returns education degree
428 | '''
429 | edu = {}
430 | # Extract education degree
431 | try:
432 | for index, text in enumerate(nlp_text):
433 | for tex in text.split():
434 | tex = re.sub(r'[?|$|.|!|,]', r'', tex)
435 | if tex.upper() in cs.EDUCATION and tex not in cs.STOPWORDS:
436 | edu[tex] = text + nlp_text[index + 1]
437 | except IndexError:
438 | pass
439 |
440 | # Extract year
441 | education = []
442 | for key in edu.keys():
443 | year = re.search(re.compile(cs.YEAR), edu[key])
444 | if year:
445 | education.append((key, ''.join(year.group(0))))
446 | else:
447 | education.append(key)
448 | return education
449 |
450 |
451 | def extract_experience(resume_text):
452 | '''
453 | Helper function to extract experience from resume text
454 |
455 | :param resume_text: Plain resume text
456 | :return: list of experience
457 | '''
458 | wordnet_lemmatizer = WordNetLemmatizer()
459 | stop_words = set(stopwords.words('english'))
460 |
461 | # word tokenization
462 | word_tokens = nltk.word_tokenize(resume_text)
463 |
464 | # remove stop words and lemmatize
465 | filtered_sentence = [
466 | w for w in word_tokens if w not
467 | in stop_words and wordnet_lemmatizer.lemmatize(w)
468 | not in stop_words
469 | ]
470 | sent = nltk.pos_tag(filtered_sentence)
471 |
472 | # parse regex
473 | cp = nltk.RegexpParser('P: {+}')
474 | cs = cp.parse(sent)
475 |
476 | # for i in cs.subtrees(filter=lambda x: x.label() == 'P'):
477 | # print(i)
478 |
479 | test = []
480 |
481 | for vp in list(
482 | cs.subtrees(filter=lambda x: x.label() == 'P')
483 | ):
484 | test.append(" ".join([
485 | i[0] for i in vp.leaves()
486 | if len(vp.leaves()) >= 2])
487 | )
488 |
489 | # Search the word 'experience' in the chunk and
490 | # then print out the text after it
491 | x = [
492 | x[x.lower().index('experience') + 10:]
493 | for i, x in enumerate(test)
494 | if x and 'experience' in x.lower()
495 | ]
496 | return x
497 |
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/rank_candidate.py:
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1 | from multiprocessing import cpu_count, Pool
2 | from typing import Set
3 | from pyresparser.utils import extract_skills
4 | import pandas as pd
5 | import spacy
6 |
7 |
8 | def get_candidate_score(
9 | job_skill_count: int,
10 | job_skills: Set[str],
11 | candidate_skills: Set[str]
12 | ) -> float:
13 | """
14 | This function compares the candidate skills with job description skills
15 | and rates the candidate based on the fraction of common skills
16 |
17 | :param job_skill_count: Number of job skills
18 | :param job_skills: Set of job skills
19 | :param candidate_skills: Set of candidate skills
20 | :return: Candidate score
21 | """
22 |
23 | common_skills = job_skills.intersection(candidate_skills)
24 | candidate_score = float(len(common_skills)) / job_skill_count
25 | return candidate_score * 100
26 |
27 |
28 | def get_candidate_score_wrapper(args: tuple) -> float:
29 | """
30 | A wrapper function to de-structure the tuple of arguments
31 | for multiprocessing.Pool.map()
32 |
33 | :param args: Tuple of arguments for the wrapped function
34 | :return: Result of the wrapped function
35 | """
36 | return get_candidate_score(*args)
37 |
38 |
39 | def sort_candidates(
40 | job_desc_text: str,
41 | candidates_df: pd.DataFrame
42 | ) -> pd.DataFrame:
43 | """
44 | This function compares the skills of a number of candidates
45 | against the skills required of a given job description
46 |
47 | :param job_desc_text: Job description text
48 | :param candidates_df: DataFrame containing candidate details and skills
49 | :return: DataFrame with candidates sorted as per their match
50 | with the given job description
51 | """
52 |
53 | # Get the list of required skills from the Job description
54 | # and convert them to a set
55 | nlp = spacy.load("en_core_web_sm")
56 | doc = nlp(job_desc_text)
57 | job_skills = set([
58 | skill.lower() for skill in extract_skills(doc, doc.noun_chunks)
59 | ])
60 | job_skill_count = len(job_skills)
61 |
62 | # Get the candidate skills from the dataframe and create
63 | # a set of skills for each candidate
64 | candidates_skills = candidates_df["Skills"].values.tolist()
65 | candidates_skills = [
66 | set([
67 | skill.strip().lower() for skill in skill_list.split(",")
68 | ])
69 | for skill_list in candidates_skills
70 | ]
71 |
72 | # Use multiprocessing to evaluate multiple candidates for
73 | # a given job in parallel
74 | num_executors = cpu_count()
75 | processing_data = [
76 | (job_skill_count, job_skills, person_skills)
77 | for person_skills in candidates_skills
78 | ]
79 | with Pool(num_executors) as process_pool:
80 | candidates_df["Score"] = process_pool.map(get_candidate_score_wrapper, processing_data)
81 |
82 | return candidates_df
83 |
84 |
85 | if __name__ == '__main__':
86 | # Read candidate details
87 | df = pd.read_csv("resumes.csv", usecols=["Email", "Skills"])
88 |
89 | try:
90 | with open("sample_job_description.txt", "r") as fp:
91 | job_description = fp.read()
92 | except FileNotFoundError:
93 | job_description = None
94 |
95 | if job_description:
96 | ranked_df = sort_candidates(job_description, df)
97 | # Sort candidates in descending order of score
98 | ranked_df.sort_values(by="Score", ascending=False, inplace=True)
99 | ranked_df.to_csv("ranked.csv", index=False)
100 |
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/requirements.txt:
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1 | attrs==19.1.0
2 | blis==0.2.4
3 | certifi==2019.6.16
4 | chardet==3.0.4
5 | cymem==2.0.2
6 | docx2txt==0.7
7 | idna==2.8
8 | jsonschema==3.0.1
9 | murmurhash==1.0.2
10 | nltk==3.4.5
11 | numpy==1.16.4
12 | pandas==0.24.2
13 | pdfminer.six==20181108
14 | plac==0.9.6
15 | preshed==2.0.1
16 | pycryptodome==3.8.2
17 | pyrsistent==0.15.2
18 | python-dateutil==2.8.0
19 | pytz==2019.1
20 | requests==2.22.0
21 | six==1.12.0
22 | sortedcontainers==2.1.0
23 | spacy==2.1.4
24 | srsly==0.0.7
25 | textract==1.6.1
26 | thinc==7.0.4
27 | tqdm==4.32.2
28 | urllib3==1.25.3
29 | wasabi==0.2.2
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/setup.py:
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1 | from setuptools import setup, find_packages
2 | from os import path
3 |
4 | here = path.abspath(path.dirname(__file__))
5 |
6 | setup(
7 | name='pyresparser',
8 | version='1.0.6',
9 | description='A simple resume parser used for extracting information from resumes',
10 | long_description=open('README.rst').read(),
11 | url='https://github.com/OmkarPathak/pyresparser',
12 | author='Omkar Pathak',
13 | author_email='omkarpathak27@gmail.com',
14 | license='GPL-3.0',
15 | include_package_data=True,
16 | classifiers=[
17 | 'Intended Audience :: Developers',
18 | 'Topic :: Software Development :: Libraries',
19 | 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
20 | 'Programming Language :: Python :: 3',
21 | 'Programming Language :: Python :: 3.3',
22 | 'Programming Language :: Python :: 3.4',
23 | 'Programming Language :: Python :: 3.5',
24 | 'Programming Language :: Python :: 3.6',
25 | 'Programming Language :: Python :: 3.7',
26 | ],
27 | packages=find_packages(),
28 | install_requires=[
29 | 'attrs>=19.1.0',
30 | 'blis>=0.2.4',
31 | 'certifi>=2019.6.16',
32 | 'chardet>=3.0.4',
33 | 'cymem>=2.0.2',
34 | 'docx2txt>=0.7',
35 | 'idna>=2.8',
36 | 'jsonschema>=3.0.1',
37 | 'nltk>=3.4.3',
38 | 'numpy>=1.16.4',
39 | 'pandas>=0.24.2',
40 | 'pdfminer.six>=20181108',
41 | 'preshed>=2.0.1',
42 | 'pycryptodome>=3.8.2',
43 | 'pyrsistent>=0.15.2',
44 | 'python-dateutil>=2.8.0',
45 | 'pytz>=2019.1',
46 | 'requests>=2.22.0',
47 | 'six>=1.12.0',
48 | 'sortedcontainers>=2.1.0',
49 | 'spacy>=2.1.4',
50 | 'srsly>=0.0.7',
51 | 'thinc>=7.0.4',
52 | 'tqdm>=4.32.2',
53 | 'urllib3>=1.25.3',
54 | 'wasabi>=0.2.2'
55 | ],
56 | zip_safe=False,
57 | entry_points = {
58 | 'console_scripts': ['pyresparser=pyresparser.command_line:main'],
59 | }
60 | )
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/test_name.py:
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1 | import os
2 | import argparse
3 | from pprint import pprint
4 | import io
5 | import multiprocessing as mp
6 | import urllib
7 | from urllib.request import Request, urlopen
8 | from pyresparser import ResumeParser
9 |
10 | def get_remote_data():
11 | try:
12 | remote_file = 'https://www.omkarpathak.in/downloads/OmkarResume.pdf'
13 | print('Extracting data from: {}'.format(remote_file))
14 | req = Request(remote_file, headers={'User-Agent': 'Mozilla/5.0'})
15 | webpage = urlopen(req).read()
16 | _file = io.BytesIO(webpage)
17 | _file.name = remote_file.split('/')[-1]
18 | resume_parser = ResumeParser(_file)
19 | return [resume_parser.get_extracted_data()]
20 | except urllib.error.HTTPError:
21 | return 'File not found. Please provide correct URL for resume file.'
22 |
23 | def get_local_data():
24 | data = ResumeParser('OmkarResume.pdf').get_extracted_data()
25 | return data
26 |
27 | def test_remote_name():
28 | data = get_remote_data()
29 | assert 'Omkar Pathak' == data[0]['name']
30 |
31 | def test_remote_phone_number():
32 | data = get_remote_data()
33 | assert '8087996634' == data[0]['mobile_number']
34 |
35 | def test_local_name():
36 | data = get_local_data()
37 | assert 'Omkar Pathak' == data['name']
38 |
39 | def test_local_phone_number():
40 | data = get_local_data()
41 | assert '8087996634' == data['mobile_number']
42 |
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