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linguine-python

Overview

linguine-python is a Python web server for use in the Linguine natural language processing workbench. The server accepts requests in a JSON format, and performs text analysis operations as they are implemented in Python. The implemented operations can be found in /linguine/ops.

Adding an operation

To add a new analysis or cleanup operation to this project:

  1. Create a new Python file in /linguine/ops.
  2. Fill the operation in using the template below.
  3. Import the op in /linguine/operation_builder.py and add the operation to the get_operation_handler function body.
  4. Any unit tests should go in /test.

Operation template

# A sample cleanup operation# Used to modify the existing text in a corpus set for easier analysis.# Data will be passed to the op in the form of a collection of corpora.# The op transforms the contents of each corpus and returns the results.classFooOp:
defrun(self, data):
forcorpusindata:
corpus.contents=Bar(corpus.contents)
returndata
# A sample analysis operation# Used to generate meaningful data from a corpus set.# Data will be passed to the op in the form of a collection of corpora.# The op runs analysis on each corpus (or the set as a whole).# It builds a set of results which are then returned in place of corpora.classFooOp:
defrun(self, data):
results= []
forcorpusindata:
results.append({'corpus_id': corpus.id, 'bar': Bar(corpus.contents)})
returnresults

API

  • HTTP POST '/': It expects a JSON payload in the provided format.
{"corpora_ids": ["12345"],//Collection of corpora to pipe into analysis"cleanup": ["stopwords"],//Cleanup steps to add"operation": "nlp-relation",//Type of analysis to be preformed"tokenizer": "",//Tokenizer used (if required)"library": "",//Library associated w/ analysis (if required)"transaction_id": "",//(Field to be populated by linguine-python)"analysis_name": "Relation Extraction (Stanford CoreNLP)",//Name to display in text fields"time_created": 1461342250445,//Used to calculate ETA of analyses"user_id": "12345"//Unique identifier of user who created analysis}

Currently implemented operations

  • Term Frequency
  • Part of Speech Tagging
  • Sentiment
  • Named Entity Recognition
  • Relation Extraction
  • Coreference Resolution

Dependencies

  • Python 3.9.1 or newer (Requires implementation of Future object)
  • MongoDB
  • NLTK Punkt model
  • Stanford CoreNLP Pywrapper (Installation instructions can be found here).

Development

  1. Install Stanford CoreNLP module following docs here.
  2. sudo pip install -r requirements.txt
  3. python -m textblob.download_corpora
  4. python -m linguine.webserver --port <port> --database <database>

Note:

  • For Linguine 1: port: 5555, database: linguine-development
  • For Linguine 2: port: 5551, database: linguine2-development

To run tests:

  1. sudo pip install -r requirements.txt
  2. pytest test

Note: running the program from a directory other than the linguine-python root directory will cause directory linking errors.

About

Linguine python server.

nlp.rit.edu/linguine/

Resources

Readme
MIT license
Activity
Custom properties

Stars

1 star

Watchers

1 watching

Forks

1 fork
Report repository

Releases

Packages

Used by

Contributors

Languages

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var __re = new RegExp('^' + "github\\.com" + '
GitHub - ritlinguine/linguine-python: Linguine python server. · GitHub
Skip to content
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Build-Indicator
linguine-python
Overview
linguine-python is a Python web server for use in the Linguine natural language processing workbench. The server accepts requests in a JSON format, and performs text analysis operations as they are implemented in Python.
The implemented operations can be found in /linguine/ops.
Adding an operation
To add a new analysis or cleanup operation to this project:
  1. Create a new Python file in /linguine/ops.
  2. Fill the operation in using the template below.
  3. Import the op in /linguine/operation_builder.py and add the operation to the get_operation_handler function body.
  4. Any unit tests should go in /test.
Operation template
# A sample cleanup operation# Used to modify the existing text in a corpus set for easier analysis.# Data will be passed to the op in the form of a collection of corpora.# The op transforms the contents of each corpus and returns the results.classFooOp:
defrun(self, data):
forcorpusindata:
corpus.contents=Bar(corpus.contents)
returndata
# A sample analysis operation# Used to generate meaningful data from a corpus set.# Data will be passed to the op in the form of a collection of corpora.# The op runs analysis on each corpus (or the set as a whole).# It builds a set of results which are then returned in place of corpora.classFooOp:
defrun(self, data):
results= []
forcorpusindata:
results.append({'corpus_id': corpus.id, 'bar': Bar(corpus.contents)})
returnresults
API
  • HTTP POST '/': It expects a JSON payload in the provided format.
{"corpora_ids": ["12345"],//Collection of corpora to pipe into analysis"cleanup": ["stopwords"],//Cleanup steps to add"operation": "nlp-relation",//Type of analysis to be preformed"tokenizer": "",//Tokenizer used (if required)"library": "",//Library associated w/ analysis (if required)"transaction_id": "",//(Field to be populated by linguine-python)"analysis_name": "Relation Extraction (Stanford CoreNLP)",//Name to display in text fields"time_created": 1461342250445,//Used to calculate ETA of analyses"user_id": "12345"//Unique identifier of user who created analysis}
Currently implemented operations
  • Term Frequency
  • Part of Speech Tagging
  • Sentiment
  • Named Entity Recognition
  • Relation Extraction
  • Coreference Resolution
Dependencies
  • Python 3.9.1 or newer (Requires implementation of Future object)
  • MongoDB
  • NLTK Punkt model
  • Stanford CoreNLP Pywrapper (Installation instructions can be found here).
Development
  1. Install Stanford CoreNLP module following docs here.
  2. sudo pip install -r requirements.txt
  3. python -m textblob.download_corpora
  4. python -m linguine.webserver --port <port> --database <database>
Note:
  • For Linguine 1: port: 5555, database: linguine-development
  • For Linguine 2: port: 5551, database: linguine2-development
To run tests:
  1. sudo pip install -r requirements.txt
  2. pytest test
Note: running the program from a directory other than the linguine-python root directory will cause directory linking errors.
About
Linguine python server.
nlp.rit.edu/linguine/
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - ritlinguine/linguine-python: Linguine python server. · GitHub
Skip to content
BranchesTags
Repository files navigation
Build-Indicator
linguine-python
Overview
linguine-python is a Python web server for use in the Linguine natural language processing workbench. The server accepts requests in a JSON format, and performs text analysis operations as they are implemented in Python.
The implemented operations can be found in /linguine/ops.
Adding an operation
To add a new analysis or cleanup operation to this project:
  1. Create a new Python file in /linguine/ops.
  2. Fill the operation in using the template below.
  3. Import the op in /linguine/operation_builder.py and add the operation to the get_operation_handler function body.
  4. Any unit tests should go in /test.
Operation template
# A sample cleanup operation# Used to modify the existing text in a corpus set for easier analysis.# Data will be passed to the op in the form of a collection of corpora.# The op transforms the contents of each corpus and returns the results.classFooOp:
defrun(self, data):
forcorpusindata:
corpus.contents=Bar(corpus.contents)
returndata
# A sample analysis operation# Used to generate meaningful data from a corpus set.# Data will be passed to the op in the form of a collection of corpora.# The op runs analysis on each corpus (or the set as a whole).# It builds a set of results which are then returned in place of corpora.classFooOp:
defrun(self, data):
results= []
forcorpusindata:
results.append({'corpus_id': corpus.id, 'bar': Bar(corpus.contents)})
returnresults
API
  • HTTP POST '/': It expects a JSON payload in the provided format.
{"corpora_ids": ["12345"],//Collection of corpora to pipe into analysis"cleanup": ["stopwords"],//Cleanup steps to add"operation": "nlp-relation",//Type of analysis to be preformed"tokenizer": "",//Tokenizer used (if required)"library": "",//Library associated w/ analysis (if required)"transaction_id": "",//(Field to be populated by linguine-python)"analysis_name": "Relation Extraction (Stanford CoreNLP)",//Name to display in text fields"time_created": 1461342250445,//Used to calculate ETA of analyses"user_id": "12345"//Unique identifier of user who created analysis}
Currently implemented operations
  • Term Frequency
  • Part of Speech Tagging
  • Sentiment
  • Named Entity Recognition
  • Relation Extraction
  • Coreference Resolution
Dependencies
  • Python 3.9.1 or newer (Requires implementation of Future object)
  • MongoDB
  • NLTK Punkt model
  • Stanford CoreNLP Pywrapper (Installation instructions can be found here).
Development
  1. Install Stanford CoreNLP module following docs here.
  2. sudo pip install -r requirements.txt
  3. python -m textblob.download_corpora
  4. python -m linguine.webserver --port <port> --database <database>
Note:
  • For Linguine 1: port: 5555, database: linguine-development
  • For Linguine 2: port: 5551, database: linguine2-development
To run tests:
  1. sudo pip install -r requirements.txt
  2. pytest test
Note: running the program from a directory other than the linguine-python root directory will cause directory linking errors.
About
Linguine python server.
nlp.rit.edu/linguine/
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
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var ref = document.referrer;
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - ritlinguine/linguine-python: Linguine python server. · GitHub
Skip to content
BranchesTags
Repository files navigation
Build-Indicator
linguine-python
Overview
linguine-python is a Python web server for use in the Linguine natural language processing workbench. The server accepts requests in a JSON format, and performs text analysis operations as they are implemented in Python.
The implemented operations can be found in /linguine/ops.
Adding an operation
To add a new analysis or cleanup operation to this project:
  1. Create a new Python file in /linguine/ops.
  2. Fill the operation in using the template below.
  3. Import the op in /linguine/operation_builder.py and add the operation to the get_operation_handler function body.
  4. Any unit tests should go in /test.
Operation template
# A sample cleanup operation# Used to modify the existing text in a corpus set for easier analysis.# Data will be passed to the op in the form of a collection of corpora.# The op transforms the contents of each corpus and returns the results.classFooOp:
defrun(self, data):
forcorpusindata:
corpus.contents=Bar(corpus.contents)
returndata
# A sample analysis operation# Used to generate meaningful data from a corpus set.# Data will be passed to the op in the form of a collection of corpora.# The op runs analysis on each corpus (or the set as a whole).# It builds a set of results which are then returned in place of corpora.classFooOp:
defrun(self, data):
results= []
forcorpusindata:
results.append({'corpus_id': corpus.id, 'bar': Bar(corpus.contents)})
returnresults
API
  • HTTP POST '/': It expects a JSON payload in the provided format.
{"corpora_ids": ["12345"],//Collection of corpora to pipe into analysis"cleanup": ["stopwords"],//Cleanup steps to add"operation": "nlp-relation",//Type of analysis to be preformed"tokenizer": "",//Tokenizer used (if required)"library": "",//Library associated w/ analysis (if required)"transaction_id": "",//(Field to be populated by linguine-python)"analysis_name": "Relation Extraction (Stanford CoreNLP)",//Name to display in text fields"time_created": 1461342250445,//Used to calculate ETA of analyses"user_id": "12345"//Unique identifier of user who created analysis}
Currently implemented operations
  • Term Frequency
  • Part of Speech Tagging
  • Sentiment
  • Named Entity Recognition
  • Relation Extraction
  • Coreference Resolution
Dependencies
  • Python 3.9.1 or newer (Requires implementation of Future object)
  • MongoDB
  • NLTK Punkt model
  • Stanford CoreNLP Pywrapper (Installation instructions can be found here).
Development
  1. Install Stanford CoreNLP module following docs here.
  2. sudo pip install -r requirements.txt
  3. python -m textblob.download_corpora
  4. python -m linguine.webserver --port <port> --database <database>
Note:
  • For Linguine 1: port: 5555, database: linguine-development
  • For Linguine 2: port: 5551, database: linguine2-development
To run tests:
  1. sudo pip install -r requirements.txt
  2. pytest test
Note: running the program from a directory other than the linguine-python root directory will cause directory linking errors.
About
Linguine python server.
nlp.rit.edu/linguine/
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - ritlinguine/linguine-python: Linguine python server. · GitHub
Skip to content
BranchesTags
Repository files navigation
Build-Indicator
linguine-python
Overview
linguine-python is a Python web server for use in the Linguine natural language processing workbench. The server accepts requests in a JSON format, and performs text analysis operations as they are implemented in Python.
The implemented operations can be found in /linguine/ops.
Adding an operation
To add a new analysis or cleanup operation to this project:
  1. Create a new Python file in /linguine/ops.
  2. Fill the operation in using the template below.
  3. Import the op in /linguine/operation_builder.py and add the operation to the get_operation_handler function body.
  4. Any unit tests should go in /test.
Operation template
# A sample cleanup operation# Used to modify the existing text in a corpus set for easier analysis.# Data will be passed to the op in the form of a collection of corpora.# The op transforms the contents of each corpus and returns the results.classFooOp:
defrun(self, data):
forcorpusindata:
corpus.contents=Bar(corpus.contents)
returndata
# A sample analysis operation# Used to generate meaningful data from a corpus set.# Data will be passed to the op in the form of a collection of corpora.# The op runs analysis on each corpus (or the set as a whole).# It builds a set of results which are then returned in place of corpora.classFooOp:
defrun(self, data):
results= []
forcorpusindata:
results.append({'corpus_id': corpus.id, 'bar': Bar(corpus.contents)})
returnresults
API
  • HTTP POST '/': It expects a JSON payload in the provided format.
{"corpora_ids": ["12345"],//Collection of corpora to pipe into analysis"cleanup": ["stopwords"],//Cleanup steps to add"operation": "nlp-relation",//Type of analysis to be preformed"tokenizer": "",//Tokenizer used (if required)"library": "",//Library associated w/ analysis (if required)"transaction_id": "",//(Field to be populated by linguine-python)"analysis_name": "Relation Extraction (Stanford CoreNLP)",//Name to display in text fields"time_created": 1461342250445,//Used to calculate ETA of analyses"user_id": "12345"//Unique identifier of user who created analysis}
Currently implemented operations
  • Term Frequency
  • Part of Speech Tagging
  • Sentiment
  • Named Entity Recognition
  • Relation Extraction
  • Coreference Resolution
Dependencies
  • Python 3.9.1 or newer (Requires implementation of Future object)
  • MongoDB
  • NLTK Punkt model
  • Stanford CoreNLP Pywrapper (Installation instructions can be found here).
Development
  1. Install Stanford CoreNLP module following docs here.
  2. sudo pip install -r requirements.txt
  3. python -m textblob.download_corpora
  4. python -m linguine.webserver --port <port> --database <database>
Note:
  • For Linguine 1: port: 5555, database: linguine-development
  • For Linguine 2: port: 5551, database: linguine2-development
To run tests:
  1. sudo pip install -r requirements.txt
  2. pytest test
Note: running the program from a directory other than the linguine-python root directory will cause directory linking errors.
About
Linguine python server.
nlp.rit.edu/linguine/
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - ritlinguine/linguine-python: Linguine python server. · GitHub
Skip to content
BranchesTags
Repository files navigation
Build-Indicator
linguine-python
Overview
linguine-python is a Python web server for use in the Linguine natural language processing workbench. The server accepts requests in a JSON format, and performs text analysis operations as they are implemented in Python.
The implemented operations can be found in /linguine/ops.
Adding an operation
To add a new analysis or cleanup operation to this project:
  1. Create a new Python file in /linguine/ops.
  2. Fill the operation in using the template below.
  3. Import the op in /linguine/operation_builder.py and add the operation to the get_operation_handler function body.
  4. Any unit tests should go in /test.
Operation template
# A sample cleanup operation# Used to modify the existing text in a corpus set for easier analysis.# Data will be passed to the op in the form of a collection of corpora.# The op transforms the contents of each corpus and returns the results.classFooOp:
defrun(self, data):
forcorpusindata:
corpus.contents=Bar(corpus.contents)
returndata
# A sample analysis operation# Used to generate meaningful data from a corpus set.# Data will be passed to the op in the form of a collection of corpora.# The op runs analysis on each corpus (or the set as a whole).# It builds a set of results which are then returned in place of corpora.classFooOp:
defrun(self, data):
results= []
forcorpusindata:
results.append({'corpus_id': corpus.id, 'bar': Bar(corpus.contents)})
returnresults
API
  • HTTP POST '/': It expects a JSON payload in the provided format.
{"corpora_ids": ["12345"],//Collection of corpora to pipe into analysis"cleanup": ["stopwords"],//Cleanup steps to add"operation": "nlp-relation",//Type of analysis to be preformed"tokenizer": "",//Tokenizer used (if required)"library": "",//Library associated w/ analysis (if required)"transaction_id": "",//(Field to be populated by linguine-python)"analysis_name": "Relation Extraction (Stanford CoreNLP)",//Name to display in text fields"time_created": 1461342250445,//Used to calculate ETA of analyses"user_id": "12345"//Unique identifier of user who created analysis}
Currently implemented operations
  • Term Frequency
  • Part of Speech Tagging
  • Sentiment
  • Named Entity Recognition
  • Relation Extraction
  • Coreference Resolution
Dependencies
  • Python 3.9.1 or newer (Requires implementation of Future object)
  • MongoDB
  • NLTK Punkt model
  • Stanford CoreNLP Pywrapper (Installation instructions can be found here).
Development
  1. Install Stanford CoreNLP module following docs here.
  2. sudo pip install -r requirements.txt
  3. python -m textblob.download_corpora
  4. python -m linguine.webserver --port <port> --database <database>
Note:
  • For Linguine 1: port: 5555, database: linguine-development
  • For Linguine 2: port: 5551, database: linguine2-development
To run tests:
  1. sudo pip install -r requirements.txt
  2. pytest test
Note: running the program from a directory other than the linguine-python root directory will cause directory linking errors.
About
Linguine python server.
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GitHub - ritlinguine/linguine-python: Linguine python server. · GitHub
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linguine-python
Overview
linguine-python is a Python web server for use in the Linguine natural language processing workbench. The server accepts requests in a JSON format, and performs text analysis operations as they are implemented in Python.
The implemented operations can be found in /linguine/ops.
Adding an operation
To add a new analysis or cleanup operation to this project:
  1. Create a new Python file in /linguine/ops.
  2. Fill the operation in using the template below.
  3. Import the op in /linguine/operation_builder.py and add the operation to the get_operation_handler function body.
  4. Any unit tests should go in /test.
Operation template
# A sample cleanup operation# Used to modify the existing text in a corpus set for easier analysis.# Data will be passed to the op in the form of a collection of corpora.# The op transforms the contents of each corpus and returns the results.classFooOp:
defrun(self, data):
forcorpusindata:
corpus.contents=Bar(corpus.contents)
returndata
# A sample analysis operation# Used to generate meaningful data from a corpus set.# Data will be passed to the op in the form of a collection of corpora.# The op runs analysis on each corpus (or the set as a whole).# It builds a set of results which are then returned in place of corpora.classFooOp:
defrun(self, data):
results= []
forcorpusindata:
results.append({'corpus_id': corpus.id, 'bar': Bar(corpus.contents)})
returnresults
API
  • HTTP POST '/': It expects a JSON payload in the provided format.
{"corpora_ids": ["12345"],//Collection of corpora to pipe into analysis"cleanup": ["stopwords"],//Cleanup steps to add"operation": "nlp-relation",//Type of analysis to be preformed"tokenizer": "",//Tokenizer used (if required)"library": "",//Library associated w/ analysis (if required)"transaction_id": "",//(Field to be populated by linguine-python)"analysis_name": "Relation Extraction (Stanford CoreNLP)",//Name to display in text fields"time_created": 1461342250445,//Used to calculate ETA of analyses"user_id": "12345"//Unique identifier of user who created analysis}
Currently implemented operations
  • Term Frequency
  • Part of Speech Tagging
  • Sentiment
  • Named Entity Recognition
  • Relation Extraction
  • Coreference Resolution
Dependencies
  • Python 3.9.1 or newer (Requires implementation of Future object)
  • MongoDB
  • NLTK Punkt model
  • Stanford CoreNLP Pywrapper (Installation instructions can be found here).
Development
  1. Install Stanford CoreNLP module following docs here.
  2. sudo pip install -r requirements.txt
  3. python -m textblob.download_corpora
  4. python -m linguine.webserver --port <port> --database <database>
Note:
  • For Linguine 1: port: 5555, database: linguine-development
  • For Linguine 2: port: 5551, database: linguine2-development
To run tests:
  1. sudo pip install -r requirements.txt
  2. pytest test
Note: running the program from a directory other than the linguine-python root directory will cause directory linking errors.
About
Linguine python server.
nlp.rit.edu/linguine/
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages