Skip to content

GitLab

Commit 188c3333 authored by J. Fernando Sánchez's avatar J. Fernando Sánchez
Browse files

Removed nbsphinx 

It requires pandoc, which cannot be installed with pip.

We can either link to the nbfile or convert the file
manually/automatically:

```
nbconvert SenpyClientUse.ipynb --to rst
```
parent 955e17eb
Hide whitespace changes
Inline Side-by-side
docs/SenpyClientUse.ipynb
View file @ 188c3333
......@@ -2,14 +2,35 @@
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-10T17:05:31.465571Z",
"start_time": "2017-04-10T19:05:31.458282+02:00"
},
"deletable": true,
"editable": true
},
"source": [
"# Client"
]
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"The built-in senpy client allows you to query any Senpy endpoint. We will illustrate how to use it with the public demo endpoint, and then show you how to spin up your own endpoint using docker."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Demo Endpoint\n",
"-------------"
......@@ -17,213 +38,235 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Import Client and send a request"
"To start using senpy, simply create a new Client and point it to your endpoint. In this case, the latest version of Senpy at GSI."
]
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"metadata": {
"collapsed": false
"ExecuteTime": {
"end_time": "2017-04-10T17:29:12.827640Z",
"start_time": "2017-04-10T19:29:12.818617+02:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from senpy.client import Client\n",
"\n",
"c = Client('http://latest.senpy.cluster.gsi.dit.upm.es/api')\n",
"r = c.analyse('I like Pizza', algorithm='sentiment140')"
"c = Client('http://latest.senpy.cluster.gsi.dit.upm.es/api')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Print response"
"Now, let's use that client analyse some queries:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"metadata": {
"collapsed": false
"ExecuteTime": {
"end_time": "2017-04-10T17:29:14.011657Z",
"start_time": "2017-04-10T19:29:13.701808+02:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"I like Pizza -> marl:Positive\n"
]
}
],
"outputs": [],
"source": [
"for entry in r.entries:\n",
" print('{} -> {}'.format(entry['text'], entry['sentiments'][0]['marl:hasPolarity']))"
"r = c.analyse('I like sugar!!', algorithm='sentiment140')\n",
"r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-10T17:08:19.616754Z",
"start_time": "2017-04-10T19:08:19.610767+02:00"
},
"deletable": true,
"editable": true
},
"source": [
"Obtain a list of available plugins "
"As you can see, that gave us the full JSON result. A more concise way to print it would be:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"metadata": {
"collapsed": false
"ExecuteTime": {
"end_time": "2017-04-10T17:29:14.854213Z",
"start_time": "2017-04-10T19:29:14.842068+02:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"emoRand\n",
"rand\n",
"sentiment140\n"
]
}
],
"source": [
"for plugin in c.request('/plugins')['plugins']:\n",
" print(plugin['name'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"Local Endpoint\n",
"--------------"
"for entry in r.entries:\n",
" print('{} -> {}'.format(entry['text'], entry['sentiments'][0]['marl:hasPolarity']))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Run a docker container with Senpy image and default plugins"
"We can also obtain a list of available plugins with the client:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"metadata": {
"collapsed": false
"ExecuteTime": {
"end_time": "2017-04-10T17:29:16.245198Z",
"start_time": "2017-04-10T19:29:16.056545+02:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a0157cd98057072388bfebeed78a830da7cf0a796f4f1a3fd9188f9f2e5fe562\r\n"
]
}
],
"outputs": [],
"source": [
"!docker run -ti --name 'SenpyEndpoint' -d -p 5000:5000 gsiupm/senpy:0.8.6 --host 0.0.0.0 --default-plugins"
"c.plugins()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Import client and send a request to localhost"
"Or, more concisely:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"metadata": {
"collapsed": false
"ExecuteTime": {
"end_time": "2017-04-10T17:29:17.663275Z",
"start_time": "2017-04-10T19:29:17.484623+02:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"c_local = Client('http://127.0.0.1:5000/api')\n",
"r = c_local.analyse('Hello world', algorithm='sentiment140')"
"c.plugins().keys()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Print response"
"Local Endpoint\n",
"--------------"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"To run your own instance of senpy, just create a docker container with the latest Senpy image. Using `--default-plugins` you will get some extra plugins to start playing with the API."
]
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"metadata": {
"collapsed": false
"ExecuteTime": {
"end_time": "2017-04-10T17:29:20.637539Z",
"start_time": "2017-04-10T19:29:19.938322+02:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello world -> marl:Neutral\n"
]
}
],
"outputs": [],
"source": [
"for entry in r.entries:\n",
" print('{} -> {}'.format(entry['text'], entry['sentiments'][0]['marl:hasPolarity']))"
"!docker run -ti --name 'SenpyEndpoint' -d -p 6000:5000 gsiupm/senpy:0.8.6 --host 0.0.0.0 --default-plugins"
]
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Obtain a list of available plugins deployed locally"
"To use this endpoint:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": null,
"metadata": {
"collapsed": false
"ExecuteTime": {
"end_time": "2017-04-10T17:29:21.263976Z",
"start_time": "2017-04-10T19:29:21.260595+02:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"rand\n",
"sentiment140\n",
"emoRand\n"
]
}
],
"outputs": [],
"source": [
"for plugin in c_local.request('/plugins')['plugins']:\n",
" print(plugin['name'])"
"c_local = Client('http://127.0.0.1:6000/api')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Stop the docker container"
"That's all! After you are done with your analysis, stop the docker container:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"metadata": {
"collapsed": false
"ExecuteTime": {
"end_time": "2017-04-10T17:29:33.226686Z",
"start_time": "2017-04-10T19:29:22.392121+02:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SenpyEndpoint\n",
"SenpyEndpoint\n"
]
}
],
"outputs": [],
"source": [
"!docker stop SenpyEndpoint\n",
"!docker rm SenpyEndpoint"
......@@ -233,7 +276,7 @@
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
......@@ -247,7 +290,26 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
"version": "3.6.0"
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "68px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false
}
},
"nbformat": 4,
......
%% Cell type:markdown id: tags:
# Client
%% Cell type:markdown id: tags:
The built-in senpy client allows you to query any Senpy endpoint. We will illustrate how to use it with the public demo endpoint, and then show you how to spin up your own endpoint using docker.
%% Cell type:markdown id: tags:
Demo Endpoint
-------------
%% Cell type:markdown id: tags:
Import Client and send a request
To start using senpy, simply create a new Client and point it to your endpoint. In this case, the latest version of Senpy at GSI.
%% Cell type:code id: tags:
``` python
from senpy.client import Client
c = Client('http://latest.senpy.cluster.gsi.dit.upm.es/api')
r = c.analyse('I like Pizza', algorithm='sentiment140')
```
%% Cell type:markdown id: tags:
Print response
Now, let's use that client analyse some queries:
%% Cell type:code id: tags:
``` python
for entry in r.entries:
print('{} -> {}'.format(entry['text'], entry['sentiments'][0]['marl:hasPolarity']))
r = c.analyse('I like sugar!!', algorithm='sentiment140')
r
```
%%%% Output: stream
I like Pizza -> marl:Positive
%% Cell type:markdown id: tags:
Obtain a list of available plugins
As you can see, that gave us the full JSON result. A more concise way to print it would be:
%% Cell type:code id: tags:
``` python
for plugin in c.request('/plugins')['plugins']:
print(plugin['name'])
for entry in r.entries:
print('{} -> {}'.format(entry['text'], entry['sentiments'][0]['marl:hasPolarity']))
```
%%%% Output: stream
emoRand
rand
sentiment140
%% Cell type:markdown id: tags:
Local Endpoint
--------------
%% Cell type:markdown id: tags:
Run a docker container with Senpy image and default plugins
We can also obtain a list of available plugins with the client:
%% Cell type:code id: tags:
``` python
!docker run -ti --name 'SenpyEndpoint' -d -p 5000:5000 gsiupm/senpy:0.8.6 --host 0.0.0.0 --default-plugins
c.plugins()
```
%%%% Output: stream
a0157cd98057072388bfebeed78a830da7cf0a796f4f1a3fd9188f9f2e5fe562
%% Cell type:markdown id: tags:
Import client and send a request to localhost
Or, more concisely:
%% Cell type:code id: tags:
``` python
c_local = Client('http://127.0.0.1:5000/api')
r = c_local.analyse('Hello world', algorithm='sentiment140')
c.plugins().keys()
```
%% Cell type:markdown id: tags:
Print response
Local Endpoint
--------------
%% Cell type:markdown id: tags:
To run your own instance of senpy, just create a docker container with the latest Senpy image. Using `--default-plugins` you will get some extra plugins to start playing with the API.
%% Cell type:code id: tags:
``` python
for entry in r.entries:
print('{} -> {}'.format(entry['text'], entry['sentiments'][0]['marl:hasPolarity']))
!docker run -ti --name 'SenpyEndpoint' -d -p 6000:5000 gsiupm/senpy:0.8.6 --host 0.0.0.0 --default-plugins
```
%%%% Output: stream
Hello world -> marl:Neutral
%% Cell type:markdown id: tags:
Obtain a list of available plugins deployed locally
To use this endpoint:
%% Cell type:code id: tags:
``` python
for plugin in c_local.request('/plugins')['plugins']:
print(plugin['name'])
c_local = Client('http://127.0.0.1:6000/api')
```
%%%% Output: stream
rand
sentiment140
emoRand
%% Cell type:markdown id: tags:
Stop the docker container
That's all! After you are done with your analysis, stop the docker container:
%% Cell type:code id: tags:
``` python
!docker stop SenpyEndpoint
!docker rm SenpyEndpoint
```
%%%% Output: stream
SenpyEndpoint
SenpyEndpoint
......
docs/clidemo.rst
View file @ 188c3333
Client demo
===========
This video shows how to use senpy through command-line tool.
.. image:: https://asciinema.org/a/9uwef1ghkjk062cw2t4mhzpyk.png
:width: 100%
:target: https://asciinema.org/a/9uwef1ghkjk062cw2t4mhzpyk
:alt: CLI demo
docs/commandline.rst
View file @ 188c3333
Command line
============
In case you want to load modules, which are located in different folders under the root folder, use the next option.
This video shows how to analyse text directly on the command line using the senpy tool.
.. code:: bash
senpy -f .
The default port used by senpy is 5000, but you can change it using the `--port` flag.
.. code:: bash
senpy --port 8080
Also, the host can be changed where senpy is deployed. The default value is `127.0.0.1`.
.. code:: bash
senpy --host 0.0.0.0
For more options, see the `--help` page.
Alternatively, you can use the modules included in senpy to build your own application.
.. image:: https://asciinema.org/a/9uwef1ghkjk062cw2t4mhzpyk.png
:width: 100%
:target: https://asciinema.org/a/9uwef1ghkjk062cw2t4mhzpyk
:alt: CLI demo
docs/conf.py
View file @ 188c3333
......@@ -38,7 +38,6 @@ extensions = [
'sphinxcontrib.httpdomain',
'sphinx.ext.coverage',
'sphinx.ext.autosectionlabel',
'nbsphinx'
]
# Add any paths that contain templates here, relative to this directory.
......
docs/index.rst
View file @ 188c3333
Welcome to Senpy's documentation!
=================================
.. image:: https://travis-ci.org/gsi-upm/senpy.svg?branch=master
:target: https://travis-ci.org/gsi-upm/senpy
.. image:: https://readthedocs.org/projects/senpy/badge/?version=latest
:target: http://senpy.readthedocs.io/en/latest/
.. image:: https://badge.fury.io/py/senpy.svg
:target: https://badge.fury.io/py/senpy
.. image:: https://lab.cluster.gsi.dit.upm.es/senpy/senpy/badges/master/build.svg
:target: https://lab.cluster.gsi.dit.upm.es/senpy/senpy/commits/master
.. image:: https://lab.cluster.gsi.dit.upm.es/senpy/senpy/badges/master/coverage.svg
:target: https://lab.cluster.gsi.dit.upm.es/senpy/senpy/commits/master
.. image:: https://img.shields.io/pypi/l/requests.svg
:target: https://lab.cluster.gsi.dit.upm.es/senpy/senpy/
With Senpy, you can easily turn your sentiment or emotion analysis algorithm into a full blown semantic service.
Sharing your sentiment analysis with the world has never been easier.
......
docs/server.rst
View file @ 188c3333
Senpy server
============
Once the server is launched, there is a basic endpoint in the server, which provides a playground to use the plugins that have been loaded.
The senpy server is launched via the `senpy` command:
In case you want to know the different endpoints of the server, there is more information available in the NIF API section_.
::
usage: senpy [-h] [--level logging_level] [--debug] [--default-plugins]
[--host HOST] [--port PORT] [--plugins-folder PLUGINS_FOLDER]
[--only-install]
Run a Senpy server
optional arguments:
-h, --help show this help message and exit
--level logging_level, -l logging_level
Logging level
--debug, -d Run the application in debug mode
--default-plugins Load the default plugins
--host HOST Use 0.0.0.0 to accept requests from any host.
--port PORT, -p PORT Port to listen on.
--plugins-folder PLUGINS_FOLDER, -f PLUGINS_FOLDER
Where to look for plugins.
--only-install, -i Do not run a server, only install plugin dependencies
When launched, the server will recursively look for plugins in the specified plugins folder (the current working directory by default).
The default server includes a playground and an endpoint with all plugins found.
By default, senpy will listen only on the `127.0.0.1` address.
That means you can only access the API from your (or localhost).
You can listen on a different address using the `--host` flag.
The default port is 5000.
You can change bothbut you can change it using the `--port` flag.
For instance, to accept connections on port 6000 on any interface: