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John Andersen
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docs: Complete integration example
Signed-off-by: John Andersen <[email protected]>
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‎docs/usage/integration.rst‎

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@@ -439,5 +439,150 @@ Modifying the Legacy App
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Now let's add a 'Predict' button to the app. The button will trigger the
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operations to be run on the new URL, and then the prediction. The demo app will
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then take the predicted classification and rcord that as the classification in
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then take the predicted classification and record that as the classification in
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the database.
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**examples/maintained/index.html**
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.. code-block:: html
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<div>
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<p>Submit or change the maintenance status of a repo.</p>
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<input id="URL" placeholder="git://server.git/repo"></input>
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<button id="maintained" class="good">Maintained</button>
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<button id="unmaintained" class="dangerous">Unmaintained</button>
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<button id="predict">Predict</button>
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</div>
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The predict button will trigger an HTTP call to the CGI API. The CGI script will
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run the same commands we just ran on the command line, and parse the output,
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which is in JSON format, and set the resulting prediction classification in the
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database.
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**examples/maintained/cgi-bin/api.py**
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.. code-block:: python
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# Add the imports we'll be using to the top of api.py
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import subprocess
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from datetime import datetime
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# ...
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elif action == 'predict':
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today = datetime.now().strftime('%Y-%m-%d %H:%M')
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operations = [
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'group_by',
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'quarters_back_to_date',
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'check_if_valid_git_repository_URL',
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'clone_git_repo',
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'git_repo_default_branch',
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'git_repo_checkout',
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'git_repo_commit_from_date',
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'git_repo_author_lines_for_dates',
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'work',
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'git_repo_release',
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'git_commits',
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'count_authors',
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'cleanup_git_repo'
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]
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subprocess.check_call(([
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'dffml', 'operations', 'repo',
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'-log', 'debug',
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'-sources', 'db=demoapp',
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'-update',
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'-keys', query['URL'],
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'-repo-def', 'URL',
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'-remap',
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'group_by.work=work',
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'group_by.commits=commits',
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'group_by.authors=authors',
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'-dff-memory-operation-network-ops'] + operations + [
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'-dff-memory-opimp-network-opimps'] + operations + [
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'-inputs'] + \
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['%d=quarter' % (i,) for i in range(0, 10)] + [
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'\'%s\'=quarter_start_date' % (today,),
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'True=no_git_branch_given',
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'-output-specs', '''{
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"authors": {
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"group": "quarter",
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"by": "author_count",
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"fill": 0
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},
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"work": {
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"group": "quarter",
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"by": "work_spread",
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"fill": 0
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},
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"commits": {
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"group": "quarter",
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"by": "commit_count",
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"fill": 0
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}
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}=group_by_spec''']))
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result = subprocess.check_output([
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'dffml', 'predict', 'repo',
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'-keys', query['URL'],
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'-model', 'dnn',
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'-sources', 'db=demoapp',
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'-classifications', '0', '1',
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'-features',
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'def:authors:int:10',
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'def:commits:int:10',
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'def:work:int:10',
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'-log', 'critical',
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'-update'])
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result = json.loads(result)
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cursor.execute("REPLACE INTO status (src_url, maintained) VALUES(%s, %s)",
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(query['URL'], result[0]['prediction']['classification'],))
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cnx.commit()
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print json.dumps(dict(success=True))
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Hook up the predict button to call our new API.
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**examples/maintained/ml.js**
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.. code-block:: javascript
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function predict(URL) {
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return fetch('cgi-bin/api.py?action=predict' +
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'&maintained=' + Number(maintained) +
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'&URL=' + URL)
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.then(function(response) {
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return response.json()
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}.bind(this));
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}
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window.addEventListener('DOMContentLoaded', function(event) {
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var tableDOM = document.getElementById('table');
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var URLDOM = document.getElementById('URL');
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var predictDOM = document.getElementById('predict');
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predictDOM.addEventListener('click', function(event) {
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predict(URLDOM.value)
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.then(function() {
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refreshTable(tableDOM);
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});
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});
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});
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Finally, import the new script into the main page.
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**examples/maintained/index.html**
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.. code-block:: html
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<head>
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<title>Git Repo Maintenance Tracker</title>
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<script type="text/javascript" src="main.js"></script>
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<script type="text/javascript" src="ml.js"></script>
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<link rel="stylesheet" type="text/css" href="theme.css">
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</head>
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Visit the web app, and input a Git repo to evaluate into the input field. Then
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click predict. The demo gif has had all the entries DROPed from the database.
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But you can look through the table and you'll find that a prediction has been
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run on the repo. Congratulations! You've automated a manual classification
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process, and integrated machine learning into a legacy application.
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.. image:: /images/integration_demo.gif

‎examples/maintained/cgi-bin/api-ml.py‎

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#!/usr/bin/env python2
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import os
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import cgi
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import sys
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import json
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import urlparse
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import subprocess
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import mysql.connector
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from datetime import datetime
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from conf import MYSQL_USER, MYSQL_PASSWORD, MYSQL_DATABASE, MYSQL_PORT
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print 'Content-Type: application/json'
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print ''
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query = dict(urlparse.parse_qsl(os.getenv('QUERY_STRING', default='')))
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action = query.get('action', None)
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if action is None:
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print json.dumps(dict(error='Need method query parameter'))
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sys.exit(1)
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cnx = mysql.connector.connect(
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user=MYSQL_USER,
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passwd=MYSQL_PASSWORD,
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database=MYSQL_DATABASE,
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port=MYSQL_PORT
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)
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cursor = cnx.cursor()
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if action == 'dump':
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cursor.execute("SELECT src_url, maintained FROM status")
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print json.dumps(dict(cursor))
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elif action == 'set':
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cursor.execute("REPLACE INTO status (src_url, maintained) VALUES(%s, %s)",
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(query['URL'], query['maintained'],))
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cnx.commit()
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print json.dumps(dict(success=True))
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elif action == 'predict':
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today = datetime.now().strftime('%Y-%m-%d %H:%M')
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operations = [
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'group_by',
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'quarters_back_to_date',
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'check_if_valid_git_repository_URL',
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'clone_git_repo',
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'git_repo_default_branch',
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'git_repo_checkout',
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'git_repo_commit_from_date',
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'git_repo_author_lines_for_dates',
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'work',
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'git_repo_release',
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'git_commits',
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'count_authors',
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'cleanup_git_repo'
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]
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subprocess.check_call(([
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'dffml', 'operations', 'repo',
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'-log', 'debug',
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'-sources', 'db=demoapp',
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'-update',
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'-keys', query['URL'],
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'-repo-def', 'URL',
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'-remap',
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'group_by.work=work',
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'group_by.commits=commits',
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'group_by.authors=authors',
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'-dff-memory-operation-network-ops'] + operations + [
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'-dff-memory-opimp-network-opimps'] + operations + [
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'-inputs'] + \
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['%d=quarter' % (i,) for i in range(0, 10)] + [
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'\'%s\'=quarter_start_date' % (today,),
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'True=no_git_branch_given',
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'-output-specs', '''{
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"authors": {
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"group": "quarter",
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"by": "author_count",
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"fill": 0
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},
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"work": {
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"group": "quarter",
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"by": "work_spread",
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"fill": 0
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},
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"commits": {
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"group": "quarter",
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"by": "commit_count",
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"fill": 0
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}
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}=group_by_spec''']))
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result = subprocess.check_output([
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'dffml', 'predict', 'repo',
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'-keys', query['URL'],
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'-model', 'dnn',
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'-sources', 'db=demoapp',
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'-classifications', '0', '1',
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'-features',
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'def:authors:int:10',
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'def:commits:int:10',
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'def:work:int:10',
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'-log', 'critical',
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'-update'])
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result = json.loads(result)
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cursor.execute("REPLACE INTO status (src_url, maintained) VALUES(%s, %s)",
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(query['URL'], result[0]['prediction']['classification'],))
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cnx.commit()
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print json.dumps(dict(success=True))
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else:
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print json.dumps(dict(error='Unknown action'))
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sys.stdout.flush()
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cursor.close()
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cnx.close()

‎examples/maintained/cgi-bin/api.py‎

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else:
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print json.dumps(dict(error='Unknown action'))
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sys.stdout.flush()
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cursor.close()
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cnx.close()

‎examples/maintained/ml.html‎

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<!DOCTYPE html>
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<html>
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<head>
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<title>Git Repo Maintenance Tracker</title>
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<script type="text/javascript" src="main.js"></script>
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<script type="text/javascript" src="ml.js"></script>
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<link rel="stylesheet" type="text/css" href="theme.css">
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</head>
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<body>
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<h1>Git Repo Maintenance Tracker</h1>
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<hr />
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<div>
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<p>Submit or change the maintenance status of a repo.</p>
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<input id="URL" placeholder="git://server.git/repo"></input>
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<button id="maintained" class="good">Maintained</button>
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<button id="unmaintained" class="dangerous">Unmaintained</button>
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<button id="predict">Predict</button>
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</div>
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<br />
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<div>
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<table id="table" style="width:100%">
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</table>
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</div>
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</body>
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</html>

‎examples/maintained/ml.js‎

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function predict(URL) {
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return fetch('cgi-bin/api-ml.py?action=predict' +
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'&maintained=' + Number(maintained) +
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'&URL=' + URL)
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.then(function(response) {
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return response.json()
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}.bind(this));
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}
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window.addEventListener('DOMContentLoaded', function(event) {
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var tableDOM = document.getElementById('table');
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var URLDOM = document.getElementById('URL');
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var predictDOM = document.getElementById('predict');
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predictDOM.addEventListener('click', function(event) {
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predict(URLDOM.value)
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.then(function() {
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refreshTable(tableDOM);
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});
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});
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});

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