In [84]:

    

import json
from elasticsearch import Elasticsearch

ES_URL = 'http://192.168.11.2:9201'
INDEX = 'movies_4_blogs_pquery'
DATA_INDEX = 'movies_4_blogs_fbeat'
DOC_TYPE = '_doc'
DATA_DOC_TYPE = 'doc'

_es = Elasticsearch(hosts=[ ES_URL ])


# ----------------------------
#  get percolate queries 
# ----------------------------
def getPercolateQuery(userId):
    _q = ''
    if userId != None:
        _q = '{"query":{"match":{"payload.user_id":"' + userId + '"}}}'
        res = _es.search(index=INDEX, doc_type=DOC_TYPE, body=json.loads(_q))
    else:
        res = _es.search(index=INDEX, doc_type=DOC_TYPE, body=_q)    
    return res

# --------------------------------
#  create significant text aggs
# --------------------------------
def generateNRunSigTextAggsByPQuery(jpquery):
    # print(type(jpquery)) # type of the jpquery object -> dict
    _qObj = jpquery['_source']['pquery']
    _qObjStr = json.dumps(_qObj)
    
    # query part 1
    _dsl = """
    {
        "size": 0,
        "aggs": {
            "_s_overview_text": {
                "significant_text": {
                    "field": "overview"
                }
            }
        },
        "query": 
    """
    # query part 2
    _dsl += _qObjStr + '}'

    return _runSigTextAggs(_dsl)

# --------------------------------
#  get sig.text aggs results
# --------------------------------
def _runSigTextAggs(_dsl):
    res = _es.search(index=DATA_INDEX, doc_type=DATA_DOC_TYPE, body=json.loads(_dsl))
    
    # parse the response dictionary
    keywords = []
    buckets = res['aggregations']['_s_overview_text']['buckets']
    for bucket in buckets:
        keywords.append(bucket['key'])
    
    return keywords

# ----------------------------------------
#  generate query dsl from the keywords
# ----------------------------------------
def generateNRunRecommendationQuery(_keywords):
    # create keyword criteria
    _kstr = ''
    for key in _keywords:
        _kstr += key + ' '
    
    _dsl = """
    {
        "sort": [
            {
              "_score": {
                "order": "asc"
              }
            }
          ],
        "query": {
            "bool": {
              "must": [    
                {
                    "match": {
                        "overview": 

    """
    _dsl += '"' + _kstr + '"' + "}} ]}}}"
    
    # run query
    res = _es.search(index=DATA_INDEX, doc_type=DATA_DOC_TYPE, body=json.loads(_dsl))
    
    return res

# --------------------
#  mail template
# --------------------
def prepareMailByTemplate(templateId, jres, jpayload):
    # TODO: add template loading mechanism here. For demo purpose, just hardcode a html email template...
    _tmpl = """
        <p>
        Dear {},
        </p><br/>
        We have found some movies you might be interested!
        <ul>
    """
    # create the recommendation content... 
    # TODO: could encapsulate this content logic to another function
    recContent = ''
    recs = jres['hits']['hits']
    for rec in recs:
        recContent += '<li>' + rec['_source']['title'] + '</li>'
        
    # dsl 2nd part
    _tmpl += recContent + "</ul>" + """
        <p>
        Thank you for visiting our website: http://awesome-awesome-movies.web!
        </p>
    """
    _tmpl = _tmpl.format( jpayload['user_name'] ) 
    
    # TODO: load the smtplib for email sending (remember there is a field "user_email" within the payload object)
    # :) 
    print(_tmpl)


# ----------------------------------------------------------------------------
# print(getPercolateQuery(None)) # every percolate query entry
# ----------------------------------------------------------------------------
jres = getPercolateQuery('123sdvTyue_23') # dedicated percolate query entry based on payload.user_id
payload = jres['hits']['hits'][0]['_source']['payload']
sigKeywords = generateNRunSigTextAggsByPQuery(jres['hits']['hits'][0])
jres = generateNRunRecommendationQuery(sigKeywords)
# create email template and send (etc)
prepareMailByTemplate(None, jres, payload)


    

    




        <p>
        Dear Bruce Sato,
        </p><br/>
        We have found some movies you might be interested!
        <ul>
    <li>Justice Ninja Style</li><li>Powwow Highway</li><li>Beirut: The Encounter</li><li>Yours and Mine</li><li>An Amorous Woman of the Tang Dynasty</li><li>After the Rain</li><li>Kinderraub in Rio - Eine Mutter schlägt zurück</li><li>Yes Madam</li><li>Army of One</li><li>Passion Ration</li></ul>
        <p>
        Thank you for visiting our website: http://awesome-awesome-movies.web!
        </p>
    

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