ashokc

[program:gunicorn]
command=.../virtualenvs/.../bin/gunicorn -c .../quoteserver/config.py -b 0.0.0.0:9999 quotes:app
directory=.../quoteserver
autostart=true
redirect_stderr=true
stdout_logfile=/var/log/supervisor/gunicorn.log

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@app.route('/css/<path:path>')
def css(path):
    return send_from_directory(app.static_folder + '/css/', path, mimetype='text/css')
@app.route('/images/<path:path>')
def image(path):
    return send_from_directory(app.static_folder + '/images/', path, mimetype='image/jpg')

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from flask import Flask, request, render_template, logging
from elasticsearch import Elasticsearch
client = Elasticsearch([{'host':'localhost','port':9200}])
app = Flask(__name__)

@app.route('/quotes/byId', methods=['GET'])
def getById():
    docId = request.args.get('id')
    quote = client.get(index=index, id=docId)
    return render_template('quote.html',quote=quote)

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def runModel (train_docs, train_labels, test_docs, test_labels, vectorizer):
    model = LogisticRegression(tol=1.0e-6,random_state=0,max_iter=20000, class_weight='balanced')
    train_X = vectorizer.transform(train_docs)
    test_X = vectorizer.transform(test_docs)
    model.fit(train_X, train_labels)
    predicted_labels = model.predict (test_X)
    scores = model.predict_proba(test_X)
    return findMetrics (scores, predicted_labels, test_labels)

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def scoreBySigTerms (test_docs, sig_words, n_sig_terns):
    scoresByLabel = {}  
    for label in [0,1]:
        useSigWords = sig_words[label][0:n_sig_terns]  
        vectorizer = CountVectorizer(analyzer=lambda x: x, min_df=1, vocabulary=useSigWords)
        test_doc_vectors = vectorizer.transform(test_docs)
        a = np.sum(test_doc_vectors,axis=1)
        b = []
        for i in range(len(a)):
            b.append(a[i,0])

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def findSignificantTerms (ids, label):
    body = { "query": { "bool" : { "must" : [ {"term": { "readmit": label} }, { "ids" : {'values' : ids} } ] } }, "aggregations": { "driver_words": { "significant_terms": { "field": "TOKENS", "size": max_sig_terms, chi_square : {}, "background_filter": { "terms": { "readmit": [ 0, 1 ] } } } } }, "size": 0 }

    response = client.search(index=index,body=body, request_timeout=6000)
    max_score = response['aggregations']['driver_words']['buckets'][0]['score']
    words = [bucket['key'] for bucket in response['aggregations']['driver_words']['buckets']]
    if (boosting == 'yes'):
        boosts = [bucket['score']/max_score for bucket in response['aggregations']['driver_words']['buckets']]
    else:
        boosts = [1.0] * len(words)

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{
  "query": {
    "term": {
      "readmit": 1
    }
  },
  "aggregations": {
    "driver_words": {
      "significant_terms": {
        "field": "TOKENS",

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def removeEmptyNotes():
    body = { "bool": { "must_not": { "exists": { "field": "TOKENS" } } } }
    update_by_query = { "script": { "source": "ctx._source.readmit=-1", "lang": "painless" }, "query": body}
    client.update_by_query(index=index, body=update_by_query, timeout='2m')

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def removeExpired ():
    body = { "bool": { "must": { "term": { "CONTENT": "expired" } } } }
    update_by_query = { "script": { "source": "ctx._source.readmit=-1", "lang": "painless" }, "query": body}
    client.update_by_query(index=index, body=update_by_query, timeout='2m')

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def removeDeaths():
    body = { "bool": { "must": [ { "match": { "HOSPITAL_EXPIRE_FLAG": 1 } }, { "match": { "readmit": 0 } } ] } }
    update_by_query = { "script": { "source": "ctx._source.readmit=-1", "lang": "painless" }, "query": body}
    client.update_by_query(index=index, body=update_by_query, timeout='2m')