CS398VL GAE 2

import requests, json

apikey = 'Something'
req = requests.request('GET', 'http://access.alchemyapi.com/')

showSourceText = '0'

def UrlKeywords(url):
    url = 'http://access.alchemyapi.com/calls/url/URLGetRankedKeywords'
    payload = {'apikey': apikey, 'url': url, 'outputMode': 'json', 'sentiment': '1', 'showSourceText': showSourceText}
    r = requests.post(url, params=payload)
    result = json.loads(r.text)
    return result#['keywords']


def Entities(s):
    url = 'http://access.alchemyapi.com/calls/text/TextGetRankedNamedEntities'
    payload = {'apikey': apikey, 'text': s, 'outputMode': 'json', 'sentiment': '1', 'disambiguate': '1','coreference':'1', 'quotations':'1', 'showSourceText': showSourceText}
    r = requests.post(url, params=payload)
    result = json.loads(r.text)
    return result['entities']

def Keywords(s):
    url = 'http://access.alchemyapi.com/calls/text/TextGetRankedKeywords'
    payload = {'apikey': apikey, 'text': s, 'outputMode': 'json', 'sentiment': '1', 'showSourceText': showSourceText}
    r = requests.post(url, params=payload)
    result = json.loads(r.text)
    return result['keywords']

def Taxonomy(s):
    url = 'http://access.alchemyapi.com/calls/text/TextGetRankedTaxonomy'
    payload = {'apikey': apikey, 'text': s, 'outputMode': 'json', 'showSourceText': showSourceText}
    r = requests.post(url, params=payload)
    result = json.loads(r.text)
    return result["taxonomy"]

def Concepts(s):
    url = 'http://access.alchemyapi.com/calls/text/TextGetRankedConcepts'
    payload = {'apikey': apikey, 'text': s, 'outputMode': 'json', 'showSourceText': showSourceText}
    r = requests.post(url, params=payload)
    result = json.loads(r.text)
    return result["concepts"]

def cleanEntity(s):
    EntDict = {}
    for i in Entities(s):
        if float(i['relevance']) > .60:
            (type, text) = (str(i['type']), str(i['text']))
            try:
                a = EntDict[type]
                a.append(text)
                EntDict[type] = a
            except:
                EntDict[type] = [text]
    return EntDict

def GenerateSummary(s):
    EntDict = cleanEntity(s)
    (Con, Top) = cleanTaxonomy(s)
    character = ""
    topic = ""
    relavance = ""
    nb = 0
    for i in Con:
        if nb == 0:
            relavance = relavance+' '+i
        elif (nb+1==len(Top)):
            relavance = relavance+' and '+i+'.'
        else:
            relavance = relavance+', '+i
        nb = nb+1
    nb = 0
    for i in Top:
        if nb == 0:
            topic = topic+' '+i
        elif (nb+1==len(Top)):
            topic = topic+' and '+i+'.'
        else:
            topic = topic+', '+i
        nb = nb+1
    nb = 0
    for i in EntDict['Person']:
        if nb == 0:
            character = character+' '+i
        elif (nb+1==len(EntDict['Person'])):
            character = character+' and '+i+'.'
        else:
            character = character+', '+i
        nb = nb+1
    return "The text is on the topic of" + topic + "It involves character" + character + ' And it is relevant to'+ relavance

def cleanKey(s):
    EntDict = {}
    neg = []
    pos = []
    neu = []
    for i in Keywords(s):
        if float(i['relevance']) > .60:
            (sentiment, text) = (str(i['sentiment']['type']), str(i['text']))
            EntDict[text] = sentiment
            if sentiment == 'negative':
                neg.append(text)
            elif sentiment == 'neutral':
                neu.append(text)
            else:
                pos.append(text)
    print pos
    print neu
    print neg
    return EntDict

def cleanTaxonomy(s):
    Con = []
    Top = []
    for i in Concepts(s):
        if float(i['relevance']) > .8:
            Con.append(str(i['text'] ))
    for i in Taxonomy(s):
        t = str(i['label'])
        t = t[t.rfind('/')+1:]
        Top.append(t)
    return (Con, Top)