for i, j in dic.iteritems():

text = t.replace(i, j)

if not text==t:

print t

print i,j

print text

break

#

#Step 0: Seperate if user is asking for meaning or for a debate response

#

word, isMeaning = MeaningExtractor.getIfMeaning(str(raw_input_string))

if isMeaning:

meaning = MeaningExtractor.getMeaning(word)

return meaning, True

#

#Step 1: Obtain input from the user

#

s = str(raw_input_string)

#

#Step 2: Convert the sentence into blob and MBSP Sentence objects respectively

#

input_string = TextBlob(s)

clipsSentence = MBSP.Sentence(MBSP.parse(s), token=[MBSP.WORD,MBSP.POS,MBSP.CHUNK,MBSP.PNP,MBSP.REL,MBSP.ANCHOR,MBSP.LEMMA])

#

#Step 3: Define the variable required for the analysis and interpretation of input

#

query = ""

w = []

subjPhrases, verbPhrases, predPhrases = [], [], []

pNouns, verbs = [], []

pnps, anchors = [], []

#

#Step 4: Obtain all the proper nouns from the sentence

#

for sentence in input_string.sentences:

tagged = sentence.tags

for word_tag in tagged:

if word_tag[1]=='NNP' or word_tag[1]=='NNPS':

pNouns.append(word_tag[0])

#

#Step 5: Obtain the different parts, i.e. subject, predicate, object of the sentence

#

for chunk in clipsSentence.chunks:

if chunk.role == 'SBJ' and chunk not in subjPhrases:

subjPhrases.append(chunk.string)

elif (chunk.type == 'VP' or chunk.type == 'ADVP') and chunk not in verbPhrases:

verbPhrases.append(chunk.string)

elif (chunk.role == 'PRD' or chunk.role == 'OBJ') and chunk not in predPhrases:

predPhrases.append(chunk.string)

#

#Step 6: Detect the noun phrase and the anchors corresponding to them(Ref:CLIPS docs)

#

pnps = clipsSentence.pnp

for item in pnps:

if item.anchor not in anchors:

anchors.append(item.anchor)

#

#Step 7.0: Train the classifier for sentiment data

#

###with open('sentiment_training_formatted.csv', 'r') as fp:

### classifier = NaiveBayesClassifier(fp, format='csv')

#

#Step 7.1: Classify the user input and record the sentiment

#

'''def getSentiment(sentence):

sentiObj= TextBlob(s, analyzer=NaiveBayesAnalyzer()).sentiment

pos_ratio = sentiObj.p_pos

neg_ratio = sentiObj.p_neg

if pos_ratio>=neg_ratio:

return 'pos'

else:

return 'neg'

input_sentiment = getSentiment(input_string)'''

#

#Step 8: Generate the final query

#

for pNoun in pNouns:

for sbj in subjPhrases:

if pNoun not in subjPhrases:

query = query+pNoun+" "

#print 'pNoun:'+pNoun

for sbj in subjPhrases:

query = query+sbj+" "

#print 'sbj:'+sbj

for prd in predPhrases:

query = query+prd+" "

#print 'prd:'+prd

for vr in verbPhrases:

query = query+vr+" "

#print 'vr:'+vr

for anc in anchors:

anc = anc.string

query = query+anc+" "

#print 'anc:'+anc

for pnp in pnps:

pnp = pnp.string

query = query+pnp+" "

#print 'pnp:'+pnp

#

#Step 9: [Blank]

#

#

#Step 10: Remove repetitive words from the sentence

#

query_blob = TextBlob(query)

wrds = query_blob.words

final_words = []

for wrd in wrds:

#print wrd

if str(wrd).lower() not in final_words:

final_words.append(wrd)

final_query = ""

for wrd in final_words:

final_query = final_query+wrd+" "

#

#Step 11: Sort the query words in order of the input

#

index_dict = {}

indexes = []

for word in TextBlob(final_query).words:

try:

index_dict[input_string.index(str(word))] = str(word)

indexes.append(input_string.index(str(word)))

except(ValueError):

print "Word not in main string:", word

indexes.sort()

final_query = ""

for index in indexes:

final_query = final_query+index_dict[index]+" "

#

#Step 12.0: Filter query for articles: a,an,the,is

#

reps = {' a ':' ', ' an ':' ', ' the ':' ', ' is ':' '}

query = replace_all(query, reps)

if change_sentiment:

#

#Step 12.1: Build a dictionary of the replaceable words

#

#Note:make priority lists for different set of words

def create_replace_dict(lines):

replace_dict = {}

for line in lines:

kv = line.split(',')

replace_dict[kv[0]] = kv[1][:-1]

return replace_dict

#

#Step 12.2: Replace words from the given phrase

#

def replace_words(phrase):

l = open('replace_list.csv', 'r').readlines()

new_phrase = replace_all(phrase, create_replace_dict(l))

if new_phrase==phrase:

l=open('replace_list2.csv', 'r').readlines()

new_phrase = replace_all(phrase, create_replace_dict(l))

return new_phrase

final_query = replace_words(final_query)

#

#Step 13: Get the sentiment of the final query

#

'''final_query_sentiment = getSentiment(final_query)'''

#

#Step 14: Print out the query

#

print "<------------------------------------------->"

print "in:", s

print "out:", final_query

print "<------------------------------------------->"

return final_query, False

else:

print "<------------------------------------------->"

print "in:", s

print "out:", s

print "<------------------------------------------->"

return s, False