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Merge pull request #1 from alvations/patch-1

Specify encoding when opening file.

2 parents 486eca8 + 2009804 commit cf252cfCopy full SHA for cf252cf

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chatbot.py

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`‎chatbot.py`

Lines changed: 16 additions & 11 deletions

Original file line number	Diff line number	Diff line change
`@@ -3,22 +3,29 @@`
`3`	`3`
`4`	`4`	`# # Meet Robo: your friend`
`5`	`5`
`6`		`-import nltk`
	`6`	`+import io`
	`7`	`+import random`
	`8`	`+import string # to process standard python strings`
`7`	`9`	`import warnings`
`8`		`-warnings.filterwarnings("ignore")`
`9`	`10`
`10`		`-# nltk.download() # for downloading packages`
	`11`	`+from sklearn.feature_extraction.text import TfidfVectorizer`
	`12`	`+from sklearn.metrics.pairwise import cosine_similarity`
`11`	`13`
`12`	`14`	`import numpy as np`
`13`		`-import random`
`14`		`-import string # to process standard python strings`
`15`	`15`
	`16`	`+import nltk`
	`17`	`+from nltk.stem import WordNetLemmatizer`
	`18`	`+`
	`19`	`+warnings.filterwarnings("ignore")`
`16`	`20`
`17`		`-f=open('chatbot.txt','r',errors = 'ignore')`
`18`		`-raw=f.read()`
`19`		`-raw=raw.lower()# converts to lowercase`
	`21`	`+nltk.download('popular', quiet=True) # for downloading packages`
	`22`	`+# Includes the following already.`
`20`	`23`	`#nltk.download('punkt') # first-time use only`
`21`	`24`	`#nltk.download('wordnet') # first-time use only`
	`25`	`+`
	`26`	`+with open('chatbot.txt','r', encoding='utf8', errors ='ignore') as fin:`
	`27`	`+ raw = fin.read().lower()`
	`28`	`+`
`22`	`29`	`sent_tokens = nltk.sent_tokenize(raw)# converts to list of sentences`
`23`	`30`	`word_tokens = nltk.word_tokenize(raw)# converts to list of words`
`24`	`31`
`@@ -29,7 +36,7 @@`
`29`	`36`	`word_tokens[:5]`
`30`	`37`
`31`	`38`
`32`		`-lemmer = nltk.stem.WordNetLemmatizer()`
	`39`	`+lemmer = WordNetLemmatizer()`
`33`	`40`	`def LemTokens(tokens):`
`34`	`41`	`return [lemmer.lemmatize(token) for token in tokens]`
`35`	`42`	`remove_punct_dict = dict((ord(punct), None) for punct in string.punctuation)`
`@@ -50,8 +57,6 @@ def greeting(sentence):`
`50`	`57`	`return random.choice(GREETING_RESPONSES)`
`51`	`58`
`52`	`59`
`53`		`-from sklearn.feature_extraction.text import TfidfVectorizer`
`54`		`-from sklearn.metrics.pairwise import cosine_similarity`
`55`	`60`
`56`	`61`
`57`	`62`	`# Generating response`

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