adding project files

data5.csv

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+Anything,class
+Hii,0
+Hey,0
+Hello,0
+Hii,0
+Yo,0
+what is your name,1
+who are you,1
+hey myra,1
+wats ur name,1
+What's your name,1
+what you like to eat,2
+wat is ur favourite thing to eat,2
+what you eat most,2
+What's your favourite food,2
+wats ur favourite food,2
+what r u doing,3
+wats up,3
+what's up,3
+wassup,3
+where you like to visit,4
+wat is ur favourite place to visit,4
+what you visit most,4
+What's your favourite place,4
+wats ur favourite site to visit,4

myra.py

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+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.externals import joblib
+count_vect = joblib.load("count_vect.pkl")
+clf_count = joblib.load("clf_count.pkl")
+
+from res_map import responses
+res = responses()
+
+print 'input your query'
+ask = raw_input()
+test_example = [ask]
+test_example_count = count_vect.transform(test_example)
+probs = clf_count.predict_proba(test_example_count)[0]
+out = clf_count.predict(test_example_count)[0]
+outn = int(out)
+print res[outn]
+
+print out

res_map.py

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+res = []
+#0
+temp = ("Hii \nWassup \nHow may I help you? \n")
+res.append(temp)
+#1
+temp = ("I am Myra")
+res.append(temp)
+#2
+temp = ("Current")
+res.append(temp)
+#3
+temp = ("Chatting with you :) ")
+res.append(temp)
+#4
+temp = ("IIT Roorkee ")
+res.append(temp)
+
+def responses():
+	return res

train.py

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+
+# coding: utf-8
+
+# In[1]:
+
+
+import csv
+from res_map import responses
+res = responses()
+
+data_path = "C:\Users\GAURABH\Desktop\Chat Bot\data5.csv"
+data = []
+target=[]
+i = 0
+with open(data_path) as datafile:
+    filereader = csv.reader(datafile,delimiter =',',quotechar='|')
+    initial = 0
+    for row in filereader:
+        if initial == 0:
+            initial = 1
+            continue
+        else:
+            data.append(row[0])
+            target.append(row[1])
+    data = data[1:]
+    target = target[1:]
+
+
+# In[2]:
+
+
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.externals import joblib
+#import joblib
+
+count_vect = CountVectorizer()
+train_counts = count_vect.fit_transform(data)
+
+clf_count = MultinomialNB(alpha=1,fit_prior='false').fit(train_counts,target)
+'''
+count_path = "trained_model/naive_count/count_vect.pkl"
+clf_path = "trained_model/naive_count/clf_count.pkl"
+
+joblib.dump(count_vect,count_path)
+joblib.dump(clf_count,clf_path)
+'''
+joblib.dump(count_vect,'count_vect.pkl')
+joblib.dump(clf_count,'clf_count.pkl')
+
+# In[3]:
+
+
+#test_example = ["will kissing cause hiv"]
+#test_example_count = count_vect.transform(test_example)
+'''
+    def respond(self):
+        print 'input your query'
+        ask = raw_input()
+        vec = self.vector(ask)
+        no = self.clf.predict(vec)[0]
+        no = int(no)
+        return self.res[no]
+
+print 'input your query'
+ask = raw_input()
+test_example = [ask]
+test_example_count = count_vect.transform(test_example)
+probs = clf_count.predict_proba(test_example_count)[0]
+out = clf_count.predict(test_example_count)[0]
+outn = int(out)
+print res[outn]
+
+
+# In[4]:
+
+
+print out
+
+
+# In[1]:
+
+
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+import joblib
+
+count_vect = joblib.load("trained_model/naive_count/count_vect.pkl")
+clf_count = joblib.load("trained_model/naive_count/clf_count.pkl")
+'''
+'''
+count_vect = joblib.load("count_vect.pkl")
+clf_count = joblib.load("clf_count.pkl")
+'''
+# In[6]:
+
+'''
+test_example = ["will kissing cause hiv"]
+test_example_count = count_vect.transform(test_example)
+probs = clf_count.predict_proba(test_example_count)[0]
+out = clf_count.predict(test_example_count)
+
+
+# In[9]:
+
+
+out = out[0]
+'''
+# In[11]:
+
+'''
+from map_englishu import mapping
+
+
+# In[12]:
+
+
+mapping[out]
+'''
+# In[ ]:
+
+
+
+