class Music:
def __init__(self):
self.st = StanfordNERTagger(JarvisN.config_data.directory_path + '\\tagger\\ner-song-model.ser.gz',
JarvisN.config_data.directory_path+'\\tagger\\stanford-ner.jar')
self.db = DataDbHelper()
def tag(self, sent):
tokenized_text = word_tokenize(sent)
classified_text = self.st.tag(tokenized_text)
print(classified_text)
entity, value = self.chunk(classified_text)
return entity, value
#for tag, chunk in groupby(classified_text, lambda x:x[1]):
# if tag != "O":
# print("%-12s"%tag, " ".join(w for w, t in chunk))
def chunk(self, tagged_sent):
for tag, chunk in groupby(tagged_sent, lambda x:x[1]):
if tag != "O":
first = tag
sec = " ".join(w for w, t in chunk)
#print("%-12s"%tag, " ".join(w for w, t in chunk))
return first, sec
def playSong(self, name):
songResult = self.db.getSong(name)
location = songResult[0][1]
print(location)
webbrowser.open(location)
def playRandSong(self):
print("in rand song")
songResult = self.db.getRandomSong()
location = songResult[1]
print(location)
webbrowser.open(location)
def main():
os.chdir(config_data.directory_path)
#webbrowser.open('http://localhost/jarvis/jarvis.php')
java_path = "C:\Program Files\Java\jdk1.8.0_101\\bin\java.exe"
os.environ['JAVAHOME'] = config_data.java_path
brain = Brain()
classifier = JarvisClassifier()
commandManager = CommandManager()
db = DataDbHelper()
while (True):
msg = input()
#cmd = brain.getCommand(msg)
cmd = classifier.classify(msg, 'general')
if cmd == "greeting":
sub = "NONE"
else:
sub = classifier.classify(msg, cmd)
#React
print(cmd, sub)
entity, type = commandManager.callCommand(cmd, sub, msg)
print(cmd, sub, entity, type)
if msg == " close" or msg == "close":
break
try:
db.insertIntoNewData(msg, cmd, sub, 0, 0, type)
except:
print("entry exists")
print("closed")
def main():
os.chdir(config_data.directory_path)
webbrowser.open('http://localhost/jarvis/jarvis.php')
java_path = "C:\Program Files\Java\jdk1.8.0_101\\bin\java.exe"
os.environ['JAVAHOME'] = config_data.java_path
brain = Brain()
classifier = JarvisClassifier()
commandManager = CommandManager()
db = DataDbHelper()
async def hello(websocket, path):
#Listen ----------
print('started')
msg = await websocket.recv()
#msg = input('Enter command')
print("< {}".format(msg))
#cmd = brain.getCommand(msg)
cmd = classifier.classify(msg, 'general')
if cmd == "greeting":
sub = "NONE"
else:
sub = classifier.classify(msg, cmd)
#React
entity, type = commandManager.callCommand(cmd, sub, msg)
print(cmd, sub, entity, type)
if msg == " close" or msg == "close":
asyncio.get_event_loop().stop()
try:
db.insertIntoNewData(msg, cmd, sub, 0, 0, type)
except:
print("Entry exists")
start_server = websockets.serve(hello, 'localhost', 9999)
loop = asyncio.get_event_loop() #asyncio.get_event_loop().stop()
loop.run_until_complete(start_server)
loop.run_forever()
import sys
import config_data
sys.path.append(config_data.jarvis_folder_location)
from JarvisN.database.datahelper import DataDbHelper
td = []
dbh = DataDbHelper()
result = dbh.getResult(
"SELECT sentence, label2 FROM trainingdata WHERE label1='dictionary'")
dbh.closeConnection()
for row in result:
td.append((row[0], row[1]))
print(td)
import nltk
import pickle
from nltk.tokenize import word_tokenize
from nltk.classify.scikitlearn import SklearnClassifier
from sklearn.naive_bayes import MultinomialNB, BernoulliNB
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.svm import SVC, LinearSVC, NuSVC
import sys
import os
sys.path.append(r"C:\Users\Dhaval\Documents\GitHub"
) # Your JarvisN folder Location... Replace it
from JarvisN.database.datahelper import DataDbHelper # Rename folder to JarvisN not JarvisN-Master
#td = []
dbh = DataDbHelper()
#result = dbh.getResult("SELECT sentence, label1 FROM trainingdata") # execute ur query here
result = dbh.getResult(
"SELECT sentence, entitys, entitye, entity FROM trainingdata WHERE label1='music'"
) # execute ur query here
dbh.closeConnection()
file = open('nerData.tsv', 'w')
for row in result:
sentence = row[0].split(" ")
start = row[1]
end = row[2]
if row[3] == 'none':
continue
print(start, end)
i = 0
for word in sentence:
if i >= start and i < end:
def __init__(self): self.st = StanfordNERTagger(JarvisN.config_data.directory_path + '\\tagger\\ner-song-model.ser.gz', JarvisN.config_data.directory_path+'\\tagger\\stanford-ner.jar') self.db = DataDbHelper()