def execute_query(query, yes_no_grammar, wh_grammar, database):
query = query.translate(string.maketrans("", ""), string.punctuation)
query_list = [i for i in query.lower().split()]
yes_no = set([
"was", "did", "is", "does", "were", "could", "do", "are", "have",
"had", "should"
])
if (query_list[0] in yes_no):
cp = load_parser(yes_no_grammar, trace=3)
else:
cp = load_parser(wh_grammar) #, trace=3)
trees = list(cp.parse(query_list))
answer = trees[0].label()['SEM']
answer = [s for s in answer if s]
q = ' '.join(answer)
print(q)
results = []
rows = chat80.sql_query(database, q)
for r in rows:
results.append(r[0])
print(r[0])
if (len(results) == 0 and query_list[0] in yes_no):
print "No"
def english_to_sql():
nltk.data.show_cfg("grammars/book_grammars/sql0.fcfg")
from nltk import load_parser
cp = load_parser("grammars/book_grammars/sql0.fcfg", trace=3)
query = "What cities are located in China"
trees = cp.nbest_parse(query.split())
answer = trees[0].node['SEM']
q = " ".join(answer)
print q
from nltk.sem import chat80
rows = chat80.sql_query('corpora/city_database/city.db', q)
for r in rows:
print r[0],
def english_to_sql():
nltk.data.show_cfg("grammars/book_grammars/sql0.fcfg")
from nltk import load_parser
cp = load_parser("grammars/book_grammars/sql0.fcfg", trace=3)
query = "What cities are located in China"
trees = cp.nbest_parse(query.split())
answer = trees[0].node['SEM']
q = " ".join(answer)
print q
from nltk.sem import chat80
rows = chat80.sql_query('corpora/city_database/city.db', q)
for r in rows:
print r[0],
def sql_run(self):
self.query = input("Enter the Natural Language: ")
self.trees = list(self.parser.parse(self.query.split()))
self.answer = self.trees[0].label()['SEM']
self.answer = [s for s in self.answer if s]
self.q = ' '.join(self.answer)
print("\n the Sql query is as follows \n")
print(self.q)
print("\n Querry is running....")
self.rows = chat80.sql_query('corpora/city_database/city.db', self.q)
self.count = 0
self.my_res = []
for r in self.rows:
self.my_res.append(r)
self.count = self.count + 1
if self.count is 0:
print("no data found")
else:
for iter_var in range(self.count):
temp_str = self.my_res[iter_var][0]
print(temp_str)
self.sum_er()
from nltk.data import show_cfg
#show_cfg('grammars/book_grammars/sql0.fcfg')
show_cfg('file:sql0.fcfg')
# The FCFG grammar uses feature structures with rules represented in []; there is a SEM feature which represents the semantics of the string; semantics is an SQL representation; 'which' corresponds to 'SELECT'; 'in' and 'are' don't contribute anything; the terminal expressions are combined with other rules; ? indicates a variable (an addition to Python); + concatenates strings;
#
# ## From strings to semantic representations
# In[2]:
from nltk import load_parser
#cp = load_parser('grammars/book_grammars/sql0.fcfg') # data from (Warren and Prereira, 1982)
cp = load_parser('file:sql0.fcfg')
query = 'What cities are located in China'
trees = list(cp.parse(query.split()))
answer = trees[0].label()['SEM']
answer = [s for s in answer if s] # makes a tuple a list
q = ' '.join(answer)
print(q)
# ## Evaluating a semantic representation in a model
# In[3]:
from nltk.sem import chat80
rows = chat80.sql_query('corpora/city_database/city.db', q)
# rows = chat80.sql_query('./city.db', q)
for r in rows:
print(r[0], end=" ") # each row is a tuple from which we extract city
import nltk
nltk.data.show_cfg('grammars/book_grammars/sql1.fcfg')
from nltk import load_parser
test = load_parser('grammars/book_grammars/sql1.fcfg')
q=" What cities are in Greece"
t = list(test.parse(q.split()))
ans = t[0].label()['SEM']
ans = [s for s in ans if s]
q = ' '.join(ans)
print(q)
from nltk.sem import chat80
r = chat80.sql_query('corpora/city_database/city.db', q)
for p in r:
print(p[0], end=" ")
button.config(state=ACTIVE)
submit_button.config(state=ACTIVE)
prob_button.pack_forget()
T.pack_forget()
T2.pack_forget()
var.set(1)
def prob():
T2.pack(padx=20, pady=5, expand=True, fill='both')
#T2.pack(side=TOP , fill = X ,padx=20 , pady = 5)
q = "SELECT City FROM city_table"
CC = []
for answer in a.sql_query('corpora/city_database/city.db', q):
CC.append(("%-10s" % answer).strip())
q = "SELECT country FROM city_table"
for answer in a.sql_query('corpora/city_database/city.db', q):
CC.append(("%-10s" % answer).strip())
file = open("IndianPeopleSorted.csv", encoding="utf8")
corpus = file.read().splitlines()
Li = []
for name in corpus:
Li.extend(name.strip().split())
file.close()
class news_classifier():
features = []
