class Root(Tk):
def __init__(self):
super(Root, self).__init__()
self.title("Tkinter Browse")
self.minsize(1000, 600)
self.labelFrame = ttk.Label(self, text="")
self.labelFrame.grid(row=0, column=1)
self.label_frame = ttk.Frame(self, height=50)
self.label_frame.grid(
row=10) # Stops child widgets of label_frame from resizing it
self.entry = ttk.Entry(self.labelFrame, text="", width=50)
self.entry.grid()
self.button()
def button(self):
self.button = ttk.Button(self.labelFrame,
text="Browse",
command=self.filedialog)
self.button.grid(column=3, row=0)
#self.entry1=ttk.Entry(self.label_frame,width=50)
# self.entry1.grid(row=10)
## self.label1=ttk.Label(self.labelFrame,text="",width=100)
#self.label1.grid(row=10)
def filedialog(self):
#root.filename = tkFileDialog.askopenfilename(initialdir = "/",title = "Select file",filetypes = (("jpeg files","*.jpg"),("all files","*.*")))
self.filename = filedialog.askopenfilename(initialdir="/",
title="Select file")
self.entry.insert(0, self.filename)
self.text = docxpy.process(self.filename)
print(self.text)
self.txt = ScrolledText(self.labelFrame, width=100, height=20)
self.txt.grid(row=7)
self.txt.insert(const.END, self.text)
#self.label1.config(text=self.text)
# self.entry1.insert(0,self.text)
self.token()
def entry(self):
self.entry = ttk.Entry(self.labelFrame, text="", width=50)
self.entry.grid(column=1, row=6, padx=5, pady=60, ipady=3)
return
def token(self):
self.tkn = Tokenizer(lang='ml',
smt=True) #smt is a flag for social-media-text
#self.text = docxpy.process(self.filename)
print(self.tkn.tokenize(self.text))
def get_tokenized_sentences(text, language):
lang = ""
if language == "english": lang = "en"
if language == "hindi": lang = "hi"
if language == "telugu": lang = "te"
tk = Tokenizer(lang=lang, split_sen=True)
tokens = tk.tokenize(text)
return get_sentences(tokens)
def load_data(text_type, filename, lang, tokenize_text=False, split_sent=True):
data_tuple = []
with codecs.open(filename, 'r', encoding='utf-8') as fp:
logger.info('Loading text_type: %s format' % (text_type))
if text_type == "ssf":
start_c = -1
for line in fp:
line = line.strip()
ds = line.split()
#print("Line", line)
#print("DS", ds)
if line == "":
continue
elif line[0:2] == "<S":
sent = []
elif line[0:3] == "</S":
data_tuple.append(sent)
elif line[0] == "<":
continue
elif ds[0] == "0" or ds[0] == "))":
continue
elif ds[1] == "((":
start_c, chunk_tag = 1, ds[2]
#print "hello-chunk tag",chunk_tag
if len(ds) > 2:
if ds[2]:
#print "--",line,"--"
word, tag = ds[1], ds[2]
if start_c == -1:
sent.append((word, tag, ""))
if start_c == 1:
sent.append((word, tag, "B-%s" % (chunk_tag)))
start_c = 0
if start_c == 0:
sent.append((word, tag, "I-%s" % (chunk_tag)))
elif text_type == "conll":
sent = []
for line in fp:
line = line.strip()
ds = line.split()
if line != "":
print(line)
if len(ds) == 2:
word, tag, chunk = ds[1], "",""
if len(ds) == 3:
word, tag, chunk = ds[1], ds[2], ""
if len(ds) == 4:
word, tag, chunk = ds[1], ds[2], ds[3]
sent.append([word, tag, chunk])
else:
data_tuple.append(sent)
sent = []
elif text_type == "txt":
if split_sent == True:
text = fp.read()
tok = Tokenizer(lang=lang, split_sen=split_sent)
tokenized_sents = tok.tokenize(text)
sent = []
for tokens in tokenized_sents:
for token in tokens:
sent.append([token, "", ""])
data_tuple.append(sent)
else:
for line in fp:
sent = []
if tokenize_text:
tok = Tokenizer(lang=lang, split_sen=False)
tokenized_sents = tok.tokenize(line)
for tokens in tokenized_sents:
for token in tokens:
sent.append([token, "", ""])
data_tuple.append(sent)
else:
print("Check - text_type", text_type)
return data_tuple
def pipeline():
curr_dir = path.dirname(path.abspath(__file__))
args = get_args()
output_dir = path.join(path.dirname(path.abspath(__file__)), "outputs")
if not os.path.exists(output_dir):
os.makedirs(output_dir)
data_writer.set_logger(args.model_type, output_dir)
if True:
model_path = "%s/models/%s/%s.%s.%s.model" % (
curr_dir, args.language, args.model_type, args.tag_type,
args.encoding)
if args.model_type == "lstm":
if args.tag_type == "pos":
model_path = "%s/models/%s/lstm/" % (curr_dir, args.language)
elif args.tag_type == "chunk":
model_path = "%s/models/%s/lstm/chunk/" % (curr_dir,
args.language)
elif args.tag_type == "ner":
model_path = "%s/models/%s/lstm/ner/" % (curr_dir,
args.language)
if args.tag_type != "parse":
if not os.path.exists(model_path):
os.makedirs(model_path)
if args.pipeline_type == 'train':
logger.info('Start Training#')
logger.info('Tagger model type: %s' % (args.model_type))
data_path = "%s/data/train/%s/train.%s.%s" % (
curr_dir, args.language, args.encoding, args.data_format)
if args.tag_type == "ner":
data_path = data_path + ".ner"
data_sents = data_reader.load_data(args.data_format, data_path,
args.language)
no_words = sum(len(sent) for sent in data_sents)
logger.info("No. of words: %d" % (no_words))
logger.info("No. of sents: %d" % (len(data_sents)))
X_data = [
generate_features.sent2features(s, args.tag_type, args.model_type)
for s in data_sents
]
y_data = [
generate_features.sent2labels(s, args.tag_type) for s in data_sents
]
X_train, X_test, y_train, y_test = train_test_split(X_data,
y_data,
test_size=0.10,
random_state=42)
print('Train data size:', len(X_train), len(y_train))
print('Test data size:', len(X_test), len(y_test))
print('Lang:', args.language)
print('Train data: ', data_path)
print('Model Path: ', model_path)
if args.model_type == "crf":
tagger = CRF(model_path)
tagger.train(X_train, y_train)
tagger.load_model()
tagger.test(X_test, y_test)
elif args.model_type == "lstm":
x_data, y_data1, y_data2 = load_data_and_labels(data_path)
if args.tag_type == "pos":
x_train, x_test, y_train1, y_test1 = train_test_split(
x_data, y_data1, test_size=0.10,
random_state=42) #Split the data into train and test
model = Sequence() #Intialize BiLSTM model
model.fit(x_train, y_train1,
epochs=10) #Train the model for 10 echos
print(model.score(x_test,
y_test1)) #Run the model on test data
model.save(model_path + "/weights.h5",
model_path + "/params.json",
model_path + "/preprocessor.json")
if args.tag_type == "chunk":
x_train, x_test, y_train2, y_test2 = train_test_split(
x_data, y_data2, test_size=0.10,
random_state=42) #Split the data into train and test
model = Sequence() #Intialize BiLSTM model
model.fit(x_train, y_train2,
epochs=10) #Train the model for 10 echos
print(model.score(x_test,
y_test2)) #Run the model on test data
model.save(model_path + "/weights.h5",
model_path + "/params.json",
model_path + "/preprocessor.json")
if args.tag_type == "ner":
x_train, x_test, y_train1, y_test1 = train_test_split(
x_data, y_data1, test_size=0.10,
random_state=42) #Split the data into train and test
model = Sequence() #Intialize BiLSTM model
model.fit(x_train, y_train1,
epochs=10) #Train the model for 10 echos
print(model.score(x_test,
y_test1)) #Run the model on test data
model.save(model_path + "/weights.h5",
model_path + "/params.json",
model_path + "/preprocessor.json")
if args.pipeline_type == "test":
if args.model_type == "crf":
test_data_path = "%s/%s" % (curr_dir, args.test_data)
test_sents = data_reader.load_data(args.data_format,
test_data_path,
args.language,
tokenize_text=False)
X_test = [
generate_features.sent2features(s, args.tag_type,
args.model_type)
for s in test_sents
]
y_test = [
generate_features.sent2labels(s, args.tag_type)
for s in test_sents
]
tagger = CRF(model_path)
tagger.load_model()
tagger.test(X_test, y_test)
if args.pipeline_type == "predict":
test_data_path = "%s" % (args.test_data)
test_sents = data_reader.load_data(args.data_format,
test_data_path,
args.language,
tokenize_text=True,
split_sent=args.sent_split)
if args.tag_type == "parse":
#Pos tagging
X_test = [
generate_features.sent2features(s, "pos", args.model_type)
for s in test_sents
]
tag_model_path = "%s/models/%s/%s.%s.%s.model" % (
curr_dir, args.language, args.model_type, "pos", args.encoding)
chunk_model_path = "%s/models/%s/%s.%s.%s.model" % (
curr_dir, args.language, args.model_type, "chunk",
args.encoding)
if args.model_type == "crf":
tagger = CRF(tag_model_path)
tagger.load_model()
y_pos = tagger.predict(X_test)
test_sents_pos = generate_features.append_tags(
test_sents, "pos", y_pos)
X_test = [
generate_features.sent2features(s, "chunk",
args.model_type)
for s in test_sents_pos
]
chunker = CRF(chunk_model_path)
chunker.load_model()
y_chunk = chunker.predict(X_test)
test_fname = path.basename(test_data_path)
output_file = "%s/%s.parse" % (output_dir, test_fname)
data_writer.write_anno_to_file(output_file, test_sents_pos,
y_chunk, "chunk")
logger.info("Output in: %s" % output_file)
data_writer.write_to_screen(output_file)
else:
X_test = [
generate_features.sent2features(s, args.tag_type,
args.model_type)
for s in test_sents
]
if args.model_type == "crf":
tagger = CRF(model_path)
tagger.load_model()
y_pred = tagger.predict(X_test)
data_writer.write_anno_to_file(args.output_path, test_sents,
y_pred, args.tag_type)
data_writer.write_to_screen(args.output_path)
logger.info("Output in: %s" % args.output_path)
if args.model_type == "lstm":
model = Sequence().load(model_path + "/weights.h5",
model_path + "/params.json",
model_path + "/preprocessor.json")
f = open(args.test_data, "r")
sent = f.read()
tok = Tokenizer(lang=args.language, split_sen=True)
tokenized_sents = tok.tokenize(sent)
for tokens in tokenized_sents:
for token in tokens:
sent = sent + " " + token
sent = sent.strip()
print(model.analyze(sent))
print(lines)
output = []
if flag == True:
for l in lines:
json = {}
definitive = forward_transl_full.transform(l)
json["text"] = definitive
json["tokens"] = []
tokens = []
text_precedent = ""
tokens = tk.tokenize(l)
back_tokens = tk_back.tokenize(definitive)
for i, t in enumerate(tokens):
inner_json = {}
choosen = back_tokens[i]
suggestions = []
forward_out = forward_transl_token.transform(t)
for c in forward_out:
back_out = back_transl_token.transform(c)
if back_out == t and c != choosen:
suggestions.append(c)
from __future__ import unicode_literals from polyglot_tokenizer import Tokenizer tk = Tokenizer(lang='ml', smt=True) #smt is a flag for social-media-text text = "രണ്ട് വർഷംമുമ്പ് നടന്ന നിയമസഭാ തെരഞ്ഞെടുപ്പിൽ തിരിച്ചടി ലഭിച്ചതിനുശേഷം ഗുജറാത്തിൽ ബിജെപിയുടേത് ഒരുതരം ഞാണിൻമേൽക്കളിയാണ്. കഴിഞ്ഞ ലോക്സഭാ തെരഞ്ഞെടുപ്പിൽ ആകെയുള്ള 26 സീറ്റിലും വിജയിച്ച ബിജെപി ഇക്കുറി അത് നിലനിർത്താനായി എല്ലാ വൃത്തികെട്ട കളിയും പുറത്തെടുക്കുകയാണ്." print(tk.tokenize(text))
def process_args(args):
if not (args.ml or args.rb):
args.rb = True
if args.infile:
ifp = io.open(args.infile, encoding='utf-8')
else:
if sys.version_info[0] >= 3:
ifp = codecs.getreader('utf8')(sys.stdin.buffer)
else:
ifp = codecs.getreader('utf8')(sys.stdin)
if args.outfile:
ofp = io.open(args.outfile, mode='w', encoding='utf-8')
else:
if sys.version_info[0] >= 3:
ofp = codecs.getwriter('utf8')(sys.stdout.buffer)
else:
ofp = codecs.getwriter('utf8')(sys.stdout)
# LIMIT CASES # BUGS INDIC-TRANS
# HARD_CODED
if args.target == "urd" or args.source == 'urd':
args.build_lookup = False
# SELECT REGEX TO SEARCH WORDS OFFSETS INSIDE A DOCUMENT (INDIAN -> UTF, ENG -> ASCII)
if args.source == 'eng' and args.target in ISO_3to2 and args.target != 'eng':
# UTF8 unicode parser regex
def my_regex(word):
return r"(?<!\S){}(?!\S)".format(re.escape(word))
else:
# ASCII romanized parser regex
def my_regex(word):
return r"\b{}\b".format(re.escape(word))
if args.output_format == 'stdout':
# initialize transliterator object
trn = Transliterator(args.source,
args.target,
rb=args.rb,
build_lookup=args.build_lookup)
# transliterate text
for line in ifp:
if args.source == 'hin' and args.target == 'eng':
replacements = {
u"\u0950": "om",
u"\u0915\u092e\u0932": "kamal"
}
for script, roman in replacements.iteritems():
line = line.replace(script, roman)
tline = trn.convert(line)
if u"whatsapp" in tline and args.target == 'eng' and u"whatsapp" not in line:
tline = tline.replace("whatsapp", "vhaatsapp")
ofp.write(tline)
# close files
ifp.close()
ofp.close()
elif args.output_format == 'json':
# getting source language from terminal
source = args.source
# getting target language from terminal
target = args.target
# Full forward ( source lang -> target lang) transliterator at SENTENCE LEVEL
forward_transl_full = Transliterator(source=source,
target=target,
rb=args.rb,
build_lookup=args.build_lookup)
# forward ( source lang -> target lang) transliterator at TOKEN LEVEL, we use this to trasliterate every token indipendently
# from source to target lang with multiple choices (beamsearch)
forward_transl_token = Transliterator(source=source,
target=target,
rb=args.rb,
decode='beamsearch')
# backward ( target lang -> source lang) transliterator at TOKEN LEVEL, we use this to check backtranslitteration of result
back_transl_token = Transliterator(source=target,
target=source,
rb=args.rb,
build_lookup=args.build_lookup)
# Tokenizer of source language
tk = Tokenizer(lang=ISO_3to2[source])
# Tokenizer of target language
tk_back = Tokenizer(lang=ISO_3to2[target])
# Soundex instance object for checking phonetically similarity between words
instance = Soundex()
# array of output sentences
output = []
# seen vector to recognize words that already have been processed
seen = {}
# read entire source text to transliterate
document_input = ifp.read()
# document_input divided by lines
lines = document_input.splitlines()
# progressive translitted text
document_translitted = u""
# for every line
for l in lines:
# Treat special cases
if u"\u0950" in l and source == 'hin' and target == 'eng':
l = l.replace(u"\u0950", "om")
# prepare a json for every line
json = {}
#transform entire sentence as first choice
definitive = forward_transl_full.transform(l)
# add traslitted line to entire translitted text
document_translitted += definitive + u"\n"
# tokenize initial sentence in tokens
tokens = tk.tokenize(l)
#backtokenize text transformed
back_tokens = tk_back.tokenize(clean_str(definitive))
# text field is sentence first choice without alternatives (stdout mode)
#json["tokenization"] = back_tokens
json["tokens"] = []
# count index token inside a sentence, without punctuation
count_tokens = 0
# zip token and translitterated token
for index, (t, choosen) in enumerate(zip(tokens, back_tokens)):
inner_json = {}
# suggestions for choosen tokens
suggestions = []
exclusions = []
# translitterate token from source sentence with beamsearch ( 5 results)
forward_out = forward_transl_token.transform(t)
# open alternatives
for c in forward_out:
# for every alternatives, back-translitterate it
back_out = back_transl_token.transform(c)
# if back-translitterated token is equal to initial token, but the result of translitteration of two token is different, one is suggestion .
if back_out == t and c != choosen:
suggestions.append(clean_str(c))
else:
if c != choosen:
exclusions.append(clean_str(c))
# add choosen to all possible choices [ suggestion + choice]
all_possible_choices = list(suggestions)
all_possible_choices.insert(0, choosen)
# transform all suggestion (+ choosen) to phonetical alphabet with soundex
transformed = []
for c in all_possible_choices:
p = instance.soundex(c)
transformed.append(p)
duplicates = {}
for p, original_text in zip(transformed, all_possible_choices):
if p not in duplicates:
duplicates[p] = []
duplicates[p].append(clean_str(original_text))
else:
duplicates[p].append(clean_str(original_text))
new_duplicates = {}
suggestion_duplicates = []
# for every
for _, v in duplicates.items():
new_duplicates[v[0]] = v[1:]
suggestion_duplicates.extend(v[1:])
#my_regex = u'(\s|^)%s(\s|$)' % choosen
#my_regex = r"\b" + re.escape(choosen) + r"\b"
if source == 'kan' and target == 'eng':
new_choosen = resolveKannada(choosen, exclusions)
if new_choosen != choosen:
exclusions.remove(new_choosen)
exclusions.append(choosen)
if choosen in new_duplicates:
new_duplicates[new_choosen] = new_duplicates.pop(
choosen)
if new_choosen not in json["text"]:
json["text"] = json["text"].replace(
choosen, new_choosen)
new_last_line = document_translitted.strip().split(
u"\n")[-1].replace(choosen, new_choosen)
document_translitted = u'\n'.join(
document_translitted.split(u"\n")
[0:-2]) + "\n" + new_last_line + "\n"
choosen = new_choosen
if (source == 'hin' or source == 'kan') and target == 'eng':
new_choosen = resolveHindi(choosen, exclusions)
if new_choosen != choosen:
exclusions.remove(new_choosen)
exclusions.append(choosen)
if choosen in new_duplicates:
new_duplicates[new_choosen] = new_duplicates.pop(
choosen)
if new_choosen not in json["text"]:
json["text"] = json["text"].replace(
choosen, new_choosen)
new_last_line = document_translitted.strip().split(
u"\n")[-1].replace(choosen, new_choosen)
document_translitted = u'\n'.join(
document_translitted.split(u"\n")
[0:-2]) + "\n" + new_last_line + "\n"
choosen = new_choosen
if target == 'eng':
if choosen == "whatsapp" and t != "whatsapp":
definitive = definitive.replace(
"whatsapp", "vhaatsapp")
choosen = "vhaatsapp"
new_last_line = document_translitted.strip().split(
u"\n")[-1].replace("whatsapp", "vhaatsapp")
document_translitted = u'\n'.join(
document_translitted.split(u"\n")
[0:-2]) + "\n" + new_last_line + "\n"
exclusions.append("whatsapp")
if choosen == t:
exclusions = []
json["text"] = definitive
r = re.compile(my_regex(choosen),
flags=re.I | re.X | re.UNICODE)
# calculate length of this choosen token
length = len([1 for c in choosen if not c in UNICODE_NSM_ALL])
for m in r.finditer(document_translitted.strip()):
# take every occurrence found inside full text
word = m.group()
characterOffsetBegin = m.start()
characterOffsetEnd = characterOffsetBegin + length - 1
found = -1
if word in seen:
found = seen[word]
if characterOffsetBegin > found:
count_tokens += 1
seen[word] = characterOffsetEnd
inner_json["source"] = t
inner_json["token"] = choosen
inner_json["index"] = count_tokens
inner_json["duplicates"] = new_duplicates
inner_json["exclusions"] = exclusions
inner_json["suggestions"] = [
s for s in suggestions
if s not in suggestion_duplicates
]
inner_json[
'characterOffsetBegin'] = characterOffsetBegin
inner_json['characterOffsetEnd'] = characterOffsetEnd
json["tokens"].append(inner_json)
break
output.append(json)
final_output = {"sentences": output}
r = js.dumps(final_output)
ofp.write(r)
# close files
ifp.close()
ofp.close()
else:
# close files
ifp.close()
ofp.close()