def __init__(self, intents_file_path, model_name='data'):
self.model_name = model_name
with open(intents_file_path, 'r') as f:
intents = json.load(f)
self.all_words = []
self.tags = []
self.xy = []
self.x_train = []
self.y_train = []
# loop through each sentence in intents patterns
for intent in intents['intents']:
tag = intent['tag']
self.tags.append(tag)
for pattern in intent['patterns']:
# tokenize each word in the sentence
w = tokenize(pattern)
# add to our words list
self.all_words.extend(w)
# add to xy pair
self.xy.append((w, tag))
# stem and lower each word
ignore_words = ['?', '.', '!']
self.all_words = [
stem(w) for w in self.all_words if w not in ignore_words
]
# remove duplicates and sort
self.all_words = sorted(set(self.all_words))
self.tags = sorted(set(self.tags))
def chat():
user_responses = []
bot_responses = []
sentence1 = request.form['user_input']
user_responses.append(sentence1)
sentence = tokenize(sentence1)
stemmed_words = [ stem(w) for w in sentence]
no_of_pizza=0
order_id = 0
sts = ['Your food is being prepared', 'Our executive is out for delivery', 'Sorry for the inconvinence.. you will get you order within 10 mins']
order_sts = sts[random.randint(0,2)]
for w in stemmed_words:
if w == 'order' or w =='want' or w == 'need':
for wrd in stemmed_words:
if re.match('^[0-9]*$', wrd):
no_of_pizza = int(wrd)
choices = list(range(100))
random.shuffle(choices)
order_id = choices.pop()
sts = ['Your food is being prepared', 'Our executive is out for delivery', 'Sorry for the inconvinence.. you will get you order within 10 mins']
order_sts = sts[random.randint(0,2)]
order_details = {'_id': order_id , 'Address':'none', 'Status': order_sts}
collection.insert_one(order_details)
bot = "your order has been recorded and your order id is {order_id}, kindly provide us delivery details"
return render_template('index.html', user_input=sentence1, bot_response = bot )
elif w == 'address':
result = collection.update({"_id":order_id}, {"$set":{"Address": sentence1}})
bot = "your delivery details are recorded status of your order: {order_sts}"
return render_template('index.html', user_input=sentence1, bot_response = bot )
elif w == 'status':
results = collection.find_one({"_id":order_id})
if results == 'None':
bot = 'No orderFound in this id'
return render_template('index.html', user_input=sentence1, bot_response = bot )
else:
bot = results.get('Status')
return render_template('index.html', user_input=sentence1, bot_response = bot )
X = bag_of_words(sentence, all_words)
X = X.reshape(1, X.shape[0])
X = torch.from_numpy(X)
output = model(X)
_, predicted = torch.max(output, dim=1)
tag = tags[predicted.item()]
probs = torch.softmax(output, dim =1)
prob = probs[0][predicted.item()]
for intent in intents['intents']:
if tag == intent['tag']:
bot = random.choice(intent["responses"])
return render_template('index.html', user_input=sentence1, bot_response = bot )
def prepare_train_data():
import numpy as np
import json
from nltk.corpus import stopwords
from nltk_utils import tokenize, stem, convert_and_pad
with open( "intents.json", "r" ) as f:
data = json.load( f )
all_words = [ ]
tags = [ ]
x_y = [ ]
for d_point in data[ "intents" ]:
tag = d_point[ 'tag' ]
tags.append( tag )
for pattern in d_point[ "patterns" ]:
word = tokenize( pattern )
word = [ s_w.lower() for s_w in word ]
all_words.extend( word )
x_y.append( (word, tag) )
signs = [ '!', '?', ',', '.']
all_words = [ stem( w ) for w in all_words if w not in signs ]
all_words = sorted( set(all_words) )
tags = sorted( set(tags) )
X_train = []
y_train = []
for ( pattern_sentence, tag ) in x_y:
bag_of_words = convert_and_pad( all_words, pattern_sentence )
X_train.append( bag_of_words )
label = tags.index( tag )
y_train.append( float(label) )
y_train = np.array( y_train )
## Testing fuction/
# sents = ['hi', 'hello', 'i', 'you', 'bye', 'thank', 'cool']
# ss = ['hello', 'how', 'are', 'you']
# print(convert_and_pad(sents, ss))
return X_train, y_train, [ all_words, tags, x_y ]
def process():
if 'username' in session:
if request.method == 'POST':
sentence = request.form['user_input']
username = session['username']
print(username)
tag_result = request.form.get('tags')
tage_value = str(tag_result)
question = sentence
#sentence = tokenize(sentence)
sentence = tokenize(sentence.lower())
stopsets = [
'a', 'an', 'the', 'i', 'you', 'one', 'do', 'of', 'in', 'like',
'for', 'from', 'to', 'as', 'by', 'about', 'off', 'did', 'am',
'is', 'are', 'was', 'were', 'if', 'is', 'on', 'what', 'when',
'where', 'which', 'and', 'tell', 'me', 'my', 'must', 'can',
'could', 'would', 'that', 'or', 'anyone', 'any', 'many',
'there'
]
stopX = [stem(w) for w in sentence if w not in stopsets]
print("\n>>>[", stopX)
#X = bag_of_words(sentence, all_words)
X = bag_of_words(stopX, all_words)
X = X.reshape(1, X.shape[0])
X = torch.from_numpy(X).to(device)
output = model(X)
_, predicted = torch.max(output, dim=1)
tag = tags[predicted.item()]
probs = torch.softmax(output, dim=1)
prob = probs[0][predicted.item()]
if prob.item() > 0.75:
for intent in intents['intents']:
if tag == intent["tag"]:
if tag_result == 'Select':
return render_template(
'index.html',
user_input=question,
bot_response=random.choice(
intent['responses']),
error=
'Please select any option from the dropdown')
else:
# f = open("file.txt", "a")
# f.write(tage_value + '\n' + question + '\n' + random.choice(intent['responses']) + '\n')
print(username)
print(tage_value)
print(question)
print(random.choice(intent['responses']))
chatTable = mongo.db.Chat
chatData = chatTable.find({'name': username})
all_questions = []
all_answers = []
all_yes_reviews = []
all_no_reviews = []
if chatData:
for x in chatData:
questions = x['details']['question']
answers = x['details']['answer']
reviews = x['details']['review']
all_questions.append(questions)
all_answers.append(answers)
if reviews == 'Yes':
all_yes_reviews.append(reviews)
elif reviews == 'No':
all_no_reviews.append(reviews)
all_questions.reverse()
all_answers.reverse()
yes_count = len(all_yes_reviews)
no_count = len(all_no_reviews)
efficiency = (
int(yes_count) /
(int(yes_count) + int(no_count))) * 100
efficiency = round(efficiency, 2)
return render_template(
'review.html',
message=session['username'],
user_names=username,
tag_value=tage_value,
user_input=question,
bot_response=random.choice(
intent['responses']),
all_history=zip(all_questions,
all_answers),
yes_count=yes_count,
no_count=no_count,
efficiency=efficiency)
else:
return render_template(
'review.html',
message=session['username'],
user_names=username,
tag_value=tage_value,
user_input=question,
bot_response=random.choice(
intent['responses']),
all_history=zip(all_questions,
all_answers))
# print(f"{bot_name}: {random.choice(intent['responses'])}")
else:
print("hello")
if tag_result == 'Select':
return render_template(
'index.html',
user_input=question,
bot_response="I don't understand",
error='Please select any option the dropdown')
else:
# f = open("file.txt", "a")
# f.write(tage_value + '\n' + question + '\n' + 'I donot understand\n')
bot_response = web_scraping(question)
chatTable = mongo.db.Chat
chatData = chatTable.find({'name': username})
all_questions = []
all_answers = []
all_yes_reviews = []
all_no_reviews = []
if chatData:
for x in chatData:
questions = x['details']['question']
answers = x['details']['answer']
reviews = x['details']['review']
all_questions.append(questions)
all_answers.append(answers)
if reviews == 'Yes':
all_yes_reviews.append(reviews)
elif reviews == 'No':
all_no_reviews.append(reviews)
all_questions.reverse()
all_answers.reverse()
yes_count = len(all_yes_reviews)
no_count = len(all_no_reviews)
efficiency = (int(yes_count) /
(int(yes_count) + int(no_count))) * 100
efficiency = round(efficiency, 2)
return render_template('review.html',
message=session['username'],
user_names=username,
tag_value=tage_value,
user_input=question,
bot_response=bot_response,
all_history=zip(
all_questions, all_answers),
yes_count=yes_count,
no_count=no_count,
efficiency=efficiency)
else:
return render_template('review.html',
message=session['username'],
user_names=username,
tag_value=tage_value,
user_input=question,
bot_response=bot_response,
all_history=zip(
all_questions, all_answers))
return render_template('mainindex.html')
#collect patterns and their tags
tags = []
#this will hold all patterns and tags
xy = []
for intent in intents['intents']:
tag = intent['tag']
tags.append(tag)
for pattern in intent['patterns']:
w = tokenize(pattern)
all_words.extend(w)
xy.append((w, tag))
all_words = [stem(w) for w in all_words if w not in punctuation]
all_words = sorted(set(all_words))
tags = sorted(set(tags))
X_train = []
y_train = []
for (pattern_sentence, tag) in xy:
bag = bag_of_words(pattern_sentence, all_words)
X_train.append(bag)
label = tags.index(tag)
y_train.append(label) #1 hot
X_train = np.array(X_train)
intents = json.load(f)
# loop through each sentence in our intents patterns
for intent in intents['intents']:
tag = intent['tag']
# add to tag list
tags.append(tag)
for pattern in intent['patterns']:
# tokenize each word in the sentence
w = tokenize(pattern)
# add to our words list
all_strings.extend(w)
# add to ls pair
ls.append((w, tag))
all_strings = [stem(w) for w in all_strings if w not in ignore_words]
all_strings = sorted(set(all_strings))
tags = sorted(set(tags))
for (pattern_sentence, tag) in ls:
# X: bag of words for each pattern_sentence
bag = bag_of_words(pattern_sentence, all_strings)
x_train.append(bag)
# y: PyTorch CrossEntropyLoss needs only class labels, not one-hot
label = tags.index(tag)
y_train.append(label)
x_train = np.array(x_train)
y_train = np.array(y_train)
all_words = []
tags = []
xy = []
for intent in intents['intents']:
tag = intent['tag']
tags.append(tag)
for pattern in intent['patterns']:
w = tokenize(pattern)
all_words.extend(w)
xy.append((w, tag))
ignore_word = [",", ".", "'", '"', "?", "!", "^", "@", "#", "_",
"-"] #we need, regular expression
all_words = [stem(w) for w in all_words
if w not in ignore_word] #this is better than using map
all_words = sorted(set(all_words))
tags = sorted(set(tags)) # for order
print(all_words)
# print(tags)
# print(xy)
X_train = []
Y_train = []
for (pattern_sentence, tag) in xy:
bag = bag_of_words(pattern_sentence, all_words)
X_train.append(bag)
label = tags.index(tag)
'intents']: # Iterate through each intent which consists of a tag, pattern of text, bot response
tags.append(intent['tag']) # Get all tags
for pattern in intent[
'patterns']: # Iterate through every user input possabilities
tkn = tokenize(pattern) # tokenize the user input possabilities
all_words.extend(
tkn
) # use extend instead of append to avoid getting an array of array (avoid flattening later)
xy.append((tkn, intent['tag']
)) # (x,y) pair with input tokens and their respective tags
to_ignore = [
'?', '!', '.', ',', 'the', 'is', 'as', 'a', 'are', 'in', 'this', 'that'
] # Some stop words and useless characters
all_words = sorted(
set([stem(wrd) for wrd in all_words if wrd not in to_ignore
])) # Apply stemming to all words and remove duplicates
tags = sorted(
set(tags)) # Incase we add duplicate tags, not necessary but good practice
X_train, y_train = [], []
for (pattern_sentence, tag) in xy:
bag = bag_of_words(pattern_sentence, all_words)
X_train.append(bag)
label = tags.index(tag)
y_train.append(label)
X_train = np.array(X_train)
y_train = np.array(y_train)
# Hyper-parameters
intents = json.load(f)
all_words = []
tags = []
xy = []
for intent in intents['intents']:
tag = intent['tag']
tags.append(tag)
for pattern in intent['patterns']:
w = tokenize(pattern)
all_words.extend(w)
xy.append((w, tag))
ignore_words = {'?', '!', '.', ','}
all_words = sorted(set([stem(w) for w in all_words if w not in ignore_words]))
tags = sorted(set(tags))
x_train, y_train = [], []
for (pattern_sentence, tag) in xy:
bag = bag_of_words(pattern_sentence, all_words)
x_train.append(bag)
label = tags.index(tag)
y_train.append(
label
) # we are using cross entropy loss , therefore one hot encoding is not needed
x_train = np.array(x_train)
y_train = np.array(y_train)
all_words = []
tags = []
xy = []
for item in intents['intents']:
tag = item['tag']
tags.append(tag)
for pat in item['patterns']:
w = tokenize(pat)
all_words.extend(w)
xy.append((w, tag))
stop_words = ['?', '!', '.', ',']
all_words = [stem(w) for w in all_words if w not in stop_words]
all_words = sorted(set(all_words))
tags = sorted(set(tags))
x_train = []
y_train = []
for (pat_sent, tag) in xy:
bag = bag_of_words(pat_sent, all_words)
x_train.append(bag)
label = tags.index(tag)
y_train.append(label) # CrossEntropyLoss
x_train = np.array(x_train)
all_words = []
tags = []
xy = [] # will hold tokenized patterns and tags
# loop over intents
for intent in intents['intents']:
tag = intent['tag']
tags.append(tag)
for pattern in intent['patterns']:
tokenized_pattern = tokenize(pattern)
all_words.extend(tokenized_pattern) # extend with array of words
xy.append((tokenized_pattern, tag))
ignored_characters = ['?', "!", '.', ',']
# apply stemming to tokenized all_words list and excluding ignored_words
all_words = [stem(word) for word in all_words if word not in ignored_characters]
# remove duplicates and sort the list
all_words = sorted(set(all_words))
# create bag of words
x_train = [] # for bag of words
y_train = [] # associated number for each tag
# iterate over (patterns, tags)
for (tokenized_pattern, tag) in xy:
bag = bag_of_words(tokenized_pattern, all_words)
x_train.append(bag)
# get index of each tag (label them) and append to y_train
label = tags.index(tag)
y_train.append(label) # we want only the class labels (cross-entropy loss)
def main(read_dir="high.json", write_dir="high.pth"):
base_json_dir = "resource/jsonFile/" + read_dir
base_pth_dir = "resource/pthFile/" + write_dir
with open(base_json_dir, "r", encoding="UTF-8") as file:
intents = json.load(file)
all_words = []
tags = []
xy = []
for intent in intents['intents']:
tag = intent['tag']
tags.append(tag)
for pattern in intent['patterns']:
w = tokenize(pattern)
all_words.extend(w)
xy.append((w, tag))
ignore_word = [",", ".", "'", '"', "?", "!", "^", "@", "#", "_", "-",
"~"] #we need, regular expression
all_words = [stem(w) for w in all_words
if w not in ignore_word] #this is better than using map
all_words = sorted(set(all_words))
tags = sorted(set(tags)) # for order
X_train = []
Y_train = []
for (pattern_sentence, tag) in xy:
bag = bag_of_words(pattern_sentence, all_words)
X_train.append(bag)
label = tags.index(tag)
Y_train.append(label)
X_train = np.array(X_train)
Y_train = np.array(Y_train)
# Hyper-parameters
num_epochs = 1000
batch_size = 8
learning_rate = 0.001
input_size = len(X_train[0])
hidden_size = 8
output_size = len(tags)
class ChatDataset(Dataset):
def __init__(self):
self.n_samples = len(X_train)
self.x_data = X_train
self.y_data = Y_train
# support indexing such that dataset[i] can be used to get i-th sample
def __getitem__(self, index):
return self.x_data[index], self.y_data[index]
# we can call len(dataset) to return the size
def __len__(self):
return self.n_samples
dataset = ChatDataset()
train_loader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = NeuralNet(input_size, hidden_size, output_size).to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# Train the model
for epoch in range(num_epochs):
for (words, labels) in train_loader:
words = words.to(device)
labels = labels.to(dtype=torch.long).to(device)
# Forward pass
outputs = model(words)
# if y would be one-hot, we must apply
# labels = torch.max(labels, 1)[1]
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (epoch + 1) % 100 == 0:
print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')
print(f'final loss: {loss.item():.4f}')
data = {
"model_state": model.state_dict(),
"input_size": input_size,
"hidden_size": hidden_size,
"output_size": output_size,
"all_words": all_words,
"tags": tags
}
torch.save(data, base_pth_dir)
print(f'training complete. write_dir saved to {base_pth_dir}')
tags = []
xy = []
ignoreWords = ['?', "!", '.', ',']
for intent in intents['intents']:
tag = intent['tag']
tags.append(tag)
for pattern in intent['patterns']:
word = tokenize(pattern)
allWords.extend(word)
xy.append((word, tag))
# stem the words in the allWords list while disregarding the ignoreWords list
allWords = [stem(word) for word in allWords if word not in ignoreWords]
# remove duplicates and sort the words
allWords = sorted(set(allWords))
tags = sorted(set(tags))
XTrain = []
YTrain = []
for (patternSentence, tag) in xy:
bag = bagOfWords(patternSentence, allWords)
XTrain.append(bag)
label = tags.index(tag)
# CrossEntropyLoss
def train(num_epochs=500, learning_rate=0.001):
global intents
# Use of a JSON-File to read trough the training data
with open("intents.json", "r", encoding="UTF-8") as f:
intents = json.load(f)
# Will hold every word to tokenize and stem them
all_words = []
# Will hold every tag to classify the words
tags = []
# Will hold patterns and tags
xy = []
# the JSON-file is treated like a dictionary, therefore we have to use a key for the loop
for intent in intents["intents"]:
tag = intent["tag"]
tags.append(tag)
for pattern in intent["patterns"]:
w = tokenize(pattern)
# We don´t want to have lists in the all_words list, therefore we extend instead of appending them
all_words.extend(w)
# to be able to link the words to the different tags
xy.append((w, tag))
# setting up the excluded characters
ignore_words = ["?", "!", ".", ","]
all_words = [stem(w) for w in all_words if w not in ignore_words]
# getting a alphabetically sorted list without duplicate words (function of set)
all_words = sorted(set(all_words))
tags = sorted(set(tags))
X_train = []
Y_train = []
for pattern_sentence, tag in xy:
bag = bag_of_words(pattern_sentence, all_words)
X_train.append(bag)
# Get the index of the tag of the tags-list
label = tags.index(tag)
Y_train.append(label) # CrossEntropyLoss
# Create np.arrays, arrays with only zeros with the length of corresponding data
X_train = np.array(X_train)
Y_train = np.array(Y_train)
# Dataset-Class to train it easily
class ChatDataSet(Dataset):
def __init__(self):
self.n_samples = len(X_train)
self.x_data = X_train
self.y_data = Y_train
def __getitem__(self, index):
return self.x_data[index], self.y_data[index]
def __len__(self):
return self.n_samples
# Hyperparameters
batch_size = 8
hidden_size = 80
output_size = len(tags)
input_size = len(all_words)
# Creating a custom data-set to feed into the neural network
dataset = ChatDataSet()
train_loader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True)
# Checking if working with gpu is available
device = torch.device("cpu")
# Defining the model and using it for training
model = NeuralNet(input_size, hidden_size, output_size).to(device)
# loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
for epoch in range(num_epochs):
for words, labels in train_loader:
words = words.to(device)
labels = labels.to(device, torch.int64)
# forward
outputs = model(words)
loss = criterion(outputs, labels)
# backward and optimizer step
optimizer.zero_grad()
loss.backward()
optimizer.step()
if epoch % 100 == 0:
print("Epoch " + str(epoch) + " finished! " + f"loss={loss.item():.4}" + "\n " + str(num_epochs - epoch)
+ " remaining!")
data = {
"model_state": model.state_dict(),
"input_size": input_size,
"output_size": output_size,
"hidden_size": hidden_size,
"all_words": all_words,
"tags": tags
}
FILE = "Terra-Speak.pth"
torch.save(data, FILE)
print(f"Training complete! Model named {FILE} saved.")
xy=[]
for intent in data:
tag = intent['tags']
tags.append(tag)
for pattern in intent['patterns']:
w = tokenize(pattern)
all_words.extend(w)
xy.append((w,tag))
miss_words = ['?','!','.',',']
all_words = [stem(w) for w in all_words if w not in miss_words]
all_words = sorted(list(set(all_words)))
tags= sorted(tags)
print(tags)
x_train = []
y_train = []
for (pattern_sentence, tag) in xy:
bag = bag_of_words(pattern_sentence,all_words)
x_train.append(bag)
label = tags.index(tag)
y_train.append(label)
x_train = np.array(x_train)
def train():
nodes = retrieve('nodes')
all_words = []
ids = []
xy = []
# loop through each sentence in our node patterns
for node in nodes:
# add to id list
ids.append(node['id'])
for pattern in node['patterns']:
# tokenize each word in the sentence
w = tokenize(pattern)
# add to our words list
all_words.extend(w)
# add to xy pair
xy.append((w, node['id']))
# stem and lower each word and remove stop words
ignore_words = ['?', '.', '!', '(', ')']
stop_words = retrieve('stop_words')
all_words = [w for w in all_words if not w.lower() in stop_words]
all_words = [stem(w) for w in all_words if w not in ignore_words]
# remove duplicates and sort
all_words = sorted(set(all_words))
ids = sorted(set(ids))
# create training data
x_train = []
y_train = []
for (pattern_sentence, id) in xy:
# X: bag of words for each pattern_sentence
bag = bag_of_words(pattern_sentence, all_words)
x_train.append(bag)
# y: PyTorch CrossEntropyLoss needs only class labels, not one-hot
y_train.append(ids.index(id))
x_train = np.array(x_train)
y_train = np.array(y_train)
# Hyper-parameters
num_epochs = 1000
batch_size = 8
learning_rate = 0.001
input_size = len(x_train[0])
hidden_size = 8
output_size = len(ids)
class ChatDataset(Dataset):
def __init__(self):
self.n_samples = len(x_train)
self.x_data = x_train
self.y_data = y_train
# support indexing such that dataset[i] can be used to get i-th sample
def __getitem__(self, index):
return self.x_data[index], self.y_data[index]
# we can call len(dataset) to return the size
def __len__(self):
return self.n_samples
dataset = ChatDataset()
train_loader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
device = torch.device('cpu')
model = NeuralNet(input_size, hidden_size, output_size).to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# Train the model
for epoch in range(num_epochs):
for (words, labels) in train_loader:
words = words.to(device)
labels = labels.to(dtype=torch.long).to(device)
# Forward pass
outputs = model(words)
# if y would be one-hot, we must apply
# labels = torch.max(labels, 1)[1]
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
data = {
"model_state": model.state_dict(),
"input_size": input_size,
"hidden_size": hidden_size,
"output_size": output_size,
"all_words": all_words,
"ids": ids
}
torch.save(data, "data.pth")
inFile = open('intentions.json', 'r')
intents = json.load(inFile)
words = []
tags = []
combo = []
for i in intents['intents']:
tag = tags.append(i["tag"])
for w in i["patterns"]:
token = makeToken(w)
words.extend(token)
combo.append((w, tag))
ignore = ['?', '.', ',', '!']
words = [stem(w) for w in words if w not in ignore]
words = sorted(set(words))
tags = sorted(tags)
train_data1 = []
train_data2 = []
for (pattern, tag) in combo:
bag = makeBag(pattern, words)
train_data1.append(bag)
label = tags.index(tag)
train_data2.append(label)
np_data1 = np.array(train_data1)
np_data2 = np.array(train_data2)
# recorrer cada oración en nuestros patrones de intenciones
for inten in intenciones['intenciones']:
etiqueta = inten['etiqueta']
# agregar a la lista de etiquetas
etiquetas.append(etiqueta)
for patron in inten['patrones']:
# tokenizar cada palabra de la oración
w = tokenize(patron)
# agregar a nuestra lista de palabras
todas_las_palabras.extend(w)
# agregar al par xy
xy.append((w, etiqueta))
# ignorar palabra
ignorar_palabras = ['?', '.', '!']
todas_las_palabras = [stem(w) for w in todas_las_palabras if w not in ignorar_palabras]
# eliminar duplicados y ordenar
todas_las_palabras = sorted(set(todas_las_palabras))
etiquetas = sorted(set(etiquetas))
print(len(xy), "patrones")
print(len(etiquetas), "etiquetas:", etiquetas)
print(len(todas_las_palabras), "palabras derivadas unicas:", todas_las_palabras)
# crear datos de entrenamiento
X_cola = []
Y_cola = []
for (patron_de_oracion, etiqueta) in xy:
# X: bolsa de palabras para cada patron de sentencia
bolsa = bag_of_words(patron_de_oracion, todas_las_palabras)
X_cola.append(bolsa)
]
#Lemmatization-----------------
t_lem = ''
for w in all_words:
if w not in ignore_words:
t_lem = t_lem + ' ' + w
lemma = lemmatize(t_lem)
all_words_lemma = []
for w in lemma:
all_words_lemma.append(w[2])
#------------------------------
#Stem--------------------------
all_words = [stem(w) for w in all_words_lemma if w not in ignore_words]
#------------------------------
all_words = sorted(set(all_words))
tags = sorted(set(tags))
x_train = []
y_train = []
for (pattern_sentence, tag) in xy:
bag = bag_of_words(pattern_sentence, all_words)
x_train.append(bag)
label = tags.index(tag)
y_train.append(label)
x_train = np.array(x_train)
with open('intents.json', 'r') as f:
intents = json.load(f)
tags = []
all_words = []
xy = []
for intent in intents['intents']:
tags.append(intent['tag'])
for pattern in intent['patterns']:
w = tokenize(pattern)
all_words.extend([word.lower() for word in w])
xy.append((w,intent['tag']))
ignore_words = string.punctuation
all_words = [stem(word) for word in all_words if word not in ignore_words]
tags = sorted(set(tags))
all_words = sorted(set(all_words))
x_train = []
y_train = []
for (sentences, tag) in xy:
word_num = bag_of_words(sentences, all_words)
label = tags.index(tag)
x_train.append(word_num)
y_train.append(label)
x_train = np.array(x_train)
y_train = np.array(y_train)
word_bank = []
tags = []
xy = []
for intent in intents['intents']:
tag = intent['tag']
tags.append(tag)
for pattern in intent['patterns']:
w = tokenize(pattern)
word_bank.extend(w)
xy.append((w, tag))
exclude_punctuation = ['?', '!', '.', ',']
word_bank = [stem(w) for w in word_bank if w not in exclude_punctuation]
word_bank = sorted(set(word_bank))
tags = sorted(set(tags))
print(tags)
X_train = []
Y_train = []
for (pattern_sent, tag) in xy:
bag = bag_of_words(pattern_sent, word_bank)
X_train.append(bag)
labels = tags.index(tag)
Y_train.append(labels)
X_train = np.array(X_train)
with open('intents.json', 'r') as f:
intents = json.load(f)
all_words = []
tags = []
xy = []
for intent in intents['intents']:
tag = intent['tag']
tags.append(tag)
for pattern in intent['patterns']:
w = tokenize(pattern)
all_words.extend(w)
xy.append((w, tag))
ignore_words = ['?', '!', '.', ',']
all_words = [stem(w) for w in all_words if w not in ignore_words]
all_words = sorted(set(all_words))
tags = sorted(set(tags))
X_train = []
y_train = []
for (pattern_sentece, tag) in xy:
bag = bag_of_words(pattern_sentece, all_words)
X_train.append(bag)
label = tags.index(tag)
y_train.append(label)
X_train = np.array(X_train)
y_train = np.array(y_train)
# tokenize each word in the sentence
w = tokenize(pattern)
# add to our words list
all_words.extend(w)
# add to xy pair
xy.append((w, tag))
# stem and lower each word
ignore_words = ['?', '.', '!', '-', ',', '0-9', '(', ')']
stopset = [
'a', 'an', 'the', 'i', 'you', 'one', 'of', 'in', 'for', 'to', 'by',
'about', 'off', 'did', 'am', 'is', 'are', 'was', 'were', 'if', 'is', 'on',
'what', 'why', 'when', 'where', 'which', 'and', 'how', 'tell', 'me', 'my',
'must', 'could', 'that', 'or', 'anyone', 'any', 'many', 'there'
]
all_word = [stem(w) for w in all_words if w not in ignore_words]
# all_word = [tokenize(w) for w in all_words if w not in ignore_words]
all_words = [stem(w) for w in all_word if w not in stopset]
#all_words = [tokenize(w) for w in all_word if w not in stopset]
# remove duplicates and sort
all_words = sorted(set(all_words))
tags = sorted(set(tags))
print(">>", len(xy), "patterns")
print(">>", len(tags), "tags:", tags)
print(len(all_words), "unique stemmed words:", all_words)
# create training data
X_train = []
y_train = []
for (pattern_sentence, tag) in xy:
