def get_bot_response():
if request.method == "POST":
bot = "Convo"
user_data = request.json
sentence = user_data['message']
sentence = normalization(sentence)
sentence = tokenization(sentence)
# print(sentence)
# print(word_list)
# return jsonify(convo_response="Bot started 2...")
x = bag_of_words(sentence, word_list)
x = torch.from_numpy(x)
x = x.reshape(-1, x.shape[0])
x = x.to(device) # x=torch.tensor(x)# print(x.shape)
output, hidden = model(x)
_, predicted = torch.max(output, dim=1)
tag = tags[predicted.item()]
prob = torch.softmax(output, dim=1)
probability = prob[0][predicted.item()]
if (probability.item() > 0.80):
for i in data['data']:
if tag == i['tag']:
return jsonify(random.choice(i['bot_responses']))
else:
return jsonify("I do not understand...")
def get_bot_response2():
try:
device = torch.device("cpu")
with open('data2.json', 'r') as instances:
data = json.load(instances)
FILE = "dataserialized2.pth"
dataserialized = torch.load(FILE)
seq_length = dataserialized["seq_length"]
input_size = dataserialized["input_size"]
hidden_size = dataserialized["hidden_size"]
num_layers = dataserialized["num_layers"]
num_classes = dataserialized["num_classes"]
word_list = dataserialized["word_list"]
tags = dataserialized["tags"]
model_state = dataserialized["model_state"]
model = LSTM(seq_length, input_size, hidden_size, num_layers,
num_classes).to(device)
model.load_state_dict(model_state)
model.eval()
except Exception as e:
print(e)
if request.method == "POST":
bot = "Convo"
user_data = request.json
sentence = user_data['message'] #
sentence = normalization(sentence)
sentence = tokenization(sentence)
x = bag_of_words(sentence, word_list)
x = torch.from_numpy(x)
x = x.reshape(-1, x.shape[0])
x = x.to(device) # x=torch.tensor(x)# print(x.shape)
output, hidden = model(x)
_, predicted = torch.max(output, dim=1)
tag = tags[predicted.item()]
prob = torch.softmax(output, dim=1)
probability = prob[0][predicted.item()]
if (probability.item() > 0.80):
for i in data['data']:
if tag == i['tag']:
return jsonify(random.choice(i['bot_responses']))
else:
return jsonify("I do not understand...")
from torch.utils.data import DataLoader, Dataset
import numpy as np
from pre_processing import normalization, tokenization, stemming, bag_of_words
from model import LSTM
with open('data3.json', 'r') as instances:
data = json.load(instances)
word_list = [] #create an array with all the words
tags = []
xy = []
for i in data['data']:
tag = i['tag']
tags.append(tag)
for user_response in i['user_responses']:
normalized = normalization(user_response)
words = tokenization(normalized)
word_list.extend(words)
xy.append(
(words, tag)) #array of user responses with the respective tags
word_list = [stemming(word) for word in word_list]
word_list = sorted(set(word_list))
print(tags)
print(word_list)
print(xy)
x_train = []
y_train = []
for (tokenized, tag) in xy:
bag = bag_of_words(tokenized, word_list)