def chatbot(txt):
#chatbot code here
# Importing the dataset
metadata, idx_q, idx_a = data_preprocessing.load_data(PATH = './')
# Splitting the dataset into the Training set and the Test set
(trainX, trainY), (testX, testY), (validX, validY) = data_utils_1.split_dataset(idx_q, idx_a)
# Embedding
xseq_len = trainX.shape[-1]
yseq_len = trainY.shape[-1]
batch_size = 16
vocab_twit = metadata['idx2w']
xvocab_size = len(metadata['idx2w'])
yvocab_size = xvocab_size
emb_dim = 1024
idx2w, w2idx, limit = data_utils_2.get_metadata()
# Building the seq2seq model
model = seq2seq_wrapper.Seq2Seq(xseq_len = xseq_len,
yseq_len = yseq_len,
xvocab_size = xvocab_size,
yvocab_size = yvocab_size,
ckpt_path = './weights',
emb_dim = emb_dim,
num_layers = 3)
# Loading the weights and Running the session
session = model.restore_last_session()
# Getting the ChatBot predicted answer
def respond(question):
encoded_question = data_utils_2.encode(question, w2idx, limit['maxq'])
answer = model.predict(session, encoded_question)[0]
return data_utils_2.decode(answer, idx2w)
# Setting up the chat
#while True :
''''
engine = pyttsx3.init()
engine.runAndWait()
try:
r = sr.Recognizer()
mic = sr.Microphone()
with mic as source:
r.adjust_for_ambient_noise(source)
audio = r.listen(source)
print('You :')
x=r.recognize_google(audio)
print(x)
except:
continue
question=x.lower()'''
question=txt
#question = input("You: ")
#if question=='good bye':
#print('Ok Bye')
#break
#answer = respond(question)
return respond(question)
def load_data():
# Importing the dataset
metadata, idx_q, idx_a = data_preprocessing.load_data(PATH='./')
# Splitting the dataset into the Training set and the Test set
(trainX,
trainY), (testX,
testY), (validX,
validY) = data_utils_1.split_dataset(idx_q, idx_a)
# Embedding
xseq_len = trainX.shape[-1]
yseq_len = trainY.shape[-1]
batch_size = 16
vocab_twit = metadata['idx2w']
xvocab_size = len(metadata['idx2w'])
yvocab_size = xvocab_size
emb_dim = 1024
idx2w, w2idx, limit = data_utils_2.get_metadata()
return (xseq_len, yseq_len, xvocab_size, yvocab_size, emb_dim)
imp.reload(seq2seq_wrapper) import data_preprocessing import data_utils_1 import data_utils_2 from flask import Flask, jsonify, render_template, request ########## PART 1 - DATA PREPROCESSING ########## # Importing the dataset metadata, idx_q, idx_a = data_preprocessing.load_data(PATH = './') # Splitting the dataset into the Training set and the Test set (trainX, trainY), (testX, testY), (validX, validY) = data_utils_1.split_dataset(idx_q, idx_a) # Embedding xseq_len = trainX.shape[-1] yseq_len = trainY.shape[-1] batch_size = 16 vocab_twit = metadata['idx2w'] xvocab_size = len(metadata['idx2w']) yvocab_size = xvocab_size emb_dim = 1024 idx2w, w2idx, limit = data_utils_2.get_metadata() ########## PART 2 - BUILDING THE SEQ2SEQ MODEL ##########
import data_prep
import data_utils_1
#data preprocess
data_prep.process_data()
# load data from pickle and npy files
metadata, idx_q, idx_a = data.load_data(PATH='./')
(trainX, trainY), (testX,
testY), (validX,
validY) = data_utils_1.split_dataset(idx_q, idx_a)
# parameters
xseq_len = trainX.shape[-1]
yseq_len = trainY.shape[-1]
batch_size = 32
xvocab_size = len(metadata['idx2w'])
yvocab_size = xvocab_size
emb_dim = 1024
import seq2seq_wrapper
model = seq2seq_wrapper.Seq2Seq(xseq_len=xseq_len,
yseq_len=yseq_len,
xvocab_size=xvocab_size,
yvocab_size=yvocab_size,
ckpt_path='./ckpt/',
emb_dim=emb_dim,
num_layers=3)
val_batch_gen = data_utils_1.rand_batch_gen(validX, validY, 32)
