DEW_with_voice.py

#importing the libraries
import tensorflow as tf
import numpy as np
import pandas as pd
import json
import nltk
import speak
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.layers import Input, Embedding, LSTM , Dense,GlobalMaxPooling1D,Flatten
from tensorflow.keras.models import Model
import matplotlib.pyplot as plt

#importing the dataset
with open('content.json') as content:
  data1 = json.load(content)

#getting all the data to lists
tags = []
inputs = []
responses={}
for intent in data1['intents']:
  responses[intent['tag']]=intent['responses']
  for lines in intent['input']:
    inputs.append(lines)
    tags.append(intent['tag'])

#converting to dataframe
data = pd.DataFrame({"inputs":inputs,
                     "tags":tags})

data = data.sample(frac=1)

#removing punctuations
import string
data['inputs'] = data['inputs'].apply(lambda wrd:[ltrs.lower() for ltrs in wrd if ltrs not in string.punctuation])
data['inputs'] = data['inputs'].apply(lambda wrd: ''.join(wrd))
data

#tokenize the data
from tensorflow.keras.preprocessing.text import Tokenizer
tokenizer = Tokenizer(num_words=2000)
tokenizer.fit_on_texts(data['inputs'])
train = tokenizer.texts_to_sequences(data['inputs'])
#apply padding
from tensorflow.keras.preprocessing.sequence import pad_sequences
x_train = pad_sequences(train)

#encoding the outputs
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y_train = le.fit_transform(data['tags'])


input_shape = x_train.shape[1]
print(input_shape)

#define vocabulary
vocabulary = len(tokenizer.word_index)
print("number of unique words : ",vocabulary)
output_length = le.classes_.shape[0]
print("output length: ",output_length)

#creating the model

i = Input(shape=(input_shape,))
x = Embedding(vocabulary+1,10)(i)
x = LSTM(10,return_sequences=True)(x)
x = Flatten()(x)
x = Dense(output_length,activation="softmax")(x)
model  = Model(i,x)

#compiling the model
model.compile(loss="sparse_categorical_crossentropy",optimizer='adam',metrics=['accuracy'])

#training the model
train = model.fit(x_train,y_train,epochs=200)

#plotting model accuracy
plt.plot(train.history['accuracy'],label='training set accuracy')
plt.plot(train.history['loss'],label='training set loss')
plt.legend()

#chatting
import random


while True:
  texts_p = []
  prediction_input = input('You : ')

  #removing punctuation and converting to lowercase
  prediction_input = [letters.lower() for letters in prediction_input if letters not in string.punctuation]
  prediction_input = ''.join(prediction_input)
  texts_p.append(prediction_input)

  #tokenizing and padding
  prediction_input = tokenizer.texts_to_sequences(texts_p)
  prediction_input = np.array(prediction_input).reshape(-1)
  prediction_input = pad_sequences([prediction_input],input_shape)

  #getting output from model
  output = model.predict(prediction_input)
  output = output.argmax()

  #finding the right tag and predicting
  response_tag = le.inverse_transform([output])[0]
  print("Dew : ",random.choice(responses[response_tag]))
  speak(random.choice(responses[response_tag]))
  if response_tag == "goodbye":
    break