training_py.py

import random
import json
import pickle
import numpy as np
import pandas as pd


import nltk
nltk.download('punkt')
nltk.download('wordnet')
from nltk.stem import WordNetLemmatizer
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Activation,Dropout
from tensorflow.keras.optimizers import SGD

lemmatizer=WordNetLemmatizer()


with open('intents.json') as json_file:
    intents = json.load(json_file)
    

#print(intents)

words=[]
classes=[]
documents=[]
ignore_letters=['?','!','.',',']

for intent in intents['intents']:
  for pattern in intent['patterns']:
    word_list=nltk.word_tokenize(pattern)
    words.extend(word_list)
    documents.append((word_list,intent['tag']))
    if intent['tag'] not in classes:
      classes.append(intent['tag'])


words =[lemmatizer.lemmatize(word) for word in words if word not in ignore_letters]
words = sorted(set(words))
classes=sorted(set(classes))
pickle.dump(words,open('words.pkl','wb'))
pickle.dump(classes,open('classes.pkl','wb'))



training=[]
output_empty=[0]*len(classes)

for document in documents:
  bag=[]
  word_patterns=document[0]
  # words = [lemmatizer.lemmatize(word) for word in words if word and word not in ignore_letters]
  words = [lemmatizer.lemmatize(word) for word in words if word and word not in ignore_letters]

  for word in words:
    bag.append(1) if word in word_patterns else bag.append(0)

  output_row=list(output_empty)
  output_row[classes.index(document[1])]=1
  training.append([bag,output_row])

random.shuffle(training)
training=np.array(training)

train_x=list(training[:,0])
train_y=list(training[:,1])
model=Sequential()
model.add(Dense(128,input_shape=(len(train_x[0]),),activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(len(train_y[0]),activation='softmax'))

# sgd=SGD(lr=0.01,decay=1e-6,momentum=0.9,nesterov=True)
sgd = SGD(learning_rate=0.01, decay=1e-6, momentum=0.9, nesterov=True)

model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy'])
hist = model.fit(np.array(train_x),np.array(train_y),epochs=200,batch_size=5,verbose=1)
# model.save('chatbotmodel.h5', hist)
model.save('chatbotmodel.h5')

print('Training Done')



# import random
# import json
# import pickle
# import numpy as np
# import pandas as pd

# import nltk
# nltk.download('punkt')  # Downloading the tokenizer
# nltk.download('wordnet')  # Downloading the WordNet for lemmatization
# from nltk.stem import WordNetLemmatizer
# from tensorflow.keras.models import Sequential
# from tensorflow.keras.layers import Dense, Activation, Dropout
# from tensorflow.keras.optimizers import SGD

# # Initialize the lemmatizer
# lemmatizer = WordNetLemmatizer()

# # Load intents from the JSON file
# with open('intents.json') as json_file:
#     intents = json.load(json_file)

# # Lists to hold words, classes, and documents
# words = []
# classes = []
# documents = []
# ignore_letters = ['?', '!', '.', ',']  # Characters to ignore

# # Loop through each intent and its patterns
# for intent in intents['intents']:
#     for pattern in intent['patterns']:
#         word_list = nltk.word_tokenize(pattern)  # Tokenize each pattern
#         words.extend(word_list)  # Add words to the words list
#         documents.append((word_list, intent['tag']))  # Add to documents with tag
#         if intent['tag'] not in classes:
#             classes.append(intent['tag'])  # Add tag to classes if not already added

# # Lemmatize words and remove ignore letters
# words = [lemmatizer.lemmatize(word) for word in words if word not in ignore_letters]
# words = sorted(set(words))  # Sort and remove duplicates
# classes = sorted(set(classes))  # Sort classes

# # Save processed words and classes to pickle files
# pickle.dump(words, open('words.pkl', 'wb'))
# pickle.dump(classes, open('classes.pkl', 'wb'))

# # Prepare training data
# training = []
# output_empty = [0] * len(classes)  # Initialize empty output array

# # Create training data
# for document in documents:
#     bag = []  # Initialize bag of words
#     word_patterns = document[0]  # Get the list of tokenized words

#     # Create a bag of words
#     for word in words:
#         bag.append(1) if word in word_patterns else bag.append(0)  # 1 if word is in pattern, else 0

#     output_row = list(output_empty)  # Copy empty output array
#     output_row[classes.index(document[1])] = 1  # Set the correct class index to 1
#     training.append([bag, output_row])  # Add to training data

# # Shuffle the training data
# random.shuffle(training)
# training = np.array(training)  # Convert to numpy array

# # Split into input (X) and output (y)
# train_x = list(training[:, 0])
# train_y = list(training[:, 1])

# # Build the model
# model = Sequential()  # Initialize a sequential model
# model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))  # Input layer
# model.add(Dropout(0.5))  # Dropout layer to prevent overfitting
# model.add(Dense(64, activation='relu'))  # Hidden layer
# model.add(Dropout(0.5))  # Dropout layer
# model.add(Dense(len(train_y[0]), activation='softmax'))  # Output layer

# # Set up the optimizer
# sgd = SGD(learning_rate=0.01, decay=1e-6, momentum=0.9, nesterov=True)

# # Compile the model
# model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

# # Train the model
# hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)

# # Save the trained model
# model.save('chatbotmodel.h5')

# print('Training Done')