Chapter 12: Chatbot Project: Customer Support Chatbot
12.5 Complete Project Code
This section will include the complete Python code for the customer support chatbot project. This encompasses all the steps from data collection and preprocessing to building, training, and maintaining the chatbot.
Please note that this code is meant to be a general guide and might need to be adjusted based on the specific requirements of your chatbot project.
# Please note that this is a simplified example and the actual code might be more complex.
# Import necessary libraries
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM, Embedding
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
# Data Collection
# This part will depend on where you're getting your data from.
# For simplicity, let's assume you have a CSV file with two columns: 'user_query' and 'bot_response'.
data = pd.read_csv('chatbot_data.csv')
# Data Preprocessing
tokenizer = Tokenizer()
tokenizer.fit_on_texts(data['user_query'])
sequences = tokenizer.texts_to_sequences(data['user_query'])
padded_sequences = pad_sequences(sequences, maxlen=100)
# Building the model
model = Sequential()
model.add(Embedding(10000, 64, input_length=100))
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid'))
# Compiling the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Training the model
model.fit(padded_sequences, data['bot_response'], epochs=10, validation_split=0.2)
# Save the model for future use
model.save('customer_support_chatbot.h5')
# Define a function to use the chatbot
def respond_to_query(query):
sequence = tokenizer.texts_to_sequences([query])
padded_sequence = pad_sequences(sequence, maxlen=100)
prediction = model.predict(padded_sequence)
return 'Yes' if prediction > 0.5 else 'No'
# Test the chatbot
print(respond_to_query('What are your working hours?'))
# Note: The actual implementation of a chatbot would be much more complex than this and may require different types of models depending on the specifics of your use case.Please remember, this is a simplified version of the code and the actual code would be more complex, incorporating concepts from the entire book, including data handling, NLP, deep learning, and deployment specifics. However, this example should give you a general idea of what the complete project code might look like.
Remember to keep your code well-structured, well-documented, and modular. This not only helps others to understand your code, but also makes it easier to maintain and update your project in the future.
12.5 Complete Project Code
This section will include the complete Python code for the customer support chatbot project. This encompasses all the steps from data collection and preprocessing to building, training, and maintaining the chatbot.
Please note that this code is meant to be a general guide and might need to be adjusted based on the specific requirements of your chatbot project.
# Please note that this is a simplified example and the actual code might be more complex.
# Import necessary libraries
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM, Embedding
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
# Data Collection
# This part will depend on where you're getting your data from.
# For simplicity, let's assume you have a CSV file with two columns: 'user_query' and 'bot_response'.
data = pd.read_csv('chatbot_data.csv')
# Data Preprocessing
tokenizer = Tokenizer()
tokenizer.fit_on_texts(data['user_query'])
sequences = tokenizer.texts_to_sequences(data['user_query'])
padded_sequences = pad_sequences(sequences, maxlen=100)
# Building the model
model = Sequential()
model.add(Embedding(10000, 64, input_length=100))
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid'))
# Compiling the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Training the model
model.fit(padded_sequences, data['bot_response'], epochs=10, validation_split=0.2)
# Save the model for future use
model.save('customer_support_chatbot.h5')
# Define a function to use the chatbot
def respond_to_query(query):
sequence = tokenizer.texts_to_sequences([query])
padded_sequence = pad_sequences(sequence, maxlen=100)
prediction = model.predict(padded_sequence)
return 'Yes' if prediction > 0.5 else 'No'
# Test the chatbot
print(respond_to_query('What are your working hours?'))
# Note: The actual implementation of a chatbot would be much more complex than this and may require different types of models depending on the specifics of your use case.Please remember, this is a simplified version of the code and the actual code would be more complex, incorporating concepts from the entire book, including data handling, NLP, deep learning, and deployment specifics. However, this example should give you a general idea of what the complete project code might look like.
Remember to keep your code well-structured, well-documented, and modular. This not only helps others to understand your code, but also makes it easier to maintain and update your project in the future.
12.5 Complete Project Code
This section will include the complete Python code for the customer support chatbot project. This encompasses all the steps from data collection and preprocessing to building, training, and maintaining the chatbot.
Please note that this code is meant to be a general guide and might need to be adjusted based on the specific requirements of your chatbot project.
# Please note that this is a simplified example and the actual code might be more complex.
# Import necessary libraries
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM, Embedding
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
# Data Collection
# This part will depend on where you're getting your data from.
# For simplicity, let's assume you have a CSV file with two columns: 'user_query' and 'bot_response'.
data = pd.read_csv('chatbot_data.csv')
# Data Preprocessing
tokenizer = Tokenizer()
tokenizer.fit_on_texts(data['user_query'])
sequences = tokenizer.texts_to_sequences(data['user_query'])
padded_sequences = pad_sequences(sequences, maxlen=100)
# Building the model
model = Sequential()
model.add(Embedding(10000, 64, input_length=100))
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid'))
# Compiling the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Training the model
model.fit(padded_sequences, data['bot_response'], epochs=10, validation_split=0.2)
# Save the model for future use
model.save('customer_support_chatbot.h5')
# Define a function to use the chatbot
def respond_to_query(query):
sequence = tokenizer.texts_to_sequences([query])
padded_sequence = pad_sequences(sequence, maxlen=100)
prediction = model.predict(padded_sequence)
return 'Yes' if prediction > 0.5 else 'No'
# Test the chatbot
print(respond_to_query('What are your working hours?'))
# Note: The actual implementation of a chatbot would be much more complex than this and may require different types of models depending on the specifics of your use case.Please remember, this is a simplified version of the code and the actual code would be more complex, incorporating concepts from the entire book, including data handling, NLP, deep learning, and deployment specifics. However, this example should give you a general idea of what the complete project code might look like.
Remember to keep your code well-structured, well-documented, and modular. This not only helps others to understand your code, but also makes it easier to maintain and update your project in the future.
12.5 Complete Project Code
This section will include the complete Python code for the customer support chatbot project. This encompasses all the steps from data collection and preprocessing to building, training, and maintaining the chatbot.
Please note that this code is meant to be a general guide and might need to be adjusted based on the specific requirements of your chatbot project.
# Please note that this is a simplified example and the actual code might be more complex.
# Import necessary libraries
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM, Embedding
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
# Data Collection
# This part will depend on where you're getting your data from.
# For simplicity, let's assume you have a CSV file with two columns: 'user_query' and 'bot_response'.
data = pd.read_csv('chatbot_data.csv')
# Data Preprocessing
tokenizer = Tokenizer()
tokenizer.fit_on_texts(data['user_query'])
sequences = tokenizer.texts_to_sequences(data['user_query'])
padded_sequences = pad_sequences(sequences, maxlen=100)
# Building the model
model = Sequential()
model.add(Embedding(10000, 64, input_length=100))
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid'))
# Compiling the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Training the model
model.fit(padded_sequences, data['bot_response'], epochs=10, validation_split=0.2)
# Save the model for future use
model.save('customer_support_chatbot.h5')
# Define a function to use the chatbot
def respond_to_query(query):
sequence = tokenizer.texts_to_sequences([query])
padded_sequence = pad_sequences(sequence, maxlen=100)
prediction = model.predict(padded_sequence)
return 'Yes' if prediction > 0.5 else 'No'
# Test the chatbot
print(respond_to_query('What are your working hours?'))
# Note: The actual implementation of a chatbot would be much more complex than this and may require different types of models depending on the specifics of your use case.Please remember, this is a simplified version of the code and the actual code would be more complex, incorporating concepts from the entire book, including data handling, NLP, deep learning, and deployment specifics. However, this example should give you a general idea of what the complete project code might look like.
Remember to keep your code well-structured, well-documented, and modular. This not only helps others to understand your code, but also makes it easier to maintain and update your project in the future.