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Menu iconMenu iconNatural Language Processing with Python Updated Edition
Natural Language Processing with Python Updated Edition
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Chapter 10: Introduction to Chatbots
10.1 What is a Chatbot?
10.2 Applications of Chatbots
10.3 Types of Chatbots: Rule-Based, Self-Learning, and Hybrid
Practical Exercises
Chapter Summary
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Chapter 10: Introduction to Chatbots

10.2 Applications of Chatbots

Chatbots have become an indispensable tool across various industries, revolutionizing the way businesses and organizations interact with customers and users. By automating conversations and providing instant responses, chatbots enhance efficiency, improve user experience, and reduce operational costs. In this section, we will explore the diverse applications of chatbots, highlighting their impact and benefits in different domains.

10.2.1 Customer Service

One of the most prevalent applications of chatbots is in customer service. Chatbots can handle a wide range of customer inquiries, provide support, and resolve issues in real-time. This 24/7 availability ensures that customers receive assistance whenever they need it, improving overall satisfaction.

Example: Customer Service Chatbot

Here's the implementation of a real-world customer service chatbot using GPT-4 for more dynamic responses, while also handling predefined queries.

First, make sure you have installed the OpenAI Python client library and set up your API key.

Here's the updated script:

First, install the OpenAI Python client library if you haven't already:

pip install openai
import openai

# Replace with your own OpenAI API key
openai.api_key = 'your-api-key'

def customer_service_chatbot(user_input):
    # Predefined responses for specific questions
    predefined_responses = {
        "what are your store hours?": "Our store is open from 9 AM to 9 PM, Monday to Saturday.",
        "where is your store located?": "Our store is located at 123 Main Street, Anytown, USA.",
        "what is your return policy?": "You can return any item within 30 days of purchase with a receipt."
    }

    # Check if the input matches any predefined responses
    user_input_lower = user_input.lower()
    if user_input_lower in predefined_responses:
        return predefined_responses[user_input_lower]

    # If no predefined response matches, use GPT-4 for a more dynamic response
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=f"User: {user_input}\\nCustomer Service Bot:",
        max_tokens=150,
        n=1,
        stop=None,
        temperature=0.7,
    )

    return response.choices[0].text.strip()

# Test the chatbot
while True:
    user_input = input("You: ")
    if user_input.lower() == "exit":
        print("Customer Service Bot: Thank you for reaching out! Have a great day!")
        break
    response = customer_service_chatbot(user_input)
    print(f"Customer Service Bot: {response}")

In this script:

  1. The customer_service_chatbot function first checks if the user input matches any predefined responses.
  2. If a match is found, it returns the predefined response.
  3. If no predefined response matches, it queries GPT-4 for a dynamic response.
  4. The chatbot runs in a loop, allowing continuous interaction until the user types "exit".

Make sure to replace 'your-api-key' with your actual OpenAI API key. This implementation provides a basic structure for a customer service chatbot using GPT-4 for dynamic responses while also handling predefined queries.

10.2.2 E-commerce

In the e-commerce sector, chatbots enhance the shopping experience by assisting customers with product recommendations, order tracking, and purchasing processes. They can also handle inquiries about product details, availability, and promotions, helping customers make informed decisions.

Example: E-commerce Chatbot

Let's extend our customer service chatbot to include functionalities for an e-commerce setting, such as product recommendations and order tracking.

Here's the updated script:

import openai

# Replace with your own OpenAI API key
openai.api_key = 'your-api-key'

def ecommerce_chatbot(user_input):
    # Predefined responses for specific questions
    predefined_responses = {
        "can you recommend a product?": "Sure! I recommend our best-selling product, the Smart Home Speaker. It's currently on sale!",
        "where is my order?": "Please provide your order number, and I'll check the status for you.",
        "what are your store hours?": "Our store is open from 9 AM to 9 PM, Monday to Saturday.",
        "where is your store located?": "Our store is located at 123 Main Street, Anytown, USA.",
        "what is your return policy?": "You can return any item within 30 days of purchase with a receipt."
    }

    # Check if the input matches any predefined responses
    user_input_lower = user_input.lower()
    if user_input_lower in predefined_responses:
        return predefined_responses[user_input_lower]

    # If no predefined response matches, use GPT-4 for a more dynamic response
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=f"User: {user_input}\\nE-commerce Bot:",
        max_tokens=150,
        n=1,
        stop=None,
        temperature=0.7,
    )

    return response.choices[0].text.strip()

# Test the chatbot
while True:
    user_input = input("You: ")
    if user_input.lower() == "exit":
        print("E-commerce Bot: Thank you for reaching out! Have a great day!")
        break
    response = ecommerce_chatbot(user_input)
    print(f"E-commerce Bot: {response}")

In this script:

  1. The ecommerce_chatbot function first checks if the user input matches any predefined responses.
  2. If a match is found, it returns the predefined response.
  3. If no predefined response matches, it queries GPT-4 for a dynamic response.
  4. The chatbot runs in a loop, allowing continuous interaction until the user types "exit".

10.2.3 Healthcare

In healthcare, chatbots provide valuable support by offering health information, scheduling appointments, and even providing preliminary diagnosis based on symptoms. They help improve patient engagement and accessibility, especially for those seeking quick answers to health-related questions.

Example: Healthcare Chatbot

Let’s create a real-world chatbot implementation in Python. As before, we'll use the OpenAI API to integrate GPT-4 into the chatbot.

Next, set up your Python script as follows:

import openai

# Replace with your own OpenAI API key
openai.api_key = 'your-api-key'

def healthcare_chatbot(user_input):
    # Predefined responses for specific questions
    predefined_responses = {
        "what should I do if I have a fever?": "If you have a fever, it's important to rest, stay hydrated, and monitor your temperature. If it persists, please consult a healthcare professional.",
        "can I schedule an appointment?": "Sure! Please provide your preferred date and time for the appointment.",
        "what are your office hours?": "Our office is open from 8 AM to 5 PM, Monday to Friday.",
        "where is your clinic located?": "Our clinic is located at 456 Health Avenue, Wellness City, USA."
    }

    # Check if the input matches any predefined responses
    user_input_lower = user_input.lower()
    if user_input_lower in predefined_responses:
        return predefined_responses[user_input_lower]

    # If no predefined response matches, use GPT-4 for a more dynamic response
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=f"User: {user_input}\\nHealthcare Bot:",
        max_tokens=150,
        n=1,
        stop=None,
        temperature=0.7,
    )

    return response.choices[0].text.strip()

# Test the chatbot
while True:
    user_input = input("You: ")
    if user_input.lower() == "exit":
        print("Healthcare Bot: Thank you for reaching out! Stay healthy!")
        break
    response = healthcare_chatbot(user_input)
    print(f"Healthcare Bot: {response}")

In this script:

  1. We use the openai library to access the GPT-4 model.
  2. The healthcare_chatbot function first checks if the user input matches any predefined responses.
  3. If a match is found, it returns the predefined response.
  4. If no predefined response matches, it queries GPT-4 for a dynamic response.
  5. The chatbot runs in a loop, allowing continuous interaction until the user types "exit".

This implementation provides a basic structure for a healthcare chatbot using GPT-4 for dynamic responses while also handling predefined queries.

10.2 Applications of Chatbots

Chatbots have become an indispensable tool across various industries, revolutionizing the way businesses and organizations interact with customers and users. By automating conversations and providing instant responses, chatbots enhance efficiency, improve user experience, and reduce operational costs. In this section, we will explore the diverse applications of chatbots, highlighting their impact and benefits in different domains.

10.2.1 Customer Service

One of the most prevalent applications of chatbots is in customer service. Chatbots can handle a wide range of customer inquiries, provide support, and resolve issues in real-time. This 24/7 availability ensures that customers receive assistance whenever they need it, improving overall satisfaction.

Example: Customer Service Chatbot

Here's the implementation of a real-world customer service chatbot using GPT-4 for more dynamic responses, while also handling predefined queries.

First, make sure you have installed the OpenAI Python client library and set up your API key.

Here's the updated script:

First, install the OpenAI Python client library if you haven't already:

pip install openai
import openai

# Replace with your own OpenAI API key
openai.api_key = 'your-api-key'

def customer_service_chatbot(user_input):
    # Predefined responses for specific questions
    predefined_responses = {
        "what are your store hours?": "Our store is open from 9 AM to 9 PM, Monday to Saturday.",
        "where is your store located?": "Our store is located at 123 Main Street, Anytown, USA.",
        "what is your return policy?": "You can return any item within 30 days of purchase with a receipt."
    }

    # Check if the input matches any predefined responses
    user_input_lower = user_input.lower()
    if user_input_lower in predefined_responses:
        return predefined_responses[user_input_lower]

    # If no predefined response matches, use GPT-4 for a more dynamic response
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=f"User: {user_input}\\nCustomer Service Bot:",
        max_tokens=150,
        n=1,
        stop=None,
        temperature=0.7,
    )

    return response.choices[0].text.strip()

# Test the chatbot
while True:
    user_input = input("You: ")
    if user_input.lower() == "exit":
        print("Customer Service Bot: Thank you for reaching out! Have a great day!")
        break
    response = customer_service_chatbot(user_input)
    print(f"Customer Service Bot: {response}")

In this script:

  1. The customer_service_chatbot function first checks if the user input matches any predefined responses.
  2. If a match is found, it returns the predefined response.
  3. If no predefined response matches, it queries GPT-4 for a dynamic response.
  4. The chatbot runs in a loop, allowing continuous interaction until the user types "exit".

Make sure to replace 'your-api-key' with your actual OpenAI API key. This implementation provides a basic structure for a customer service chatbot using GPT-4 for dynamic responses while also handling predefined queries.

10.2.2 E-commerce

In the e-commerce sector, chatbots enhance the shopping experience by assisting customers with product recommendations, order tracking, and purchasing processes. They can also handle inquiries about product details, availability, and promotions, helping customers make informed decisions.

Example: E-commerce Chatbot

Let's extend our customer service chatbot to include functionalities for an e-commerce setting, such as product recommendations and order tracking.

Here's the updated script:

import openai

# Replace with your own OpenAI API key
openai.api_key = 'your-api-key'

def ecommerce_chatbot(user_input):
    # Predefined responses for specific questions
    predefined_responses = {
        "can you recommend a product?": "Sure! I recommend our best-selling product, the Smart Home Speaker. It's currently on sale!",
        "where is my order?": "Please provide your order number, and I'll check the status for you.",
        "what are your store hours?": "Our store is open from 9 AM to 9 PM, Monday to Saturday.",
        "where is your store located?": "Our store is located at 123 Main Street, Anytown, USA.",
        "what is your return policy?": "You can return any item within 30 days of purchase with a receipt."
    }

    # Check if the input matches any predefined responses
    user_input_lower = user_input.lower()
    if user_input_lower in predefined_responses:
        return predefined_responses[user_input_lower]

    # If no predefined response matches, use GPT-4 for a more dynamic response
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=f"User: {user_input}\\nE-commerce Bot:",
        max_tokens=150,
        n=1,
        stop=None,
        temperature=0.7,
    )

    return response.choices[0].text.strip()

# Test the chatbot
while True:
    user_input = input("You: ")
    if user_input.lower() == "exit":
        print("E-commerce Bot: Thank you for reaching out! Have a great day!")
        break
    response = ecommerce_chatbot(user_input)
    print(f"E-commerce Bot: {response}")

In this script:

  1. The ecommerce_chatbot function first checks if the user input matches any predefined responses.
  2. If a match is found, it returns the predefined response.
  3. If no predefined response matches, it queries GPT-4 for a dynamic response.
  4. The chatbot runs in a loop, allowing continuous interaction until the user types "exit".

10.2.3 Healthcare

In healthcare, chatbots provide valuable support by offering health information, scheduling appointments, and even providing preliminary diagnosis based on symptoms. They help improve patient engagement and accessibility, especially for those seeking quick answers to health-related questions.

Example: Healthcare Chatbot

Let’s create a real-world chatbot implementation in Python. As before, we'll use the OpenAI API to integrate GPT-4 into the chatbot.

Next, set up your Python script as follows:

import openai

# Replace with your own OpenAI API key
openai.api_key = 'your-api-key'

def healthcare_chatbot(user_input):
    # Predefined responses for specific questions
    predefined_responses = {
        "what should I do if I have a fever?": "If you have a fever, it's important to rest, stay hydrated, and monitor your temperature. If it persists, please consult a healthcare professional.",
        "can I schedule an appointment?": "Sure! Please provide your preferred date and time for the appointment.",
        "what are your office hours?": "Our office is open from 8 AM to 5 PM, Monday to Friday.",
        "where is your clinic located?": "Our clinic is located at 456 Health Avenue, Wellness City, USA."
    }

    # Check if the input matches any predefined responses
    user_input_lower = user_input.lower()
    if user_input_lower in predefined_responses:
        return predefined_responses[user_input_lower]

    # If no predefined response matches, use GPT-4 for a more dynamic response
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=f"User: {user_input}\\nHealthcare Bot:",
        max_tokens=150,
        n=1,
        stop=None,
        temperature=0.7,
    )

    return response.choices[0].text.strip()

# Test the chatbot
while True:
    user_input = input("You: ")
    if user_input.lower() == "exit":
        print("Healthcare Bot: Thank you for reaching out! Stay healthy!")
        break
    response = healthcare_chatbot(user_input)
    print(f"Healthcare Bot: {response}")

In this script:

  1. We use the openai library to access the GPT-4 model.
  2. The healthcare_chatbot function first checks if the user input matches any predefined responses.
  3. If a match is found, it returns the predefined response.
  4. If no predefined response matches, it queries GPT-4 for a dynamic response.
  5. The chatbot runs in a loop, allowing continuous interaction until the user types "exit".

This implementation provides a basic structure for a healthcare chatbot using GPT-4 for dynamic responses while also handling predefined queries.

10.2 Applications of Chatbots

Chatbots have become an indispensable tool across various industries, revolutionizing the way businesses and organizations interact with customers and users. By automating conversations and providing instant responses, chatbots enhance efficiency, improve user experience, and reduce operational costs. In this section, we will explore the diverse applications of chatbots, highlighting their impact and benefits in different domains.

10.2.1 Customer Service

One of the most prevalent applications of chatbots is in customer service. Chatbots can handle a wide range of customer inquiries, provide support, and resolve issues in real-time. This 24/7 availability ensures that customers receive assistance whenever they need it, improving overall satisfaction.

Example: Customer Service Chatbot

Here's the implementation of a real-world customer service chatbot using GPT-4 for more dynamic responses, while also handling predefined queries.

First, make sure you have installed the OpenAI Python client library and set up your API key.

Here's the updated script:

First, install the OpenAI Python client library if you haven't already:

pip install openai
import openai

# Replace with your own OpenAI API key
openai.api_key = 'your-api-key'

def customer_service_chatbot(user_input):
    # Predefined responses for specific questions
    predefined_responses = {
        "what are your store hours?": "Our store is open from 9 AM to 9 PM, Monday to Saturday.",
        "where is your store located?": "Our store is located at 123 Main Street, Anytown, USA.",
        "what is your return policy?": "You can return any item within 30 days of purchase with a receipt."
    }

    # Check if the input matches any predefined responses
    user_input_lower = user_input.lower()
    if user_input_lower in predefined_responses:
        return predefined_responses[user_input_lower]

    # If no predefined response matches, use GPT-4 for a more dynamic response
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=f"User: {user_input}\\nCustomer Service Bot:",
        max_tokens=150,
        n=1,
        stop=None,
        temperature=0.7,
    )

    return response.choices[0].text.strip()

# Test the chatbot
while True:
    user_input = input("You: ")
    if user_input.lower() == "exit":
        print("Customer Service Bot: Thank you for reaching out! Have a great day!")
        break
    response = customer_service_chatbot(user_input)
    print(f"Customer Service Bot: {response}")

In this script:

  1. The customer_service_chatbot function first checks if the user input matches any predefined responses.
  2. If a match is found, it returns the predefined response.
  3. If no predefined response matches, it queries GPT-4 for a dynamic response.
  4. The chatbot runs in a loop, allowing continuous interaction until the user types "exit".

Make sure to replace 'your-api-key' with your actual OpenAI API key. This implementation provides a basic structure for a customer service chatbot using GPT-4 for dynamic responses while also handling predefined queries.

10.2.2 E-commerce

In the e-commerce sector, chatbots enhance the shopping experience by assisting customers with product recommendations, order tracking, and purchasing processes. They can also handle inquiries about product details, availability, and promotions, helping customers make informed decisions.

Example: E-commerce Chatbot

Let's extend our customer service chatbot to include functionalities for an e-commerce setting, such as product recommendations and order tracking.

Here's the updated script:

import openai

# Replace with your own OpenAI API key
openai.api_key = 'your-api-key'

def ecommerce_chatbot(user_input):
    # Predefined responses for specific questions
    predefined_responses = {
        "can you recommend a product?": "Sure! I recommend our best-selling product, the Smart Home Speaker. It's currently on sale!",
        "where is my order?": "Please provide your order number, and I'll check the status for you.",
        "what are your store hours?": "Our store is open from 9 AM to 9 PM, Monday to Saturday.",
        "where is your store located?": "Our store is located at 123 Main Street, Anytown, USA.",
        "what is your return policy?": "You can return any item within 30 days of purchase with a receipt."
    }

    # Check if the input matches any predefined responses
    user_input_lower = user_input.lower()
    if user_input_lower in predefined_responses:
        return predefined_responses[user_input_lower]

    # If no predefined response matches, use GPT-4 for a more dynamic response
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=f"User: {user_input}\\nE-commerce Bot:",
        max_tokens=150,
        n=1,
        stop=None,
        temperature=0.7,
    )

    return response.choices[0].text.strip()

# Test the chatbot
while True:
    user_input = input("You: ")
    if user_input.lower() == "exit":
        print("E-commerce Bot: Thank you for reaching out! Have a great day!")
        break
    response = ecommerce_chatbot(user_input)
    print(f"E-commerce Bot: {response}")

In this script:

  1. The ecommerce_chatbot function first checks if the user input matches any predefined responses.
  2. If a match is found, it returns the predefined response.
  3. If no predefined response matches, it queries GPT-4 for a dynamic response.
  4. The chatbot runs in a loop, allowing continuous interaction until the user types "exit".

10.2.3 Healthcare

In healthcare, chatbots provide valuable support by offering health information, scheduling appointments, and even providing preliminary diagnosis based on symptoms. They help improve patient engagement and accessibility, especially for those seeking quick answers to health-related questions.

Example: Healthcare Chatbot

Let’s create a real-world chatbot implementation in Python. As before, we'll use the OpenAI API to integrate GPT-4 into the chatbot.

Next, set up your Python script as follows:

import openai

# Replace with your own OpenAI API key
openai.api_key = 'your-api-key'

def healthcare_chatbot(user_input):
    # Predefined responses for specific questions
    predefined_responses = {
        "what should I do if I have a fever?": "If you have a fever, it's important to rest, stay hydrated, and monitor your temperature. If it persists, please consult a healthcare professional.",
        "can I schedule an appointment?": "Sure! Please provide your preferred date and time for the appointment.",
        "what are your office hours?": "Our office is open from 8 AM to 5 PM, Monday to Friday.",
        "where is your clinic located?": "Our clinic is located at 456 Health Avenue, Wellness City, USA."
    }

    # Check if the input matches any predefined responses
    user_input_lower = user_input.lower()
    if user_input_lower in predefined_responses:
        return predefined_responses[user_input_lower]

    # If no predefined response matches, use GPT-4 for a more dynamic response
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=f"User: {user_input}\\nHealthcare Bot:",
        max_tokens=150,
        n=1,
        stop=None,
        temperature=0.7,
    )

    return response.choices[0].text.strip()

# Test the chatbot
while True:
    user_input = input("You: ")
    if user_input.lower() == "exit":
        print("Healthcare Bot: Thank you for reaching out! Stay healthy!")
        break
    response = healthcare_chatbot(user_input)
    print(f"Healthcare Bot: {response}")

In this script:

  1. We use the openai library to access the GPT-4 model.
  2. The healthcare_chatbot function first checks if the user input matches any predefined responses.
  3. If a match is found, it returns the predefined response.
  4. If no predefined response matches, it queries GPT-4 for a dynamic response.
  5. The chatbot runs in a loop, allowing continuous interaction until the user types "exit".

This implementation provides a basic structure for a healthcare chatbot using GPT-4 for dynamic responses while also handling predefined queries.

10.2 Applications of Chatbots

Chatbots have become an indispensable tool across various industries, revolutionizing the way businesses and organizations interact with customers and users. By automating conversations and providing instant responses, chatbots enhance efficiency, improve user experience, and reduce operational costs. In this section, we will explore the diverse applications of chatbots, highlighting their impact and benefits in different domains.

10.2.1 Customer Service

One of the most prevalent applications of chatbots is in customer service. Chatbots can handle a wide range of customer inquiries, provide support, and resolve issues in real-time. This 24/7 availability ensures that customers receive assistance whenever they need it, improving overall satisfaction.

Example: Customer Service Chatbot

Here's the implementation of a real-world customer service chatbot using GPT-4 for more dynamic responses, while also handling predefined queries.

First, make sure you have installed the OpenAI Python client library and set up your API key.

Here's the updated script:

First, install the OpenAI Python client library if you haven't already:

pip install openai
import openai

# Replace with your own OpenAI API key
openai.api_key = 'your-api-key'

def customer_service_chatbot(user_input):
    # Predefined responses for specific questions
    predefined_responses = {
        "what are your store hours?": "Our store is open from 9 AM to 9 PM, Monday to Saturday.",
        "where is your store located?": "Our store is located at 123 Main Street, Anytown, USA.",
        "what is your return policy?": "You can return any item within 30 days of purchase with a receipt."
    }

    # Check if the input matches any predefined responses
    user_input_lower = user_input.lower()
    if user_input_lower in predefined_responses:
        return predefined_responses[user_input_lower]

    # If no predefined response matches, use GPT-4 for a more dynamic response
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=f"User: {user_input}\\nCustomer Service Bot:",
        max_tokens=150,
        n=1,
        stop=None,
        temperature=0.7,
    )

    return response.choices[0].text.strip()

# Test the chatbot
while True:
    user_input = input("You: ")
    if user_input.lower() == "exit":
        print("Customer Service Bot: Thank you for reaching out! Have a great day!")
        break
    response = customer_service_chatbot(user_input)
    print(f"Customer Service Bot: {response}")

In this script:

  1. The customer_service_chatbot function first checks if the user input matches any predefined responses.
  2. If a match is found, it returns the predefined response.
  3. If no predefined response matches, it queries GPT-4 for a dynamic response.
  4. The chatbot runs in a loop, allowing continuous interaction until the user types "exit".

Make sure to replace 'your-api-key' with your actual OpenAI API key. This implementation provides a basic structure for a customer service chatbot using GPT-4 for dynamic responses while also handling predefined queries.

10.2.2 E-commerce

In the e-commerce sector, chatbots enhance the shopping experience by assisting customers with product recommendations, order tracking, and purchasing processes. They can also handle inquiries about product details, availability, and promotions, helping customers make informed decisions.

Example: E-commerce Chatbot

Let's extend our customer service chatbot to include functionalities for an e-commerce setting, such as product recommendations and order tracking.

Here's the updated script:

import openai

# Replace with your own OpenAI API key
openai.api_key = 'your-api-key'

def ecommerce_chatbot(user_input):
    # Predefined responses for specific questions
    predefined_responses = {
        "can you recommend a product?": "Sure! I recommend our best-selling product, the Smart Home Speaker. It's currently on sale!",
        "where is my order?": "Please provide your order number, and I'll check the status for you.",
        "what are your store hours?": "Our store is open from 9 AM to 9 PM, Monday to Saturday.",
        "where is your store located?": "Our store is located at 123 Main Street, Anytown, USA.",
        "what is your return policy?": "You can return any item within 30 days of purchase with a receipt."
    }

    # Check if the input matches any predefined responses
    user_input_lower = user_input.lower()
    if user_input_lower in predefined_responses:
        return predefined_responses[user_input_lower]

    # If no predefined response matches, use GPT-4 for a more dynamic response
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=f"User: {user_input}\\nE-commerce Bot:",
        max_tokens=150,
        n=1,
        stop=None,
        temperature=0.7,
    )

    return response.choices[0].text.strip()

# Test the chatbot
while True:
    user_input = input("You: ")
    if user_input.lower() == "exit":
        print("E-commerce Bot: Thank you for reaching out! Have a great day!")
        break
    response = ecommerce_chatbot(user_input)
    print(f"E-commerce Bot: {response}")

In this script:

  1. The ecommerce_chatbot function first checks if the user input matches any predefined responses.
  2. If a match is found, it returns the predefined response.
  3. If no predefined response matches, it queries GPT-4 for a dynamic response.
  4. The chatbot runs in a loop, allowing continuous interaction until the user types "exit".

10.2.3 Healthcare

In healthcare, chatbots provide valuable support by offering health information, scheduling appointments, and even providing preliminary diagnosis based on symptoms. They help improve patient engagement and accessibility, especially for those seeking quick answers to health-related questions.

Example: Healthcare Chatbot

Let’s create a real-world chatbot implementation in Python. As before, we'll use the OpenAI API to integrate GPT-4 into the chatbot.

Next, set up your Python script as follows:

import openai

# Replace with your own OpenAI API key
openai.api_key = 'your-api-key'

def healthcare_chatbot(user_input):
    # Predefined responses for specific questions
    predefined_responses = {
        "what should I do if I have a fever?": "If you have a fever, it's important to rest, stay hydrated, and monitor your temperature. If it persists, please consult a healthcare professional.",
        "can I schedule an appointment?": "Sure! Please provide your preferred date and time for the appointment.",
        "what are your office hours?": "Our office is open from 8 AM to 5 PM, Monday to Friday.",
        "where is your clinic located?": "Our clinic is located at 456 Health Avenue, Wellness City, USA."
    }

    # Check if the input matches any predefined responses
    user_input_lower = user_input.lower()
    if user_input_lower in predefined_responses:
        return predefined_responses[user_input_lower]

    # If no predefined response matches, use GPT-4 for a more dynamic response
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=f"User: {user_input}\\nHealthcare Bot:",
        max_tokens=150,
        n=1,
        stop=None,
        temperature=0.7,
    )

    return response.choices[0].text.strip()

# Test the chatbot
while True:
    user_input = input("You: ")
    if user_input.lower() == "exit":
        print("Healthcare Bot: Thank you for reaching out! Stay healthy!")
        break
    response = healthcare_chatbot(user_input)
    print(f"Healthcare Bot: {response}")

In this script:

  1. We use the openai library to access the GPT-4 model.
  2. The healthcare_chatbot function first checks if the user input matches any predefined responses.
  3. If a match is found, it returns the predefined response.
  4. If no predefined response matches, it queries GPT-4 for a dynamic response.
  5. The chatbot runs in a loop, allowing continuous interaction until the user types "exit".

This implementation provides a basic structure for a healthcare chatbot using GPT-4 for dynamic responses while also handling predefined queries.

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