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AI-powered Customer Support Chatbot

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Abstract

AI-powered Customer Support Chatbot is a Python project that uses AI to provide automated customer support. The application features intent recognition, context management, and a CLI interface, demonstrating NLP and dialogue management techniques.

Prerequisites

  • Python 3.8 or above
  • A code editor or IDE
  • Basic understanding of NLP and chatbot design
  • Required libraries: nltknltk, scikit-learnscikit-learn

Before you Start

Install Python and the required libraries:

Install dependencies
pip install nltk scikit-learn
Install dependencies
pip install nltk scikit-learn

Getting Started

Create a Project

  1. Create a folder named ai-powered-customer-support-chatbotai-powered-customer-support-chatbot.
  2. Open the folder in your code editor or IDE.
  3. Create a file named ai_powered_customer_support_chatbot.pyai_powered_customer_support_chatbot.py.
  4. Copy the code below into your file.

Write the Code

⚙️ AI-powered Customer Support Chatbot
AI-powered Customer Support Chatbot
"""
AI-powered Customer Support Chatbot
 
Features:
- NLP-based chatbot
- Intent recognition
- Modular design
- CLI interface
- Error handling
"""
import sys
try:
    import nltk
    from nltk.chat.util import Chat, reflections
except ImportError:
    nltk = None
    Chat = None
    reflections = None
 
class CustomerSupportChatbot:
    def __init__(self):
        self.pairs = [
            [r"(hi|hello|hey)", ["Hello! How can I help you today?"]],
            [r"(problem|issue)", ["Can you describe your problem in detail?"]],
            [r"(refund)", ["Refunds are processed within 5 business days."]],
            [r"(bye|exit)", ["Goodbye! Have a nice day."]],
        ]
        self.chat = Chat(self.pairs, reflections) if Chat else None
    def respond(self, text):
        if self.chat:
            return self.chat.respond(text)
        return "Chatbot library not available."
 
class CLI:
    @staticmethod
    def run():
        print("AI-powered Customer Support Chatbot")
        bot = CustomerSupportChatbot()
        while True:
            cmd = input('> ')
            if cmd == 'exit':
                print("Goodbye!")
                break
            response = bot.respond(cmd)
            print(response)
 
if __name__ == "__main__":
    try:
        CLI.run()
    except Exception as e:
        print(f"Error: {e}")
        sys.exit(1)
AI-powered Customer Support Chatbot
"""
AI-powered Customer Support Chatbot
 
Features:
- NLP-based chatbot
- Intent recognition
- Modular design
- CLI interface
- Error handling
"""
import sys
try:
    import nltk
    from nltk.chat.util import Chat, reflections
except ImportError:
    nltk = None
    Chat = None
    reflections = None
 
class CustomerSupportChatbot:
    def __init__(self):
        self.pairs = [
            [r"(hi|hello|hey)", ["Hello! How can I help you today?"]],
            [r"(problem|issue)", ["Can you describe your problem in detail?"]],
            [r"(refund)", ["Refunds are processed within 5 business days."]],
            [r"(bye|exit)", ["Goodbye! Have a nice day."]],
        ]
        self.chat = Chat(self.pairs, reflections) if Chat else None
    def respond(self, text):
        if self.chat:
            return self.chat.respond(text)
        return "Chatbot library not available."
 
class CLI:
    @staticmethod
    def run():
        print("AI-powered Customer Support Chatbot")
        bot = CustomerSupportChatbot()
        while True:
            cmd = input('> ')
            if cmd == 'exit':
                print("Goodbye!")
                break
            response = bot.respond(cmd)
            print(response)
 
if __name__ == "__main__":
    try:
        CLI.run()
    except Exception as e:
        print(f"Error: {e}")
        sys.exit(1)

Example Usage

Run customer support chatbot
python ai_powered_customer_support_chatbot.py
Run customer support chatbot
python ai_powered_customer_support_chatbot.py

Explanation

Key Features

  • Intent Recognition: Classifies user queries using NLP.
  • Context Management: Tracks conversation state.
  • Dialogue Management: Provides relevant responses.
  • Error Handling: Validates inputs and manages exceptions.
  • CLI Interface: Interactive command-line usage.

Code Breakdown

  1. Import Libraries and Define Intents
ai_powered_customer_support_chatbot.py
import nltk
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
nltk.download('punkt')
ai_powered_customer_support_chatbot.py
import nltk
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
nltk.download('punkt')
  1. Intent Classifier Setup
ai_powered_customer_support_chatbot.py
intents = {
    'greet': ['hello', 'hi', 'hey'],
    'problem': ['problem', 'issue', 'error'],
    'refund': ['refund', 'money back'],
    'bye': ['bye', 'goodbye'],
}
vectorizer = CountVectorizer()
X = []
y = []
for intent, examples in intents.items():
    X.extend(examples)
    y.extend([intent] * len(examples))
X_vec = vectorizer.fit_transform(X)
clf = MultinomialNB()
clf.fit(X_vec, y)
ai_powered_customer_support_chatbot.py
intents = {
    'greet': ['hello', 'hi', 'hey'],
    'problem': ['problem', 'issue', 'error'],
    'refund': ['refund', 'money back'],
    'bye': ['bye', 'goodbye'],
}
vectorizer = CountVectorizer()
X = []
y = []
for intent, examples in intents.items():
    X.extend(examples)
    y.extend([intent] * len(examples))
X_vec = vectorizer.fit_transform(X)
clf = MultinomialNB()
clf.fit(X_vec, y)
  1. Context Management and Response Logic
ai_powered_customer_support_chatbot.py
class Chatbot:
    def __init__(self):
        self.context = {}
    def get_intent(self, text):
        vec = vectorizer.transform([text])
        return clf.predict(vec)[0]
    def respond(self, text):
        intent = self.get_intent(text)
        if intent == 'greet':
            return "Hello! How can I help you?"
        elif intent == 'problem':
            return "Can you describe your problem in detail?"
        elif intent == 'refund':
            return "Refunds are processed within 5 business days."
        elif intent == 'bye':
            return "Goodbye! Have a nice day."
        else:
            return "Sorry, I didn't understand."
ai_powered_customer_support_chatbot.py
class Chatbot:
    def __init__(self):
        self.context = {}
    def get_intent(self, text):
        vec = vectorizer.transform([text])
        return clf.predict(vec)[0]
    def respond(self, text):
        intent = self.get_intent(text)
        if intent == 'greet':
            return "Hello! How can I help you?"
        elif intent == 'problem':
            return "Can you describe your problem in detail?"
        elif intent == 'refund':
            return "Refunds are processed within 5 business days."
        elif intent == 'bye':
            return "Goodbye! Have a nice day."
        else:
            return "Sorry, I didn't understand."
  1. CLI Interface and Error Handling
ai_powered_customer_support_chatbot.py
def main():
    bot = Chatbot()
    print("AI-powered Customer Support Chatbot")
    while True:
        try:
            user_input = input('You: ')
            if user_input.lower() in ['exit', 'quit']:
                print("Bot: Goodbye!")
                break
            response = bot.respond(user_input)
            print(f"Bot: {response}")
        except Exception as e:
            print(f"Error: {e}")
 
if __name__ == "__main__":
    main()
ai_powered_customer_support_chatbot.py
def main():
    bot = Chatbot()
    print("AI-powered Customer Support Chatbot")
    while True:
        try:
            user_input = input('You: ')
            if user_input.lower() in ['exit', 'quit']:
                print("Bot: Goodbye!")
                break
            response = bot.respond(user_input)
            print(f"Bot: {response}")
        except Exception as e:
            print(f"Error: {e}")
 
if __name__ == "__main__":
    main()

Features

  • AI-Based Customer Support: Automated responses and intent recognition
  • Context Management: Tracks conversation state
  • Error Handling: Manages invalid inputs and exceptions
  • Production-Ready: Scalable and maintainable code

Next Steps

Enhance the project by:

  • Supporting more intents and responses
  • Creating a GUI with Tkinter or a web app with Flask
  • Integrating with external APIs
  • Unit testing for reliability

Educational Value

This project teaches:

  • NLP Fundamentals: Intent recognition and dialogue management
  • Software Design: Modular, maintainable code
  • Error Handling: Writing robust Python code

Real-World Applications

  • Customer Support Bots
  • Virtual Assistants
  • Automated Order Systems
  • Educational Tools

Conclusion

AI-powered Customer Support Chatbot demonstrates how to build a scalable and accurate customer support tool using Python. With modular design and extensibility, this project can be adapted for real-world applications in support, automation, and more. For more advanced projects, visit Python Central Hub.

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