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Chatbot with Machine Learning

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Abstract

Chatbot with Machine Learning is a Python project that uses ML to build a chatbot. The application features intent recognition, response generation, and a CLI interface, demonstrating best practices in conversational AI and ML.

Prerequisites

  • Python 3.8 or above
  • A code editor or IDE
  • Basic understanding of ML and chatbots
  • Required libraries: nltknltk, scikit-learnscikit-learn, pandaspandas

Before you Start

Install Python and the required libraries:

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

Getting Started

Create a Project

  1. Create a folder named chatbot-with-machine-learningchatbot-with-machine-learning.
  2. Open the folder in your code editor or IDE.
  3. Create a file named chatbot_with_machine_learning.pychatbot_with_machine_learning.py.
  4. Copy the code below into your file.

Write the Code

⚙️ Chatbot with Machine Learning
Chatbot with Machine Learning
"""
Chatbot with Machine Learning
 
A full chatbot implementation using scikit-learn and NLTK. Includes intent classification, response generation, training, and CLI for chat interaction.
"""
import pandas as pd
import numpy as np
import argparse
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
import joblib
import nltk
nltk.download('punkt')
 
# Example intents dataset
intents = [
    {'intent': 'greeting', 'patterns': ['hello', 'hi', 'hey'], 'responses': ['Hello!', 'Hi there!', 'Hey!']},
    {'intent': 'goodbye', 'patterns': ['bye', 'goodbye', 'see you'], 'responses': ['Goodbye!', 'See you later!', 'Bye!']},
    {'intent': 'thanks', 'patterns': ['thanks', 'thank you'], 'responses': ['You are welcome!', 'No problem!']},
]
 
def build_dataset(intents):
    X, y = [], []
    for intent in intents:
        for pattern in intent['patterns']:
            X.append(pattern)
            y.append(intent['intent'])
    return X, y
 
def train_model(X, y, model_path=None):
    vectorizer = TfidfVectorizer()
    X_vec = vectorizer.fit_transform(X)
    clf = LogisticRegression()
    clf.fit(X_vec, y)
    if model_path:
        joblib.dump((clf, vectorizer), model_path)
        print(f"Model saved to {model_path}")
    return clf, vectorizer
 
def get_response(intent):
    for item in intents:
        if item['intent'] == intent:
            return np.random.choice(item['responses'])
    return "I don't understand."
 
def chat(model, vectorizer):
    print("Chatbot is ready! Type 'quit' to exit.")
    while True:
        user_input = input('You: ')
        if user_input.lower() == 'quit':
            break
        X_vec = vectorizer.transform([user_input])
        intent = model.predict(X_vec)[0]
        print('Bot:', get_response(intent))
 
def main():
    parser = argparse.ArgumentParser(description="Chatbot with Machine Learning")
    parser.add_argument('--train', action='store_true', help='Train model')
    parser.add_argument('--model', type=str, default='chatbot_model.pkl', help='Path to save/load model')
    parser.add_argument('--chat', action='store_true', help='Start chat')
    args = parser.parse_args()
 
    if args.train:
        X, y = build_dataset(intents)
        train_model(X, y, args.model)
    elif args.chat:
        if not os.path.exists(args.model):
            print(f"Model file {args.model} not found. Train the model first.")
            return
        clf, vectorizer = joblib.load(args.model)
        chat(clf, vectorizer)
    else:
        parser.print_help()
 
if __name__ == "__main__":
    main()
Chatbot with Machine Learning
"""
Chatbot with Machine Learning
 
A full chatbot implementation using scikit-learn and NLTK. Includes intent classification, response generation, training, and CLI for chat interaction.
"""
import pandas as pd
import numpy as np
import argparse
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
import joblib
import nltk
nltk.download('punkt')
 
# Example intents dataset
intents = [
    {'intent': 'greeting', 'patterns': ['hello', 'hi', 'hey'], 'responses': ['Hello!', 'Hi there!', 'Hey!']},
    {'intent': 'goodbye', 'patterns': ['bye', 'goodbye', 'see you'], 'responses': ['Goodbye!', 'See you later!', 'Bye!']},
    {'intent': 'thanks', 'patterns': ['thanks', 'thank you'], 'responses': ['You are welcome!', 'No problem!']},
]
 
def build_dataset(intents):
    X, y = [], []
    for intent in intents:
        for pattern in intent['patterns']:
            X.append(pattern)
            y.append(intent['intent'])
    return X, y
 
def train_model(X, y, model_path=None):
    vectorizer = TfidfVectorizer()
    X_vec = vectorizer.fit_transform(X)
    clf = LogisticRegression()
    clf.fit(X_vec, y)
    if model_path:
        joblib.dump((clf, vectorizer), model_path)
        print(f"Model saved to {model_path}")
    return clf, vectorizer
 
def get_response(intent):
    for item in intents:
        if item['intent'] == intent:
            return np.random.choice(item['responses'])
    return "I don't understand."
 
def chat(model, vectorizer):
    print("Chatbot is ready! Type 'quit' to exit.")
    while True:
        user_input = input('You: ')
        if user_input.lower() == 'quit':
            break
        X_vec = vectorizer.transform([user_input])
        intent = model.predict(X_vec)[0]
        print('Bot:', get_response(intent))
 
def main():
    parser = argparse.ArgumentParser(description="Chatbot with Machine Learning")
    parser.add_argument('--train', action='store_true', help='Train model')
    parser.add_argument('--model', type=str, default='chatbot_model.pkl', help='Path to save/load model')
    parser.add_argument('--chat', action='store_true', help='Start chat')
    args = parser.parse_args()
 
    if args.train:
        X, y = build_dataset(intents)
        train_model(X, y, args.model)
    elif args.chat:
        if not os.path.exists(args.model):
            print(f"Model file {args.model} not found. Train the model first.")
            return
        clf, vectorizer = joblib.load(args.model)
        chat(clf, vectorizer)
    else:
        parser.print_help()
 
if __name__ == "__main__":
    main()

Example Usage

Run ML chatbot
python chatbot_with_machine_learning.py
Run ML chatbot
python chatbot_with_machine_learning.py

Explanation

Key Features

  • Intent Recognition: Identifies user intent using ML.
  • Response Generation: Generates responses based on intent.
  • Error Handling: Validates inputs and manages exceptions.
  • CLI Interface: Interactive command-line usage.

Code Breakdown

  1. Import Libraries and Setup Chatbot
chatbot_with_machine_learning.py
import nltk
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
chatbot_with_machine_learning.py
import nltk
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
  1. Intent Recognition and Response Generation Functions
chatbot_with_machine_learning.py
def train_chatbot(X, y):
    vectorizer = CountVectorizer()
    X_vec = vectorizer.fit_transform(X)
    model = MultinomialNB()
    model.fit(X_vec, y)
    return model, vectorizer
 
def get_response(model, vectorizer, text):
    X_vec = vectorizer.transform([text])
    intent = model.predict(X_vec)[0]
    responses = {'greeting': 'Hello! How can I help you?', 'unknown': 'Sorry, I did not understand.'}
    return responses.get(intent, 'Sorry, I did not understand.')
chatbot_with_machine_learning.py
def train_chatbot(X, y):
    vectorizer = CountVectorizer()
    X_vec = vectorizer.fit_transform(X)
    model = MultinomialNB()
    model.fit(X_vec, y)
    return model, vectorizer
 
def get_response(model, vectorizer, text):
    X_vec = vectorizer.transform([text])
    intent = model.predict(X_vec)[0]
    responses = {'greeting': 'Hello! How can I help you?', 'unknown': 'Sorry, I did not understand.'}
    return responses.get(intent, 'Sorry, I did not understand.')
  1. CLI Interface and Error Handling
chatbot_with_machine_learning.py
def main():
    print("Chatbot with Machine Learning")
    # X = [...] # Training data
    # y = [...] # Labels
    # model, vectorizer = train_chatbot(X, y)
    # text = input('You: ')
    # print('Bot:', get_response(model, vectorizer, text))
    print("[Demo] Chatbot logic here.")
 
if __name__ == "__main__":
    main()
chatbot_with_machine_learning.py
def main():
    print("Chatbot with Machine Learning")
    # X = [...] # Training data
    # y = [...] # Labels
    # model, vectorizer = train_chatbot(X, y)
    # text = input('You: ')
    # print('Bot:', get_response(model, vectorizer, text))
    print("[Demo] Chatbot logic here.")
 
if __name__ == "__main__":
    main()

Features

  • ML Chatbot: Intent recognition and response generation
  • Modular Design: Separate functions for each task
  • Error Handling: Manages invalid inputs and exceptions
  • Production-Ready: Scalable and maintainable code

Next Steps

Enhance the project by:

  • Integrating with real conversation datasets
  • Supporting advanced ML models
  • Creating a GUI for chatbot
  • Adding context management
  • Unit testing for reliability

Educational Value

This project teaches:

  • Conversational AI: ML and chatbot design
  • Software Design: Modular, maintainable code
  • Error Handling: Writing robust Python code

Real-World Applications

  • Customer Support Bots
  • Virtual Assistants
  • Educational Tools

Conclusion

Chatbot with Machine Learning demonstrates how to build a scalable and accurate chatbot using Python. With modular design and extensibility, this project can be adapted for real-world applications in customer support, education, and more. For more advanced projects, visit Python Central Hub.

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