ChatterMind AI

An advanced AI chatbot built with natural language processing (NLP) techniques. ChatterMind AI provides intelligent and context-aware responses, suitable for use in customer support, virtual assistants, and conversational apps.

Features

• Natural Language Understanding
• Context-aware Responses
• Easily Integrates with Various Platforms

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/ChatterMindAI.git
   cd ChatterMindAI

2. Install the required packages:

   pip install -r requirements.txt

Usage

Run the chatbot:

python src/chatbot.py

Example

from src.chatbot import Chatbot

chatbot = Chatbot()
response = chatbot.get_response("Hello, how are you?")
print(response)

### `requirements.txt`

transformers torch flask

### `src/chatbot.py`
```python
import transformers
from transformers import pipeline

class Chatbot:
    def __init__(self):
        self.nlp = pipeline("conversational", model="microsoft/DialoGPT-medium")

    def get_response(self, user_input):
        conversation = transformers.Conversation(user_input)
        result = self.nlp(conversation)
        return result.generated_responses[-1]

if __name__ == "__main__":
    bot = Chatbot()
    while True:
        user_input = input("You: ")
        if user_input.lower() == "exit":
            break
        response = bot.get_response(user_input)
        print(f"Bot: {response}")

src/utils.py

# Utility functions can be added here

tests/test_chatbot.py

import unittest
from src.chatbot import Chatbot

class TestChatbot(unittest.TestCase):
    def setUp(self):
        self.chatbot = Chatbot()

    def test_response(self):
        response = self.chatbot.get_response("Hello")
        self.assertIsInstance(response, str)

if __name__ == "__main__":
    unittest.main()

This is just a starting point. You can expand this structure with more features, better error handling, and additional tests as needed.