Google Colab chatbot development

Free GPU Power for DialoGPT Development

Running machine learning models and chatbots in Google Colab offers free GPU access, making it perfect for developers without high-end hardware. This guide explores implementing DialoGPT-medium in Google Colab, providing step-by-step instructions for building a functional chatbot interface.

Why Choose Google Colab for Chatbot Development?

Google Colab provides several compelling advantages for chatbot developers:

• Free access to GPU resources
• Pre-installed machine learning libraries
• Cloud-based development environment
• Collaborative features for team projects
• Jupyter notebook compatibility

Setting Up Your Chatbot Environment

Initial Setup Steps

1. Navigate to Google Colab
2. Create a new notebook
3. Upload required files:

• requirements.txt
• test_main.py
• main.py
• confest.py

Installing Dependencies

First, install the necessary packages using pip:

!pip install -r requirements.txt

Testing Your Implementation

Run the test suite to identify potential issues:

!pytest test_main.py

Testing after edit with full code. For source code you can get in below

Building the Chatbot Interface

Core Components

The chatbot implementation uses several key technologies:

• Hugging Face’s DialoGPT-medium model
• Gradio for the user interface
• PyTorch for model operations
• Regular expressions for input processing

Code Implementation

Let’s break down the main components of our chatbot:

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch
import re

# Model initialization
device = 0 if torch.cuda.is_available() else -1
model_name = "microsoft/DialoGPT-medium"
chatbot = pipeline("text-generation", model=model_name, device=device)

Key Features

The chatbot includes several sophisticated features:

1. Conversation History Management
2. Basic Arithmetic Operations
3. Natural Language Processing
4. GPU Acceleration (when available)

Handling Different Types of Inputs

The chatbot can process various input types:

• Basic greetings (“hello”, “bye”)
• Mathematical calculations (“calculate 5 + 3”)
• Natural language conversations

Response Generation

# Initialize conversation history
conversation_history = []
chat_history_ids = None

# Function to generate chatbot response
def chatbot_response(prompt):
    global conversation_history, chat_history_ids

    #write your code below
    if prompt.lower() == "hello":
        response = "Hi there! How can I help you today?"
    elif prompt.lower() == "bye":
        response = "Goodbye! Have a great day!"
    elif "calculate" in prompt.lower():
        match = re.match(r"calculate (\d+) ([+\-*/]) (\d+)", prompt.lower())
        if match:
            num1, operator, num2 = match.groups()
            num1, num2 = int(num1), int(num2)
            if operator == '+':
                result = num1 + num2
            elif operator == '-':
                result = num1 - num2
            elif operator == '*':
                result = num1 * num2
            elif operator == '/':
                result = num1 / num2
            response = f"The result is {result}"
        else:
            response = "Invalid operator and/or calculation format. Please use 'calculate <num1> <operator> <num2>'."
    else:
        # Use the chatbot model for other inputs
        # outputs = chatbot(prompt, max_length=1000, pad_token_id=50256)
        # response = outputs[0]['generated_text'][len(prompt):].strip()
        new_user_input_ids = tokenizer.encode(prompt + tokenizer.eos_token, return_tensors='pt')
        if chat_history_ids is not None:
            bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1)  
        else : 
            bot_input_ids = new_user_input_ids
        chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
        response =tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True) 
        print("DialoGPT: {}".format(response))

    # Update conversation history
    conversation_history.append(f"User: {prompt}")
    conversation_history.append(f"Bot: {response}")


    # Display conversation history (loops and data structures)
    history = "\n".join(conversation_history[-6:])  # Show last 3 interactions
    
    return history

Example Usage

iface = gr.Interface(
    fn=chatbot_response,
    inputs=gr.Textbox(lines=2, placeholder="Enter your message here..."),
    outputs="text",
    title="Chatbot Interface",
    description="A simple chatbot interface using Gradio and Hugging Face's DialoGPT."
)
iface.launch(share=True)

Limitations and Considerations

When using Google Colab for chatbot development, consider these factors:

• Session timeout after 3 hours
• Limited persistent storage
• Variable GPU availability
• Network connectivity requirements

Pro Tips for Colab Usage

1. Save your work frequently
2. Download modified files locally
3. Consider Colab Pro for extended sessions
4. Use runtime disconnection warnings

Deployment and Testing

To run your chatbot:

1. Execute the main script
   • `!python main.py`
2. Access the Gradio interface
3. Test different input scenarios
4. Monitor response quality

Conclusion

Google Colab provides an excellent platform for developing and testing chatbots, especially for those without access to powerful local hardware. While it has some limitations, the benefits of free GPU access and pre-configured environments make it an attractive option for chatbot development.

For more information about the technologies used, visit:

• Google Colab Documentation
• Hugging Face’s DialoGPT
• Gradio Documentation

Full code you can see in my github : https://github.com/teguhteja/rea-ai-bootcamp/blob/master/python-prep-project/main.py