Creating a chatbot using Taipy involves integrating OpenAI's GPT-3.5 API to generate intelligent responses. Here's a structured guide to help you build your own chatbot.

Step 1: Install Requirements

Create a `requirements.txt` file with the following content to set up the necessary software packages that the chatbot will depend on.

taipy==3.0.0
openai==1.3.7

This file lists the specific versions of Taipy and OpenAI packages required.

Install the requirements using:

pip install -r requirements.txt

Step 2: Imports

Create a `main.py` file with the necessary imports:

from taipy.gui import Gui, State, notify
import openai

taipy.gui is used for creating the graphical user interface, and openai is for interacting with the OpenAI API.

Step 3: Initialize Variables

Initialize the conversation context and variables in `main.py`:

context = "The following is a conversation with an AI assistant..."
conversation = {
    "Conversation": ["Who are you?", "Hi! I am GPT-3. How can I help you today?"]
}
current_user_message = ""

context maintains the conversation history, conversation stores the dialog, and current_user_message holds the user's input.

Step 4: Generate Responses

Initialize the OpenAI client and create a function to get responses. This function sends user messages to the OpenAI API and receives the AI's response.

client = openai.Client(api_key="ENTER_YOUR_API_KEY_HERE")

def request(state: State, prompt: str) -> str:
    response = state.client.chat.completions.create(
        messages=[{"role": "user", "content": f"{prompt}"}],
        model="gpt-3.5-turbo",
    )
    return response.choices[0].message.content

client initializes the OpenAI API with your API key. The request function sends a message to the API and returns the AI's response.

Step 5: Handle User Messages

Create a function to process user messages and update the conversation. This function updates the conversation context with the user's message and the AI's response. It appends the user's message to the context, calls the request function to get the AI's response, and updates the conversation history.

def send_message(state: State) -> None:
    state.context += f"Human: \n {state.current_user_message}\n\n AI:"
    answer = request(state, state.context).replace("\n", "")
    state.context += answer
    conv = state.conversation._dict.copy()
    conv["Conversation"] += [state.current_user_message, answer]
    state.conversation = conv
    state.current_user_message = ""

Step 6: Design the User Interface

Define the interface using a Markdown string to define the layout and appearance of the chatbot interface.

page = """
<|{conversation}|table|show_all|width=100%|>
<|{current_user_message}|input|label=Write your message here...|on_action=send_message|class_name=fullwidth|>
"""

This defines a simple interface with a table to display the conversation and an input box for the user's messages.

Step 7: Run the Application

Run the Taipy application. This code starts the Taipy GUI with the defined page layout, enabling dark mode and setting the window title.

if __name__ == "__main__":
    Gui(page).run(dark_mode=True, title="Taipy Chat")

Step 8: Add Styling

Customize the chat interface by creating a `main.css` file:

.gpt_message td { ... }
.user_message td { ... }

Apply these styles dynamically:

def style_conv(state: State, idx: int, row: int) -> str:
    if idx is None:
        return None
    elif idx % 2 == 0:
        return "user_message"
    else:
        return "gpt_message"

Update the table:

<|{conversation}|table|show_all|style=style_conv|>

Step 9: Additional Features

Enhance the chatbot with additional features like notifications, a clear conversation button, and conversation history.

1. Notifications:
   • Use the notify function to inform users of important events.
2. Clear Conversation:
   • Add a button to clear the conversation history.
3. Conversation History:
   • Store and display previous conversations for context.

The full implementation is in the GitHub repository.

Step 10: Secure API Key

Store your API key securely using an environment variable:

import os
client = openai.Client(api_key=os.environ["OPENAI_API_KEY"])

Deploy your application securely, ensuring the API key is not exposed in the code.

By following these steps, you can create a functional and stylish chatbot using Taipy and OpenAI's GPT-3.5. For a detailed guide, visit the official tutorial.