Building a Chatbot with Taipy in 8 simple steps

What is Taipy?

Taipy is a Python open-source library designed for easy development of data-driven web applications.

Taipy covers both front-end and back-end. It has been designed to expedite application development, from initial prototypes to production-ready applications.

User Interface Creation:

Understanding GUI

Taipy aims to simplify web application development:

• Accelerates application building.
• Streamlines management of variables and events.
• Offers intuitive visualization using Markdown syntax.

Scenario and Data Management:

Scenario management Overview

Taipy brings a suite of features to streamline data pipeline orchestration:

• It registers each pipeline execution, enabling users to monitor KPIs over time and benchmark different runs, providing what-if scenarios.
• Taipy includes ready-to-use UI components for pipeline interaction — allowing for the selection of inputs and parameters, execution and tracking of pipelines, and visualization of results.
• Taipy efficiently manages computations, avoiding unnecessary reruns of unchanged data.
• Taipy easily integrates with the most popular data sources.
• It supports concurrent computing, enhancing processing speed and scalability.

Let’s creating ChatBot

Creating a chatbot using Taipy involves integrating OpenAI’s GPT-3.5 API to generate intelligent responses. Here’s a structured and detailed guide to help you build your chatbot from scratch.

Step 1: Install Requirements

First, 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. Then, 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 {
  /* Your CSS for GPT messages */
}
.user_message td {
  /* Your CSS for user messages */
}

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|>

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.

References

Tutorials

Gallery