Build Your Own ChatGPT Clone in Python!

Table of Contents

1. Introduction
2. Building the Chai GPT Technology
3. Setting Up the Environment
4. Installing Required Libraries
5. Creating the Application
6. Displaying the Chat Messages
7. Getting User Input
8. Passing User Input to the AI Model
9. Storing and Displaying AI Response
10. Conclusion

Introduction

In this article, we will learn how to build a chatbot using the Chai GPT technology. The chatbot will be built using Python, Streamlit, and OpenAI. We will go through the process step by step and Create a fully functional chatbot application. By the end of this tutorial, You will be able to Interact with the chatbot and get responses to your questions.

1. Building the Chai GPT Technology

The first step in building the chatbot is understanding the technology behind it. Chai GPT is a language model developed by OpenAI. It is Based on the GPT-3 (Generative Pre-trained Transformer) architecture and can generate human-like text based on the input it receives.

2. Setting Up the Environment

Before we can start building the chatbot, we need to set up our development environment. We will be using Python and the Streamlit framework for the user interface. It's important to have Python installed on your system before proceeding.

3. Installing Required Libraries

Next, we need to install the necessary libraries and packages to build our chatbot. We will be using the following libraries:

• Streamlit: A Python framework for creating interactive web applications.
• OpenAI: The library that provides access to the OpenAI GPT-3 model.
• Python-dotenv: A library for managing environment variables in Python.

We can install these libraries using the pip Package manager. Open your terminal or command prompt and run the following commands:

pip install streamlit
pip install openai
pip install python-dotenv

4. Creating the Application

Now that our environment is set up and the required libraries are installed, we can start creating our chatbot application. We will be using the Streamlit framework to build the user interface and handle user interactions.

First, we need to import the necessary dependencies. We will be using the Streamlit library and the chat module from the longchain library.

import streamlit as st
from longchain.chat_models import chat_openai

Next, we need to set up the page configuration for our application. We can define the page title, icon, and layout using the st.set_page_config() function.

st.set_page_config(
    page_title="Chatbot",
    page_icon="🤖",
    layout="wide"
)

5. Displaying the Chat Messages

Once the application is set up, we can start displaying the chat messages on the screen. The chat messages will be stored in the session state and retrieved when needed.

messages = st.session_state.get("messages", [])

if not messages:
    messages = [
        {"role": "assistant", "content": "Hello, how can I assist you today?"}
    ]

The messages list is initially set to an empty list. If there are no messages in the session state, we add a default welcome message from the chatbot.

To display the messages, we can use the st.text() function for each message in the list.

for message in messages:
    with st.form(key=message_id):
        st.write(f"{message['role']}: {message['content']}")
        st.write("---")

6. Getting User Input

To enable user interaction with the chatbot, we need to get input from the user. We can use the st.text_input() function to get the user's input.

user_input = st.text_input("User input")

7. Passing User Input to the AI Model

Once we have the user's input, we can pass it to the AI model to generate a response. We will be using the chat_openai function from the longchain library.

response = chat_openai(user_input)

8. Storing and Displaying AI Response

After receiving the response from the AI model, we need to store it and display it on the screen. We will append the AI's response to the messages list and update the session state.

messages.append({"role": "assistant", "content": response})
st.session_state["messages"] = messages

To display the updated messages, we can use the same code as Mentioned in step 5.

for message in messages:
    with st.form(key=message_id):
        st.write(f"{message['role']}: {message['content']}")
        st.write("---")

9. Conclusion

In this tutorial, we have learned how to build a chatbot using the Chai GPT technology, Python, Streamlit, and OpenAI. We have covered the process step by step, from setting up the environment to creating the application and handling user interactions. By following this tutorial, you should now be able to create your own chatbot using the Chai GPT technology.

Highlights

• Learn how to build a chatbot using the Chai GPT technology
• Utilize Python, Streamlit, and OpenAI to create a fully functional chatbot application
• Handle user interactions and generate human-like text responses
• Set up the environment, install required libraries, and create the application
• Display chat messages, get user input, pass input to the AI model, and store/display AI responses

FAQ

Q: What is Chai GPT? A: Chai GPT is a language model developed by OpenAI. It is based on the GPT-3 architecture and can generate human-like text based on the input it receives.

Q: What libraries do I need to build the chatbot? A: You will need to install Streamlit, OpenAI, and Python-dotenv libraries to build the chatbot.

Q: How can I pass user input to the AI model? A: You can use the chat_openai function from the longchain library to pass user input to the AI model and get a response.