Build a User-Friendly GUI App for Open-Assistant API

Table of Contents

1. Introduction
2. Setting up the Environment
3. Understanding the Architectural Diagram
4. Exploring the Code
5. Running the Application
6. Processing User Input
7. Defining the Language Model Chain
8. Generating Responses
9. Working with Response Containers
10. Conclusion

Introduction

In this article, we will explore how to Create a chatbot using Streamlit and Open Assistant, a powerful large language model. The goal is to demonstrate how You can integrate language models with your own applications. We will walk through the code step by step and explain the functionality of each component. By the end of the article, you will be able to create a chatbot similar to the one demonstrated in the video.

Setting up the Environment

Before we dive into the code, we need to set up our environment. We will create a virtual environment to ensure that our Python installation remains clean. We also need to install a few packages, including Streamlit and Hugging Face. We will provide a requirements.txt file to easily install all the required packages. Additionally, we need to obtain a Hugging Face Hub API token and save it in a .env file. This token is necessary to access the Open Assistant model.

Understanding the Architectural Diagram

To better understand the chatbot's architecture, let's take a look at the diagram. We will be developing both the front-end and the back-end of the chatbot. The back-end will utilize Open Assistant as our large language model, while the front-end will use Streamlit's graphical user interface to accept user inputs and display responses. The chat messages will be sent to the Open Assistant API for generating responses, which will then be displayed back to the user.

Exploring the Code

The code for creating the chatbot is surprisingly concise, with only around 90 lines of code. We will run the app in Visual Studio Code, but it can also be run in Google Colab. The code is divided into sections, starting with the necessary Package imports. We use Streamlit for creating the graphical elements of our app, and a few other packages like Lang Chains and Hugging Face for the language model.

Running the Application

To run the Streamlit app, you simply need to execute the command streamlit run hugging_chat.py in your terminal. This will start a local web server where you can access the chatbot app. Copy the provided link and open it in your browser. You will see the user interface of the chatbot.

Processing User Input

The chatbot app uses Streamlit's text_input element to accept user inputs. When the user enters their question or prompt and presses enter, the input is passed on to the language model for generating a response. The input container displays the user's text using a custom input function. We also have a function called get_text that retrieves the text from the text_input element.

Defining the Language Model Chain

The language model chain is defined using the chain_setup function. It starts with a prompt template that follows a specific format expected by the language model. The template is then used to generate the prompt for the language model chain. In this case, We Are using the Open Assistant model with a parameter of 12 billion. The resulting language model chain is returned.

Generating Responses

To generate responses, we use the generate_response function. This function takes user input and runs it through the language model chain to generate a response. The response is then appended to the chat session. It is important to note that the language model does not have memory of previous conversations, so each message is treated independently.

Working with Response Containers

The response container is responsible for displaying the generated responses from the language model. If there is a user input, the function checks if there is a response, and if so, it creates messages for both the user input and the generated responses. These messages are then appended to the session and displayed in the response container.

Conclusion

In this article, we have learned how to create a chatbot using Streamlit and Open Assistant. We have explored the code step by step and understood how each component works together to create a functioning chatbot. By following the instructions and explanations provided, you should be able to create your own chatbot using language models. Enjoy experimenting and expanding upon the code to create more advanced chatbot applications!

Highlights

• Chatbot development using Streamlit and Open Assistant
• Integration of large language models with custom applications
• Clean environment setup with virtual environment and package installation
• Explanation of the chatbot's architectural diagram
• Concise code implementation with detailed explanations
• Running the chatbot app with Streamlit
• Processing user input and generating responses using language models
• Displaying chat messages using response containers
• Scalability and potential for further enhancements and customization

FAQs

Q: Can I use this code to create a chatbot in a different language? A: Absolutely! The code provided can be used as a foundation to create chatbots in various languages. You would just need to modify the language model and adjust the prompts accordingly.

Q: How can I add memory to the chatbot to remember previous conversations? A: The code in this article demonstrates a basic chatbot without memory. To add memory, you would need to modify the language model chain and incorporate techniques like context tracking or maintaining a conversation history.

Q: Can I deploy this chatbot on a Website or a server? A: Yes, you can deploy the chatbot on a website or a server. Streamlit provides options for deploying apps, such as using Streamlit Sharing or containerizing the app and deploying it on platforms like Heroku or AWS.

Q: Are there any limitations to the language model used in this chatbot? A: The language model used in this chatbot has a limit of 12 billion parameters. While it is powerful, it may not be suitable for every use case. If you require a more specialized or domain-specific language model, you can explore other options provided by Hugging Face.

Q: Is it possible to modify the user interface of the chatbot? A: Yes, the user interface of the chatbot can be customized to match your specific requirements. Streamlit provides a range of UI components and customization options to create a visually appealing and user-friendly interface.

Q: Can I train my own language model for the chatbot? A: Yes, Hugging Face provides tools and resources to train your own language models. You can explore their model training pipelines and adapt them to your specific needs. Training your own language model gives you more control and flexibility over the chatbot's capabilities and responses.