CustomGPT.ai, combined with Streamlit, offers a practical solution for developing interactive web applications tailored to your specific business needs. This combination allows developers to easily create a variety of applications, especially those involving chatbots. The seamless integration of CustomGPT.ai with Streamlit enhances your capability to create engaging and user-friendly applications without requiring an extensive technical background.
In this article, we’ll explore how you can create a chatbot application using Streamlit for your business. Let’s get started.
Streamlit: Web Application Development
Streamlit is a free and open-source framework to quickly create and share visually appealing web applications for machine learning and data science without any cost. It is a Python-based library specifically designed for machine learning engineers. It provides a user-friendly environment to build and showcase your project’s applications.
CustomGPT.ai
CustomGPT.ai platform provides highly customized chatbot solutions for your business. It grows revenue by increasing customer engagement. It also boosts efficiency by lowering costs for employees engaged in customer service. CustomGPT.ai is driven by advanced large language models (LLMs), providing the latest technology. It ensures swift responses for customers of your website while maintaining accuracy and reliability.
Define the Purpose of your Chatbot
Create your Custom chatbot by logging into your CustomGPT account. Define the purpose of your chatbot and train it on your website data. For example, If I want to create a custom chatbot for my online store, I will train it on my website’s product data.
See the full blog on How to create and train your CustomGPT chatbot for your website
Get your API_key
After creating a custom chatbot get your API_key from your CustomGPT.ai account. Save this key to use further for writing code to create a chatbot application for your Streamlit.
See the full blog on How to get your CustomGPT API_key
Selecting the Environment
Select your development environment. Choose the platform where you’ll be working on your chatbot development. In this guide, we’re using the Visual Studio Code editor as our preferred environment. This visual code editor provides an interactive space, seamlessly combining code and documentation, making it a user-friendly workspace for our project.
Install Dependencies
Before starting to write code for creating a Chatbot application using Streamlit, you have to install all the dependencies first. Run the command “pip install sseclient requests streamlit customgpt.ai” to install all these dependencies.
This will ensure that your development environment now has all the tools required for a successful chatbot application creation process.
Make API call: Create Backend Functionality for your chatbot application
In the code below, we define the backend functionality for a CustomGPT.ai chatbot using Python in the customchat.py file. The purpose is to integrate this chatbot with Streamlit, a web application framework.
We here defined a function create_conversation that establishes a new conversation session using the CustomGPT.ai API. It sends a request to create a conversation, and after creating it, it also retrieves and returns the unique session ID.
Then, we created a class called customGPTChat, which is designed to handle interactions with the CustomGPT.ai API. It takes the necessary parameters like API key, project ID, and conversation name. Place the actual details in this part.
Within this class customGPTChat, the chatbot is used for sending prompts to CustomGPT.ai and receiving their responses. With the user prompt It constructs the API request, a custom persona, and a streaming option. Then the response is processed and returned.
The code at the bottom demonstrates that the customBOT function is defined to interact with the CustomGPT.ai chatbot by sending a user prompt.
This backend code works like a communication bridge between the Streamlit front-end and the CustomGPT.ai API, enabling the creation of a responsive and intelligent chatbot for various applications, particularly for customer support.
Streamlit Application Code for CusomGPT Chatbot
Now we will use the Streamlit library to create the user interface for our chatbot application. We created a chatbot_app.py file for creating a Streamlit chatbot Application interface. The code begins by setting up the Streamlit application with the title, “CustomGPT.ai Chatbot.” From the customchat.py file, we will import customBOT function to send our queries to CustomGPT.ai.
Our chat history within the application will be managed using the st.session_state feature, ensuring that previous messages persist when the app is rerun. Users can input the query through a chat input box with the prompt “What is up?”
As users enter the query, the application adds their messages to the chat history and displays them in the user’s role. Simultaneously, it calls the customBOT function to interact with the CustomGPT.ai chatbot to respond to the query being asked.
To create a conversational flow, the assistant’s responses are then displayed in the chat message container. To enhance the user experience, the responses are presented gradually, simulating a real-time typing effect with a blinking cursor.
Our chatbot application coding is done now. Overall, this Streamlit application code created the front end of our CustomGPT.ai chatbot application, offering an interactive interface for users to engage with the chatbot.
Testing Your Chatbot
Now run the chatbot_app.py file to test whether your application is working.
To test your Custom Chatbot, execute the command “streamlit run chatbot_app.py ” in the terminal. This command runs your chatbot application through your web browser.
You can see below the chatbot application with the title “CustomGPT.ai Chatbot” has been created.
Here is our chatbot application in action.
Deployment
Once your chatbot has been thoroughly tested and is primed to respond effectively, the next step is deployment.
This deployment phase is crucial for leveraging the capabilities of your chatbot to enhance customer engagement, streamline interactions, and provide valuable support
Conclusion
The seamless integration of CustomGPT.ai’s language generation capabilities with the user-friendly framework of Streamlit provides an efficient solution for crafting personalized and context-aware chatbots. By following the step-by-step guide, you can effortlessly navigate the development process, from setting up the chatbot’s persona to testing and deploying it for real-time customer engagement. Unlock the potential of your business by bringing the power of CustomGPT.ai to the forefront, creating a more engaging and responsive environment for your users.
