CustomGPT.ai, combined with Streamlit, offers a practical solution for developing interactive web applications tailored to your specific business needs through custom instructions, and understanding how CustomGPT.ai works helps developers make the most of this combination. 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 as part of creating a custom GPT. 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.
Frequently Asked Questions
How hard is it to build a Streamlit chatbot for a business if you have limited developer time?
It can be lighter-weight than training a model from scratch. Dr. Michael Levin described the accessibility this way: u0022Omg finally, I can retire! A high-school student made this chat-bot trained on our papers and presentationsu0022. For a business setup, you typically split the work into three steps: train the assistant on your company data, store the API key securely, and connect a Streamlit chat UI to the OpenAI-compatible /v1/chat/completions endpoint.
What is the difference between a basic Streamlit chatbot example and a business chatbot connected to company data?
A basic Streamlit chatbot mainly gives you the interface. A business chatbot adds retrieval-augmented generation so answers are grounded in your own websites, documents, audio, video, or structured files before the model responds. That matters when accuracy affects support, policy, or product questions, and it is why RAG quality is important: CustomGPT.ai is documented as outperforming OpenAI in a RAG accuracy benchmark and supports citation-backed answers to reduce hallucination risk.
Can non-technical teams update the chatbot after a developer builds the Streamlit app?
Yes. A developer can build the Streamlit front end once, while business users keep the knowledge base current through a no-code chatbot builder. The platform supports ingesting websites, documents, audio, video, and URLs, so teams can refresh content without rewriting the app as long as the Streamlit interface continues calling the same backend assistant.
How do you secure a Streamlit chatbot that uses internal business documents?
Start by keeping the API key in server-side secrets rather than exposing it in browser code. Then put authentication and role-based access in front of the Streamlit app, and only connect approved internal data sources. For regulated or security-sensitive use cases, the platform is SOC 2 Type 2 certified, GDPR compliant, and does not use customer data for model training.
Can a Streamlit chatbot handle real employee or customer support traffic?
Yes. Biamp deployed internal and external AI assistants in under 30 days, supports 90+ languages, and uses them for both customer support and HR workflows with 24/7 availability. Toyon Nurul Huda said, u0022CustomGPT has opened new doors for how Biamp interacts with customers and internal audiences. With its advanced GPT-4 capabilities, CustomGPT allows Biamp to quickly address the most common questions and requests for information, making it far faster and more efficient to deliver answers.u0022 In a Streamlit setup, the web app can stay simple while the backend handles retrieval and response generation.
Can I start with Streamlit and later reuse the same chatbot in other apps or channels?
Yes. If your Streamlit app calls the assistant through the API, you can reuse the same knowledge-backed chatbot in other deployment formats such as an embed widget, live chat, search bar, or other applications without rebuilding the knowledge base. Bill French highlighted why that matters for user experience: u0022They’ve officially cracked the sub-second barrier, a breakthrough that fundamentally changes the user experience from merely ‘interactive’ to ‘instantaneous’.u0022 Keeping the interface separate from the retrieval layer makes expansion much easier.
Related Resources
This guide pairs well with a practical look at building a chatbot around your own content.
- Knowledge Base Chatbot Guide — Learn how to create a CustomGPT.ai-powered chatbot that answers questions using your custom knowledge base.