How to Build Chatbot Interfaces with Python
Let's create a chatbot interface using only Python and the streamlit library. In this video, I'm going to walk you through the script we have here. I'll also share the code with you, and it will be available on GitHub so you can follow along. This tutorial is ideal for Python developers, like myself, who work on AI applications but don't have the front-end skills to build fancy applications using JavaScript, HTML, CSS, and other technologies.
Step-by-Step Instructions
Step 1: Set Up a New Project
Start by creating a new and fresh project. Make sure you have a virtual environment or conda environment already set up. You can clone the project from the GitHub repository I provided. Once you have the project, install the necessary dependencies: streamlit, OpenAI, and Python. You can either use the command pip install to install the dependencies individually or run the requirements.txt file to set everything up automatically.
To test if streamlit is working properly, open a terminal, make sure your environment is activated, and run the command streamlit hello. If you see the "Welcome to Streamlit" message on your local host, then you've configured streamlit correctly.
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Step 2: Create an .env File with OpenAI API Key
Create an .env file in your project directory and add your OpenAI API key to it. You can use the example .env file provided and rename it by removing the "example" part. Fill in your API key and save the file.
Step 3: Create the app.py File and Add Imports
Create an app.py file in your project directory. You can either clone the repository or start from scratch. In this file, add the necessary imports, including streamlit and the required libraries. Set up your page title and load your environment variable (the OpenAI API key) using the load_dotenv function.
Step 4: Configure Model and Messages
In the app.py file, configure the model and messages. The code provided includes functions to load a chat history from a file and store messages using the shelve library. The load_chat_history function loads the chat history, and the store_messages function stores the messages. If there are no messages in the state, it loads the messages from the file. You can also add a sidebar with a delete button to the application.
Step 5: Build the Chat Interface
In the app.py file, you'll find the main chat interface code. This code utilizes the streamlit library to create the chat elements. You can refer to the streamlit documentation for a better understanding of how the chat elements work. The provided code sets up the chat interface and allows you to start a conversation with the chatbot. You can run the command streamlit run app.py to launch the application.
Conclusion
In this tutorial, we learned how to build chatbot interfaces with Python using the streamlit library. This approach is beneficial for Python developers who want to work on AI applications without having extensive front-end skills. With the provided code and step-by-step instructions, you can quickly set up a chatbot interface and showcase your AI projects. Although the demo is simple, you can make it more advanced and integrate it with other platforms like Slack or WhatsApp. If you're interested in deploying your app or integrating your own data using the OpenAI Assistance API, be sure to check out the additional resources.
Frequently Asked Questions
1. Can I use this tutorial if I'm not a Python developer?
Absolutely! This tutorial is designed to be accessible to both technical experts and general readers. Even if you're not a Python developer, you can follow the step-by-step instructions and create a chatbot interface using Python and the streamlit library.
2. Do I need front-end skills to build chatbot interfaces with Python?
No, that's the beauty of using the streamlit library. It simplifies the process of creating chatbot interfaces without requiring extensive front-end skills like JavaScript, HTML, or CSS. With just Python and the provided code, you can build a functional and engaging chatbot interface.
3. Can I customize the chatbot interface to match my branding?
Yes, you can customize the chatbot interface to match your branding. The provided code includes options to set icons for the user and the bot. You can modify these icons or add additional styling elements using HTML and CSS to achieve the desired look and feel.
4. Are there any limitations to the chatbot functionality?
The functionality of the chatbot depends on the underlying model you're using. In this tutorial, the code utilizes the gpt-3.5-turbo model from OpenAI. While this model is powerful, there are certain limitations to be aware of. It's recommended to consult the documentation and guidelines provided by OpenAI to understand the capabilities and restrictions of the specific model you're using.
5. How can I deploy my chatbot interface?
If you're interested in deploying your chatbot interface, you can follow the link provided in the tutorial. The linked resource provides detailed instructions on how to deploy your app, allowing you to share it with others and showcase your AI projects.
