How to Integrate GPT-4 into Your Streamlit Project: A Step-by-Step Tutorial

Integrating GPT-4 into Your Streamlit Project

OpenAI has officially released the new GPT-4 model, which outperforms its predecessor, GPT-3, in numerous tasks. Due to its enhanced general knowledge and capabilities, it is advisable to integrate GPT-4 into your applications. In this tutorial, we will guide you step-by-step on how to effectively incorporate GPT-4 within a Streamlit project.

How to Access GPT-4?

The GPT-4 API is currently available only in a limited beta phase. To gain access, you need to visit OpenAI's official website and fill out the application form. This form will require your details and the answers to several questions. If approved, you will receive an invitation email granting you access to the GPT-4 beta.

Transitioning from GPT-3 to GPT-4

Switching from GPT-3 to GPT-4 involves some adjustments as GPT-4, like the gpt-3.5-turbo model, is primarily optimized for chat completions instead of traditional text completions. Its robustness in general knowledge also supports traditional text tasks. Consequently, you will need to modify your completion functions to cater to chat completion. In this tutorial, we will utilize the Python SDK alongside lablab.ai's GPT-3 Streamlit Boilerplate.

Step 1: Clone the Repository

Begin by cloning the project repository using the following command:

git clone 

Next, navigate to the project directory.

Step 2: Install Dependencies

All necessary dependencies can be installed by executing:

pip install -r requirements.txt

Step 3: Modify the Model File

To utilize GPT-4, open the model.py file, particularly focusing on the GeneralModel class. Replace:

• openai.Completion.create with openai.ChatCompletion.create
• Adjust the parameters to suit GPT-4's requirements.
• Add the model parameter to the create function.
• Change the prompt keyword to messages and extract content differently from the final response.

Feel free to modify parameters like temperature to observe how they impact the results.

Step 4: Running Your Project

After completing the above modifications, execute your project with the following command:

streamlit run app.py

Now let's try generating some creative content, like a poem!

Is Implementing GPT-4 Worth It?

Absolutely! Transitioning from GPT-3 to GPT-4 is straightforward, requiring only a few simple changes as highlighted in this tutorial. The benefits of utilizing GPT-4 far outweigh the effort, providing a more powerful model that reduces hallucinations and introduces incredible features like image inputs.

Your imagination is the only limit to what you can create with this advanced tool. As you're currently developing with Whisper and ChatGPT, consider enhancing your project with the GPT-4 API.

Join the Community

We encourage you to connect with the lablab.ai community. Participate in upcoming hackathons centered around cutting-edge AI technologies, such as our Whisper and ChatGPT AI Hackathon. Build functional prototypes and take advantage of our slingshot program to elevate your project.

Embrace the transformative power of AI and kickstart your journey with lablab.ai today!