Create Your Own AI with PaLM2: A Step-by-Step Tutorial

Introduction

PaLM2 is a next-generation large language model developed by Google, representing a significant advancement in the field of machine learning and artificial intelligence (AI). This model builds on Google's established legacy of pioneering research, demonstrating enhanced performance in various reasoning tasks compared to its predecessors, such as PaLM. Tasks like code generation, mathematical problem-solving, classification, question answering, translation, and natural language generation have all seen marked improvements with the introduction of PaLM2.

Prerequisites

To leverage Google’s PaLM2 API, interested developers are required to join the waitlist. Additionally, to deploy applications utilizing this API on Streamlit, users must create a free account on Streamlit. It's recommended to sign up using a GitHub account so you can conveniently deploy your application on the Streamlit Sharing Cloud.

Getting Started

Step 0: Create a New Project Directory

First, create a new project directory and navigate to it in your terminal. You'll also need to:

• Create and activate a virtual environment.
• Install Streamlit along with Google’s PaLM API dependencies.

Step 1: Create a New Streamlit App

Inside your project directory, create a file named app.py. In this file, you will import the necessary packages for Streamlit and Google’s PaLM API:

import streamlit as st
import google_palm_api  # hypothetical import

Add a title to your app using Streamlit's st.title() function.

Next, you'll need to initialize the message state for your application:

if 'messages' not in st.session_state:
    st.session_state.messages = []

Create a form to capture user input, leveraging Streamlit's st.form() method for better interaction:

with st.form(key='input_form'):
    user_input = st.text_input('Your message:')
    submit_button = st.form_submit_button(label='Send')

Organize your layout using st.columns() to create a chat-like interface, with user input displayed on one side and the send button on the other.

Configure Google's PaLM API by incorporating your API key and format the flow to fetch responses:

response = google_palm_api.get_response(user_input)

If required, implement a feature to clear chat history:

if st.button('Clear History'):
    st.session_state.messages = []

Step 2: Run the App

To run your app, use the following command in your terminal:

streamlit run app.py

Upon successful execution, your application will launch and appear similar to a chat interface.

Deploying on Streamlit Sharing Cloud

Once development is complete, you can deploy your application on the Streamlit Sharing Cloud. For a comprehensive guide on deployment, refer to the Streamlit Sharing documentation.

Conclusion

This tutorial walked you through the steps to create an AI-powered virtual assistant using Google’s PaLM2 model along with Streamlit for application development. Streamlit is an excellent tool that enables developers to craft applications swiftly using pure Python.

To explore the full implementation, check out the repository here.

Thank you for following this tutorial! For any questions or feedback, feel free to connect with me on LinkedIn or Twitter. I would love to hear from you!