Groq, llama3, Streamlit to build a application

This demo is how to use promptulate chat to create a simple chatbot utilising Groq and llama3 model.

For the application frontend, there will be using streamlit, an easy-to-use open-source Python framework.

You see try the live demo here or see the code here.

Environment Setup

We can start off by creating a new conda environment with python=3.11:conda create -n streamlit_groq_chatbot python=3.11

Activate the environment:conda activate streamlit_groq_chatbot

Next, let’s install all necessary libraries:

pip install -U promptulate streamlit

Step-by-Step Implementation

Step 1

Create a app.py script and import the necessary dependencies:

import streamlit as st

import promptulate as pne

Step 2

Create a sidebar to place the user parameter configuration:

with st.sidebar:
    groq_api_key = st.text_input("Groq API Key", key="chatbot_api_key", type="password")

Step 3

Set page style:

# Set title
st.title("💬 Chat")
st.caption("🚀 Hi there! 👋 I am a simple chatbot by groq and llama3 to help you ")

# Determine whether to initialize the message variable
# otherwise initialize a message dictionary
if "messages" not in st.session_state:
    st.session_state["messages"] = [
        {"role": "assistant", "content": "How can I help you?"}
    ]

# Traverse messages in session state
for msg in st.session_state.messages:
    st.chat_message(msg["role"]).write(msg["content"])

Step 4

Set user input:

# User input
if prompt := st.chat_input():
    if not groq_api_key:
        st.info("Please add your Groq API key to continue.")
        st.stop()

    # Add the message entered by the user to the list of messages in the session state
    st.session_state.messages.append({"role": "user", "content": prompt})
    # Display in the chat interface
    st.chat_message("user").write(prompt)

    response: str = pne.chat(
        model="groq/llama3-8b-8192",
        messages=prompt,
        model_config={"api_key": groq_api_key},
    )

    st.session_state.messages.append({"role": "assistant", "content": response})
    st.chat_message("assistant").write(response)

Effect

The running effect is as follows:

Demo

There is a app.py file under the streamlit-chatbot file of example in the project folder. You can run the application directly to view the effect and debug the web page. Project Link: streamlit-groq-llama3 To run the application, follow the steps below:

• Click here to fork the project to your local machine
• Clone the project locally:

git clone https://github.com/Undertone0809/promptulate.git

• Switch the current directory to the example

cd ./example/streamlit-groq-llama3-chatbot

• Install the dependencies

pip install -r requirements.txt

• Run the application

streamlit run app.py

The running result is as follows: