Overview

Table of Contents

Go to Visual Studio Code.
Install streamlit using pip.
- You can use the copy button in the box below to automatically copy its code:

command

pip3 install streamlit

Create a new file "genai_app.py".
Add the import statements inside "genai_app.py" to use Streamlit elements.

genai_app.py

import streamlit as st #all streamlit commands will be available through the "st" alias

Set the HTML title tag which will be shown in the browser tab.

genai_app.py

import streamlit as st #all streamlit commands will be available through the "st" alias

st.set_page_config(page_title="Text Generator- Gen AI Project") #HTML title1

Add the page title which will appear on web page.

genai_app.py

import streamlit as st #all streamlit commands will be available through the "st" alias

st.set_page_config(page_title="Text Generator- Gen AI Project") #HTML title
st.title("Text Generator- Gen AI Project") #page title

Create a multiline text box for prompt input.

genai_app.py

import streamlit as st #all streamlit commands will be available through the "st" alias

st.set_page_config(page_title="Text Generator- Gen AI Project by KAUSTUBH SHARMA") #HTML title
st.title("Text Generator- Gen AI Project") #page title

input_text = st.text_area("Input text", label_visibility="collapsed") #display a multiline text box with no label

Create button to get the user's prompt and send it to Bedrock.

genai_app.py

import streamlit as st #all streamlit commands will be available through the "st" alias

st.set_page_config(page_title="Text Generator- Gen AI Project by KAUSTUBH SHARMA") #HTML title
st.title("Text Generator- Gen AI Project") #page title

input_text = st.text_area("Input text", label_visibility="collapsed") #display a multiline text box with no label
go_button = st.button("Go", type="primary") #display a primary button

Create a new file "genai_lib.py".
Install langchain using pip.
- You can use the copy button in the box below to automatically copy its code:

bash

pip3 install langchain

Add the import statements inside "genai_lib.py" to use LangChain to call Bedrock and read environment variables.
- You can use the copy button in the box below to automatically copy its code:

genai_lib.py

from langchain_community.llms import Bedrock

Create a function to call Bedrock

genai_lib.py

def get_text_response(input_content): #text-to-text client function

    llm = Bedrock( #create a Bedrock llm client
        model_id="cohere.command-text-v14", #set your foundation model here
        model_kwargs={
            "max_tokens": 512,
            "temperature": 0,
            "p": 0.01,
            "k": 0,
            "stop_sequences": [],
            "return_likelihoods": "NONE"
        }
    )
    
    return llm.invoke(input_content) #return a response to the prompt