Building a Conversational Q&A Chatbot with Streamlit and GPT-3.5-Turbo

Overview

In the ever-evolving landscape of artificial intelligence, creating interactive and intelligent chatbots has become increasingly accessible. In this blog post, we’ll guide you through building a Conversational Q&A Chatbot using Streamlit for the front end and the powerful GPT-3.5-turbo model from OpenAI. Let’s dive into the details of this exciting project!

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Introduction

Streamlit is a powerful open-source framework designed to simplify the process of building and sharing data-driven web applications. With Streamlit, developers can rapidly create interactive and visually appealing applications using simple Python scripts. It provides a seamless way to turn data scripts into shareable web apps, eliminating the need for extensive front-end development expertise.

By leveraging Streamlit’s intuitive API and built-in components, developers can focus on the core functionality of their applications without getting bogged down by the complexities of web development. Whether it’s data visualization, machine learning models, or data analysis tools, Streamlit enables users to deploy their projects easily. It is an invaluable tool for seasoned developers and newcomers to data science and web application development.

Advantages of Streamlit and GPT-3.5-turbo

1. Natural Language Understanding:

The GPT-3.5-turbo model excels in understanding and generating human-like text, providing users with a natural and engaging conversational experience.

2. Ease of Deployment with Streamlit:

Streamlit simplifies the deployment process, allowing users to access the chatbot through a web browser without complex configurations.

3. Customizable Behavior:

Parameters like temperature in the GPT-3.5-turbo model can be adjusted to customize the chatbot’s behavior, enabling developers to fine-tune the user experience.

4. Flexibility for Extensions:

The modular structure of the code allows for easy extensions. Developers can add features like sentiment analysis or integrate external APIs to enhance functionality.

Description

The Conversational Q&A Chatbot we are building combines the user-friendly interface of Streamlit with the advanced natural language processing capabilities of the GPT-3.5-turbo model. Users can interact with the chatbot by posing questions, and the model will generate contextually relevant responses conversationally. This project showcases the potential of integrating cutting-edge language models into practical applications, making it ideal for various use cases, from customer support to educational platforms.

Prerequisites

Before we begin, make sure you have the following:

Python installed on your machine (version 3.6 or higher).
An OpenAI account and the API key (OPENAI_API_KEY) accessible.

Step-by-Step Guide

Step 1: Installation

Setting Up the Environment

Install Streamlit
bash

1 2	Install Streamlit bash

pip install streamlit

1	pip install streamlit

Install OpenAI Python SDK
bash

1 2	Install OpenAI Python SDK bash

pip install openai

1	pip install openai

Install Langchain
bash

1 2	Install Langchain bash

pip install langchain

1	pip install langchain

Step 2: Implementation

Writing the Streamlit App
Now, let’s create the Streamlit application. Create a file named app.py and follow the following step:

Required libraries

#conversational Q&A Chatbot
import streamlit as st
from langchain.schema import HumanMessage,SystemMessage,AIMessage
from langchain_openai import ChatOpenAI 
from dotenv import load_dotenv
import os

#conversational Q&A Chatbot

import streamlit as st

from langchain.schema import HumanMessage,SystemMessage,AIMessage

from langchain_openai import ChatOpenAI

from dotenv import load_dotenv

import os

create .env file
variable:

‘OPEN_API_KEY’=’YOUR_OPENAI_API_KEY’

Replace “YOUR_OPENAI_API_KEY” with your actual OpenAI API key.

Load the environment variable

load_dotenv()

1	load_dotenv()

Defining Function to Get OpenAI Response

chat=ChatOpenAI(temperature=0.8) 
def get_openai_response(question):
    st.session_state['flowmessages'].append(
    HumanMessage(content=question))
    answer=chat(st.session_state['flowmessages'])
    st.session_state['flowmessages'].append(AIMessage(
    content=answer.content))
    return answer.content

chat=ChatOpenAI(temperature=0.8)

def get_openai_response(question):

st.session_state['flowmessages'].append(

HumanMessage(content=question))

answer=chat(st.session_state['flowmessages'])

st.session_state['flowmessages'].append(AIMessage(

content=answer.content))

return answer.content

Set up UI and session state

st.set_page_config(page_title="coversational Q&A Chatbot")
st.header("hi lets have a Chat")

if 'flowmessages' not in st.session_state:
    st.session_state['flowmessages']=[
        SystemMessage(content= 'you are AI Assistant')
    ]

st.set_page_config(page_title="coversational Q&A Chatbot")

st.header("hi lets have a Chat")

if 'flowmessages' not in st.session_state:

st.session_state['flowmessages']=[

SystemMessage(content= 'you are AI Assistant')

]

Handling User Input and Displaying Response

input=st.text_input('Input:', key='input')
response=get_openai_response(input)

submit=st.button('Ask me question')
if submit:
    st.subheader("The Response is")
    st.write(response)

input=st.text_input('Input:', key='input')

response=get_openai_response(input)

submit=st.button('Ask me question')

if submit:

st.subheader("The Response is")

st.write(response)

Step 3: Running the Application

Save the app.py file and run the following command in your terminal:

bash

bash

streamlit run app.py

1	streamlit run app.py

Step 4: Visit the provided URL in your web browser to interact with your chatbot

chatbot

Conclusion

Building a Conversational Q&A Chatbot with Streamlit and GPT-3.5-turbo opens up a world of possibilities for creating intelligent and interactive applications. The combination of a user-friendly interface and advanced language models provides a powerful tool for developers and businesses alike.

Experiment, customize, and explore the potential of this chatbot to elevate your projects to new heights.

Drop a query if you have any questions regarding Q&A Chatbot and we will get back to you quickly.

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FAQs

1. What is the purpose of this chatbot?

ANS: – This chatbot serves as a conversational question and answer system. Users can input questions, and the chatbot, powered by the GPT-3.5-turbo model, provides relevant responses.

2. How does the chatbot work?

ANS: – The chatbot utilizes the Streamlit library for the front end and the GPT-3.5-turbo model from OpenAI. User input is processed, and the model generates responses based on the given context.

3. Can I customize the behavior of the chatbot?

ANS: – Yes, the behavior of the chatbot can be customized by adjusting parameters such as temperature, which controls the randomness of the model’s responses. Feel free to experiment with different settings for a more tailored experience.

WRITTEN BY Shantanu Singh

Shantanu Singh is a Research Associate at CloudThat with expertise in Data Analytics and Generative AI applications. Driven by a passion for technology, he has chosen data science as his career path and is committed to continuous learning. Shantanu enjoys exploring emerging technologies to enhance both his technical knowledge and interpersonal skills. His dedication to work, eagerness to embrace new advancements, and love for innovation make him a valuable asset to any team.