GUI of a chatbot using streamlit Library

GUI of an AI chatbot 

Creating a GUI for an AI chatbot using the streamlit library in Python is straightforward. Streamlit is a powerful tool that makes it easy to build web applications with minimal code. Below is a step-by-step guide to building a simple AI chatbot GUI using Streamlit.

Step 1: Install Required Libraries

First, you'll need to install streamlit and any AI model or library you want to use (e.g., OpenAI's GPT-3 or a simple rule-based chatbot). If you're using OpenAI's GPT-3, you'll also need the openai library.

pip install streamlit openai

Step 2: Set Up OpenAI API (Optional)

If you're using OpenAI's GPT-3 for your chatbot, make sure you have an API key and set it up as an environment variable:

export OPENAI_API_KEY='your-openai-api-key'

Step 3: Create the Streamlit Chatbot Application

Here's a basic example of a chatbot using OpenAI's GPT-3 and Streamlit:

import streamlit as st
import openai

# Set the OpenAI API key (if you're using OpenAI's API)
openai.api_key = st.secrets["openai_api_key"]

# Initialize session state to keep track of the chat
if "messages" not in st.session_state:
    st.session_state.messages = []

# Function to get a response from the chatbot (using OpenAI GPT-3)
def get_response(user_input):
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=user_input,
        max_tokens=150
    )
    return response.choices[0].text.strip()

# Title of the app
st.title("AI Chatbot")

# Input from the user
user_input = st.text_input("You:", "")

# If user submits a message
if user_input:
    # Store user input
    st.session_state.messages.append({"role": "user", "content": user_input})

    # Get response from the chatbot
    response = get_response(user_input)

    # Store the chatbot response
    st.session_state.messages.append({"role": "bot", "content": response})

# Display the chat history
if st.session_state.messages:
    for message in st.session_state.messages:
        if message["role"] == "user":
            st.text_area("You:", message["content"], height=100, max_chars=None, key=f"user_{len(st.session_state.messages)}")
        else:
            st.text_area("AI:", message["content"], height=100, max_chars=None, key=f"bot_{len(st.session_state.messages)}")

# Keep the text input focused for the next user input
st.text_input("You:", "", key="new_input", on_change=lambda: st.session_state.messages.append({"role": "user", "content": st.session_state.new_input}))

Step 4: Run the Streamlit Application

Save the above code into a Python file, e.g., chatbot.py, and run it using Streamlit:

streamlit run chatbot.py

Explanation:

1. Streamlit Setup: The streamlit library (st) is used to create the GUI. The st.text_input is used to capture user input, and st.text_area displays the conversation history.

2. Session State: st.session_state is used to store chat history, allowing the conversation to persist between user interactions.

3. Chatbot Response: The get_response function sends the user's input to OpenAI's GPT-3 and retrieves a response. This can be modified to use other AI models or even a custom rule-based response generator.

4. Continuous Interaction: The text input automatically focuses on new user input after each interaction, creating a smooth chat experience.

Step 5: Customize and Deploy

You can customize the chatbot by modifying the prompt, using a different AI model, or adding additional features like buttons or file uploads. Streamlit also makes it easy to deploy your app using platforms like Streamlit Sharing, Heroku, or any cloud service.

This basic example should get you started with building and deploying an AI chatbot using Streamlit!