Develop an LLM Application using Openai

Language Models (LMs) play a crucial role in natural language processing applications, enabling the development of tools that generate human-like text. OpenAI's Generative Pre-Trained Transformer (GPT) models, such as GPT-3.5-turbo, are widely used in this domain. They excel in understanding context, learning language patterns, and generating coherent text. You can also try what we are about to build right here:- GeekBot

1. Definition: LMs are algorithms or neural networks trained to understand and generate human-like text.
2. GPT Models: OpenAI's GPT models, like GPT-3.5-turbo, are built on the Transformer architecture and pre-trained on diverse text data.
3. Capabilities: Versatility: GPT models can perform various natural language processing tasks.
4. Human-Like Text Generation: They produce text with a natural quality.
5. OpenAI API: OpenAI provides an API for easy integration of GPT models into applications.

LLM Application Development

The provided Python code uses the Streamlit framework to create an interactive web application.

It leverages the OpenAI API for language generation.

Developers can utilize GPT models to build applications for creative writing, content generation, and more. As language models advance, their impact on natural language understanding and generation continues to grow, driving innovation in NLP.

Understanding the Concepts

1. Streamlit Framework: Streamlit is a popular Python library for creating web applications with minimal effort. It allows developers to turn data scripts into shareable web apps.
2. OpenAI API Integration: The code uses the OpenAI API to interact with the GPT model. The OpenAI API key is provided by the user through the Streamlit sidebar.
3. Token Counting with Tiktoken: The count_tokens function utilizes the Tiktoken library to count the number of tokens in the input text. Tokens are the basic units of text that models read.
4. Language Model Generation: The generate_response function initializes an OpenAI language model and generates a response based on the input text. The generated response and the token count are displayed using Streamlit.

How to Obtain OpenAI API Key:

1. Go to OpenAI's website.
2. Sign in or create a new account.
3. Navigate to API Section:
4. After signing in, navigate to the API section.
5. Create an API Key:
6. Create a new API key by following the instructions.

Once generated, copy the API key and keep it secure.

Important - You'll need to have a balance in your OpenAI account to get started with their models.

Getting Started with the Project

1. Setting Up the Environment

To run this application, follow these steps:

• Create a Virtual Environment (Optional but Recommended)

python -m venv venv
source venv/bin/activate  # On Windows, use 'venv\Scripts\activate'

• Install Required Libraries

1. Streamlit:
   • Streamlit is a Python library used for creating web applications with minimal effort.
   • It allows developers to create interactive and user-friendly interfaces with simple Python scripts.
2. OpenAI (langchain.llms):
   • The OpenAI class is part of the langchain library, specifically for interacting with OpenAI's language models.
   • It is used here to generate responses based on user input.
3. Tiktoken:
   • tiktoken is a Python library for counting tokens in a text string without making API calls.
   • It helps in managing and tracking the token usage of OpenAI language models.

pip install streamlit openai tiktoken

• Run the Application
  • Save the code in a file (e.g., llm_app.py) and run:

streamlit run llm_app.py

Open the provided URL in your web browser to interact with the GeekBot application.

2. Streamlit Setup

• Import the necessary libraries: Streamlit for the web application, OpenAI language model (OpenAI), and tiktoken for token counting.
• Set the title of the application as "GeekBot."

import streamlit as st
from langchain.llms import OpenAI
import tiktoken
st.title('GeekBot')

3. OpenAI API Key Input:

• Create a text input field in the sidebar for the user to enter their OpenAI API key.

openai_api_key = st.sidebar.text_input('OpenAI API Key')

4. Token Counting Function (count_tokens):

• This function takes a string as input and counts the number of tokens using the tiktoken library.
• The encoding for the OpenAI language model (gpt-3.5-turbo) is specified, and token counting is performed.

def count_tokens(string: str) -> int:
    encoding_name = "p50k_base"
    encoding = tiktoken.get_encoding(encoding_name)
    num_tokens = len(encoding.encode(string))
    return num_tokens
Response Generation Function (generate_response):

5. Response Generation Function (generate_response):

• Uses the OpenAI language model (OpenAI) to generate a response based on the input text.
• The temperature parameter controls the randomness of the output.
• The function also displays the number of tokens in the input.

def generate_response(input_text):
    try:
        llm = OpenAI(temperature=0.7, openai_api_key=openai_api_key)
        response = llm(input_text)
        num_tokens = count_tokens(input_text)
        st.info(f"Input contains {num_tokens} tokens.")
        st.info(response)
    except Exception as e:
        st.error(f"An error occurred: {str(e)}")

6. Streamlit Form:

• A form is created to take user input.
• If the OpenAI API key is not provided or invalid, a warning is displayed.
• Upon form submission with a valid API key, the generate_response function is called to generate and display the response.

with st.form('my_form'):
    text = st.text_area('Enter text:', 'How to get started with DSA')
    submitted = st.form_submit_button('Submit')
    if not openai_api_key.startswith('sk-'):
        st.warning('Please enter your OpenAI API key!', icon='⚠')
    if submitted and openai_api_key.startswith('sk-'):
        generate_response(text)

Code

import streamlit as st
from langchain.llms import OpenAI
import tiktoken

st.title('GeekBot')

openai_api_key = st.sidebar.text_input('OpenAI API Key')

def count_tokens(string: str) -> int:
    # Load the encoding for gpt-3.5-turbo (which uses cl100k_base)
    encoding_name = "p50k_base"
    encoding = tiktoken.get_encoding(encoding_name) 
    # Encode the input string and count the tokens
    num_tokens = len(encoding.encode(string))
    return num_tokens

def generate_response(input_text):
    try:
        llm = OpenAI(temperature=0.7, openai_api_key=openai_api_key)
        response = llm(input_text)
        num_tokens = count_tokens(input_text)
        st.info(f"Input contains {num_tokens} tokens.")
        st.info(response)
    except Exception as e:
        st.error(f"An error occurred: {str(e)}")

with st.form('my_form'):
  text = st.text_area('Enter text:', 'How to get started with DSA')
  submitted = st.form_submit_button('Submit')
  if not openai_api_key.startswith('sk-'):
    st.warning('Please enter your OpenAI API key!', icon='⚠')
  if submitted and openai_api_key.startswith('sk-'):
    generate_response(text)

Output:

You can also try the app here: - App

Conclusion

Developing Language Model applications using OpenAI and Streamlit provides a straightforward way to create interactive and engaging tools. As the field of natural language processing continues to evolve, such applications play a crucial role in harnessing the power of advanced language models. Experiment, explore, and enjoy building your own language-driven applications!