app.py

import streamlit as st
import os
from openai import OpenAI


class ChatBot:
    def __init__(self):
        self.client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
        self.history = [{"role": "system", "content": "You are a helpful assistant."}]

    def generate_response(self, prompt: str) -> str:
        self.history.append({"role": "user", "content": prompt})
        
        completion = self.client.chat.completions.create(
            model="gpt-4o",
            messages=self.history
        )
        
        response = completion.choices[0].message.content
        self.history.append({"role": "assistant", "content": response})
        
        return response

    def get_history(self) -> list:
        return self.history


st.set_page_config(layout="wide")
st.title("OpenAI GPT-4o 🤖")


with st.sidebar:
    with st.expander("Instruction Manual"):
        st.markdown("""
            ## OpenAI GPT-4o 🤖 Chatbot
            This Streamlit app allows you to chat with GPT-4o model.
            ### How to Use:
            1. **Input**: Type your prompt into the chat input box labeled "What is up?".
            2. **Response**: The app will display a response from GPT-4o.
            3. **Chat History**: Previous conversations will be shown on the app.
            ### Credits:
            - **Developer**: [Yiqiao Yin](https://www.y-yin.io/) | [App URL](https://huggingface.co/spaces/eagle0504/meta-llama) | [LinkedIn](https://www.linkedin.com/in/yiqiaoyin/) | [YouTube](https://youtube.com/YiqiaoYin/)
            Enjoy chatting with Meta's Llama3 model!
        """)

    # Example:
    st.success("Example: Explain what is supervised learning.")
    st.success("Example: What is large language model?")
    st.success("Example: How to conduct an AI experiment?")
    st.success("Example: Write a tensorflow flow code with a 3-layer neural network model.")


    # Add a button to clear the session state
    if st.button("Clear Session"):
        st.session_state.messages = []
        st.session_state["history"] = [{"role": "system", "content": "You are a helpful assistant."}]
        st.experimental_rerun()


# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []


# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])


# React to user input
if prompt := st.chat_input("😉 Ask any question or feel free to use the examples provided in the left sidebar."):

    # Display user message in chat message container
    st.chat_message("user").markdown(prompt)

    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})
    st.session_state["history"].append({"role": "user", "content": prompt})

    # API Call
    bot = ChatBot(history=st.session_state["history"])
    response = bot.generate_response(prompt)

    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        st.markdown(response)

    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": response})
    st.session_state["history"].append({"role": "assistant", "content": response})