Stramlit ChatGPT-like app. How to.

Finally, they reach the climax of the tutorial: building a ChatGPT-like app. It is possible by using the OpenAI API to generate responses. And of course, for that you need to get an API key to follow along.

In summary, this tutorial takes you on a thrilling journey of building chat-based applications with Streamlit and GPT models. Enjoy!

import openai
import streamlit as st

st.title("ChatGPT-like clone")

openai.api_key = st.secrets["OPENAI_API_KEY"]

if "openai_model" not in st.session_state:
    st.session_state["openai_model"] = "gpt-4"

if "messages" not in st.session_state:
    st.session_state.messages = []

for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

if prompt := st.chat_input("What is up?"):
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)

    with st.chat_message("assistant"):
        message_placeholder = st.empty()
        full_response = ""
        for response in openai.ChatCompletion.create(
            model=st.session_state["openai_model"],
            messages=[
                {"role": m["role"], "content": m["content"]}
                for m in st.session_state.messages
            ],
            stream=True,
        ):
            full_response += response.choices[0].delta.get("content", "")
            message_placeholder.markdown(full_response + "▌")
        message_placeholder.markdown(full_response)
    st.session_state.messages.append({"role": "assistant", "content": full_response})

You could find this and more code examples here

Three keys on how to build chat based applications

You can find here three general tips in order to boost your Streamlit application:

• User-Centric Design: Prioritize the needs and preferences of your users when developing a chat-based application. Consider their goals, pain points, and communication preferences to ensure a seamless and intuitive user experience.

• Fine-tune your LLM or other NLP model: Implement robust NLP techniques to enhance the application’s ability to understand and interpret user input accurately. Also, it seems like an awesome idea to train specifically to your use case in order to provide more contextually relevant and personalized responses.

• Scalability and Performance: Build a scalable architecture that can handle high volumes of concurrent users and maintain optimal performance. Implement efficient data storage, caching mechanisms, and use cloud-based services to handle the computational demands of processing and serving chat-based interactions.

Stay updated on Streamlit and Python tips

By harnessing the power of Streamlit and GPT, you can unlock a world of possibilities and create highly engaging and interactive conversational applications that will captivate your users and elevate your online presence.

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