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| import openai | |
| import streamlit as st | |
| st.title("ChatGPT-like clone") | |
| with st.expander("ℹ️ Disclaimer"): | |
| st.caption( | |
| "We appreciate your engagement! Please note, this demo is designed to process a maximum of 10 interactions. Thank you for your understanding." | |
| ) | |
| openai.api_key = st.secrets["OPENAI_API_KEY"] | |
| if "openai_model" not in st.session_state: | |
| st.session_state["openai_model"] = "gpt-3.5-turbo" | |
| 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"]) | |
| # Maximum allowed messages | |
| max_messages = ( | |
| 20 # Counting both user and assistant messages, so 10 iterations of conversation | |
| ) | |
| if len(st.session_state.messages) >= max_messages: | |
| st.info( | |
| """Notice: The maximum message limit for this demo version has been reached. We value your interest! | |
| We encourage you to experience further interactions by building your own application with instructions | |
| from Streamlit's [Build conversational apps](https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps) | |
| tutorial. Thank you for your understanding.""" | |
| ) | |
| else: | |
| 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} | |
| ) | |