timmy / Streamlit 聊天應用示範

import streamlit as st import random import time st.write("Streamlit loves LLMs! 🤖 [Build your own chat app](https://docs.streamlit.io/develop/tutorials/llms/build-conversational-apps) in minutes, then make it powerful by adding images, dataframes, or even input widgets to the chat.") st.caption("Note that this demo app isn't actually connected to any LLMs. Those are expensive ;)") # Initialize chat history if "messages" not in st.session_state: st.session_state.messages = [{"role": "assistant", "content": "Let's start chatting! 👇"}] # 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"]) # Accept user input if prompt := st.chat_input("What is up?"): # Add user message to chat history st.session_state.messages.append({"role": "user", "content": prompt}) # Display user message in chat message container with st.chat_message("user"): st.markdown(prompt) # Display assistant response in chat message container with st.chat_message("assistant"): message_placeholder = st.empty() full_response = "" assistant_response = random.choice( [ "Hello there! How can I assist you today?", "Hi, human! Is there anything I can help you with?", "Do you need help?", ] ) # Simulate stream of response with milliseconds delay for chunk in assistant_response.split(): full_response += chunk + " " time.sleep(0.05) # Add a blinking cursor to simulate typing message_placeholder.markdown(full_response + "▌") message_placeholder.markdown(full_response) # Add assistant response to chat history st.session_state.messages.append({"role": "assistant", "content": full_response})