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import streamlit as st | |
from infer import run | |
PAGE_TITLE: str = "Nano Shakespeare GPT " | |
PAGE_ICON: str = "π€" | |
st.set_page_config(page_title=PAGE_TITLE, page_icon=PAGE_ICON) | |
st.title("Nano Shakespeare GPT") | |
def clear_chat() -> None: | |
st.session_state.messages = [] | |
# 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"]) | |
# 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 = "" | |
shakespeare_response = run(prompt) | |
# Simulate stream of response with milliseconds delay | |
for chunk in shakespeare_response.split(): | |
full_response += chunk + " " | |
# time.sleep(0.05) | |
# Add a blinking cursor to simulate typing | |
message_placeholder.markdown(full_response + "β") | |
message_placeholder.markdown(full_response) | |
# Add assistant response to chat history | |
st.session_state.messages.append({"role": "assistant", "content": full_response}) | |
st.button(label="Clear Chat", on_click=clear_chat) | |