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import streamlit as st
import random
import time
from transformers import GPT2LMHeadModel, GPT2Tokenizer
st.title("Simple chat with Hugging Face")
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Load pre-trained model and tokenizer
model = GPT2LMHeadModel.from_pretrained("gpt2")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
# 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?"):
# Display user message in chat message container
with st.chat_message("user"):
st.markdown(prompt)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
# Encode the new user input and add end of sentence token
inputs = tokenizer.encode(prompt + tokenizer.eos_token, return_tensors="pt")
# Generate a response
outputs = model.generate(inputs, max_length=50, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
# Decode the response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Display the response in the chat
with st.chat_message("bot"):
st.markdown(response)
# Add bot message to chat history
st.session_state.messages.append({"role": "bot", "content": response})
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