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import streamlit as st
from transformers import GPT2Tokenizer, GPT2LMHeadModel
model_name = 'KhantKyaw/Chat_GPT-2'
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)
special_tokens_dict = {'bos_token': '<BOS>', 'eos_token': '<EOS>', 'sep_token': '<SEP>', 'pad_token': '<PAD>'}
tokenizer.add_special_tokens(special_tokens_dict)
model.resize_token_embeddings(len(tokenizer))
# Function to generate a response
def generate_response(input_text):
# Adjusted input to include the [Bot] marker
#adjusted_input = f"{input_text} [Bot]"
# Encode the adjusted input
inputs = tokenizer(input_text, return_tensors="pt")
# Generate a sequence of text with a slightly increased max_length to account for the prompt length
output_sequences = model.generate(
input_ids=inputs['input_ids'],
attention_mask=inputs['attention_mask'],
max_length=100, # Adjusted max_length
temperature=0.7,
top_k=50,
top_p=0.95,
no_repeat_ngram_size=2,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
#early_stopping=True,
do_sample=True
)
# Decode the generated sequence
full_generated_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
# Extract the generated response after the [Bot] marker
bot_response_start = full_generated_text.find('[Bot]') + len('[Bot]')
bot_response = full_generated_text[bot_response_start:]
return bot_response
# Load pre-trained model tokenizer (vocabulary) and model
model_name = 'KhantKyaw/Chat_GPT-2'
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)
st.title("Chat_GPT-2 Bot")
# 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"])
# React to user input
if prompt := st.chat_input("What is up?"):
# Display user message in chat message container
st.chat_message("user").markdown(prompt)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
response = generate_response(prompt)
# Display assistant response in chat message container
with st.chat_message("assistant"):
st.markdown(response)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response})
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