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})