import streamlit as st
# from transformers import T5Tokenizer,AutoModelForCausalLM

model_name = "rinna/japanese-gpt2-small"

# tokenizer = T5Tokenizer.from_pretrained(model_name)
# model = AutoModelForCausalLM.from_pretrained(model_name)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the pre-trained GPT-2 model and tokenizer

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# App title
st.set_page_config(page_title="ChatBot")

if "messages" not in st.session_state.keys():
    st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]

# Display chat messages
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.write(message["content"])

# Function for generating LLM response
# def generate_response(prompt_input):
#     input = tokenizer.encode(prompt_input, return_tensors="pt")
#     output = model.generate(input, do_sample=True, max_length=30, num_return_sequences=1)

#     return tokenizer.batch_decode(output)

def generate_response(prompt, max_length=50):
    input_ids = tokenizer.encode(prompt, return_tensors="pt")

    # Generate response
    with torch.no_grad():
        output = model.generate(input_ids, max_length=max_length, num_return_sequences=1, pad_token_id=50256)

    response = tokenizer.decode(output[0], skip_special_tokens=True)
    return response

# User-provided prompt
if prompt := st.chat_input():
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.write(prompt)

# Generate a new response if last message is not from assistant
if st.session_state.messages[-1]["role"] != "assistant":
    with st.chat_message("assistant"):
        with st.spinner("Thinking..."):
            response = generate_response(prompt)
            st.write(response) 
    message = {"role": "assistant", "content": response}
    st.session_state.messages.append(message)