import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load pre-trained DialoGPT-small model and tokenizer
model_name = "microsoft/DialoGPT-small"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Set device to GPU if available for faster inference, otherwise fallback to CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Initialize chat history
if 'history' not in st.session_state:
    st.session_state['history'] = []

def generate_response(input_text):
    # Encode the new user input, add end of string token
    new_user_input_ids = tokenizer.encode(input_text + tokenizer.eos_token, return_tensors='pt').to(device)

    # Append the new user input tokens to the chat history
    bot_input_ids = torch.cat([torch.tensor(st.session_state['history']).to(device), new_user_input_ids], dim=-1) if st.session_state['history'] else new_user_input_ids

    # Generate a response from the model
    chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id, top_k=50, top_p=0.95, temperature=0.7)

    # Decode the model's output and add it to the history
    chat_history_ids = chat_history_ids[:, bot_input_ids.shape[-1]:]  # only take the latest generated tokens
    bot_output = tokenizer.decode(chat_history_ids[0], skip_special_tokens=True)

    # Update session state history with the new tokens
    st.session_state['history'] = chat_history_ids[0].tolist()

    return bot_output

# Streamlit Interface
st.title("Chat with DialoGPT")

# Create input box for user
user_input = st.text_input("You: ", "")

if user_input:
    # Generate and display the bot's response
    response = generate_response(user_input)
    st.write(f"Bot: {response}")