app.py

import streamlit as st
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

@st.cache_resource
def load_model():
    tokenizer = AutoTokenizer.from_pretrained("google/mt5-small", padding_side="left", use_fast=False)
    model = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-small")
    return tokenizer, model

st.title("Український Чат-бот")

if "history" not in st.session_state:
    st.session_state.history = []

if "user_input" not in st.session_state:
    st.session_state.user_input = ""

tokenizer, model = load_model()

def send_message():
    if st.session_state.user_input:
        inputs = tokenizer(st.session_state.history + [st.session_state.user_input], return_tensors="pt", padding=True, truncation=True)
        with torch.no_grad():
            outputs = model.generate(**inputs, max_length=100)
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        st.session_state.history.extend([st.session_state.user_input, response])
        st.session_state.user_input = "" # clear the stored user input
        st.session_state.temp_user_input = "" # clear the text input field

def update_user_input():
    st.session_state.user_input = st.session_state.temp_user_input

st.text_input("Ви:", key="temp_user_input", on_change=update_user_input)

if st.button("Надіслати") or st.session_state.get("user_input", "") != "":
    if st.session_state.get("user_input", "") != "":
        send_message()

if st.session_state.history:
    for i in range(0, len(st.session_state.history), 2):
        st.write(f"Ви: {st.session_state.history[i]}")
        if i + 1 < len(st.session_state.history):
            st.write(f"Бот: {st.session_state.history[i+1]}")