import streamlit as st
from transformers import T5Tokenizer, T5ForConditionalGeneration

# Create the app layout
st.title("Text Translation")
input_text = st.text_input("Enter text to translate:")
# Create a list of options for the select box
options = ["English", "Romanian", "German", "French", "Spanish"]
models = ["t5-base", "t5-small", "t5-large", "google/mt5-base", "helsinki-nlp/opus-mt-de-ro"]
# Create the select box
sselected_language = st.selectbox("Select a source language:", options)
tselected_language = st.selectbox("Select a target language:", options)
model_name = st.selectbox("Select a model:", models)
st.session_state["sselected_language"] = sselected_language
st.session_state["tselected_language"] = tselected_language
st.session_state["model_name"] = model_name
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
st.write("Selected language combination:", sselected_language, " - ", tselected_language, "Selected model:", model_name)
submit_button = st.button("Translate")
translated_textarea = st.text("")

# Handle the submit button click
if submit_button:
    prompt = f'Prompt: translate {sselected_language} to {tselected_language}: {input_text}'
    print(prompt)
    input_ids = tokenizer.encode(prompt, return_tensors='pt')
    # Perform translation
    output_ids = model.generate(input_ids)
    # Decode the translated text
    translated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    # Display the translated text
    print(translated_text)
    st.write(f"Translated text from {sselected_language} to {tselected_language}:", translated_text)
    translated_textarea = st.text(translated_text)