app.py

import streamlit as st
from transformers import T5Tokenizer, T5ForConditionalGeneration, AutoTokenizer, AutoModelForSeq2SeqLM

# 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 = ["German", "Romanian", "English", "French", "Spanish"]
langs = {"English":"en", "Romanian":"ro", "German":"de", "French":"fr", "Spanish":"es"}
models = ["Helsinki-NLP", "t5-base", "t5-small", "t5-large"]

# Create two columns
scol, tcol = st.columns(2)
# Place select boxes in columns
with scol:
    sselected_language = st.selectbox("Source language:", options, index=0, placeholder="Select source language")
with tcol:
    tselected_language = st.selectbox("Target language:", options, index=1, placeholder="Select target language")
model_name = st.selectbox("Select a model:", models, index=0, placeholder="Select language model")

sl = langs[sselected_language]  
tl = langs[tselected_language]

st.session_state["sselected_language"] = sselected_language
st.session_state["tselected_language"] = tselected_language
st.session_state["model_name"] = model_name

if model_name == 'Helsinki-NLP':
    try:
        model_name = f"Helsinki-NLP/opus-mt-{sl}-{tl}"
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
    except EnvironmentError:
        model_name = f"Helsinki-NLP/opus-tatoeba-{sl}-{tl}"
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
else:
    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:
    if model_name.startswith('Helsinki-NLP'):
        prompt = input_text
    else:
        prompt = f'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} using {model_name}")
    translated_textarea = st.text(translated_text)