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import streamlit as st
from transformers import T5Tokenizer, T5ForConditionalGeneration
# tokenizer = T5Tokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-ro")
# 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", "opus-mt-en-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)
# Display the selected language
st.session_state["slanguage_selector"] = sselected_language
st.session_state["tlanguage_selector"] = 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:", selected_language, ' - ', selected_language)
submit_button = st.button("Translate")
translated_text = st.text("")
# Handle the submit button click
if submit_button:
# Encode the input text
# encoded = tokenizer(text_input, return_tensors="pt")
input_ids = tokenizer.encode(f'Translate {sselected_language} to {tselected_language}: {input_text}', 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
st.write("Translated Text:", translated_text) |