File size: 1,105 Bytes
0f80043 c1b6fa4 0f80043 c1b6fa4 167f186 c1b6fa4 167f186 c1b6fa4 167f186 c1b6fa4 167f186 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 |
import streamlit as st
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = 't5-base'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
# model = T5ForTranslation.from_pretrained("Helsinki-NLP/opus-mt-en-ro")
# tokenizer = T5Tokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-ro")
# Create the app layout
st.title("Text Translation App")
text_input = st.text_input("Enter text to translate:")
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(input_text, return_tensors='pt')
# Perform translation
output_ids = model.generate(input_ids)
translated = tokenizer.decode(output_ids[0], skip_special_tokens=True)
# 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[0]) |