app.py

import streamlit as st
from transformers import T5Tokenizer, T5ForConditionalGeneration

# Load the T5 model and tokenizer
@st.cache_resource
def load_model():
    model_name = "t5-small"
    tokenizer = T5Tokenizer.from_pretrained(model_name)
    model = T5ForConditionalGeneration.from_pretrained(model_name)
    return model, tokenizer

model, tokenizer = load_model()

def translate_text(text, model, tokenizer):
    input_text = f"translate English to Urdu: {text}"
    inputs = tokenizer.encode(input_text, return_tensors="pt", truncation=True)
    outputs = model.generate(inputs, max_length=512, num_beams=5, early_stopping=True)
    translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return translated_text

# Streamlit UI
st.title("English to Urdu Translation with T5")

# Input text from the user
text_to_translate = st.text_area("Enter English text to translate:")

if text_to_translate.strip():
    with st.spinner("Translating..."):
        translated_text = translate_text(text_to_translate, model, tokenizer)
    st.markdown(f"### Translated Text:\n{translated_text}")