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from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
import streamlit as st
from torch import cuda
if cuda.is_available():
device='cuda'
else:
device='cpu'
@st.cache_resource()
def load_model():
model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
model.to(device)
tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M")
return model,tokenizer
model,tokenizer=load_model()
st.title("Multilingual translation app")
st.write("This app demonstrates translation capabilities of LLM.The app leverages M2M100_418M model by facebook")
col1,col2 = st.columns(2)
with col1:
source_language=st.radio("Select source language",["ar","zh","de","bn","Kn","ta"])
user_text = st.text_area("Enter text for translation")
with col2:
target_language=st.radio("Select target language",["en","de","bn","hi","kn","ta"])
if user_text:
tokenizer.src_lang = source_language#"zh"#"hi"
encoded_text = tokenizer(user_text, return_tensors="pt").to(device)
generated_tokens = model.generate(**encoded_text, forced_bos_token_id=tokenizer.get_lang_id(target_language))
m2m_translated=tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
st.write(m2m_translated)
#st.snow()
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