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import streamlit as st | |
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
# Initialize tokenizer and model | |
model_name = "your_hugging_face_model_name_or_url" | |
tokenizer = AutoTokenizer.from_pretrained(model_name) | |
model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
def translate(text): | |
# Tokenize the text | |
input_ids = tokenizer.batch_encode_plus([text], return_tensors="pt")["input_ids"] | |
# Generate the translation | |
outputs = model.generate(input_ids, max_length=100) | |
# Decode the translation | |
translation = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] | |
return translation | |
def main(): | |
# Get the user's input | |
text = st.text_input("Enter a Russian text to translate:") | |
# Translate the text | |
translation = translate(text) | |
# Display the translation | |
st.text(translation) | |
if __name__ == "__main__": | |
main() | |