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import streamlit as st
from transformers import T5Tokenizer, T5ForConditionalGeneration
from pdfminer.high_level import extract_text
import nltk
from nltk import sent_tokenize
# Download the punkt tokenizer for sentence segmentation
nltk.download('punkt')
def main():
st.title("PDF Translation")
# Upload the pdf
uploaded_file = st.file_uploader("Upload a PDF file and we will translate the text inside to German and French", type=["pdf"])
if uploaded_file is not None:
# Extract text from pdf
text = extract_text(uploaded_file)
tokenizer = T5Tokenizer.from_pretrained("t5-base")
model = T5ForConditionalGeneration.from_pretrained("t5-base")
# Define translation prefixes for each language
translation_prefixes = {
"german": "translate English to German: ",
"french": "translate English to French: "
}
# Variables to track translation state
translated_german = False
translated_french = False
# Buttons to trigger translation
translate_german = st.button("Translate to German")
translate_french = st.button("Translate to French")
# Translate and display for German
if translate_german and not translated_german:
translated_sentences_german = translate_text(text, translation_prefixes["german"], tokenizer, model)
display_translation(translated_sentences_german, "German")
translated_german = True
# Translate and display for French
if translate_french and not translated_french:
translated_sentences_french = translate_text(text, translation_prefixes["french"], tokenizer, model)
display_translation(translated_sentences_french, "French")
translated_french = True
def translate_text(text, prefix, tokenizer, model):
# Split text into sentences
sentences = sent_tokenize(text)
# Translate each sentence
translated_sentences = []
for sentence in sentences:
text_to_translate = prefix + sentence
input_ids = tokenizer(text_to_translate, return_tensors="pt").input_ids
outputs = model.generate(input_ids=input_ids, max_length=500, num_beams=4, no_repeat_ngram_size=2)
translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
translated_sentences.append(translated_text)
return translated_sentences
def display_translation(translations, language):
st.write(f"\nLanguage: {language}")
st.write(f"Translation:\n {' '.join(translations)}")
if __name__ == "__main__":
main()
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