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| import streamlit as st | |
| from transformers import T5Tokenizer, T5ForConditionalGeneration | |
| from pdfminer.high_level import extract_text | |
| 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-small") | |
| model = T5ForConditionalGeneration.from_pretrained("t5-small") | |
| # Define translation prefixes for each language | |
| translation_prefixes = { | |
| "german": "translate English to German: ", | |
| "french": "translate English to French: " | |
| } | |
| # Generate translations for each language | |
| translations = {} | |
| # Buttons to trigger translation | |
| translate_german = st.button("Translate to German") | |
| translate_french = st.button("Translate to French") | |
| for language, prefix in translation_prefixes.items(): | |
| # Translate the entire text, not page by page | |
| text_to_translate = prefix + text | |
| input_ids = tokenizer(text_to_translate, return_tensors="pt").input_ids | |
| outputs = model.generate(input_ids=input_ids, max_length=150, num_beams=4, no_repeat_ngram_size=2) | |
| translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| translations[language] = translated_text | |
| # Display the translations based on the button clicked | |
| if translate_german: | |
| display_translation(translations["german"], "German") | |
| if translate_french: | |
| display_translation(translations["french"], "French") | |
| def display_translation(translation, language): | |
| st.write(f"\nLanguage: {language}") | |
| st.write(f"Translation: {translation}") | |
| if __name__ == "__main__": | |
| main() | |