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import streamlit as st |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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from configs.download_files import FileDownloader |
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from configs.db_configs import add_one_item |
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from streamlit.components.v1 import html |
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from configs.html_features import set_image |
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from sacrebleu.compat import corpus_bleu |
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import pandas as pd |
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def translate_text_to_text(text, source_lang, target_lang): |
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prefix = f'translate {source_lang} to {target_lang}: ' |
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text = prefix + text |
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tokenizer = AutoTokenizer.from_pretrained('stevhliu/my_awesome_opus_books_model') |
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input_ids = tokenizer(text, return_tensors='pt').input_ids |
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model = AutoModelForSeq2SeqLM.from_pretrained('stevhliu/my_awesome_opus_books_model') |
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output_ids = model.generate(input_ids, max_new_tokens=len(input_ids[0]) * 3, do_sample=False, top_k=30, top_p=0.95) |
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translated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) |
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return translated_text |
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def validate_translation(original_text, translated_text): |
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return corpus_bleu(translated_text, [original_text]) |
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def main(): |
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st.title('Text Translator') |
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im1, im2, im3 = st.columns([1, 5.3, 1]) |
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with im1: |
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pass |
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with im2: |
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url = "https://i.postimg.cc/jdF1hPng/combined.png" |
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html(set_image(url), height=400, width=400) |
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with im3: |
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pass |
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languages = ['English', 'French'] |
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source_lang = st.sidebar.selectbox('Source Language', languages) |
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target_lang = st.sidebar.selectbox('Target Language', languages, index=1) |
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text = st.text_area('Text Translator', placeholder='Enter your input text here ...', height=200, label_visibility='hidden') |
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if st.button('translate it'): |
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if text != '': |
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if (source_lang == 'English' and target_lang == 'English') or (source_lang == 'French' and target_lang == 'French'): |
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st.error('Expected different values for source and target languages, but got the same values!') |
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else: |
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with st.expander('Original Text'): |
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st.write(text) |
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add_one_item(text, 'Text Translator') |
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with st.expander('Translated Text'): |
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translated_text = translate_text_to_text(text, source_lang, target_lang) |
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st.write(translated_text) |
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col1, col2 = st.columns(2) |
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with col1: |
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with st.expander('Download Translated Text'): |
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FileDownloader(translated_text, 'txt').download() |
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with col2: |
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with st.expander('Translated Text Validation'): |
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bleu_score = validate_translation(text, translated_text) |
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df = pd.DataFrame({ |
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'Brevity Penalty' : bleu_score.bp, |
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'length of original text' : bleu_score.ref_len, |
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'length of translated text' : bleu_score.sys_len, |
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'Ratio' : bleu_score.ratio |
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}, index=[1]) |
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st.dataframe(df) |
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else: |
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st.error('Please enter a non-empty text.') |
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if __name__ == '__main__': |
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main() |