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""" |
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translation program for simple text |
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1. detect language from langdetect |
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2. translate to target language given by user |
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Example from |
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https://www.thepythoncode.com/article/machine-translation-using-huggingface-transformers-in-python |
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user_input: |
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string: string to be translated |
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target_lang: language to be translated to |
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Returns: |
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string: translated string of text |
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""" |
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import gradio as gr |
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import argparse |
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import langid |
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from transformers import pipeline |
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def detect_lang(article, target_lang): |
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""" |
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Language Detection using library langid |
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Args: |
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article (string): article that user wish to translate |
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target_lang (string): language user want to translate article into |
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Returns: |
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string: detected language short form |
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""" |
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result_lang = langid.classify(article) |
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print(result_lang[0]) |
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if result_lang == target_lang: |
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return result_lang[0] |
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else: |
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return result_lang[0] |
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def opus_trans(article, target_language): |
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""" |
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Translation by Helsinki-NLP model |
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Args: |
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article (string): article that user wishes to translate |
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result_lang (string): detected language in short form |
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target_language (string): language that user wishes to translate article into |
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Returns: |
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string: translated piece of article based off target_language |
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""" |
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result_lang = detect_lang(article, target_language) |
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if target_language == "English": |
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target_lang = "en" |
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elif target_language == "Chinese": |
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target_lang = "zh" |
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task_name = f"translation_{result_lang}_to_{target_lang}" |
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model_name = f"Helsinki-NLP/opus-mt-{result_lang}-{target_lang}" |
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translator = pipeline(task_name, model=model_name, tokenizer=model_name) |
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translated = translator(article)[0]["translation_text"] |
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print(translated) |
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return translated |
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article = gr.Textbox() |
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lang_select = gr.Radio(["English", "Chinese"]) |
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translate = gr.Interface( |
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opus_trans, |
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[ |
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article, |
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lang_select, |
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], |
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outputs=gr.Textbox(), |
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) |
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translate.launch() |
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