run python jupyter_translate.py, and you will be asked for 3 inputs:
A new notebook will be created in target language in the same path as notebook.
It will preserve original language cells too, for easier human debugging of the translation.
You need a DeepL API
| import json | |
| import os | |
| import sys | |
| import deepl | |
| import readline | |
| my_key = open("./my_key.txt", "r").read().strip() | |
| translator = deepl.Translator(my_key) | |
| def needs_translate(cell): | |
| if is_toc_cell(cell): | |
| return False | |
| if is_code_cell(cell): | |
| return False | |
| if is_html_cell(cell): | |
| return False | |
| return True | |
| def get_cell_text(cell): | |
| return "\n".join(cell["source"]) | |
| def is_latex_cell(cell): | |
| cell_text = get_cell_text(cell) | |
| return cell_text.startswith("$") | |
| def is_html_cell(cell): | |
| cell_text = get_cell_text(cell) | |
| return cell_text.startswith("<") | |
| def is_toc_cell(cell): | |
| return cell["metadata"].get("toc") | |
| def is_code_cell(cell): | |
| return cell["cell_type"] == "code" | |
| def deepl_translate(text, from_lang, to_lang): | |
| result = translator.translate_text(text, source_lang=from_lang, target_lang=to_lang) | |
| translated_text = result.text | |
| return translated_text | |
| def generate_translated_notebook(source_lang, target_lang, filename): | |
| with open(filename, "rb") as f: | |
| notebook = json.load(f) | |
| # clone old notebook (maintain metadata, indent, etc) and reset cells | |
| new_notebook = notebook.copy() | |
| new_notebook["cells"] = [] | |
| n_translations = 0 | |
| for i, original_cell in enumerate(notebook["cells"]): | |
| if i % 10 == 0: | |
| print(f"Evaluating cell #{i}") | |
| new_notebook["cells"].append(original_cell) | |
| if not needs_translate(original_cell): | |
| continue | |
| # else: needs translation | |
| n_translations += 1 | |
| new_cell = original_cell.copy() | |
| original_text = get_cell_text(original_cell) | |
| translated_text = deepl_translate(original_text, source_lang, target_lang) | |
| new_cell['source'] = translated_text | |
| new_notebook["cells"].append(new_cell) | |
| print(f"Translated {n_translations}/{i} cells") | |
| filepath_translated = f"{filename.split('.ipynb')[0]}_{target_lang}.ipynb" | |
| with open(filepath_translated, "w") as f: | |
| print(f"You can find translated notebook here: {filepath_translated}") | |
| json.dump(new_notebook, f, indent=2) | |
| if __name__ == "__main__": | |
| readline.set_completer_delims(' \t\n=') | |
| readline.parse_and_bind("tab: complete") | |
| filename = input("File path: ") | |
| print(f"You entered: {filename}") | |
| if not os.path.exists(filename): | |
| print("That file path does not exist") | |
| sys.exit() | |
| source_lang = input("Source language: ") | |
| target_lang = input("Target language: ") | |
| generate_translated_notebook(source_lang, target_lang, filename) |