| Conditions | 5 | 
| Total Lines | 54 | 
| Code Lines | 27 | 
| Lines | 0 | 
| Ratio | 0 % | 
| Changes | 0 | ||
Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.
For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.
Commonly applied refactorings include:
If many parameters/temporary variables are present:
| 1 | # -*- coding: utf-8 -*- | ||
| 33 | @rate_limit(3, 'message') | ||
| 34 | @dp.message_handler() | ||
| 35 | async def processing_message(message: types.Message, state: FSMContext): | ||
| 36 | |||
| 37 | """ | ||
| 38 | |||
| 39 | The function is designed to receive messages in russian and generate a response. | ||
| 40 | |||
| 41 | """ | ||
| 42 | |||
| 43 | await types.ChatActions.typing() | ||
| 44 | |||
| 45 | data_storage = await state.get_data() | ||
| 46 | text = message.text.lower() | ||
| 47 | |||
| 48 | await state.update_data(history_text=text) | ||
| 49 | await state.update_data(chat_id=message.chat.id) | ||
| 50 | await state.update_data(first_name=message.from_user.first_name) | ||
| 51 | |||
| 52 | input_text, check_question = encoding_text(text_encode=text) | ||
| 53 | |||
| 54 | if 'history_text' in data_storage: | ||
| 55 | |||
| 56 | if text == data_storage['history_text']: | ||
| 57 | |||
| 58 | await message.answer(text="Ой... Где-то я уже это видел! 🥱") | ||
| 59 | |||
| 60 | # input_text = torch.cat([context.chat_data['output'][-1], input_text[0]], dim=0) | ||
| 61 | # input_text = input_text.unsqueeze(0) | ||
| 62 | |||
| 63 | return | ||
| 64 | |||
| 65 | text_gpt3 = get_text_gpt3(text_gpt=input_text, check_question=check_question) | ||
| 66 | output_text = decoding_text(text_decode=text_gpt3) | ||
| 67 | |||
| 68 | if len(output_text.split()) < 1: | ||
| 69 | |||
| 70 | await zero_output(message) | ||
| 71 | |||
| 72 | return | ||
| 73 | |||
| 74 | await message.answer(text=output_text) | ||
| 75 | |||
| 76 | if 'input' in data_storage: | ||
| 77 | |||
| 78 | await state.update_data(input=input_text) | ||
| 79 | await state.update_data(output=text_gpt3) | ||
| 80 | |||
| 81 | return | ||
| 82 | |||
| 83 |     data_history = {'input': [input_text], | ||
| 84 | 'output': [text_gpt3]} | ||
| 85 | |||
| 86 | await state.update_data(data_history) | ||
| 87 |