Non1ce /
Russian-conversational-chat_bot
| Conditions | 4 |
| Total Lines | 52 |
| Code Lines | 26 |
| 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 -*- |
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| 36 | @rate_limit(3, 'message') |
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| 37 | @dp.message_handler() |
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| 38 | async def processing_message(message: types.Message, state: FSMContext) -> types.Message: |
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| 39 | |||
| 40 | """ |
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| 41 | |||
| 42 | The function is designed to receive messages in russian and generate a response. |
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| 43 | |||
| 44 | """ |
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| 45 | |||
| 46 | await types.ChatActions.typing() |
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| 47 | |||
| 48 | data_storage = await state.get_data() |
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| 49 | text = message.text.lower() |
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| 50 | |||
| 51 | await state.update_data(history_text=text) |
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| 52 | await state.update_data(chat_id=message.chat.id) |
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| 53 | await state.update_data(first_name=message.from_user.first_name) |
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| 54 | |||
| 55 | input_text, check_question = encoding_text(text_encode=text) |
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| 56 | |||
| 57 | if text == data_storage.get('history_text'): |
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| 58 | |||
| 59 | await message.answer(text="Ой... Где-то я уже это видел! 🥱") |
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| 60 | |||
| 61 | # input_text = torch.cat([context.chat_data['output'][-1], input_text[0]], dim=0) |
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| 62 | # input_text = input_text.unsqueeze(0) |
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| 63 | |||
| 64 | return |
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| 65 | |||
| 66 | text_gpt3 = get_text_gpt3(text_gpt=input_text, check_question=check_question) |
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| 67 | output_text = decoding_text(text_decode=text_gpt3) |
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| 68 | |||
| 69 | if len(output_text.split()) < 1: |
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| 70 | |||
| 71 | await zero_output(message) |
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| 72 | |||
| 73 | return |
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| 74 | |||
| 75 | await message.answer(text=output_text) |
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| 76 | |||
| 77 | if 'input' in data_storage: |
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| 78 | |||
| 79 | await state.update_data(input=input_text) |
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| 80 | await state.update_data(output=text_gpt3) |
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| 81 | |||
| 82 | return |
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| 83 | |||
| 84 | data_history = {'input': [input_text], |
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| 85 | 'output': [text_gpt3]} |
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| 86 | |||
| 87 | await state.update_data(data_history) |
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| 88 |
