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from gradio_client import Client
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from deep_translator import GoogleTranslator
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# Обработка запроса генерации
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def predict(prompt, client, model = "0.1"):
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global iddb
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if model == "0.1":
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result = client.predict(
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prompt,
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"Default",
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api_name="/chat"
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)
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elif model == "0.2":
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result = client.predict(
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prompt,
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0.5, # 'Температура'
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128, # 'Длина'
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0.8, # 'Top-p (nucleus sampling)'
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1.8, # 'Repetition penalty'
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api_name="/chat"
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)
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elif model == "RWKV":
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result = client.predict(
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prompt,
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333,
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0.6,
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0, # int | float representing numeric value between 0.0 and 1.0 in 'Top P' Slider component
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0, # int | float representing numeric value between 0.0 and 1.0 in 'Presence Penalty' Slider component
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0, # int | float representing numeric value between 0.0 and 1.0 in 'Count Penalty' Slider component
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fn_index=0
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)
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else:
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print("INCORRECT MODEL: ", model)
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print(type(model))
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return result
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# Определяем что это код
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def iscode(text):
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# Теги
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langs = ['sql','php','js','java','c','cpp','python','go']
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is_code = False
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for i in langs:
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if i + r'\n' in text:
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is_code = True
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break
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# Тег ассемблера
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spec = ['section .']
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if not is_code:
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for i in spec:
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if i in text:
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is_code = True
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break
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return is_code
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# text IN language IN
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def translate(text, source):
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if source == "ru":
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target = "en"
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elif source == "en":
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target = "ru"
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# Исправление перевода кода
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if '```' in text:
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out = ''
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for i in text.split('```'):
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if iscode(i):
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out += '```' + i + '```'
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else:
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out += GoogleTranslator(source = source, target = target).translate(i)
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else:
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out = GoogleTranslator(source = source, target = target).translate(text)
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return out
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# Словарь пользователь - сессия
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iddb = {}
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def gen(prompt, id, model):
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global iddb
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# Если нету сессии
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if str(id) not in iddb:
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if model == "0.1":
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client = Client("https://afischer1985-ai-interface.hf.space/")
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elif model == "0.2":
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client = Client("https://nonamed33-minigpt-api.hf.space/")
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elif model == "RWKV":
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client = Client("https://blinkdl-rwkv-gradio-1.hf.space/")
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iddb[str(id)] = client
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else:
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client = iddb[str(id)]
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try:
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if model == "0.1" or model == "0.2":
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prompt = translate(prompt, "ru")
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predicted = predict(prompt, client, model).replace("</s>", "")
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predicted = translate(predicted, "en")
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elif model == "RWKV":
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predicted = predict(prompt, client, model)
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else:
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print("INCORRECT MODEL: ", model)
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print(type(model))
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except:
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pass
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fixed = predicted.replace(r'\n', '\n').replace('\\ n', '\n')
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return fixed
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