mirror of
https://github.com/illian64/llm-translate.git
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99 lines
4.1 KiB
Python
99 lines
4.1 KiB
Python
import os
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import lmstudio
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from lmstudio import LLM, LlmPredictionConfig
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from tqdm import tqdm
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from app import params, translate_func
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from app.app_core import AppCore
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from app.dto import TranslatePluginInitInfo, TranslateStruct
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from app.lang_dict import get_lang_by_2_chars_code
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plugin_name = os.path.basename(__file__)[:-3] # calculating modname
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llm_model: LLM | None = None
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def start(core: AppCore):
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manifest = {
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"name": "LM-Studio Translator", # name
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"version": "1.0", # version
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"default_options": {
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"custom_url": "http://localhost:1234", #
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"prompt": "You are a professional translator from %%from_lang%% to %%to_lang%%. Your task is to translate a text provided below.\n%%context_prompt%%\nINSTRUCTION:Carefully analyze the context. Pay special attention to Terminology, Style, Consistency. Provide only the translation. Do not include any additional information, explanations, notes, or comments in your response. The output should be the pure translated text only.\nTEXT TO TRANSLATE:",
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"prompt_postfix": "",
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"prompt_no_think_postfix": False,
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"use_library_for_request": True,
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},
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"translate": {
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"lm_studio": (init, translate) # 1 function - init, 2 - translate
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}
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}
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return manifest
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def start_with_options(core: AppCore, manifest: dict):
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params.read_plugin_translate_params(manifest)
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pass
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def init(core: AppCore) -> TranslatePluginInitInfo:
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options = core.plugin_options(plugin_name)
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custom_url: str = options['custom_url']
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use_library_for_request = options["use_library_for_request"]
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if use_library_for_request:
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lmstudio.configure_default_client(custom_url.replace("http://", ""))
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loaded_models = lmstudio.list_loaded_models("llm")
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if len(loaded_models) > 0:
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model_identifier = loaded_models[0].identifier
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global llm_model
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llm_model = lmstudio.llm(model_identifier)
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return TranslatePluginInitInfo(plugin_name=plugin_name, model_name=model_identifier)
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else:
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raise ValueError('List loaded models is empty. Please load model before init this plugin')
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else:
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postfix = translate_func.get_prompt_postfix(options["prompt_postfix"], options['prompt_no_think_postfix'])
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prompt = "You are assistant. " + postfix
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req = translate_func.get_open_ai_request(prompt, "init")
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resp = translate_func.post_request(req, options['custom_url'] + "/v1/chat/completions")
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return TranslatePluginInitInfo(plugin_name=plugin_name, model_name=resp["model"])
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def translate(core: AppCore, ts: TranslateStruct) -> TranslateStruct:
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options = core.plugin_options(plugin_name)
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from_lang_name = get_lang_by_2_chars_code(ts.req.from_lang)
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to_lang_name = get_lang_by_2_chars_code(ts.req.to_lang)
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prompt = translate_func.generate_prompt(prompt_param=options["prompt"], from_lang_name=from_lang_name,
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to_lang_name=to_lang_name, postfix_param=options["prompt_postfix"],
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prompt_no_think_postfix_param=options['prompt_no_think_postfix'],
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context=ts.req.context)
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use_library_for_request = options["use_library_for_request"]
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for part in tqdm(ts.parts, unit=params.tp.unit, ascii=params.tp.ascii, desc=params.tp.desc):
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if part.need_to_translate():
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content: str
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if use_library_for_request:
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content = library_request(llm_model, prompt, part.text)
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else:
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req = translate_func.get_open_ai_request(prompt, part.text)
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resp = translate_func.post_request(req, options['custom_url'] + "/v1/chat/completions")
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content = resp["choices"][0]['message']['content']
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part.translate = translate_func.remove_think_text(content)
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return ts
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def library_request(model: LLM, prompt: str, text: str) -> str:
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chat = lmstudio.Chat(prompt)
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chat.add_user_message(text)
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result = model.respond(chat, config=LlmPredictionConfig(temperature=0.0))
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return result.content
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