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import os
import gradio as gr
from langchain.llms import HuggingFaceHub
llama_repo = os.getenv('HF_MODEL_LLAMA_REPO')
starchat_repo = os.getenv('HF_MODEL_STARCHAT_REPO')
bloom_repo = os.getenv('HF_MODEL_BLOOM_REPO')
llamma_template = """<s>[INST]<<SYS>>I want you to act as document language translator. You do translation {source} texts in document into then you return to me the translated document AND DO NOTHING ELSE.<</SYS>>[/INST]
[INST]Begin of the document:
{query}
End of the document.[/INST]
{target} translated document:
"""
starchat_template = """<|system|>I want you to act as document language translator. You do translation {source} texts in document into then you return to me the translated document AND DO NOTHING ELSE.<</SYS>>
Begin of the document:
{query}
End of the document<|end|>
<|assistant|>
{target} translated document:
"""
starchat_template = """Translation {source} texts into {target}. then you return to me the translated document AND DO NOTHING ELSE.<</SYS>>
Begin of the texts:
{query}
End of the texts
{target} translated texts:
"""
model_kwargs={
"max_new_tokens":2048,
"temperature": 0.01,
"truncate": 4096,
"seed" : 256,
"stop" : ["</s>","<|endoftext|>","<|end|>"],
}
llm1 = HuggingFaceHub(repo_id=llama_repo, task="text-generation", model_kwargs=model_kwargs)
llm2 = HuggingFaceHub(repo_id=starchat_repo, task="text-generation", model_kwargs=model_kwargs)
llm3 = HuggingFaceHub(repo_id=bloom_repo, task="text-generation", model_kwargs=model_kwargs)
def translation(source, target, text):
response = text
try:
input_prompt = llamma_template.replace("{source}", source)
input_prompt = input_prompt.replace("{target}", target)
input_prompt = input_prompt.replace("{query}", text)
response=llm3(input_prompt)
except Exception as e:
print(f"ERROR: LLM show {e}")
input_prompt = starchat_template.replace("{source}", source)
input_prompt = input_prompt.replace("{target}", target)
input_prompt = input_prompt.replace("{query}", text)
response=llm1(input_prompt).replace("<|end|>","")
return response
gr.Interface(translation, inputs=["text","text","text"], outputs="text").launch() |