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from huggingface_hub import InferenceClient
import gradio as gr
import os
APIKEY = os.environ["MT_TOKEN"]
EP = os.environ["MT_EP"]
import requests
def translate(source_lang,target_lang,text):
params = {
'auth_key' : APIKEY,
'text' : text,
'source_lang' : source_lang,
"target_lang": target_lang
}
request = requests.post(EP, data=params)
result = request.json()
return result["translations"][0]["text"]
def translate_en_to_ja(text):
if text is None:
return None
return translate("EN","JA",text)
def translate_ja_to_en(text):
if text is None:
return None
return translate("JA","EN",text)
client = InferenceClient(
"mistralai/Mistral-7B-Instruct-v0.1"
)
message_en = None
real_history = []
def format_prompt(message, history):
global message_en
print(message)
message_en = translate_ja_to_en(message)
print(message_en)
prompt = "<s>"
for user_prompt, bot_response in real_history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
prompt += f"[INST] {message_en} [/INST]"
return prompt
def generate(
prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
):
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42,
)
formatted_prompt = format_prompt(prompt, history)
output = client.text_generation(formatted_prompt, **generate_kwargs, stream=False, details=True, return_full_text=False)
last_eng_respose = output.generated_text
real_history.append([message_en, last_eng_respose])
return translate_en_to_ja(output.generated_text)
additional_inputs=[
gr.Slider(
label="Temperature",
value=0.9,
minimum=0.0,
maximum=1.0,
step=0.05,
interactive=True,
info="Higher values produce more diverse outputs",
),
gr.Slider(
label="Max new tokens",
value=256,
minimum=0,
maximum=1048,
step=64,
interactive=True,
info="The maximum numbers of new tokens",
),
gr.Slider(
label="Top-p (nucleus sampling)",
value=0.90,
minimum=0.0,
maximum=1,
step=0.05,
interactive=True,
info="Higher values sample more low-probability tokens",
),
gr.Slider(
label="Repetition penalty",
value=1.2,
minimum=1.0,
maximum=2.0,
step=0.05,
interactive=True,
info="Penalize repeated tokens",
)
]
css = """
#mkd {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
"""
with gr.Blocks(css=css) as demo:
gr.HTML("<h1><center>Mistral 7B + Japanese MT <h1><center>")
gr.HTML("<h3><center><a href='https://huggingface.co./mistralai/Mistral-7B-Instruct-v0.1'>Mistral-7B-Instruct</a> の入出力に機械翻訳をかけたものです。💬<h3><center>")
gr.HTML("<h3><center>モデルの詳細については<a href='https://huggingface.co./docs/transformers/main/model_doc/mistral'>ここから</a>. 📚<h3><center>")
gr.ChatInterface(
generate,
additional_inputs=additional_inputs,
examples=[["人生の秘密は何ですか？"], ["パンケーキのレシピを書いて。"]]
)
demo.queue().launch(debug=True) |