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)