import gradio as gr
from functools import lru_cache
import openai  # 用于调用外部API
import os
import spaces
import gradio as gr
from transformers import AutoTokenizer, AutoModel,AutoModelForCausalLM
import platform
import torch
import nltk
from functools import lru_cache

# 假设openai_client已定义，例如：

device = "cuda"
MODEL_NAME = "ByteDance-Seed/Seed-X-PPO-7B"

def load_model():
    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME,torch_dtype="bfloat16").to(device)
    print(f"Model loaded in {device}")
    return model


model = load_model()

# Loading the tokenizer once, because re-loading it takes about 1.5 seconds each time
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)


@lru_cache(maxsize=100)
def translate(text: str):
    return _translate(text)

# Only assign GPU if cache not used
@spaces.GPU
def _translate(text: str):
    input_tokens = (
        tokenizer(text, return_tensors="pt")
        .input_ids[0]
        .cpu()
        .numpy()
        .tolist()
    )
    translated_chunk = model.generate(
        input_ids=torch.tensor([input_tokens]).to(device),
        max_length=len(input_tokens) + 2048,
        num_return_sequences=1,
    )
    full_output = tokenizer.decode(translated_chunk[0], skip_special_tokens=True).strip()
    print(full_output)
    return full_output

# def _translate(text: str, src_lang: str, tgt_lang: str):
#     prompt = f"Translate the following text from {src_lang} to {tgt_lang}. Direct output translation result without any explaination:\n\n{text}"
#     key=os.getenv('key')
#     openai_client = openai.OpenAI(base_url="https://ssapi.cppbear.site/v1", api_key=key)
#     response = openai_client.chat.completions.create(
#         model="tbai.xin-dpsk-deepseek-v3",  # 如gpt-3.5-turbo或其他兼容模型
#         messages=[{"role": "user", "content": prompt}],
#         max_tokens=30240,
#         temperature=0.0
#     )
#     print(response)
#     return response.choices[0].message.content.strip()


description = """
<div style="text-align: center;">
    <img src="https://github.com/user-attachments/assets/c42e675e-497c-4508-8bb9-093ad4d1f216" alt="UNESCO Meta Hugging Face Banner" style="max-width: 800px; width: 100%; margin: 0 auto;">
    <h1 style="color: #0077be; font-size: 3em;">Seed-X, powered by Bytedance</h1>
</div>
Seed-X, a powerful series of open-source multilingual translation language models, including an instruction model, a reinforcement learning model, and a reward model. It pushes the boundaries of translation capabilities within 7 billion parameters. We develop Seed-X as an accessible, off-the-shelf tool to support the community in advancing translation research and applications:
"""

examples_inputs = [["Seed-X is indeed a good translation model ","English","Chinese"],]

with gr.Blocks() as demo:
    gr.Markdown(description)
    with gr.Row():
        input_text = gr.Textbox(label="Input Text", lines=6)
    with gr.Row():
        btn = gr.Button("Translate text")
    with gr.Row():
        output = gr.Textbox(label="Output Text", lines=6)
    btn.click(
        translate,
        inputs=[input_text],
        outputs=output,
    )
    examples = gr.Examples(examples=examples_inputs,inputs=[input_text, src_lang,target_lang], fn=translate, outputs=output, cache_examples=True)
demo.launch()