# -*- coding: utf-8 -*-
"""Hugging Face Space App with CPU Quantization"""

import os
import gradio as gr
from huggingface_hub import login
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

# 登錄 Hugging Face，使用訪問令牌進行身份驗證
HF_TOKEN = os.getenv("HF_TOKEN")
if not HF_TOKEN:
    raise ValueError(
        "未找到 Hugging Face 訪問令牌！請設置環境變數 'HF_TOKEN'，或者直接提供有效的訪問令牌。"
    )

login(HF_TOKEN)

# 配置 4-bit 量化
MODEL_NAME = "meta-llama/Llama-2-13b-chat-hf"
quantization_config = BitsAndBytesConfig(load_in_4bit=True)

# 加載量化模型
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    quantization_config=quantization_config,
    device_map="auto",
    token=HF_TOKEN
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=HF_TOKEN)

# 定義推理函數
def generate_text(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(
        inputs.input_ids,
        max_length=200,
        num_beams=5,
        repetition_penalty=1.2,
        early_stopping=True
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# 使用 Gradio 構建界面
interface = gr.Interface(
    fn=generate_text,
    inputs=gr.Textbox(lines=5, placeholder="Enter your prompt here..."),
    outputs="text",
    title="Llama 2 Text Generator (CPU Quantized)",
    description="Generate text using the Llama-2-13b-chat-hf model with CPU quantization hosted on Hugging Face Spaces."
)

# 啟動應用
if __name__ == "__main__":
    interface.launch()