import os
import time
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from peft import PeftModel
import gradio as gr
from threading import Thread

# 从环境变量中获取 Hugging Face 模型信息
HF_TOKEN = os.environ.get("HF_TOKEN", None)
BASE_MODEL_ID = "Qwen/Qwen2.5-Coder-7B-Instruct"  # 基础模型
LORA_MODEL_PATH = "QLWD/test-7b"  # LoRA 模型路径

# 定义界面标题和描述
TITLE = "<h1><center>漏洞检测 微调模型测试</center></h1>"

DESCRIPTION = f"""
<h3>模型: <a href="https://huggingface.co/{LORA_MODEL_PATH}">漏洞检测 微调模型</a></h3>
<center>
<p>测试基础模型 + LoRA 补丁的生成效果。</p>
</center>
"""

PLACEHOLDER = """
<center>
<p>请输入您要分析的代码...</p>
</center>
"""

CSS = """
.duplicate-button {
    margin: auto !important;
    color: white !important;
    background: black !important;
    border-radius: 100vh !important;
}
h3 {
    text-align: center;
}
"""

device = "cuda"

# 加载tokenizer和基础模型
tokenizer = AutoTokenizer.from_pretrained(
    BASE_MODEL_ID, 
    use_fast=False,
    trust_remote_code=True,
    force_download=True
)

base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
    ignore_mismatched_sizes=True,
    force_download=True
)

# 加载 LoRA 微调权重
model = PeftModel.from_pretrained(
    base_model,
    LORA_MODEL_PATH,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

def format_chat(system_prompt, history, message):
    formatted_chat = f"<|im_start|>system\n{system_prompt}<|im_end|>\n"
    
    for prompt, answer in history:
        formatted_chat += f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n{answer}<|im_end|>\n"
    formatted_chat += f"<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n"
    return formatted_chat

@spaces.GPU()
def stream_chat(
    message: str, 
    history: list,
    system_prompt: str,
    temperature: float = 0.3, 
    max_new_tokens: int = 256, 
    top_p: float = 1.0, 
    top_k: int = 20, 
    repetition_penalty: float = 1.2,
):
    print(f'message: {message}')
    print(f'history: {history}')

    formatted_prompt = format_chat(system_prompt, history, message)
    inputs = tokenizer(formatted_prompt, return_tensors="pt").to(device)
    
    streamer = TextIteratorStreamer(tokenizer, timeout=5000.0, skip_prompt=True, skip_special_tokens=True)
    
    generate_kwargs = dict(
        input_ids=inputs.input_ids,
        max_new_tokens=max_new_tokens,
        do_sample=False if temperature == 0 else True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        repetition_penalty=repetition_penalty,
        streamer=streamer,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id
    )

    with torch.no_grad():
        thread = Thread(target=model.generate, kwargs=generate_kwargs)
        thread.start()
        
    buffer = ""
    for new_text in streamer:
        buffer += new_text
        if "<|endoftext|>" in buffer:
            yield buffer.split("<|endoftext|>")[0]
            break
        yield buffer

chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER)

SYSTEM_PROMPT = '''你是一位代码审计和漏洞修复专家，请仔细分析下面提供的代码，扫描并输出所有存在的漏洞和潜在的风险。每个漏洞或风险之间用分隔符 "--------" 隔开，报告内容左对齐。
从高危到低危的顺序来列出漏洞和风险，每个漏洞或风险的格式如下：
- **类型**：明确描述漏洞的类型或名称(如果已经有对应名称)，或潜在的风险类型（如资源泄露、边界条件问题等）。
- **风险等级**：根据漏洞或风险的严重性进行评级（如高危、中危、低危）。
- **漏洞/风险描述**：以专业的角度详细解释漏洞的技术细节和成因，或描述潜在的风险。
- **影响**：说明该漏洞或风险可能对系统、数据或用户造成的具体影响。
- **修复建议**：提供修复该漏洞或风险的具体步骤或建议（不是给出修复代码，而是修复的实现方法）。
- **漏洞所在的代码段**：给出代码中存在漏洞的具体位置和代码段（如适用）。
- **修复的代码段**：给出修复漏洞的替换代码段，以便开发者使用（如适用）。
请确保扫描并**输出所有**漏洞和风险，请确保扫描并**输出所有你能够笃定和大概率存在的**漏洞和风险。
分隔符 "--------" 用于每个漏洞或风险之间。'''

with gr.Blocks(css=CSS, theme="soft") as demo:
    gr.HTML(TITLE)
    gr.HTML(DESCRIPTION)
    gr.DuplicateButton(value="复制此 Space 进行私有部署", elem_classes="duplicate-button")
    gr.ChatInterface(
        fn=stream_chat,
        chatbot=chatbot,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ 参数设置", open=False, render=False),
        additional_inputs=[
            gr.Textbox(
                value=SYSTEM_PROMPT,
                label="系统提示词",
                render=False,
            ),
            gr.Slider(
                minimum=0,
                maximum=1,
                step=0.1,
                value=0.1,
                label="Temperature",
                render=False,
            ),
            gr.Slider(
                minimum=128,
                maximum=8192,
                step=1,
                value=8192,
                label="最大生成长度",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=1.0,
                step=0.1,
                value=1.0,
                label="Top-p",
                render=False,
            ),
            gr.Slider(
                minimum=1,
                maximum=50,
                step=1,
                value=20,
                label="Top-k",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=2.0,
                step=0.1,
                value=1.2,
                label="重复惩罚",
                render=False,
            ),
        ],
        examples=None,
        cache_examples=False,
    )

if __name__ == "__main__":
    demo.launch(share=True)