# 配置环境
import subprocess
# 克隆GitHub仓库
subprocess.run(["git", "clone", "https://github.com/hiyouga/LLaMA-Factory.git"], check=True)
# 切换到仓库目录
import os
os.chdir("LLaMA-Factory")
# 安装unsloth
subprocess.run(["pip", "install", "unsloth[colab-new]@git+https://github.com/unslothai/unsloth.git"], check=True)
# 安装xformers
subprocess.run(["pip", "install", "-U", "xformers==0.0.25"], check=True)
# 安装当前目录下的依赖
subprocess.run(["pip", "install", ".[torch,bitsandbytes]"], check=True)



import gradio as gr
from llamafactory.chat import ChatModel
from llamafactory.extras.misc import torch_gc
import re

def split_into_sentences(text):
    sentence_endings = re.compile(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?|\!)\s')
    sentences = sentence_endings.split(text)
    return [sentence.strip() for sentence in sentences if sentence]

def process_paragraph(paragraph, progress=gr.Progress()):
    sentences = split_into_sentences(paragraph)
    results = []
    total_sentences = len(sentences)
    for i, sentence in enumerate(sentences):
        progress((i + 1) / total_sentences)
        messages.append({"role": "user", "content": sentence})
        sentence_response = ""
        for new_text in chat_model.stream_chat(messages, temperature=0.7, top_p=0.9, top_k=50, max_new_tokens=300):
            sentence_response += new_text.strip()
        category = sentence_response.strip().lower().replace(' ', '_')
        if category != "fair":
            results.append((sentence, category))
        else:
            results.append((sentence, "fair"))
        messages.append({"role": "assistant", "content": sentence_response})
        torch_gc()
    return results

%cd /root/autodl-tmp/LLaMA-Factory/

args = dict(
  model_name_or_path="princeton-nlp/Llama-3-Instruct-8B-SimPO",  # 使用量化的 Llama-3-8B-Instruct 模型
  # model_name_or_path="StevenChen16/llama3-8b-compliance-review",
  adapter_name_or_path="StevenChen16/llama3-8b-compliance-review-adapter",                 # 加载保存的 LoRA 适配器
  template="llama3",                                      # 与训练时使用的模板相同
  finetuning_type="lora",                                 # 与训练时使用的微调类型相同
  quantization_bit=8,                                     # 加载 4-bit 量化模型
  use_unsloth=True,                                       # 使用 UnslothAI 的 LoRA 优化以加速生成
)
chat_model = ChatModel(args)
messages = []

# 定义类型到颜色的映射
label_to_color = {
    "fair": "green",
    "limitation_of_liability": "red",
    "unilateral_termination": "orange",
    "unilateral_change": "yellow",
    "content_removal": "purple",
    "contract_by_using": "blue",
    "choice_of_law": "cyan",
    "jurisdiction": "magenta",
    "arbitration": "brown",
}

with gr.Blocks() as demo:
    
    with gr.Row(equal_height=True):
        with gr.Column():
            input_text = gr.Textbox(label="Input Paragraph", lines=10, placeholder="Enter the paragraph here...")
            btn = gr.Button("Process")
        with gr.Column():
            output = gr.HighlightedText(label="Processed Paragraph", color_map=label_to_color)
            progress = gr.Progress()

    def on_click(paragraph):
        results = process_paragraph(paragraph, progress=progress)
        return results

    btn.click(on_click, inputs=input_text, outputs=[output])

demo.launch(share=True)