import gradio as gr
import os  # 导入os模块以便扫描文件夹
import sys
from pathlib import Path
from unsloth import FastLanguageModel
import torch

sys.path.append(str(Path(__file__).resolve().parent.parent))
from global_var import model,tokenizer

def model_manage_page():
    workdir = "workdir"  # 假设workdir是当前工作目录下的一个文件夹
    models_dir = os.path.join(workdir, "models")
    model_folders = [name for name in os.listdir(models_dir) if os.path.isdir(os.path.join(models_dir, name))]  # 扫描models文件夹下的所有子文件夹

    with gr.Blocks() as demo:
        gr.Markdown("## 模型管理")
        dropdown = gr.Dropdown(choices=model_folders, label="选择模型", interactive=True)  # 将子文件夹列表添加到Dropdown组件中，并设置为可选
        max_seq_length_input = gr.Number(label="最大序列长度", value=4096, precision=0)
        load_in_4bit_input = gr.Checkbox(label="使用4位量化", value=True)
        with gr.Row():
            load_button = gr.Button("加载模型", variant="primary")
            unload_button = gr.Button("卸载模型", variant="stop")
        output_text = gr.Textbox(label="操作结果", interactive=False)

        def load_model(selected_model, max_seq_length, load_in_4bit):
            try:
                global model, tokenizer
                # 判空操作，如果模型已加载，则先卸载
                if model is not None:
                    unload_model()
                
                model_path = os.path.join(models_dir, selected_model)
                model, tokenizer = FastLanguageModel.from_pretrained(
                    model_name=model_path,
                    max_seq_length=max_seq_length,
                    load_in_4bit=load_in_4bit,
                )
                return f"模型 {selected_model} 已加载"
            except Exception as e:
                return f"加载模型时出错: {str(e)}"

        load_button.click(fn=load_model, inputs=[dropdown, max_seq_length_input, load_in_4bit_input], outputs=output_text)

        def unload_model():
            try:
                global model, tokenizer
                # 将模型移动到 CPU
                if model is not None:
                    model.cpu()
                
                # 如果提供了 tokenizer，也将其设置为 None
                if tokenizer is not None:
                    tokenizer = None
                
                # 清空 CUDA 缓存
                torch.cuda.empty_cache()
                
                # 将模型设置为 None
                model = None

                return "模型已卸载"
            except Exception as e:
                return f"卸载模型时出错: {str(e)}"
        
        unload_button.click(fn=unload_model, inputs=None, outputs=output_text)

    return demo

if __name__ == "__main__":
    demo = model_manage_page()
    demo.queue()
    demo.launch()