code_inference/all_in_normal.py

import os
import json
import subprocess
import pandas as pd
import shutil
from pathlib import Path

GLOBAL_MODEL_PATH = "../stage2_object_v8_1200"
GLOBAL_NORMAL_PATH = "正常-normal_data"###正常图的绝对路径

# 任务配置：(脚本名, 规则显示名称)
TASK_CONFIGS = [
    ("rule_11_vllm.py", "文字占比"),
    ("rule_12_vllm.py", "样式数量"),
    #("rule_13_vllm.py", "排布位置"),
    #("rule_14_vllm.py", "设计搭配"),
    ("rule_17_vllm.py", "信息量"),
    ("rule_18_vllm.py", "排布间距"),
    ("rule_19_vllm.py", "内容构图"),
]

SUMMARY_OUTPUT = "vllm_audit_summary_normal.csv"

def run_vllm_task(script, label_name):
    normal_path = Path(GLOBAL_NORMAL_PATH)
    original_json = normal_path / f"audit_result_{normal_path.name}.json"
    unique_json = normal_path / f"temp_{script.replace('.py', '')}.json"

    if unique_json.exists():
        unique_json.unlink()

    cmd = [
        "python", script,
        "--input_dir", GLOBAL_NORMAL_PATH,
        "--model_path", GLOBAL_MODEL_PATH
    ]
    
    print(f"\n[EXEC] 正在运行规则: {label_name} ({script})")
    
    try:
        subprocess.run(cmd, check=True)
        
        if original_json.exists():
            shutil.move(str(original_json), str(unique_json))
            print(f"[DONE] 结果已固化至: {unique_json.name}")
            return unique_json
        else:
            print(f"[ERROR] 脚本运行完成但未找到生成文件: {original_json}")
            return None
            
    except subprocess.CalledProcessError as e:
        print(f"[ERROR] 脚本 {script} 崩溃: {e}")
        return None

def extract_metrics(json_path, script, label_name):
    """从重命名后的唯一 JSON 文件中读取数据"""
    if not json_path or not json_path.exists():
        return {"规则名称": label_name, "状态": "失败"}

    with open(json_path, 'r', encoding='utf-8') as f:
        data = json.load(f)
    
    total = len(data)
    unsuitable = sum(1 for item in data if item["label"] == "Unsuitable")
    suitable = sum(1 for item in data if item["label"] == "Suitable")
    fp_rate = (unsuitable / total) if total > 0 else 0

    return {
        "规则脚本": script,
        "规则维度": label_name,
        "测试总数": total,
        "误报数(Unsuitable)": unsuitable,
        "正确通过数(Suitable)": suitable,
        "误报率(FP Rate)": f"{fp_rate:.2%}",
        "结果文件": json_path.name
    }

def main():
    final_results = []
    
    for script, label_name in TASK_CONFIGS:
        unique_json_path = run_vllm_task(script, label_name)
        
        if unique_json_path:
            metrics = extract_metrics(unique_json_path, script, label_name)
            final_results.append(metrics)
    
    if final_results:
        df = pd.DataFrame(final_results)
        df.to_csv(SUMMARY_OUTPUT, index=False, encoding='utf-8-sig')
        
        print("\n" + "="*80)
        print(f"测试完成！汇总已写入: {SUMMARY_OUTPUT}")
        print("-" * 80)
        print(df.to_string(index=False))
        print("="*80)
    else:
        print("[CRITICAL] 没有收集到任何有效数据。")

if __name__ == "__main__":
    main()