女神把我送的花扔进了垃圾桶，当我回去捡时发现里面夹了一张小纸条：DS.OLLAMA.API 含(671)

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]

import pandas as pd
import requests
import json
import logging
from typing import List, Dict
import threading
from queue import Queue
import math
from concurrent.futures import ThreadPoolExecutor, as_completed
import os

# 设置日志
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler("server_search.log"),
        logging.StreamHandler()
    ]
)

# 全局变量用于存储结果
result_queue = Queue()
deepseek_models = []
original_df = None  # 用于存储原始Excel数据
processed_count = 0
total_count = 0
progress_lock = threading.Lock()

def update_progress():
    """更新进度"""
    global processed_count
    with progress_lock:
        processed_count += 1
        if processed_count % 10 == 0:  # 每处理10个请求才输出一次进度
            logging.info(f"进度: {processed_count}/{total_count} ({(processed_count/total_count*100):.2f}%)")

def read_server_list(excel_path: str) -> List[str]:
    """读取Excel文件中的服务器链接列表"""
    try:
        global original_df, total_count
        original_df = pd.read_excel(excel_path)
        servers = original_df.iloc[:, 8].tolist()
        total_count = len(servers)
        return servers
    except Exception as e:
        logging.error(f"读取Excel文件失败: {e}")
        return []

def get_models_from_server(server_url: str, row_index: int) -> Dict:
    """从服务器获取模型列表"""
    try:
        url = f"{server_url}/api/tags"
        response = requests.get(url, timeout=10)

        if response.status_code == 200:
            result = {
                "server_url": server_url,
                "status": "success",
                "models": response.json().get("models", [])
            }
            # 检查是否同时包含 deepseek-r1 和 671
            for model in result["models"]:
                model_name = model.get("model", "").lower()
                if "deepseek-r1" in model_name and "671" in model_name:
                    row_data = original_df.iloc[row_index].to_dict()
                    row_data["model"] = model["model"]
                    deepseek_models.append(row_data)
            update_progress()  # 更新进度
            return result
        else:
            update_progress()  # 更新进度
            return {
                "server_url": server_url,
                "status": "error",
                "error": f"HTTP错误: {response.status_code}"
            }
    except Exception as e:
        update_progress()  # 更新进度
        return {
            "server_url": server_url,
            "status": "error",
            "error": str(e)
        }

def process_single_server(server_url: str, row_index: int) -> Dict:
    """处理单个服务器"""
    result = get_models_from_server(server_url, row_index)
    # 只记录错误情况，减少日志量
    if result["status"] == "error":
        logging.error(f"服务器 {server_url} 查询失败: {result['error']}")
    return result

def process_server_batch(servers: List[str], start_idx: int) -> List[Dict]:
    """处理一批服务器"""
    with ThreadPoolExecutor(max_workers=10) as executor:  # 每个批次内部再用10个线程
        futures = [
            executor.submit(process_single_server, server_url, start_idx + i)
            for i, server_url in enumerate(servers)
        ]
        return [future.result() for future in as_completed(futures)]

def ensure_output_dirs():
    """确保输出目录存在"""
    os.makedirs("data/v3", exist_ok=True)

def save_models_txt(server_models: List[Dict]) -> None:
    """将服务器模型信息保存为txt格式"""
    try:
        with open("data/v3/models.txt", "w", encoding="utf-8") as f:
            for server_info in server_models:
                if server_info["status"] == "success":
                    server_url = server_info["server_url"]
                    model_names = [model["name"] for model in server_info["models"]]
                    line = f"{server_url}#{'#'.join(model_names)}\n"
                    f.write(line)
        logging.info("模型信息已保存到 models.txt")
    except Exception as e:
        logging.error(f"保存TXT文件失败: {e}")

def save_deepseek_models():
    """保存deepseek模型到Excel"""
    try:
        if deepseek_models:
            df = pd.DataFrame(deepseek_models)
            df.to_excel("data/v3/deepseek_671_models.xlsx", index=False)
            logging.info(f"找到 {len(deepseek_models)} 个包含 deepseek-r1 和 671 的模型")
    except Exception as e:
        logging.error(f"保存筛选后的模型Excel失败: {e}")

def main():
    excel_path = "ds.xlsx"
    
    # 确保输出目录存在
    ensure_output_dirs()

    # 读取服务器列表
    servers = read_server_list(excel_path)
    if not servers:
        logging.error("没有找到服务器列表")
        return

    # 检查数据量
    if total_count > 5000:
        logging.error(f"数据量过大（{total_count}条），请分批处理，每批建议不超过5000条")
        return

    # 计算需要的线程数
    batch_size = 50
    num_batches = math.ceil(total_count / batch_size)
    logging.info(f"总计 {total_count} 条数据，分为 {num_batches} 批处理")

    # 使用线程池处理数据
    all_server_models = []
    with ThreadPoolExecutor(max_workers=min(num_batches, 20)) as executor:
        future_to_batch = {}
        for i in range(num_batches):
            start_idx = i * batch_size
            end_idx = min((i + 1) * batch_size, total_count)
            batch = servers[start_idx:end_idx]
            future = executor.submit(process_server_batch, batch, start_idx)
            future_to_batch[future] = (start_idx, end_idx)

        # 收集结果
        for future in as_completed(future_to_batch):
            start_idx, end_idx = future_to_batch[future]
            try:
                batch_results = future.result()
                all_server_models.extend(batch_results)
            except Exception as e:
                logging.error(f"处理批次 {start_idx + 1} - {end_idx} 失败: {str(e)}")

    # 保存结果
    try:
        with open("data/v3/models.json", "w", encoding="utf-8") as f:
            json.dump(all_server_models, f, ensure_ascii=False, indent=2)
        logging.info("所有结果已保存")
    except Exception as e:
        logging.error(f"保存JSON文件失败: {e}")

    save_models_txt(all_server_models)
    save_deepseek_models()

if __name__ == "__main__":
    # 总计 2461 条数据，分为 50 批处理
    print()
    main()

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