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+#!/usr/bin/env python
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+# coding=utf-8
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+
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+import argparse
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+import base64
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+
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+import pandas as pd
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+from openai import OpenAI
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+from tqdm import tqdm
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+
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+model_name_or_path = 'Qwen/Qwen2.5-VL-7B-Instruct'
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+
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+system = """
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+根据中老年用户在社交平台上的姓名/昵称和头像，分析其性别倾向。
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+先基于姓名/昵称分析，不能判断时，再结合头像分析；
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+头像中的儿童/青少年，不能作为判断依据；
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+当头像不可用时，仅基于姓名/昵称分析。
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+直接返回[男性|女性|未知]，不要返回多余的信息。
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+"""
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+system = system.replace("\n", "")
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+prompt = "姓名/昵称：%s, 头像："
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+
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+
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+class ModelService:
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+ def __init__(self, host, port, model_name):
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+ base_url = 'http://%s:%d/v1' % (host, port)
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+ self.client = OpenAI(api_key="0", base_url=base_url)
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+ self.model_name = model_name
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+
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+
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+def get_image_base64(image_path):
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+ with open(image_path, "rb") as f:
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+ encoded_image = base64.b64encode(f.read())
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+ encoded_image_text = encoded_image.decode("utf-8")
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+ return f"data:image/jpeg;base64,{encoded_image_text}"
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+
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+
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+def get_system_message():
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+ return {
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+ "role": "system",
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+ "content": system}
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+
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+
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+def get_user_message(nick_name, image_path):
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+ return {
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+ "role": "user",
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+ "content": [
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+ {"type": "text", "text": prompt % nick_name},
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+ {"type": "image_url", "image_url": {"url": get_image_base64(image_path)}}
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+ ]
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+ }
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+
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+
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+def get_assistant_message(content):
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+ return {
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+ "role": "assistant",
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+ "content": content}
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+
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+
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+def get_messages(row):
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+ messages = [
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+ get_system_message(),
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+ get_user_message(row["nick_name"], row["image"])
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+ # get_assistant_message(row["gender"])
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+ ]
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+ return messages
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+
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+
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+def inference(model_service, messages):
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+ try:
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+ top_p = 0.5 # 1.0
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+ extra_body = {"do_sample": False} # True
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+ result = model_service.client.chat.completions.create(messages=messages, model=model_service.model_name,
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+ extra_body=extra_body)
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+ return result.choices[0].message.content
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+ except Exception as e:
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+ print(f"获取结果时发生错误：{messages} {e}")
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+ return 'error'
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+
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+
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+def process(model_service, df, output_file):
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+ row_list = []
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+ for index, row in tqdm(df.iterrows(), total=len(df)):
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+ messages = get_messages(row)
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+ result = inference(model_service, messages)
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+ new_row = {
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+ 'uid': row['uid'],
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+ 'nick_name': row['nick_name'],
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+ 'gender': row['gender'],
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+ 'predict': result,
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+ 'valid': row['valid'],
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+ 'avatar_url': row['avatar_url']}
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+ row_list.append(new_row)
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+ if row_list:
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+ new_df = pd.DataFrame(row_list)
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+ new_df.to_csv(output_file, encoding='utf-8', index=False)
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+
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+
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+if __name__ == '__main__':
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+ parser = argparse.ArgumentParser()
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+ parser.add_argument('--host', default='0.0.0.0', type=str, help='service ip')
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+ parser.add_argument('--port', default=8000, type=int, help='service port')
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+ parser.add_argument('--input_file', required=True, help='input files')
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+ parser.add_argument('--output_file', required=True, help='output file')
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+ args = parser.parse_args()
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+ print('\n\n')
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+ print(args)
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+ print('\n\n')
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+
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+ # load
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+ all_df = pd.read_csv(args.input_file)
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+
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+ # model
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+ model = ModelService(args.host, args.port, model_name_or_path)
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+
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+ # process
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+ process(model, all_df, args.output_file)
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jch