ai_api_web/main.py

import requests
import json
import sys

headers = {
    'accept': 'application/json, text/event-stream',
    'accept-language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
    'authorization': 'Bearer sk-hOdY292wluz2aFrLCe5f1cdscnjdksnjkNKJNJNJKNKJ1769082037063',
    'cache-control': 'no-cache',
    'content-type': 'application/json',
    'dnt': '1',
    'newauth': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpZCI6Mjk4MSwic2Vzc2lvbiI6IjQ3YmFlYTBlNDY0ZDA0YWU3YjYwYjJiZDQ5YTQ2Y2RlIn0.PWjowAqaBepISCdp1XvwwbjRZi8jrrwNE8BntT6VQHs',
    'origin': 'https://proai520.com',
    'pragma': 'no-cache',
    'priority': 'u=1, i',
    'referer': 'https://proai520.com/',
    'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Microsoft Edge";v="144"',
    'sec-ch-ua-mobile': '?0',
    'sec-ch-ua-platform': '"Windows"',
    'sec-fetch-dest': 'empty',
    'sec-fetch-mode': 'cors',
    'sec-fetch-site': 'same-site',
    'token': '85bb99fa1ae0b45caa50346f26479285',
    'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36 Edg/144.0.0.0',
    'username': 'ojAPQ6mcTExxYLTrA7HjGnG7803I',
}

json_data = {
    'messages': [
        {
            'role': 'system',
            'content': '\nYou are ChatGPT, a large language model trained by OpenAI.\nKnowledge cutoff: 2021-09\nCurrent model: gpt-5.2\nCurrent time: Thu Jan 22 2026 19:40:37 GMT+0800 (中国标准时间)\nLatex inline: \\(x^2\\) \nLatex block: $$e=mc^2$$\n\n',
        },
        {
            'role': 'user',
            'content': '你好，你是谁',
        },
    ],
    'stream': True,
    'model': 'gpt-5.2',
    'temperature': 0.5,
    'presence_penalty': 0,
    'frequency_penalty': 0,
    'top_p': 1,
    'max_tokens': 8000,
}


def parse_sse_stream(response):
    """
    解析 SSE (Server-Sent Events) 流式响应
    返回 OpenAI 兼容格式的完整响应
    """
    full_content = ""
    response_id = None
    model = None
    created = None
    usage = None
    
    # 逐行解析 SSE 流
    for line in response.iter_lines(decode_unicode=True):
        if not line:
            continue
            
        # SSE 格式: data: {json}
        if line.startswith('data: '):
            data_str = line[6:]  # 移除 'data: ' 前缀
            
            # 检查是否是结束标记
            if data_str.strip() == '[DONE]':
                break
            
            try:
                chunk = json.loads(data_str)
                
                # 提取元数据
                if response_id is None and 'id' in chunk:
                    response_id = chunk['id']
                if model is None and 'model' in chunk:
                    model = chunk['model']
                if created is None and 'created' in chunk:
                    created = chunk['created']
                if usage is None and 'usage' in chunk:
                    usage = chunk['usage']
                
                # 提取内容增量
                if 'choices' in chunk and len(chunk['choices']) > 0:
                    choice = chunk['choices'][0]
                    if 'delta' in choice and 'content' in choice['delta']:
                        content_delta = choice['delta']['content']
                        full_content += content_delta
                        
            except json.JSONDecodeError as e:
                # 忽略无法解析的行
                continue
    
    # 构建 OpenAI 兼容格式的完整响应
    openai_response = {
        "id": response_id or "chatcmpl-unknown",
        "object": "chat.completion",
        "created": created or 0,
        "model": model or json_data.get('model', 'unknown'),
        "choices": [
            {
                "index": 0,
                "message": {
                    "role": "assistant",
                    "content": full_content
                },
                "finish_reason": "stop"
            }
        ],
        "usage": usage or {
            "prompt_tokens": 0,
            "completion_tokens": 0,
            "total_tokens": 0
        }
    }
    
    return openai_response


def stream_sse_response(response):
    """
    流式输出 SSE 响应，转换为 OpenAI 兼容格式
    每个 chunk 输出为 OpenAI 流式格式
    """
    for line in response.iter_lines(decode_unicode=True):
        if not line:
            continue
            
        if line.startswith('data: '):
            data_str = line[6:]
            
            if data_str.strip() == '[DONE]':
                # 输出结束标记
                print('data: [DONE]\n')
                break
            
            try:
                chunk = json.loads(data_str)
                
                # 转换为 OpenAI 流式格式
                openai_chunk = {
                    "id": chunk.get('id', 'chatcmpl-unknown'),
                    "object": "chat.completion.chunk",
                    "created": chunk.get('created', 0),
                    "model": chunk.get('model', json_data.get('model', 'unknown')),
                    "choices": []
                }
                
                if 'choices' in chunk and len(chunk['choices']) > 0:
                    original_choice = chunk['choices'][0]
                    openai_choice = {
                        "index": 0,
                        "delta": original_choice.get('delta', {}),
                        "finish_reason": original_choice.get('finish_reason')
                    }
                    openai_chunk["choices"] = [openai_choice]
                
                # 输出 OpenAI 格式的 chunk
                print(f"data: {json.dumps(openai_chunk, ensure_ascii=False)}\n")
                sys.stdout.flush()
                
            except json.JSONDecodeError:
                continue


# 发送请求
response = requests.post(
    'https://api.proai520.com/v1/chat/completions',
    headers=headers,
    json=json_data,
    stream=True  # 启用流式响应
)

# 方式1: 解析为完整的 OpenAI 格式响应（非流式）
print("=" * 60)
print("方式1: 完整响应格式（非流式）")
print("=" * 60)
complete_response = parse_sse_stream(response)
print(json.dumps(complete_response, ensure_ascii=False, indent=2))

# 如果需要流式输出，可以使用以下代码（需要重新发送请求）
# print("\n" + "=" * 60)
# print("方式2: 流式响应格式")
# print("=" * 60)
# response2 = requests.post(
#     'https://api.proai520.com/v1/chat/completions',
#     headers=headers,
#     json=json_data,
#     stream=True
# )
# stream_sse_response(response2)