useapi.py

import asyncio
import httpx
import json
import requests
import math
import os
client = httpx.AsyncClient()
# 请求URL
recommand_base_url = "https://" + os.getenv("recommand_base_url")
chat_url = "https://" + os.getenv("chat_url")
model_url = "https://" + os.getenv("model_url")
character_url = "https://" + os.getenv("character_url")
avatar_url = "https://" + os.getenv("avatar_url")
image_url = "https://" + os.getenv("image_url")
auth = os.getenv("auth")
#headers
def create_headers(language):
    # 映射
    language_mapping = {
        'Chinese': 'zh',
        'English': 'en',
        'Japanese': 'ja',
        'Korean': 'ko'
    }
    
    # 获取对应的语言代码，如果不存在则默认为 'zh'
    language_code = language_mapping.get(language, 'zh')
    
    return {
        'X-Refresh-Token': '',
        'X-Language': language_code,
        'accept-language': '',
        'User-Agent': 'Apifox/1.0.0 (https://apifox.com)',
        'Authorization': auth,
        'Accept': '*/*',
        'Connection': 'keep-alive',
        'X-nsfw': '1'
    }

def recommand_character(language):
    response = requests.get(character_url, headers=create_headers(language))
    json_data = response.json()
    characters = [{
        "name": item["name"],
        "_id": item["_id"],
        "avatar_url": str(avatar_url + item['_id'] + "_avatar.webp")
    } for item in json_data['data']]
    return characters

def id_to_avatar(char_id):
    return str(avatar_url + char_id + "_avatar.webp")

#GET模型列表
def get_models():
    class ModelStorage:
        def __init__(self):
            self.models = []

        def add_models(self, models):
            for model_info in models:
                # 过滤掉 'gpt-4o' 和 'gpt-4o-mini'
                if model_info['model'] not in ['mythomax-13b']:
                    if model_info['model'] in ['qwen-2-7b', 'gemma-2-9b', 'llama-3.1-8b', 'internLM-2.5-7b']:
                        weight = 13
                    else:
                        weight = int(math.ceil(24 / model_info['price'] + 0.5))
                    self.models.extend([model_info['model']] * weight)

    model_storage = ModelStorage()

    # 从指定的 URL 获取 JSON 数据
    response = requests.get(model_url)

    if response.status_code == 200:
        data = response.json()
        # 添加模型到 self.models
        model_storage.add_models(data['data'])
        return model_storage.models

#解析推荐json
def extract_recommand(data):
    return [
        {
            "character_id": item["character_id"],
            "avatar_url" : str(avatar_url+item["character_id"]+"_avatar.webp"),
            "_id": item["_id"],
            "image_url" : str(image_url+item["_id"]+"_large.webp"),
            "description": item["description"],
            "name": item["title"],
            "opening": item["opening"]
        }
        for item in data["data"]["moments"]
    ]

#请求推荐API
async def recommand(char_id, language):
    recommand_url = str(recommand_base_url + char_id)
    response = await client.get(recommand_url, headers=create_headers(language))
    json_data = response.json()
    return extract_recommand(json_data)

async def fetch_stream(query, model, moment_id, session_id, bio, request_name, queue, language):
    payload = {"query": query, "model": model, "bio": bio, "moment_id": moment_id}
    if session_id:
        payload["session_id"] = session_id
    async with client.stream(
        "POST", chat_url, json=payload, headers=create_headers(language)
    ) as response:
        # 获取并返回 header
        if response.status_code != 200:
            await queue.put((request_name, "content", "Error Occur!"))
            await queue.put((request_name, "end", None))
            return
        response_headers = dict(response.headers)
        session_id = response_headers.get("x-session-id")
        await queue.put((request_name, "header", response_headers))

        # 流式处理响应内容
        async for chunk in response.aiter_bytes():
            await queue.put((request_name, "content", chunk.decode()))

    # 标记流结束
    await queue.put((request_name, "end", None))

    return session_id


async def combine_streams(
    query_a,
    query_b,
    model_a,
    model_b,
    moment_id_a,
    moment_id_b,
    session_id_a,
    session_id_b,
    bio_a,
    bio_b,
    language
):
    queue = asyncio.Queue()
    task_a = asyncio.create_task(
        fetch_stream(
            query_a, model_a, moment_id_a, session_id_a, bio_a, "requestA", queue, language
        )
    )
    task_b = asyncio.create_task(
        fetch_stream(
            query_b, model_b, moment_id_b, session_id_b, bio_b, "requestB", queue, language
        )
    )

    headers = {}
    content = {"requestA": "", "requestB": ""}
    active_streams = 2

    while active_streams > 0:
        request_name, data_type, data = await queue.get()

        if data_type == "header":
            headers[f"{request_name}_header"] = data
            if len(headers) == 2:
                yield headers
        elif data_type == "content":
            content[request_name] = data.strip()
            if content["requestA"] or content["requestB"]:
                yield content
                content = {"requestA": "", "requestB": ""}
        elif data_type == "end":
            active_streams -= 1

    session_id_a = await task_a
    session_id_b = await task_b