腾讯云AI实战技巧：高效调用API的最佳实践

import json
from tencentcloud.common import credential
from tencentcloud.common.profile import ClientProfile
from tencentcloud.common.profile import HttpProfile
from tencentcloud.iai.v20180301 import iai_client, models

# 替换为你的API密钥
secret_id = "YOUR_SECRET_ID"
secret_key = "YOUR_SECRET_KEY"

# 初始化凭证
cred = credential.Credential(secret_id, secret_key)
http_profile = HttpProfile()
http_profile.endpoint = "iai.tencentcloudapi.com"

client_profile = ClientProfile()
client_profile.httpProfile = http_profile

# 创建AI客户端
client = iai_client.IaiClient(cred, "ap-guangzhou", client_profile)

# 设置请求参数
req = models.DetectFaceRequest()
params = {
    "ImageBase64": "BASE64_ENCODED_IMAGE",  # 你的图像的Base64编码
}
req.from_json_string(json.dumps(params))

# 发送请求
response = client.DetectFace(req)

# 解析返回结果
print(response.to_json_string())

import asyncio
from tencentcloud.common import credential
from tencentcloud.common.profile import ClientProfile
from tencentcloud.common.profile import HttpProfile
from tencentcloud.iai.v20180301 import iai_client, models

async def async_detect_face(image_base64):
    # 初始化凭证
    cred = credential.Credential("YOUR_SECRET_ID", "YOUR_SECRET_KEY")
    http_profile = HttpProfile()
    http_profile.endpoint = "iai.tencentcloudapi.com"
    client_profile = ClientProfile()
    client_profile.httpProfile = http_profile

    # 创建AI客户端
    client = iai_client.IaiClient(cred, "ap-guangzhou", client_profile)

    # 设置请求参数
    req = models.DetectFaceRequest()
    req.from_json_string(json.dumps({"ImageBase64": image_base64}))

    # 异步发送请求
    response = await client.DetectFace(req)
    return response.to_json_string()

# 运行异步任务
async def main():
    tasks = [
        async_detect_face("BASE64_ENCODED_IMAGE_1"),
        async_detect_face("BASE64_ENCODED_IMAGE_2"),
    ]
    results = await asyncio.gather(*tasks)
    for result in results:
        print(result)

# 执行
asyncio.run(main())

import hashlib
import json

# 简单的缓存机制
cache = {}

def get_cached_response(image_base64):
    # 计算图像的哈希值，作为缓存的键
    hash_key = hashlib.md5(image_base64.encode('utf-8')).hexdigest()
    if hash_key in cache:
        print("使用缓存")
        return cache[hash_key]
    else:
        return None

def cache_response(image_base64, response):
    hash_key = hashlib.md5(image_base64.encode('utf-8')).hexdigest()
    cache[hash_key] = response

# 假设我们已经有一个函数获取AI接口的响应
def get_face_recognition_result(image_base64):
    cached_result = get_cached_response(image_base64)
    if cached_result:
        return cached_result
    
    # 调用腾讯云API（这里用伪代码代表调用）
    response = api_call_to_recognize_face(image_base64)
    
    # 缓存结果
    cache_response(image_base64, response)
    
    return response

import time
import logging

def api_call_with_retry(api_function, retries=3):
    for attempt in range(retries):
        try:
            return api_function()
        except Exception as e:
            logging.error(f"API调用失败，错误：{str(e)}")
            if attempt < retries - 1:
                time.sleep(2 ** attempt)  # 指数退避重试策略
            else:
                raise Exception("API调用失败，已达最大重试次数")

# 使用示例
response = api_call_with_retry(lambda: get_face_recognition_result("BASE64_ENCODED_IMAGE"))