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import requests | |
import json | |
from http import HTTPStatus | |
from dashscope import Application | |
ak = "" | |
sk = "" | |
def init_param(access_key,secret_key): | |
global ak, sk | |
ak = access_key | |
sk = secret_key | |
def baidu_client(input): | |
global ak, sk | |
if ak == "" or sk == "": | |
return "" | |
url = "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-lite-8k?access_token=" + get_access_token() | |
payload = json.dumps({ | |
"temperature": 0.95, | |
"top_p": 0.7, | |
"penalty_score": 1, | |
"messages": [ | |
{ | |
"role": "user", | |
"content": input | |
} | |
], | |
"system": "" | |
}) | |
headers = { | |
'Content-Type': 'application/json' | |
} | |
response = requests.request("POST", url, headers=headers, data=payload) | |
print("baidu_client",response.text) | |
return response.json()["result"] | |
def get_access_token(): | |
""" | |
使用 AK,SK 生成鉴权签名(Access Token) | |
:return: access_token,或是None(如果错误) | |
""" | |
url = "https://aip.baidubce.com/oauth/2.0/token" | |
params = {"grant_type": "client_credentials", "client_id": ak, "client_secret": sk} | |
return str(requests.post(url, params=params).json().get("access_token")) | |
def qwen_agent_app(input): | |
global ak, sk | |
if ak == "" or sk == "": | |
return "" | |
response = Application.call(app_id=ak, | |
prompt=input, | |
api_key=sk, | |
) | |
if response.status_code != HTTPStatus.OK: | |
print('request_id=%s, code=%s, message=%s\n' % (response.request_id, response.status_code, response.message)) | |
return "" | |
else: | |
print('request_id=%s\n output=%s\n usage=%s\n' % (response.request_id, response.output, response.usage)) | |
return response.output["text"] | |
def hg_client(input): | |
global ak, sk | |
if sk == "": | |
return "" | |
import requests | |
API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3" | |
headers = {"Authorization": f"Bearer {sk}"} | |
def query(payload): | |
response = requests.post(API_URL, headers=headers, json=payload) | |
return response.json() | |
output = query({ | |
"inputs": input, | |
}) | |
print(output) | |
if len(output) >0: | |
return output[0]['generated_text'] | |
return "" |