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from toolbox import get_conf, get_pictures_list, encode_image
import base64
import datetime
import hashlib
import hmac
import json
from urllib.parse import urlparse
import ssl
from datetime import datetime
from time import mktime
from urllib.parse import urlencode
from wsgiref.handlers import format_date_time
import websocket
import threading, time
timeout_bot_msg = '[Local Message] Request timeout. Network error.'
class Ws_Param(object):
# 初始化
def __init__(self, APPID, APIKey, APISecret, gpt_url):
self.APPID = APPID
self.APIKey = APIKey
self.APISecret = APISecret
self.host = urlparse(gpt_url).netloc
self.path = urlparse(gpt_url).path
self.gpt_url = gpt_url
# 生成url
def create_url(self):
# 生成RFC1123格式的时间戳
now = datetime.now()
date = format_date_time(mktime(now.timetuple()))
# 拼接字符串
signature_origin = "host: " + self.host + "\n"
signature_origin += "date: " + date + "\n"
signature_origin += "GET " + self.path + " HTTP/1.1"
# 进行hmac-sha256进行加密
signature_sha = hmac.new(self.APISecret.encode('utf-8'), signature_origin.encode('utf-8'), digestmod=hashlib.sha256).digest()
signature_sha_base64 = base64.b64encode(signature_sha).decode(encoding='utf-8')
authorization_origin = f'api_key="{self.APIKey}", algorithm="hmac-sha256", headers="host date request-line", signature="{signature_sha_base64}"'
authorization = base64.b64encode(authorization_origin.encode('utf-8')).decode(encoding='utf-8')
# 将请求的鉴权参数组合为字典
v = {
"authorization": authorization,
"date": date,
"host": self.host
}
# 拼接鉴权参数，生成url
url = self.gpt_url + '?' + urlencode(v)
# 此处打印出建立连接时候的url,参考本demo的时候可取消上方打印的注释，比对相同参数时生成的url与自己代码生成的url是否一致
return url
class SparkRequestInstance():
def __init__(self):
XFYUN_APPID, XFYUN_API_SECRET, XFYUN_API_KEY = get_conf('XFYUN_APPID', 'XFYUN_API_SECRET', 'XFYUN_API_KEY')
if XFYUN_APPID == '00000000' or XFYUN_APPID == '': raise RuntimeError('请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET')
self.appid = XFYUN_APPID
self.api_secret = XFYUN_API_SECRET
self.api_key = XFYUN_API_KEY
self.gpt_url = "ws://spark-api.xf-yun.com/v1.1/chat"
self.gpt_url_v2 = "ws://spark-api.xf-yun.com/v2.1/chat"
self.gpt_url_v3 = "ws://spark-api.xf-yun.com/v3.1/chat"
self.gpt_url_img = "wss://spark-api.cn-huabei-1.xf-yun.com/v2.1/image"
self.time_to_yield_event = threading.Event()
self.time_to_exit_event = threading.Event()
self.result_buf = ""
def generate(self, inputs, llm_kwargs, history, system_prompt, use_image_api=False):
llm_kwargs = llm_kwargs
history = history
system_prompt = system_prompt
import _thread as thread
thread.start_new_thread(self.create_blocking_request, (inputs, llm_kwargs, history, system_prompt, use_image_api))
while True:
self.time_to_yield_event.wait(timeout=1)
if self.time_to_yield_event.is_set():
yield self.result_buf
if self.time_to_exit_event.is_set():
return self.result_buf
def create_blocking_request(self, inputs, llm_kwargs, history, system_prompt, use_image_api):
if llm_kwargs['llm_model'] == 'sparkv2':
gpt_url = self.gpt_url_v2
elif llm_kwargs['llm_model'] == 'sparkv3':
gpt_url = self.gpt_url_v3
else:
gpt_url = self.gpt_url
file_manifest = []
if use_image_api and llm_kwargs.get('most_recent_uploaded'):
if llm_kwargs['most_recent_uploaded'].get('path'):
file_manifest = get_pictures_list(llm_kwargs['most_recent_uploaded']['path'])
if len(file_manifest) > 0:
print('正在使用讯飞图片理解API')
gpt_url = self.gpt_url_img
wsParam = Ws_Param(self.appid, self.api_key, self.api_secret, gpt_url)
websocket.enableTrace(False)
wsUrl = wsParam.create_url()
# 收到websocket连接建立的处理
def on_open(ws):
import _thread as thread
thread.start_new_thread(run, (ws,))
def run(ws, *args):
data = json.dumps(gen_params(ws.appid, *ws.all_args, file_manifest))
ws.send(data)
# 收到websocket消息的处理
def on_message(ws, message):
data = json.loads(message)
code = data['header']['code']
if code != 0:
print(f'请求错误: {code}, {data}')
self.result_buf += str(data)
ws.close()
self.time_to_exit_event.set()
else:
choices = data["payload"]["choices"]
status = choices["status"]
content = choices["text"][0]["content"]
ws.content += content
self.result_buf += content
if status == 2:
ws.close()
self.time_to_exit_event.set()
self.time_to_yield_event.set()
# 收到websocket错误的处理
def on_error(ws, error):
print("error:", error)
self.time_to_exit_event.set()
# 收到websocket关闭的处理
def on_close(ws, *args):
self.time_to_exit_event.set()
# websocket
ws = websocket.WebSocketApp(wsUrl, on_message=on_message, on_error=on_error, on_close=on_close, on_open=on_open)
ws.appid = self.appid
ws.content = ""
ws.all_args = (inputs, llm_kwargs, history, system_prompt)
ws.run_forever(sslopt={"cert_reqs": ssl.CERT_NONE})
def generate_message_payload(inputs, llm_kwargs, history, system_prompt, file_manifest):
conversation_cnt = len(history) // 2
messages = []
if file_manifest:
base64_images = []
for image_path in file_manifest:
base64_images.append(encode_image(image_path))
for img_s in base64_images:
if img_s not in str(messages):
messages.append({"role": "user", "content": img_s, "content_type": "image"})
else:
messages = [{"role": "system", "content": system_prompt}]
if conversation_cnt:
for index in range(0, 2*conversation_cnt, 2):
what_i_have_asked = {}
what_i_have_asked["role"] = "user"
what_i_have_asked["content"] = history[index]
what_gpt_answer = {}
what_gpt_answer["role"] = "assistant"
what_gpt_answer["content"] = history[index+1]
if what_i_have_asked["content"] != "":
if what_gpt_answer["content"] == "": continue
if what_gpt_answer["content"] == timeout_bot_msg: continue
messages.append(what_i_have_asked)
messages.append(what_gpt_answer)
else:
messages[-1]['content'] = what_gpt_answer['content']
what_i_ask_now = {}
what_i_ask_now["role"] = "user"
what_i_ask_now["content"] = inputs
messages.append(what_i_ask_now)
return messages
def gen_params(appid, inputs, llm_kwargs, history, system_prompt, file_manifest):
"""
通过appid和用户的提问来生成请参数
"""
domains = {
"spark": "general",
"sparkv2": "generalv2",
"sparkv3": "generalv3",
}
domains_select = domains[llm_kwargs['llm_model']]
if file_manifest: domains_select = 'image'
data = {
"header": {
"app_id": appid,
"uid": "1234"
},
"parameter": {
"chat": {
"domain": domains_select,
"temperature": llm_kwargs["temperature"],
"random_threshold": 0.5,
"max_tokens": 4096,
"auditing": "default"
}
},
"payload": {
"message": {
"text": generate_message_payload(inputs, llm_kwargs, history, system_prompt, file_manifest)
}
}
}
return data
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