Husnain
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•
b5f45b3
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Parent(s):
85bab23
💎 [Feature] Enable gpt-3.5 in chat_api
Browse files- networks/huggingface_streamer.py +199 -0
networks/huggingface_streamer.py
ADDED
@@ -0,0 +1,199 @@
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1 |
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import json
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2 |
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import re
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import requests
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from tclogger import logger
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from transformers import AutoTokenizer
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from constants.models import (
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MODEL_MAP,
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STOP_SEQUENCES_MAP,
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TOKEN_LIMIT_MAP,
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TOKEN_RESERVED,
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)
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from constants.envs import PROXIES
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from messagers.message_outputer import OpenaiStreamOutputer
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class HuggingfaceStreamer:
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def __init__(self, model: str):
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if model in MODEL_MAP.keys():
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self.model = model
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else:
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self.model = "default"
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self.model_fullname = MODEL_MAP[self.model]
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self.message_outputer = OpenaiStreamOutputer(model=self.model)
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if self.model == "gemma-7b":
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# this is not wrong, as repo `google/gemma-7b-it` is gated and must authenticate to access it
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# so I use mistral-7b as a fallback
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self.tokenizer = AutoTokenizer.from_pretrained(MODEL_MAP["mistral-7b"])
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else:
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_fullname)
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def parse_line(self, line):
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line = line.decode("utf-8")
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line = re.sub(r"data:\s*", "", line)
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data = json.loads(line)
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try:
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content = data["token"]["text"]
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except:
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logger.err(data)
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return content
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def count_tokens(self, text):
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tokens = self.tokenizer.encode(text)
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token_count = len(tokens)
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logger.note(f"Prompt Token Count: {token_count}")
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return token_count
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def chat_response(
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self,
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prompt: str = None,
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temperature: float = 0.5,
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top_p: float = 0.95,
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max_new_tokens: int = None,
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api_key: str = None,
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use_cache: bool = False,
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):
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# https://huggingface.co/docs/api-inference/detailed_parameters?code=curl
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# curl --proxy http://<server>:<port> https://api-inference.huggingface.co/models/<org>/<model_name> -X POST -d '{"inputs":"who are you?","parameters":{"max_new_token":64}}' -H 'Content-Type: application/json' -H 'Authorization: Bearer <HF_TOKEN>'
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self.request_url = (
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f"https://api-inference.huggingface.co/models/{self.model_fullname}"
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64 |
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)
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self.request_headers = {
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"Content-Type": "application/json",
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}
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if api_key:
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logger.note(
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f"Using API Key: {api_key[:3]}{(len(api_key)-7)*'*'}{api_key[-4:]}"
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)
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self.request_headers["Authorization"] = f"Bearer {api_key}"
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if temperature is None or temperature < 0:
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temperature = 0.0
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# temperature must 0 < and < 1 for HF LLM models
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temperature = max(temperature, 0.01)
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79 |
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temperature = min(temperature, 0.99)
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top_p = max(top_p, 0.01)
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top_p = min(top_p, 0.99)
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83 |
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token_limit = int(
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84 |
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TOKEN_LIMIT_MAP[self.model] - TOKEN_RESERVED - self.count_tokens(prompt)
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)
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if token_limit <= 0:
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raise ValueError("Prompt exceeded token limit!")
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89 |
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if max_new_tokens is None or max_new_tokens <= 0:
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max_new_tokens = token_limit
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else:
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max_new_tokens = min(max_new_tokens, token_limit)
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# References:
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# huggingface_hub/inference/_client.py:
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# class InferenceClient > def text_generation()
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# huggingface_hub/inference/_text_generation.py:
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# class TextGenerationRequest > param `stream`
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# https://huggingface.co/docs/text-generation-inference/conceptual/streaming#streaming-with-curl
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# https://huggingface.co/docs/api-inference/detailed_parameters#text-generation-task
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self.request_body = {
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"inputs": prompt,
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"parameters": {
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"temperature": temperature,
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"top_p": top_p,
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"max_new_tokens": max_new_tokens,
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"return_full_text": False,
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},
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"options": {
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"use_cache": use_cache,
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},
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"stream": True,
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}
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if self.model in STOP_SEQUENCES_MAP.keys():
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self.stop_sequences = STOP_SEQUENCES_MAP[self.model]
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# self.request_body["parameters"]["stop_sequences"] = [
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# self.STOP_SEQUENCES[self.model]
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# ]
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logger.back(self.request_url)
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stream_response = requests.post(
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self.request_url,
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headers=self.request_headers,
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json=self.request_body,
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proxies=PROXIES,
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stream=True,
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)
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status_code = stream_response.status_code
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130 |
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if status_code == 200:
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logger.success(status_code)
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else:
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logger.err(status_code)
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return stream_response
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def chat_return_dict(self, stream_response):
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# https://platform.openai.com/docs/guides/text-generation/chat-completions-response-format
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final_output = self.message_outputer.default_data.copy()
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final_output["choices"] = [
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141 |
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{
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142 |
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"index": 0,
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"finish_reason": "stop",
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"message": {
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"role": "assistant",
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"content": "",
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},
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}
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]
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logger.back(final_output)
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+
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152 |
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final_content = ""
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153 |
+
for line in stream_response.iter_lines():
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154 |
+
if not line:
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continue
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156 |
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content = self.parse_line(line)
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157 |
+
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158 |
+
if content.strip() == self.stop_sequences:
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159 |
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logger.success("\n[Finished]")
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160 |
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break
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161 |
+
else:
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162 |
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logger.back(content, end="")
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163 |
+
final_content += content
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164 |
+
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165 |
+
if self.model in STOP_SEQUENCES_MAP.keys():
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166 |
+
final_content = final_content.replace(self.stop_sequences, "")
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167 |
+
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168 |
+
final_content = final_content.strip()
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169 |
+
final_output["choices"][0]["message"]["content"] = final_content
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170 |
+
return final_output
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171 |
+
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172 |
+
def chat_return_generator(self, stream_response):
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173 |
+
is_finished = False
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174 |
+
line_count = 0
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175 |
+
for line in stream_response.iter_lines():
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176 |
+
if line:
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177 |
+
line_count += 1
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178 |
+
else:
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179 |
+
continue
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180 |
+
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181 |
+
content = self.parse_line(line)
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182 |
+
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183 |
+
if content.strip() == self.stop_sequences:
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184 |
+
content_type = "Finished"
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185 |
+
logger.success("\n[Finished]")
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186 |
+
is_finished = True
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187 |
+
else:
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188 |
+
content_type = "Completions"
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189 |
+
if line_count == 1:
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190 |
+
content = content.lstrip()
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191 |
+
logger.back(content, end="")
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192 |
+
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193 |
+
output = self.message_outputer.output(
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194 |
+
content=content, content_type=content_type
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195 |
+
)
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196 |
+
yield output
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197 |
+
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198 |
+
if not is_finished:
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199 |
+
yield self.message_outputer.output(content="", content_type="Finished")
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