162 lines
6.3 KiB
Python
162 lines
6.3 KiB
Python
# Copyright (C) 2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import asyncio
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import os
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import sys
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from comps import MegaServiceEndpoint, MicroService, ServiceOrchestrator, ServiceRoleType, ServiceType
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from comps.cores.proto.api_protocol import AudioChatCompletionRequest, ChatCompletionResponse
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from comps.cores.proto.docarray import LLMParams
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from fastapi import Request
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MEGA_SERVICE_PORT = int(os.getenv("MEGA_SERVICE_PORT", 8888))
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WHISPER_SERVER_HOST_IP = os.getenv("WHISPER_SERVER_HOST_IP", "0.0.0.0")
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WHISPER_SERVER_PORT = int(os.getenv("WHISPER_SERVER_PORT", 7066))
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LLM_SERVER_HOST_IP = os.getenv("LLM_SERVER_HOST_IP", "0.0.0.0")
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LLM_SERVER_PORT = int(os.getenv("LLM_SERVER_PORT", 3006))
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SPEECHT5_SERVER_HOST_IP = os.getenv("SPEECHT5_SERVER_HOST_IP", "0.0.0.0")
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SPEECHT5_SERVER_PORT = int(os.getenv("SPEECHT5_SERVER_PORT", 7055))
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ANIMATION_SERVICE_HOST_IP = os.getenv("ANIMATION_SERVICE_HOST_IP", "0.0.0.0")
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ANIMATION_SERVICE_PORT = int(os.getenv("ANIMATION_SERVICE_PORT", 9066))
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def align_inputs(self, inputs, cur_node, runtime_graph, llm_parameters_dict, **kwargs):
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if self.services[cur_node].service_type == ServiceType.LLM:
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# convert TGI/vLLM to unified OpenAI /v1/chat/completions format
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next_inputs = {}
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next_inputs["model"] = "tgi" # specifically clarify the fake model to make the format unified
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next_inputs["messages"] = [{"role": "user", "content": inputs["asr_result"]}]
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next_inputs["max_tokens"] = llm_parameters_dict["max_tokens"]
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next_inputs["top_p"] = llm_parameters_dict["top_p"]
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next_inputs["stream"] = inputs["stream"] # False as default
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next_inputs["frequency_penalty"] = inputs["frequency_penalty"]
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# next_inputs["presence_penalty"] = inputs["presence_penalty"]
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# next_inputs["repetition_penalty"] = inputs["repetition_penalty"]
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next_inputs["temperature"] = inputs["temperature"]
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inputs = next_inputs
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elif self.services[cur_node].service_type == ServiceType.TTS:
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next_inputs = {}
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next_inputs["text"] = inputs["choices"][0]["message"]["content"]
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next_inputs["voice"] = kwargs["voice"]
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inputs = next_inputs
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elif self.services[cur_node].service_type == ServiceType.ANIMATION:
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next_inputs = {}
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next_inputs["byte_str"] = inputs["tts_result"]
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inputs = next_inputs
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return inputs
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def check_env_vars(env_var_list):
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for var in env_var_list:
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if os.getenv(var) is None:
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print(f"Error: The environment variable '{var}' is not set.")
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sys.exit(1) # Exit the program with a non-zero status code
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print("All environment variables are set.")
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class AvatarChatbotService:
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def __init__(self, host="0.0.0.0", port=8000):
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self.host = host
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self.port = port
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ServiceOrchestrator.align_inputs = align_inputs
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self.megaservice = ServiceOrchestrator()
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self.endpoint = str(MegaServiceEndpoint.AVATAR_CHATBOT)
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def add_remote_service(self):
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asr = MicroService(
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name="asr",
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host=WHISPER_SERVER_HOST_IP,
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port=WHISPER_SERVER_PORT,
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endpoint="/v1/asr",
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use_remote_service=True,
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service_type=ServiceType.ASR,
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)
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llm = MicroService(
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name="llm",
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host=LLM_SERVER_HOST_IP,
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port=LLM_SERVER_PORT,
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endpoint="/v1/chat/completions",
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use_remote_service=True,
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service_type=ServiceType.LLM,
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)
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tts = MicroService(
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name="tts",
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host=SPEECHT5_SERVER_HOST_IP,
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port=SPEECHT5_SERVER_PORT,
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endpoint="/v1/tts",
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use_remote_service=True,
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service_type=ServiceType.TTS,
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)
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animation = MicroService(
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name="animation",
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host=ANIMATION_SERVICE_HOST_IP,
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port=ANIMATION_SERVICE_PORT,
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endpoint="/v1/animation",
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use_remote_service=True,
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service_type=ServiceType.ANIMATION,
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)
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self.megaservice.add(asr).add(llm).add(tts).add(animation)
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self.megaservice.flow_to(asr, llm)
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self.megaservice.flow_to(llm, tts)
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self.megaservice.flow_to(tts, animation)
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async def handle_request(self, request: Request):
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data = await request.json()
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chat_request = AudioChatCompletionRequest.model_validate(data)
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parameters = LLMParams(
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# relatively lower max_tokens for audio conversation
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max_tokens=chat_request.max_tokens if chat_request.max_tokens else 128,
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top_k=chat_request.top_k if chat_request.top_k else 10,
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top_p=chat_request.top_p if chat_request.top_p else 0.95,
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temperature=chat_request.temperature if chat_request.temperature else 0.01,
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repetition_penalty=chat_request.presence_penalty if chat_request.presence_penalty else 1.03,
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stream=False, # TODO add stream LLM output as input to TTS
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)
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# print(parameters)
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result_dict, runtime_graph = await self.megaservice.schedule(
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initial_inputs={"audio": chat_request.audio},
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llm_parameters=parameters,
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voice=chat_request.voice if hasattr(chat_request, "voice") else "default",
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)
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last_node = runtime_graph.all_leaves()[-1]
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response = result_dict[last_node]["video_path"]
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return response
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def start(self):
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self.service = MicroService(
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self.__class__.__name__,
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service_role=ServiceRoleType.MEGASERVICE,
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host=self.host,
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port=self.port,
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endpoint=self.endpoint,
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input_datatype=AudioChatCompletionRequest,
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output_datatype=ChatCompletionResponse,
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)
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self.service.add_route(self.endpoint, self.handle_request, methods=["POST"])
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self.service.start()
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if __name__ == "__main__":
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check_env_vars(
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[
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"MEGA_SERVICE_HOST_IP",
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"MEGA_SERVICE_PORT",
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"WHISPER_SERVER_HOST_IP",
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"WHISPER_SERVER_PORT",
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"LLM_SERVER_HOST_IP",
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"LLM_SERVER_PORT",
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"SPEECHT5_SERVER_HOST_IP",
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"SPEECHT5_SERVER_PORT",
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"ANIMATION_SERVICE_HOST_IP",
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"ANIMATION_SERVICE_PORT",
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]
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)
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avatarchatbot = AvatarChatbotService(port=MEGA_SERVICE_PORT)
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avatarchatbot.add_remote_service()
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avatarchatbot.start()
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