import os import nltk import openai import time import gradio as gr import tiktoken from threading import Thread #线程 用于定时器 from assets.char_poses_base64 import ( #角色动作 CHAR_IDLE_HTML, CHAR_THINKING_HTML, CHAR_TALKING_HTML) from app_utils import ( get_chat_history, initialize_knowledge_base, text_to_speech_gen, logging, buzz_user) global max_response_tokens global token_limit max_response_tokens = 500 token_limit= 15000 global FUNC_CALL #全局变量 用于判断角色动作 FUNC_CALL = 0 global BUZZ_TIMEOUT #全局变量 用于定时器 BUZZ_TIMEOUT = 60 global MESSAGES GENERAL_RSPONSE_TRIGGERS = ["I don't understand the question.", "I don't know", "Hello, my name is", "mentioned in the context provided","I don't know the answer"] MESSAGES = [{"role": "system", "content": "你现在是一个优秀的展览馆讲解员,你可以通过文字或语音与客户交流,你可以讲述上海老建筑和历史人物之间的关系。"}] LOGGER = logging.getLogger('voice_agent') #日志 AUDIO_HTML = '' # Uncomment If this is your first Run: nltk.download('averaged_perceptron_tagger') #下载语料库 conv_model, voice_model = initialize_knowledge_base() #初始化知识库 def num_tokens_from_messages(messages, model="gpt-3.5-turbo-16k"): encoding = tiktoken.encoding_for_model(model) num_tokens = 0 for message in messages: num_tokens += 4 # every message follows {role/name}\n{content}\n for key, value in message.items(): num_tokens += len(encoding.encode(value)) if key == "name": # if there's a name, the role is omitted num_tokens += -1 # role is always required and always 1 token num_tokens += 2 # every reply is primed with assistant return num_tokens def idle_timer(): global BUZZ_TIMEOUT while True: time.sleep(BUZZ_TIMEOUT) buzz_user() if BUZZ_TIMEOUT == 80: time.sleep(BUZZ_TIMEOUT) BUZZ_TIMEOUT = 60 def update_img(): global FUNC_CALL FUNC_CALL += 1 if FUNC_CALL % 2== 0: return CHAR_TALKING_HTML else: return CHAR_THINKING_HTML def get_response(history, audio_input): query_type = 'text' question =history[-1][0] conv_history_tokens = 0 global BUZZ_TIMEOUT BUZZ_TIMEOUT = 80 if not question: if audio_input: query_type = 'audio' os.rename(audio_input, audio_input + '.wav') audio_file = open(audio_input + '.wav', "rb") transcript = openai.Audio.transcribe("whisper-1", audio_file) question = transcript['text'] else: return None, None LOGGER.info("\nquery_type: %s", query_type) LOGGER.info("query_text: %s", question) print('\nquery_type:', query_type) print('\nquery_text:', question) if question.lower().strip() == 'hi': question = 'hello' answer = conv_model.run(question) LOGGER.info("\ndocument_response: %s", answer) print('\ndocument_response:', answer) conv_history_tokens = num_tokens_from_messages(MESSAGES) print("conv_history_tokens: ", conv_history_tokens) print("MESSAGES", MESSAGES) while (conv_history_tokens + max_response_tokens >= token_limit): del MESSAGES[1] conv_history_tokens = num_tokens_from_messages(MESSAGES) print("conv_history_tokens_ajust: ", conv_history_tokens) MESSAGES.append({"role": "user", "content": question}) MESSAGES.append({"role": "assistant", "content": answer}) for trigger in GENERAL_RSPONSE_TRIGGERS: if trigger in answer: MESSAGES.append({"role": "user", "content": question}) chat = openai.ChatCompletion.create( model="gpt-3.5-turbo-16k", messages=MESSAGES, max_tokens=500, temperature=0.7, n=128, stop="\n" ) answer = chat.choices[0].message.content MESSAGES.append({"role": "assistant", "content": answer}) LOGGER.info("general_response: %s", answer) print('\ngeneral_response:', answer) AUDIO_HTML = text_to_speech_gen(answer) history[-1][1] = answer return history, AUDIO_HTML # buzz_usr_proc = Thread(target=idle_timer) with gr.Blocks(css = """#col_image{width:800px; height:800px; margin-left: auto; margin-right: auto;}""") as demo: with gr.Row(scale=0.7): output_html = gr.HTML(label="Felix's Voice", value=AUDIO_HTML) output_html.visible = False image1= gr.Image("assets/NPCtest1.png").style(height=700) #elem_id = "col_image" #assistant_character = gr.HTML(label=None, value=CHAR_IDLE_HTML, show_label=False) with gr.Column(scale=0.3): chatbot = gr.Chatbot(label='Send a text or a voice input').style(height=285) with gr.Column(): msg = gr.Textbox(placeholder='Write a chat & press Enter.', show_label=False).style(container=False) with gr.Column(scale=0.5): audio_input = gr.Audio(source="microphone", type='filepath', show_label=False).style(container=False) button = gr.Button(value="Send") msg.submit(get_chat_history, [msg, chatbot], [msg, chatbot] ).then(get_response, [chatbot, audio_input], [chatbot, output_html] ) button.click(get_chat_history, [msg, chatbot], [msg, chatbot] ).then(get_response, [chatbot, audio_input], [chatbot, output_html] ) # buzz_usr_proc.start() demo.launch(debug=False, favicon_path='assets/favicon.png', show_api=False, share=False)