app.py

import os
import nltk 
import openai
import time
import gradio as gr
from threading import Thread #线程  用于定时器
import testlinkspark

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 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

GENERAL_RSPONSE_TRIGGERS = ["I don't understand the question.", "I don't know", "Hello, my name is", "mentioned in the context provided"]
MESSAGES = [{"role": "system", "content": "You are a helpful assistant.You accompany me to practice English and engage in scene dialogue. As a hotel attendant, I am checking in. You introduce the hotel to me and recommend hotel services to me. After receiving my needs, arrange for the service personnel to work. Please remember, my English is not very good. Please have a conversation with me in simple English. After you ask questions in English, please give me some English prompts so that I know how to answer you. Let's start the conversation. You first say hello to me."}]

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 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]

    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)
    answer = testlinkspark.main(appid="d2ff57e0",
            api_secret="YjlmNDdkYjFmMGMzYjc5MmJiODFjN2Fi",
            api_key="07963fbc530a42f4ad223517decfd5fe",
            gpt_url="ws://spark-api.xf-yun.com/v1.1/chat",
            question= question)
    LOGGER.info("\ndocument_response: %s", answer)
    print('\ndocument_response:', 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", 
                    messages=MESSAGES,
                    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)