import os
import nltk 
import openai
import time
import gradio as gr
from threading import Thread

from assets.char_poses_base64 import (
    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
FUNC_CALL = 0

global BUZZ_TIMEOUT
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.."}]

LOGGER = logging.getLogger('voice_agent')
AUDIO_HTML = ''

# Uncomment If this is your first Run: 
nltk.download('averaged_perceptron_tagger')
conv_model, voice_model = initialize_knowledge_base()


def idle_timer():
    global BUZZ_TIMEOUT

    while True:
        print('started countdown')
        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)
    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(title="Your Assistance Pal!") as demo: 

    with gr.Row():
        output_html = gr.HTML(label="Felix's Voice", value=AUDIO_HTML)
        output_html.visible = False
        assistant_character = gr.HTML(label=None, value=CHAR_IDLE_HTML, show_label=False)
        with gr.Column(scale=0.1):
            chatbot = gr.Chatbot(label='Send a text or a voice input').style(height=285)
            with gr.Row():
                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(update_img, outputs=[assistant_character]
                ).then(get_response, [chatbot, audio_input], [chatbot, output_html]
                ).then(update_img, outputs=[assistant_character])

    button.click(get_chat_history, [msg, chatbot], [msg, chatbot]
                ).then(update_img, outputs=[assistant_character]
                ).then(get_response, [chatbot, audio_input], [chatbot, output_html]
                ).then(update_img, outputs=[assistant_character])
    
    buzz_usr_proc.start()
    
demo.launch(debug=False, favicon_path='assets/favicon.png', show_api=False, share=True)