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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') # logger object
AUDIO_HTML = ''
# Uncomment If this is your first Run:
nltk.download('averaged_perceptron_tagger') #download the nltk model
conv_model, voice_model = initialize_knowledge_base() # initialize the knowledge base
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)
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) # audio input
button = gr.Button(value="Send") # button to send the message
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=False) |