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import gradio as gr
import pandas as pd
from asr_openai import AutomaticSpeechRecognition
from tts_elevenlabs import ElevenLabsTTS
from falcon_7b_llm import Falcon_7b_llm
from order_parser import Order_Parser
import logging
import os
logging.basicConfig(level=logging.INFO)
def delete_files_in_folder(folder_path):
if not os.path.exists(folder_path):
os.makedirs(folder_path)
for filename in os.listdir(folder_path):
file_path = os.path.join(folder_path, filename)
# Check if it's a file (and not a directory)
if os.path.isfile(file_path):
os.remove(file_path)
def generate_response(input_audio):
sentence = asr.run_transcription(input_audio)
# sentence = 'how are you?'
print(sentence)
global order_dict
try:
order_dict = order_taking.order_parser(sentence['text'])
print(order_dict)
except Exception as e:
print('order parsing failed')
print(e)
llm_response = llm.get_llm_response(sentence['text'])
print(llm_response)
output_audio = tts.tts_generate_audio(llm_response)
# output_audio = tts.tts_generate_audio(sentence)
chatbot_history.append(((input_audio,), (output_audio,)))
return chatbot_history
delete_files_in_folder('data//tts_responses')
title = "<h1 style='text-align: center; color: #ffffff; font-size: 40px;'> Falcon Barista - Proof of Concept (POC)"
asr = AutomaticSpeechRecognition()
tts = ElevenLabsTTS()
llm = Falcon_7b_llm()
order_taking = Order_Parser()
chatbot_history = []
order_display=False
order_dict={}
df = pd.DataFrame({
"item" : [],
"quantity" : [],
})
s = df#.style.format("{:.2f}")
with gr.Blocks() as demo:
gr.Markdown(title)
gr.Markdown('''Note: This is just a POC and has several issues
1. High Latency
2. Models are still in fine tuning phase and get confused easily
''')
order_title = gr.Markdown('### Your Order', visible=False)
with gr.Row():
gr.Image('https://i.imgur.com/fHCFI2T.png', label="Look how cute is Falcon Barista")
with gr.Column():
chatbot = gr.Chatbot(label='Chat with Falcon Barista', avatar_images=('data//user_avatar_logo.png','data//falcon_logo_transparent.png'), scale=2)
mic = gr.Audio(source="microphone", type='filepath', scale=1)
mic.stop_recording(generate_response, mic, chatbot)
with gr.Row():
restart_btn = gr.Button(value="Restart Chat", scale=1, variant='stop')
# restart_btn.click(restart_chat, outputs=[chatbot])
end_btn = gr.Button(value="End Chat and Confirm Order", scale=2, variant='primary')
with gr.Column(visible=False) as output_col:
order_title = gr.Markdown('### Your Order')
order_summary = gr.DataFrame(s)
def restart_chat():
delete_files_in_folder('data//tts_responses')
global chatbot_history
chatbot_history = []
global order_dict
order_dict = {}
global df
df = pd.DataFrame({
"item" : [],
"quantity" : [],
})
order_taking.restart_state()
tts.restart_state()
llm.restart_state()
return {
chatbot: [],
output_col: gr.Column(visible=False)
}
def end_chat():
df = pd.DataFrame(list(order_dict.items()), columns=['item', 'quantity'])
s = df#.style.format("{:.2f}")
return {
output_col: gr.Column(visible=True),
order_summary: gr.DataFrame(s, visible=True)
}
restart_btn.click(restart_chat, outputs=[chatbot, output_col])
end_btn.click(end_chat, outputs=[output_col, order_summary])
if __name__ == "__main__":
demo.launch() |