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import streamlit as st
import base64
from audio_recorder_streamlit import audio_recorder
from streamlit_float import *
from utils.stt import speech_to_text
from utils.tts import text_to_speech
from utils.qa import chain
recorded_audio = r".\media\recorded.mp3"
output_audio = r".\media\ouput_file.mp3"
def autoplay_audio(file_path: str):
with open(file_path, "rb") as f:
data = f.read()
b64 = base64.b64encode(data).decode("utf-8")
md = f"""
<audio autoplay>
<source src="data:audio/mp3;base64,{b64}" type="audio/mp3">
</audio>
"""
st.markdown(md, unsafe_allow_html=True)
def get_answer(query):
response = chain.invoke(query)
return response['result']
def audio_d():
float_init()
st.title("Ai Doubt resolver")
# Initialize session state
if "messages" not in st.session_state:
st.session_state.messages = [
{"role": "assistant", "content": "Hi! How may I assist you today?"}
]
footer_container = st.container()
with footer_container:
audio_bytes = audio_recorder()
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.write(message["content"])
if audio_bytes:
with st.spinner("Transcribing..."):
webm_file_path = recorded_audio
with open(webm_file_path, "wb") as f:
f.write(audio_bytes)
transcript = speech_to_text()
if transcript:
st.session_state.messages.append({"role": "user", "content": transcript})
with st.chat_message("user"):
st.write(transcript)
if st.session_state.messages[-1]["role"] != "assistant":
with st.chat_message("assistant"):
with st.spinner("Thinking🤔..."):
final_response = get_answer(str(st.session_state.messages))
with st.spinner("Generating audio response..."):
text_to_speech(final_response)
audio_file = output_audio
autoplay_audio(audio_file)
st.write(final_response)
st.session_state.messages.append({"role": "assistant", "content": final_response})
#os.remove(audio_file)
# Float the footer container and provide CSS to target it with
footer_container.float("bottom: 0rem;")
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