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import tempfile
import openai
import streamlit as st
from streamlit_chat import message
from audio_recorder_streamlit import audio_recorder
import os
from dotenv import load_dotenv
from speech_to_text import transcribe_speech_recognition, transcribe_whisper
from utils import (
get_initial_message,
get_chatgpt_response,
update_chat,
)
# Carga las claves
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")
# Streamlit Application
def main():
st.title("Smart Form")
st.markdown(
"""
Hackaton 2023 - Team Spaidermen
"""
)
if 'messages' not in st.session_state:
with st.spinner("Inicializando nuevo entorno..."):
st.session_state['messages'] = get_initial_message()
if st.session_state['messages']:
for i, msg in enumerate(st.session_state['messages']):
if msg['role'] == 'user':
message(msg['content'], is_user=True, key=str(i))
else:
message(msg['content'], key=str(i))
# Inicializar el texto transcrito como vacío
if 'transcribed_text' not in st.session_state:
st.session_state['transcribed_text'] = ''
# Grabar audio
st.session_state['text'] = ''
audio_bytes = audio_recorder(pause_threshold=5, sample_rate=16_000)
if audio_bytes:
with st.spinner('Transcribiendo...'):
# Guardar audio grabado en un archivo temporal
with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as temp_audio:
temp_path = temp_audio.name
temp_audio.write(audio_bytes)
text = transcribe_speech_recognition(temp_path)
print(f"Texto transcrito: {text}")
# Actualizar el valor de transcribed_text con el texto transcrito
st.session_state['transcribed_text'] = text
# Mostrar el texto transcrito en el textarea
query = st.text_input("Ingresa tu texto", value=st.session_state['transcribed_text'])
if st.button("Enviar") and query:
with st.spinner("Enviando mensaje..."):
st.session_state['messages'] = update_chat(st.session_state['messages'], "user", query)
response = get_chatgpt_response(st.session_state['messages'])
st.session_state['messages'] = update_chat(st.session_state['messages'], "assistant", response)
if __name__ == "__main__":
main()
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