nutri / app.py
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Update app.py
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import os
import streamlit as st
import subprocess
from gtts import gTTS
import cv2
from huggingface_hub import InferenceClient
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print('Using {} for inference.'.format(device))
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
def generate_output(prompt):
if not prompt:
return None, "El campo de la pregunta es obligatorio."
response = client.text_generation(prompt, max_new_tokens=50, temperature=0.6)
gpt3_output = response.strip()
personalized_response = f"{gpt3_output}"
try:
tts = gTTS(personalized_response, lang='es')
audio_path = "audio.mp3"
tts.save(audio_path)
except Exception as e:
return None, f"No se pudo generar el audio: {str(e)}"
video_path = "video.mp4"
command = f"CUDA_VISIBLE_DEVICES='' python3 inference.py --checkpoint_path checkpoints/wav2lip_gan.pth --face face.jpg --audio {audio_path} --outfile {video_path} --nosmooth --resize_factor 4"
process = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
if process.returncode != 0:
error_message = process.stderr
return None, f"No se pudo generar el video: {error_message}"
if os.path.isfile(video_path):
return video_path, None
return None, "No se pudo generar el video"
st.title("Lypsinc + Inteligencia Artificial")
prompt = st.text_input("Pregunta")
if st.button("Generar Video"):
video_path, error_message = generate_output(prompt)
if error_message:
st.error(f"Error: {error_message}")
else:
with open(video_path, "rb") as video_file:
st.video(video_file.read())