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import os
import asyncio
import logging
import tempfile
import requests
from datetime import datetime
import edge_tts
import gradio as gr
import torch
import re
from keybert import KeyBERT
# Configuraci贸n b谩sica de logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
# Clave API de Pexels (configurar en Secrets de Hugging Face)
PEXELS_API_KEY = os.environ.get("PEXELS_API_KEY", "YOUR_DEFAULT_API_KEY")
# Inicializaci贸n del modelo KeyBERT
try:
kw_model = KeyBERT('distilbert-base-nli-mean-tokens')
logger.info("Modelo KeyBERT cargado exitosamente.")
except Exception as e:
logger.error(f"Error al cargar KeyBERT: {e}")
kw_model = None
# --- Funciones principales optimizadas para Spaces ---
async def text_to_speech(text, output_path, voice="es-ES-ElviraNeural"):
"""Genera audio TTS usando edge-tts"""
try:
communicate = edge_tts.Communicate(text, voice)
await communicate.save(output_path)
return True
except Exception as e:
logger.error(f"Error en TTS: {e}")
return False
def download_video(url, temp_dir):
"""Descarga un video desde una URL a un directorio temporal"""
try:
response = requests.get(url, stream=True, timeout=30)
response.raise_for_status()
filename = f"video_{datetime.now().strftime('%H%M%S%f')}.mp4"
filepath = os.path.join(temp_dir, filename)
with open(filepath, 'wb') as f:
for chunk in response.iter_content(chunk_size=8192):
f.write(chunk)
return filepath
except Exception as e:
logger.error(f"Error descargando video: {e}")
return None
def extract_keywords(text, max_keywords=3):
"""Extrae palabras clave usando KeyBERT o m茅todo simple como fallback"""
if kw_model:
try:
keywords = kw_model.extract_keywords(
text,
keyphrase_ngram_range=(1, 2),
top_n=max_keywords,
use_mmr=True,
diversity=0.7
)
return [kw[0].replace(" ", "+") for kw in keywords]
except Exception as e:
logger.warning(f"Error KeyBERT: {e}")
# Fallback: m茅todo simple
words = re.findall(r'\b\w+\b', text.lower())
stop_words = {"el", "la", "los", "las", "de", "en", "y", "a", "que", "es", "por"}
return list(set([w for w in words if len(w) > 3 and w not in stop_words][:max_keywords]))
def search_pexels_videos(query_list, per_query=2):
"""Busca videos en Pexels usando su API oficial"""
if not PEXELS_API_KEY:
logger.error("API_KEY de Pexels no configurada")
return []
headers = {"Authorization": PEXELS_API_KEY}
video_urls = []
for query in query_list:
try:
params = {
"query": query,
"per_page": per_query,
"orientation": "landscape",
"size": "medium"
}
response = requests.get(
"https://api.pexels.com/videos/search",
headers=headers,
params=params,
timeout=15
)
if response.status_code == 200:
videos = response.json().get("videos", [])
for video in videos:
video_files = video.get("video_files", [])
if video_files:
# Seleccionar el video con la mejor resoluci贸n
best_quality = max(
video_files,
key=lambda x: x.get("width", 0) * x.get("height", 0)
)
video_urls.append(best_quality["link"])
except Exception as e:
logger.error(f"Error buscando videos: {e}")
return video_urls
def create_video(audio_path, video_paths, output_path):
"""Crea el video final usando FFmpeg"""
try:
# Crear archivo de lista para concatenaci贸n
with open("input_list.txt", "w") as f:
for path in video_paths:
f.write(f"file '{path}'\n")
# Comando FFmpeg para concatenar videos y a帽adir audio
cmd = [
"ffmpeg", "-y",
"-f", "concat",
"-safe", "0",
"-i", "input_list.txt",
"-i", audio_path,
"-c", "copy",
"-shortest",
output_path
]
subprocess.run(cmd, check=True)
return True
except Exception as e:
logger.error(f"Error creando video: {e}")
return False
async def generate_video(text, music_url=None):
"""Funci贸n principal para generar el video"""
temp_dir = tempfile.mkdtemp()
all_files = []
try:
# 1. Generar audio TTS
tts_path = os.path.join(temp_dir, "audio.mp3")
if not await text_to_speech(text, tts_path):
return None, "Error generando voz"
all_files.append(tts_path)
# 2. Extraer palabras clave
keywords = extract_keywords(text)
logger.info(f"Palabras clave identificadas: {keywords}")
# 3. Buscar y descargar videos
video_urls = search_pexels_videos(keywords)
if not video_urls:
return None, "No se encontraron videos para las palabras clave"
video_paths = []
for url in video_urls:
path = download_video(url, temp_dir)
if path:
video_paths.append(path)
all_files.append(path)
if not video_paths:
return None, "Error descargando videos"
# 4. Crear video final
output_path = os.path.join(temp_dir, "final_video.mp4")
if create_video(tts_path, video_paths, output_path):
return output_path, "Video creado exitosamente"
else:
return None, "Error en la creaci贸n del video"
except Exception as e:
logger.exception("Error inesperado")
return None, f"Error: {str(e)}"
finally:
# Limpieza opcional (Hugging Face limpia autom谩ticamente)
pass
# --- Interfaz de Gradio ---
with gr.Blocks(title="Generador Autom谩tico de Videos con IA", theme="soft") as demo:
gr.Markdown("# 馃幀 Generador Autom谩tico de Videos con IA")
gr.Markdown("Transforma texto en videos usando contenido de Pexels y voz sintetizada")
with gr.Row():
with gr.Column():
text_input = gr.Textbox(
label="Texto para el video",
placeholder="Describe el contenido que quieres en el video...",
lines=5
)
generate_btn = gr.Button("Generar Video", variant="primary")
with gr.Column():
video_output = gr.Video(label="Video Generado")
status_output = gr.Textbox(label="Estado")
generate_btn.click(
fn=generate_video,
inputs=[text_input],
outputs=[video_output, status_output]
)
gr.Markdown("### C贸mo funciona:")
gr.Markdown("""
1. Ingresa un texto descriptivo
2. Nuestra IA extrae palabras clave
3. Buscamos videos relacionados en Pexels
4. Generamos voz con Edge TTS
5. Combinamos todo en un video final
""")
# Para Hugging Face Spaces
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860) |