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Update app.py
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app.py
CHANGED
@@ -9,13 +9,15 @@ import re
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import torch
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from transformers import GPT2LMHeadModel, GPT2Tokenizer
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import warnings
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# Configuraci贸n inicial
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Suprimir warnings espec铆ficos
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warnings.filterwarnings("ignore", category=UserWarning, module="transformers")
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PEXELS_API_KEY = os.getenv("PEXELS_API_KEY")
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@@ -24,245 +26,154 @@ VOICES = [
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"es-MX-DaliaNeural", "es-ES-ElviraNeural", "es-AR-ElenaNeural",
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"es-MX-JorgeNeural", "es-ES-AlvaroNeural", "es-AR-TomasNeural",
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"en-US-JennyNeural", "fr-FR-DeniseNeural", "de-DE-KatjaNeural",
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"it-IT-ElsaNeural", "pt-BR-FranciscaNeural", "ja-JP-NanamiNeural"
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"en-GB-SoniaNeural", "es-CL-CatalinaNeural", "es-CO-GonzaloNeural"
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]
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# Cargar modelo
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if custom_script:
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return custom_script
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if model is None or tokenizer is None:
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return f"Texto
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try:
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prompt = f"Escribe un texto largo y detallado sobre {tema}"
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inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
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# Generar texto con configuraci贸n corregida
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outputs = model.generate(
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inputs.input_ids,
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max_length=
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do_sample=True,
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temperature=0.
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top_k=50,
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top_p=0.95,
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num_return_sequences=1,
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pad_token_id=tokenizer.eos_token_id
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# early_stopping=True # Eliminado para evitar warnings
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)
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# Limpiar texto
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guion = re.sub(r'<.*?>', '', guion)
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guion = re.sub(r'\n+', '\n', guion)
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guion = re.sub(r'\s+', ' ', guion).strip()
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logger.info(f"Guion generado: {len(guion)} caracteres")
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return guion
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except Exception as e:
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logger.error(f"Error generando
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return f"
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def
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"""Busca videos en Pexels
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try:
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headers = {"Authorization": PEXELS_API_KEY}
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logger.info(f"Buscando videos para: {tema}")
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response = requests.get(
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f"https://api.pexels.com/videos/search?query={tema}&per_page=
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headers=headers,
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timeout=
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)
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if response.status_code != 200:
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return []
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return response.json().get("videos", [])[:num_videos]
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except Exception as e:
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logger.error(f"Error buscando videos: {str(e)}")
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return []
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def descargar_video(url, output_path):
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"""Descarga un video de manera eficiente"""
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try:
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with requests.get(url, stream=True, timeout=30) as r:
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r.raise_for_status()
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with open(output_path, 'wb') as f:
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for chunk in r.iter_content(chunk_size=8192):
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f.write(chunk)
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return True
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except Exception as e:
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logger.error(f"Error descargando video: {str(e)}")
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return False
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def crear_video(prompt, custom_script, voz_seleccionada, musica=None):
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try:
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# 2. Generar
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subprocess.run([
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'edge-tts',
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'--voice', voz_seleccionada,
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'--text', guion,
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'--write-media',
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], check=True)
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duracion_total = audio.duration
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# 4. Buscar videos relevantes
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videos_data = buscar_videos_pexels(prompt)
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if not videos_data:
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logger.warning("No se encontraron videos. Usando videos gen茅ricos...")
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videos_data = buscar_videos_pexels("nature")
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#
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clips = []
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try:
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video_url = video_file['link']
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temp_path = f"temp_video_{i}.mp4"
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# Calcular duraci贸n proporcional para cada clip
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duracion_clip = duracion_total / len(videos_data)
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# Ajustar clip a la duraci贸n requerida
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if clip.duration < duracion_clip:
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clip = clip.loop(duration=duracion_clip)
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else:
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clip = clip.subclip(0, duracion_clip)
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clips.append(clip)
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except Exception as e:
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logger.error(f"Error procesando video {i}: {str(e)}")
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if not clips:
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# 6. Combinar videos
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final_clip = concatenate_videoclips(clips, method="compose")
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final_clip = final_clip.set_audio(audio)
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if musica:
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try:
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musica_clip = AudioFileClip(musica.name)
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if musica_clip.duration < duracion_total:
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musica_clip = musica_clip.loop(duration=duracion_total)
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else:
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musica_clip = musica_clip.subclip(0, duracion_total)
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audio_final = CompositeAudioClip([
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audio.volumex(1.0),
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musica_clip.volumex(0.25)
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])
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final_clip = final_clip.set_audio(audio_final)
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except Exception as e:
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logger.error(f"Error procesando m煤sica: {str(e)}")
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# 8. Exportar video final
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output_path = f"video_{datetime.now().strftime('%Y%m%d_%H%M%S')}.mp4"
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final_clip.write_videofile(
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fps=24,
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codec="libx264",
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audio_codec="aac",
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threads=
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preset='fast'
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)
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logger.info(f"Video
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return
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except Exception as e:
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logger.error(f"
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return None
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finally:
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# Limpieza
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os.
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temp_file = f"temp_video_{i}.mp4"
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if os.path.exists(temp_file):
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os.remove(temp_file)
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# Interfaz
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with gr.Blocks(title="Generador de Videos"
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gr.Markdown("# 馃幀 GENERADOR AUTOM脕TICO DE VIDEOS")
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with gr.Row():
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with gr.Column():
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custom_script = gr.TextArea(
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label="Guion personalizado (opcional)",
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placeholder="Pega tu guion completo aqu铆...",
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lines=8,
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max_lines=20
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)
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voz = gr.Dropdown(
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label="Voz Narradora",
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choices=VOICES,
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value=VOICES[0],
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interactive=True
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)
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musica = gr.File(
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label="M煤sica de fondo (opcional)",
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file_types=["audio"],
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type="filepath"
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)
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btn = gr.Button("Generar Video", variant="primary")
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with gr.Column():
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format="mp4",
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interactive=False
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)
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btn.click(
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fn=crear_video,
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inputs=[
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outputs=
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)
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if __name__ == "__main__":
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app.launch(
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import torch
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from transformers import GPT2LMHeadModel, GPT2Tokenizer
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import warnings
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import time
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# Configuraci贸n inicial
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Suprimir warnings espec铆ficos
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warnings.filterwarnings("ignore", category=UserWarning, module="transformers")
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warnings.filterwarnings("ignore", category=DeprecationWarning)
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PEXELS_API_KEY = os.getenv("PEXELS_API_KEY")
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"es-MX-DaliaNeural", "es-ES-ElviraNeural", "es-AR-ElenaNeural",
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"es-MX-JorgeNeural", "es-ES-AlvaroNeural", "es-AR-TomasNeural",
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"en-US-JennyNeural", "fr-FR-DeniseNeural", "de-DE-KatjaNeural",
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"it-IT-ElsaNeural", "pt-BR-FranciscaNeural", "ja-JP-NanamiNeural"
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]
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# Cargar modelo GPT-2 con manejo de errores mejorado
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def cargar_modelo():
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try:
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tokenizer = GPT2Tokenizer.from_pretrained("datificate/gpt2-small-spanish")
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model = GPT2LMHeadModel.from_pretrained("datificate/gpt2-small-spanish")
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logger.info("Modelo GPT-2 cargado correctamente")
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return tokenizer, model
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except Exception as e:
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logger.error(f"Error cargando modelo: {str(e)}")
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return None, None
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tokenizer, model = cargar_modelo()
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def generar_guion(tema, custom_script=None):
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"""Genera texto basado en el tema sin estructuras predefinidas"""
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if custom_script:
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return custom_script
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if model is None or tokenizer is None:
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return f"Texto sobre {tema}. " * 100
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try:
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inputs = tokenizer(f"Escribe un texto largo sobre {tema}", return_tensors="pt", truncation=True)
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outputs = model.generate(
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inputs.input_ids,
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max_length=1000,
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do_sample=True,
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temperature=0.7,
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top_k=50,
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num_return_sequences=1,
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pad_token_id=tokenizer.eos_token_id
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)
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return tokenizer.decode(outputs[0], skip_special_tokens=True)
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except Exception as e:
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logger.error(f"Error generando texto: {str(e)}")
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return f"Contenido generado sobre {tema}. " * 100
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def buscar_videos(tema):
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"""Busca videos en Pexels con manejo de errores mejorado"""
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try:
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headers = {"Authorization": PEXELS_API_KEY}
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response = requests.get(
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f"https://api.pexels.com/videos/search?query={tema}&per_page=3",
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headers=headers,
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timeout=10
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return response.json().get("videos", [])[:3]
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except Exception as e:
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logger.error(f"Error buscando videos: {str(e)}")
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return []
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def crear_video(prompt, custom_script, voz_seleccionada, musica=None):
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try:
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start_time = time.time()
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# 1. Generar gui贸n
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guion = generar_guion(prompt, custom_script)
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logger.info(f"Guion generado ({len(guion)} caracteres)")
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# 2. Generar voz
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voz_file = "narracion.mp3"
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subprocess.run([
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'edge-tts',
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'--voice', voz_seleccionada,
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'--text', guion,
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'--write-media', voz_file
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], check=True)
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audio = AudioFileClip(voz_file)
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duracion = audio.duration
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# 3. Buscar y descargar videos
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videos = buscar_videos(prompt) or buscar_videos("nature")
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clips = []
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for i, video in enumerate(videos):
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try:
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video_file = max(video['video_files'], key=lambda x: x.get('width', 0))
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temp_file = f"temp_vid_{i}.mp4"
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with requests.get(video_file['link'], stream=True) as r:
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r.raise_for_status()
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with open(temp_file, 'wb') as f:
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for chunk in r.iter_content(chunk_size=8192):
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f.write(chunk)
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clip = VideoFileClip(temp_file)
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clip = clip.subclip(0, min(10, clip.duration)) # M谩ximo 10 segundos por clip
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clips.append(clip)
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except Exception as e:
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logger.error(f"Error procesando video {i}: {str(e)}")
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# 4. Crear video final
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if not clips:
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final_clip = ColorClip((1280, 720), (0, 0, 0), duration=duracion)
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else:
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final_clip = concatenate_videoclips(clips).set_duration(duracion)
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final_clip = final_clip.set_audio(audio)
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output_file = f"video_{datetime.now().strftime('%Y%m%d_%H%M%S')}.mp4"
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final_clip.write_videofile(
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output_file,
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fps=24,
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codec="libx264",
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audio_codec="aac",
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threads=2,
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preset='fast'
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)
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logger.info(f"Video creado en {time.time()-start_time:.2f} segundos")
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return output_file
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except Exception as e:
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logger.error(f"Error cr铆tico: {str(e)}")
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return None
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finally:
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# Limpieza
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for f in [voz_file, *[f"temp_vid_{i}.mp4" for i in range(3)]]:
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if os.path.exists(f):
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os.remove(f)
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# Interfaz simplificada y funcional
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with gr.Blocks(title="Generador de Videos") as app:
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with gr.Row():
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with gr.Column():
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tema = gr.Textbox(label="Tema del video")
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guion = gr.TextArea(label="Guion personalizado (opcional)", lines=5)
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voz = gr.Dropdown(label="Voz", choices=VOICES, value=VOICES[0])
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btn = gr.Button("Generar Video")
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with gr.Column():
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+
salida = gr.Video(label="Resultado")
|
167 |
+
|
|
|
|
|
|
|
|
|
168 |
btn.click(
|
169 |
fn=crear_video,
|
170 |
+
inputs=[tema, guion, voz],
|
171 |
+
outputs=salida
|
172 |
)
|
173 |
|
174 |
if __name__ == "__main__":
|
175 |
+
app.launch(
|
176 |
+
server_name="0.0.0.0",
|
177 |
+
server_port=7860,
|
178 |
+
share=False # Desactivado para evitar problemas
|
179 |
+
)
|