app.py

import gradio as gr
import torch
import soundfile as sf
import edge_tts
import asyncio
from transformers import GPT2Tokenizer, GPT2LMHeadModel
from keybert import KeyBERT
from moviepy.editor import (
    VideoFileClip,
    AudioFileClip,
    concatenate_videoclips,
    concatenate_audioclips,
    CompositeAudioClip,
    AudioClip,
    TextClip,
    CompositeVideoClip,
    VideoClip
)
import numpy as np
import json
import logging
import os
import requests
import re
import math
import tempfile
import shutil
import uuid
import threading
import time
from datetime import datetime, timedelta

# ------------------- Configuración de Timeout -------------------
os.environ["GRADIO_SERVER_TIMEOUT"] = "1800"  # 30 minutos en segundos

# ------------------- Configuración & Globals -------------------
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)

PEXELS_API_KEY = os.getenv("PEXELS_API_KEY")
if not PEXELS_API_KEY:
    logger.warning("PEXELS_API_KEY no definido. Los videos no funcionarán.")

tokenizer, gpt2_model, kw_model = None, None, None
RESULTS_DIR = "video_results"
os.makedirs(RESULTS_DIR, exist_ok=True)
TASKS = {}

# ------------------- Motor Edge TTS -------------------
class EdgeTTSEngine:
    def __init__(self, voice="es-ES-AlvaroNeural"):
        self.voice = voice
        logger.info(f"Inicializando Edge TTS con voz: {voice}")
    
    async def _synthesize_async(self, text, output_path):
        """Sintetiza texto a voz usando Edge TTS de forma asíncrona"""
        try:
            communicate = edge_tts.Communicate(text, self.voice)
            await communicate.save(output_path)
            return True
        except Exception as e:
            logger.error(f"Error en Edge TTS: {e}")
            return False
    
    def synthesize(self, text, output_path):
        """Sintetiza texto a voz (wrapper síncrono)"""
        try:
            # Ejecutar la función async en un nuevo loop
            return asyncio.run(self._synthesize_async(text, output_path))
        except Exception as e:
            logger.error(f"Error al sintetizar con Edge TTS: {e}")
            return False

# Instancia global del motor TTS
tts_engine = EdgeTTSEngine()

# ------------------- Carga Perezosa de Modelos -------------------
def get_tokenizer():
    global tokenizer
    if tokenizer is None:
        logger.info("Cargando tokenizer GPT2 español...")
        tokenizer = GPT2Tokenizer.from_pretrained("datificate/gpt2-small-spanish")
        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.eos_token
    return tokenizer

def get_gpt2_model():
    global gpt2_model
    if gpt2_model is None:
        logger.info("Cargando modelo GPT-2 español...")
        gpt2_model = GPT2LMHeadModel.from_pretrained("datificate/gpt2-small-spanish").eval()
    return gpt2_model

def get_kw_model():
    global kw_model
    if kw_model is None:
        logger.info("Cargando modelo KeyBERT multilingüe...")
        kw_model = KeyBERT("paraphrase-multilingual-MiniLM-L12-v2")
    return kw_model

# ------------------- Funciones del Pipeline -------------------
def update_task_progress(task_id, message):
    if task_id in TASKS:
        TASKS[task_id]['progress_log'] = message
        logger.info(f"[{task_id}] {message}")

def gpt2_script(prompt: str) -> str:
    """Genera un guión usando GPT-2"""
    try:
        local_tokenizer = get_tokenizer()
        local_gpt2_model = get_gpt2_model()
        
        instruction = f"Escribe un guion corto y coherente sobre: {prompt}"
        inputs = local_tokenizer(instruction, return_tensors="pt", truncation=True, max_length=512)
        
        outputs = local_gpt2_model.generate(
            **inputs,
            max_length=160 + inputs["input_ids"].shape[1],
            do_sample=True,
            top_p=0.9,
            top_k=40,
            temperature=0.7,
            no_repeat_ngram_size=3,
            pad_token_id=local_tokenizer.pad_token_id,
            eos_token_id=local_tokenizer.eos_token_id,
        )
        
        text = local_tokenizer.decode(outputs[0], skip_special_tokens=True)
        generated = text.split("sobre:")[-1].strip()
        return generated if generated else prompt
        
    except Exception as e:
        logger.error(f"Error generando guión: {e}")
        return f"Hoy hablaremos sobre {prompt}. Este es un tema fascinante que merece nuestra atención."

def generate_tts_audio(text: str, output_path: str) -> bool:
    """Genera audio usando Edge TTS"""
    try:
        logger.info("Generando audio con Edge TTS...")
        success = tts_engine.synthesize(text, output_path)
        if success and os.path.exists(output_path) and os.path.getsize(output_path) > 0:
            logger.info(f"Audio generado exitosamente: {output_path}")
            return True
        else:
            logger.error("El archivo de audio no se generó correctamente")
            return False
    except Exception as e:
        logger.error(f"Error generando TTS: {e}")
        return False

def extract_keywords(text: str) -> list[str]:
    """Extrae palabras clave del texto para búsqueda de videos"""
    try:
        local_kw_model = get_kw_model()
        clean_text = re.sub(r"[^\w\sáéíóúñÁÉÍÓÚÑ]", "", text.lower())
        kws = local_kw_model.extract_keywords(clean_text, stop_words="spanish", top_n=5)
        keywords = [k.replace(" ", "+") for k, _ in kws if k]
        return keywords if keywords else ["naturaleza", "paisaje"]
    except Exception as e:
        logger.error(f"Error extrayendo keywords: {e}")
        return ["mystery", "conspiracy", "alien", "UFO", "secret", "cover-up", "illusion", "paranoia", 
                "secret society", "lie", "simulation", "matrix", "terror", "darkness", "shadow", "enigma", 
                "urban legend", "unknown", "hidden", "mistrust", "experiment", "government", "control", 
                "surveillance", "propaganda", "deception", "whistleblower", "anomaly", "extraterrestrial", 
                "shadow government", "cabal", "deep state", "new world order", "mind control", "brainwashing", 
                "disinformation", "false flag", "assassin", "black ops", "anomaly", "men in black", "abduction", 
                "hybrid", "ancient aliens", "hollow earth", "simulation theory", "alternate reality", "predictive programming", 
                "symbolism", "occult", "eerie", "haunting", "unexplained", "forbidden knowledge", "redacted", "conspiracy theorist"]

def search_pexels_videos(query: str, count: int = 3) -> list[dict]:
    """Busca videos en Pexels"""
    if not PEXELS_API_KEY:
        return []
    
    try:
        response = requests.get(
            "https://api.pexels.com/videos/search",
            headers={"Authorization": PEXELS_API_KEY},
            params={"query": query, "per_page": count, "orientation": "landscape"},
            timeout=20
        )
        response.raise_for_status()
        return response.json().get("videos", [])
    except Exception as e:
        logger.error(f"Error buscando videos en Pexels: {e}")
        return []

def download_video(url: str, folder: str) -> str | None:
    """Descarga un video desde URL"""
    try:
        filename = f"{uuid.uuid4().hex}.mp4"
        filepath = os.path.join(folder, filename)
        
        with requests.get(url, stream=True, timeout=60) as response:
            response.raise_for_status()
            with open(filepath, "wb") as f:
                for chunk in response.iter_content(chunk_size=1024*1024):
                    f.write(chunk)
        
        if os.path.exists(filepath) and os.path.getsize(filepath) > 1000:
            return filepath
        else:
            logger.error(f"Archivo descargado inválido: {filepath}")
            return None
            
    except Exception as e:
        logger.error(f"Error descargando video {url}: {e}")
        return None

def create_subtitle_clips(script: str, video_width: int, video_height: int, duration: float):
    """Crea clips de subtítulos"""
    try:
        sentences = [s.strip() for s in re.split(r"[.!?¿¡]", script) if s.strip()]
        if not sentences:
            return []
        
        total_words = sum(len(s.split()) for s in sentences) or 1
        time_per_word = duration / total_words
        
        clips = []
        current_time = 0.0
        
        for sentence in sentences:
            num_words = len(sentence.split())
            sentence_duration = num_words * time_per_word
            
            if sentence_duration < 0.5:
                continue
                
            txt_clip = (
                TextClip(
                    sentence,
                    fontsize=max(20, int(video_height * 0.05)),
                    color="white",
                    stroke_color="black",
                    stroke_width=2,
                    method="caption",
                    size=(int(video_width * 0.9), None),
                    font="Arial-Bold"
                )
                .set_start(current_time)
                .set_duration(sentence_duration)
                .set_position(("center", "bottom"))
            )
            clips.append(txt_clip)
            current_time += sentence_duration
            
        return clips
    except Exception as e:
        logger.error(f"Error creando subtítulos: {e}")
        return []

def loop_audio_to_duration(audio_clip: AudioFileClip, target_duration: float) -> AudioFileClip:
    """Hace loop del audio hasta alcanzar la duración objetivo"""
    try:
        if audio_clip.duration >= target_duration:
            return audio_clip.subclip(0, target_duration)
        
        loops_needed = math.ceil(target_duration / audio_clip.duration)
        looped_audio = concatenate_audioclips([audio_clip] * loops_needed)
        return looped_audio.subclip(0, target_duration)
    except Exception as e:
        logger.error(f"Error haciendo loop del audio: {e}")
        return audio_clip

def create_video(script_text: str, generate_script: bool, music_path: str | None, task_id: str) -> str:
    """Función principal para crear el video"""
    temp_dir = tempfile.mkdtemp()
    
    try:
        # Paso 1: Generar o usar guión
        update_task_progress(task_id, "Paso 1/7: Preparando guión...")
        if generate_script:
            script = gpt2_script(script_text)
        else:
            script = script_text.strip()
        
        if not script:
            raise ValueError("El guión está vacío")
        
        # Paso 2: Generar audio TTS
        update_task_progress(task_id, "Paso 2/7: Generando audio con Edge TTS...")
        audio_path = os.path.join(temp_dir, "voice.wav")
        
        if not generate_tts_audio(script, audio_path):
            raise RuntimeError("Error generando el audio TTS")
        
        voice_clip = AudioFileClip(audio_path)
        video_duration = voice_clip.duration
        
        if video_duration < 1:
            raise ValueError("El audio generado es demasiado corto")
        
        # Paso 3: Buscar y descargar videos
        update_task_progress(task_id, "Paso 3/7: Buscando videos en Pexels...")
        video_paths = []
        keywords = extract_keywords(script)
        
        for i, keyword in enumerate(keywords[:3]):  # Límite de 3 keywords
            update_task_progress(task_id, f"Paso 3/7: Buscando videos para '{keyword}' ({i+1}/{len(keywords[:3])})")
            
            videos = search_pexels_videos(keyword, 2)
            for video_data in videos:
                if len(video_paths) >= 6:  # Límite de 6 videos
                    break
                
                video_files = video_data.get("video_files", [])
                if video_files:
                    # Tomar el video de mejor calidad
                    best_file = max(video_files, key=lambda f: f.get("width", 0))
                    video_url = best_file.get("link")
                    
                    if video_url:
                        downloaded_path = download_video(video_url, temp_dir)
                        if downloaded_path:
                            video_paths.append(downloaded_path)
        
        if not video_paths:
            raise RuntimeError("No se pudieron descargar videos de Pexels")
        
        # Paso 4: Procesar videos
        update_task_progress(task_id, f"Paso 4/7: Procesando {len(video_paths)} videos...")
        video_clips = []
        
        for path in video_paths:
            try:
                clip = VideoFileClip(path)
                # Tomar máximo 8 segundos de cada clip
                duration = min(8, clip.duration)
                video_clips.append(clip.subclip(0, duration))
            except Exception as e:
                logger.error(f"Error procesando video {path}: {e}")
                continue
        
        if not video_clips:
            raise RuntimeError("No se pudieron procesar los videos")
        
        # Concatenar videos
        base_video = concatenate_videoclips(video_clips, method="chain")
        
        # Extender video si es más corto que el audio
        if base_video.duration < video_duration:
            loops_needed = math.ceil(video_duration / base_video.duration)
            base_video = concatenate_videoclips([base_video] * loops_needed)
        
        # Cortar al tiempo exacto del audio
        base_video = base_video.subclip(0, video_duration)
        
        # Paso 5: Componer audio final
        update_task_progress(task_id, "Paso 5/7: Componiendo audio...")
        if music_path and os.path.exists(music_path):
            try:
                music_clip = AudioFileClip(music_path)
                music_clip = loop_audio_to_duration(music_clip, video_duration).volumex(0.2)
                final_audio = CompositeAudioClip([music_clip, voice_clip])
            except Exception as e:
                logger.error(f"Error con música: {e}")
                final_audio = voice_clip
        else:
            final_audio = voice_clip
        
        # Paso 6: Crear subtítulos
        update_task_progress(task_id, "Paso 6/7: Agregando subtítulos...")
        subtitle_clips = create_subtitle_clips(script, base_video.w, base_video.h, video_duration)
        
        # Paso 7: Renderizar video final
        update_task_progress(task_id, "Paso 7/7: Renderizando video final...")
        final_video = CompositeVideoClip([base_video] + subtitle_clips).set_audio(final_audio)
        
        output_path = os.path.join(RESULTS_DIR, f"video_{task_id}.mp4")
        final_video.write_videofile(
            output_path,
            fps=24,
            codec="libx264",
            audio_codec="aac",
            threads=2,
            logger=None,
            verbose=False
        )
        
        # Limpiar clips
        voice_clip.close()
        if 'music_clip' in locals():
            music_clip.close()
        base_video.close()
        final_video.close()
        for clip in video_clips:
            clip.close()
        
        return output_path
        
    except Exception as e:
        logger.error(f"Error creando video: {e}")
        raise
    finally:
        # Limpiar directorio temporal
        try:
            shutil.rmtree(temp_dir)
        except:
            pass

def worker_thread(task_id: str, mode: str, topic: str, user_script: str, music_path: str | None):
    """Hilo worker para procesamiento de video"""
    try:
        generate_script = (mode == "Generar Guion con IA")
        content = topic if generate_script else user_script
        
        output_path = create_video(content, generate_script, music_path, task_id)
        
        TASKS[task_id].update({
            "status": "done",
            "result": output_path,
            "progress_log": "✅ ¡Video completado exitosamente!"
        })
        
    except Exception as e:
        logger.error(f"Error en worker {task_id}: {e}")
        TASKS[task_id].update({
            "status": "error",
            "error": str(e),
            "progress_log": f"❌ Error: {str(e)}"
        })

def generate_video_with_progress(mode, topic, user_script, music):
    """Función principal que maneja la generación con progreso en tiempo real"""
    # Validar entrada
    content = topic if mode == "Generar Guion con IA" else user_script
    if not content or not content.strip():
        yield "❌ Error: Por favor, ingresa un tema o guion.", None, None
        return
    
    # Crear tarea
    task_id = uuid.uuid4().hex[:8]
    TASKS[task_id] = {
        "status": "processing",
        "progress_log": "🚀 Iniciando generación de video...",
        "timestamp": datetime.utcnow()
    }
    
    # Iniciar worker
    worker = threading.Thread(
        target=worker_thread,
        args=(task_id, mode, topic, user_script, music),
        daemon=True
    )
    worker.start()
    
    # Monitorear progreso
    while TASKS[task_id]["status"] == "processing":
        yield TASKS[task_id]['progress_log'], None, None
        time.sleep(1)
    
    # Retornar resultado final
    if TASKS[task_id]["status"] == "error":
        yield TASKS[task_id]['progress_log'], None, None
    elif TASKS[task_id]["status"] == "done":
        result_path = TASKS[task_id]['result']
        yield TASKS[task_id]['progress_log'], result_path, result_path

# ------------------- Limpieza automática -------------------
def cleanup_old_files():
    """Limpia archivos antiguos cada hora"""
    while True:
        try:
            time.sleep(3600)  # 1 hora
            now = datetime.utcnow()
            logger.info("Ejecutando limpieza de archivos antiguos...")
            
            for task_id, info in list(TASKS.items()):
                if "timestamp" in info and now - info["timestamp"] > timedelta(hours=24):
                    if info.get("result") and os.path.exists(info.get("result")):
                        try:
                            os.remove(info["result"])
                            logger.info(f"Archivo eliminado: {info['result']}")
                        except Exception as e:
                            logger.error(f"Error eliminando archivo: {e}")
                    del TASKS[task_id]
                    
        except Exception as e:
            logger.error(f"Error en cleanup: {e}")

# Iniciar hilo de limpieza
threading.Thread(target=cleanup_old_files, daemon=True).start()

# ------------------- Interfaz Gradio -------------------
def toggle_input_fields(mode):
    """Alterna los campos de entrada según el modo seleccionado"""
    return (
        gr.update(visible=mode == "Generar Guion con IA"),
        gr.update(visible=mode != "Generar Guion con IA")
    )

# Crear interfaz
with gr.Blocks(title="🎬 Generador de Videos IA", theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # 🎬 Generador de Videos con IA
    
    Crea videos profesionales a partir de texto usando:
    - **Edge TTS** para voz en español
    - **GPT-2** para generación de guiones
    - **Pexels API** para videos de stock
    - **Subtítulos automáticos** y efectos visuales
    
    El progreso se mostrará en tiempo real.
    """)
    
    with gr.Row():
        with gr.Column(scale=2):
            gr.Markdown("### ⚙️ Configuración")
            
            mode_radio = gr.Radio(
                choices=["Generar Guion con IA", "Usar Mi Guion"],
                value="Generar Guion con IA",
                label="Método de creación"
            )
            
            topic_input = gr.Textbox(
                label="💡 Tema para la IA",
                placeholder="Ej: Los misterios del océano profundo",
                lines=2
            )
            
            script_input = gr.Textbox(
                label="📝 Tu Guion Completo",
                placeholder="Escribe aquí tu guion personalizado...",
                lines=8,
                visible=False
            )
            
            music_input = gr.Audio(
                type="filepath",
                label="🎵 Música de fondo (opcional)"
            )
            
            generate_btn = gr.Button(
                "🎬 Generar Video",
                variant="primary",
                size="lg"
            )
        
        with gr.Column(scale=2):
            gr.Markdown("### 📊 Progreso y Resultados")
            
            progress_output = gr.Textbox(
                label="📋 Log de progreso en tiempo real",
                lines=12,
                interactive=False,
                show_copy_button=True
            )
            
            video_output = gr.Video(
                label="🎥 Video generado",
                height=400
            )
            
            download_output = gr.File(
                label="📥 Descargar archivo"
            )
    
    # Event handlers
    mode_radio.change(
        fn=toggle_input_fields,
        inputs=[mode_radio],
        outputs=[topic_input, script_input]
    )
    
    generate_btn.click(
        fn=generate_video_with_progress,
        inputs=[mode_radio, topic_input, script_input, music_input],
        outputs=[progress_output, video_output, download_output]
    )
    
    gr.Markdown("""
    ### 📋 Instrucciones:
    1. **Elige el método**: Genera un guion con IA o usa el tuyo propio
    2. **Configura el contenido**: Ingresa un tema interesante o tu guion
    3. **Música opcional**: Sube un archivo de audio para fondo musical
    4. **Genera**: Presiona el botón y observa el progreso en tiempo real
    
    ⏱️ **Tiempo estimado**: 2-5 minutos dependiendo de la duración del contenido.
    """)

# Ejecutar aplicación
if __name__ == "__main__":
    logger.info("🚀 Iniciando aplicación Generador de Videos IA...")
    
    # Configurar la cola (versión compatible)
    demo.queue(max_size=10)
    
    # Lanzar aplicación (parámetros básicos compatibles)
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        show_api=False
    )