app.py

import os, re, math, uuid, time, shutil, logging, tempfile, threading, requests, asyncio, numpy as np
from datetime import datetime, timedelta
from collections import Counter

import gradio as gr
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
from keybert import KeyBERT
import edge_tts
from moviepy.editor import (
    VideoFileClip, AudioFileClip, concatenate_videoclips, concatenate_audioclips,
    CompositeAudioClip, AudioClip, TextClip, CompositeVideoClip, VideoClip
)

# ------------------- 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:
    raise RuntimeError("Debes definir PEXELS_API_KEY en 'Settings' -> 'Variables & secrets'")

tokenizer, gpt2_model, kw_model = None, None, None
RESULTS_DIR = "video_results"
os.makedirs(RESULTS_DIR, exist_ok=True)
TASKS = {} # Diccionario para almacenar estado y progreso de tareas

SPANISH_VOICES = [
    "es-ES-ElviraNeural", "es-ES-AlvaroNeural", "es-MX-DaliaNeural", "es-MX-JorgeNeural",
    "es-AR-ElenaNeural", "es-AR-TomasNeural", "es-CO-SalomeNeural", "es-CO-GonzaloNeural"
]

# ------------------- Carga Perezosa de Modelos -------------------
def get_tokenizer():
    global tokenizer
    if tokenizer is None:
        logger.info("Cargando tokenizer (primera vez)...")
        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 (primera vez)...")
        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 (primera vez)...")
        kw_model = KeyBERT("distilbert-base-multilingual-cased")
    return kw_model

# ------------------- Funciones del Pipeline de Vídeo -------------------
def update_task_progress(task_id, message):
    """Actualiza el log de progreso para una tarea."""
    if task_id in TASKS:
        TASKS[task_id]['progress_log'] = message
        logger.info(f"[{task_id}] {message}")

def gpt2_script(prompt: str) -> str:
    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)
    return text.split("sobre:")[-1].strip()

async def edge_tts_synth(text: str, voice: str, path: str):
    communicate = edge_tts.Communicate(text, voice)
    await communicate.save(path)

def keywords(text: str) -> list[str]:
    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)
    return [k.replace(" ", "+") for k, _ in kws if k] or ["naturaleza"]

def pexels_search(query: str, count: int) -> list[dict]:
    res = requests.get("https://api.pexels.com/videos/search", headers={"Authorization": PEXELS_API_KEY},
                       params={"query": query, "per_page": count, "orientation": "landscape"}, timeout=20)
    res.raise_for_status()
    return res.json().get("videos", [])

def download_file(url: str, folder: str) -> str | None:
    try:
        name = uuid.uuid4().hex + ".mp4"
        path = os.path.join(folder, name)
        with requests.get(url, stream=True, timeout=60) as r:
            r.raise_for_status()
            with open(path, "wb") as f:
                for chunk in r.iter_content(1024 * 1024): f.write(chunk)
        return path if os.path.exists(path) and os.path.getsize(path) > 1000 else None
    except Exception as e:
        logger.error(f"Fallo al descargar {url}: {e}")
        return None

def loop_audio(audio_clip: AudioFileClip, duration: float) -> AudioFileClip:
    if audio_clip.duration >= duration: return audio_clip.subclip(0, duration)
    loops = math.ceil(duration / audio_clip.duration)
    return concatenate_audioclips([audio_clip] * loops).subclip(0, duration)

def make_subtitle_clips(script: str, video_w: int, video_h: int, duration: float):
    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 < 0.1: continue
        txt_clip = (TextClip(sentence, fontsize=int(video_h * 0.05), color="white",
                             stroke_color="black", stroke_width=1.5, method="caption",
                             size=(int(video_w * 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

def make_grain_clip(size: tuple[int, int], duration: float):
    w, h = size
    def make_frame(t):
        noise = np.random.randint(0, 40, (h, w, 1), dtype=np.uint8)
        return np.repeat(noise, 3, axis=2)
    return VideoClip(make_frame, duration=duration).set_opacity(0.15)

def build_video(script_text: str, generate_script_flag: bool, voice: str, music_path: str | None, task_id: str) -> str:
    tmp_dir = tempfile.mkdtemp()
    try:
        update_task_progress(task_id, "Paso 1/7: Generando guion...")
        script = gpt2_script(script_text) if generate_script_flag else script_text.strip()
        
        update_task_progress(task_id, f"Paso 2/7: Creando audio con voz '{voice}'...")
        voice_path = os.path.join(tmp_dir, "voice.mp3")
        asyncio.run(edge_tts_synth(script, voice, voice_path))
        voice_clip = AudioFileClip(voice_path)
        video_duration = voice_clip.duration
        if video_duration < 1: raise ValueError("El audio generado es demasiado corto.")

        update_task_progress(task_id, "Paso 3/7: Buscando clips de vídeo en Pexels...")
        video_paths = []
        kws = keywords(script)
        for i, kw in enumerate(kws):
            update_task_progress(task_id, f"Paso 3/7: Buscando clips... (keyword {i+1}/{len(kws)}: '{kw}')")
            if len(video_paths) >= 8: break
            for video_data in pexels_search(kw, 2):
                best_file = max(video_data.get("video_files", []), key=lambda f: f.get("width", 0))
                if best_file:
                    path = download_file(best_file.get('link'), tmp_dir)
                    if path: video_paths.append(path)
                if len(video_paths) >= 8: break
        if not video_paths: raise RuntimeError("No se encontraron vídeos en Pexels.")
        
        update_task_progress(task_id, f"Paso 4/7: Ensamblando {len(video_paths)} clips de vídeo...")
        segments = [VideoFileClip(p).subclip(0, min(8, VideoFileClip(p).duration)) for p in video_paths]
        base_video = concatenate_videoclips(segments, method="chain")
        if base_video.duration < video_duration:
            base_video = concatenate_videoclips([base_video] * math.ceil(video_duration / base_video.duration))
        base_video = base_video.subclip(0, video_duration)

        update_task_progress(task_id, "Paso 5/7: Componiendo audio final...")
        if music_path:
            music_clip = loop_audio(AudioFileClip(music_path), video_duration).volumex(0.20)
            final_audio = CompositeAudioClip([music_clip, voice_clip])
        else: final_audio = voice_clip

        update_task_progress(task_id, "Paso 6/7: Añadiendo subtítulos y efectos...")
        subtitles = make_subtitle_clips(script, base_video.w, base_video.h, video_duration)
        grain_effect = make_grain_clip(base_video.size, video_duration)
        
        update_task_progress(task_id, "Paso 7/7: Renderizando vídeo final (esto puede tardar varios minutos)...")
        final_video = CompositeVideoClip([base_video, grain_effect, *subtitles]).set_audio(final_audio)
        output_path = os.path.join(tmp_dir, "final_video.mp4")
        final_video.write_videofile(output_path, fps=24, codec="libx64", audio_codec="aac", threads=2, logger=None)
        
        return output_path
    finally:
        if 'voice_clip' in locals(): voice_clip.close()
        if 'music_clip' in locals(): music_clip.close()
        if 'base_video' in locals(): base_video.close()
        if 'final_video' in locals(): final_video.close()
        if 'segments' in locals():
            for seg in segments: seg.close()

def worker(task_id: str, mode: str, topic: str, user_script: str, voice: str, music: str | None):
    try:
        text = topic if mode == "Generar Guion con IA" else user_script
        result_tmp_path = build_video(text, mode == "Generar Guion con IA", voice, music, task_id)
        final_path = os.path.join(RESULTS_DIR, f"{task_id}.mp4")
        shutil.copy2(result_tmp_path, final_path)
        TASKS[task_id].update({"status": "done", "result": final_path})
        shutil.rmtree(os.path.dirname(result_tmp_path))
    except Exception as e:
        logger.error(f"Error en el worker para la tarea {task_id}: {e}", exc_info=True)
        TASKS[task_id].update({"status": "error", "error": str(e)})

def janitor_thread():
    while True:
        time.sleep(3600)
        now = datetime.utcnow()
        logger.info("[JANITOR] Realizando limpieza de vídeos 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"[JANITOR] Eliminado: {info['result']}")
                    except Exception as e:
                        logger.error(f"[JANITOR] Error al eliminar {info['result']}: {e}")
                del TASKS[task_id]

threading.Thread(target=janitor_thread, daemon=True).start()

def generate_and_monitor(mode, topic, user_script, voice, music):
    content = topic if mode == "Generar Guion con IA" else user_script
    if not content.strip():
        yield "Por favor, ingresa un tema o guion.", None, None
        return

    task_id = uuid.uuid4().hex[:8]
    TASKS[task_id] = {"status": "processing", "progress_log": "Iniciando tarea...", "timestamp": datetime.utcnow()}
    
    worker_thread = threading.Thread(target=worker, args=(task_id, mode, topic, user_script, voice, music), daemon=True)
    worker_thread.start()

    while TASKS[task_id]["status"] == "processing":
        yield TASKS[task_id]['progress_log'], None, None
        time.sleep(1)

    if TASKS[task_id]["status"] == "error":
        yield f"❌ Error: {TASKS[task_id]['error']}", None, None
    elif TASKS[task_id]["status"] == "done":
        yield "✅ ¡Vídeo completado!", TASKS[task_id]['result'], TASKS[task_id]['result']

with gr.Blocks(title="Generador de Vídeos IA", theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🎬 Generador de Vídeos con IA")
    gr.Markdown("Crea vídeos a partir de texto con voz, música y efectos visuales. El progreso se mostrará en tiempo real.")

    with gr.Row():
        with gr.Column(scale=2):
            mode_radio = gr.Radio(["Generar Guion con IA", "Usar Mi Guion"], value="Generar Guion con IA", label="Elige el método")
            topic_textbox = gr.Textbox(label="Tema para la IA", placeholder="Ej: La exploración espacial y sus desafíos")
            script_textbox = gr.Textbox(label="Tu Guion Completo", lines=5, visible=False, placeholder="Pega aquí tu guion...")
            voice_dropdown = gr.Dropdown(SPANISH_VOICES, value=SPANISH_VOICES[0], label="Elige una voz")
            music_upload = gr.Audio(type="filepath", label="Música de fondo (opcional)")
            submit_button = gr.Button("✨ Generar Vídeo", variant="primary")
        
        with gr.Column(scale=2):
            gr.Markdown("## Progreso y Resultados")
            progress_log = gr.Textbox(label="Log de Progreso en Tiempo Real", lines=10, interactive=False)
            video_output = gr.Video(label="Resultado del Vídeo")
            download_file_output = gr.File(label="Descargar Fichero")

    def toggle_textboxes(mode):
        return gr.update(visible=mode == "Generar Guion con IA"), gr.update(visible=mode != "Generar Guion con IA")

    mode_radio.change(toggle_textboxes, inputs=mode_radio, outputs=[topic_textbox, script_textbox])
    
    submit_button.click(
        fn=generate_and_monitor,
        inputs=[mode_radio, topic_textbox, script_textbox, voice_dropdown, music_upload],
        outputs=[progress_log, video_output, download_file_output]
    )

if __name__ == "__main__":
    demo.launch()