core/visual_engine.py

from PIL import Image, ImageDraw, ImageFont, ImageOps
import base64
import json
from moviepy.editor import (ImageClip, VideoFileClip, concatenate_videoclips, TextClip,
                            CompositeVideoClip, AudioFileClip)
import moviepy.video.fx.all as vfx
import numpy as np
import os
import openai
import requests
import io
import time
import random
import logging
import mimetypes

logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)

# --- MONKEY PATCH ---
try:
    if hasattr(Image, 'Resampling') and hasattr(Image.Resampling, 'LANCZOS'):
        if not hasattr(Image, 'ANTIALIAS'):
            Image.ANTIALIAS = Image.Resampling.LANCZOS
    elif hasattr(Image, 'LANCZOS'):
        if not hasattr(Image, 'ANTIALIAS'):
            Image.ANTIALIAS = Image.LANCZOS
    elif not hasattr(Image, 'ANTIALIAS'):
        print("WARNING: Pillow ANTIALIAS/Resampling issue.")
except Exception as e_mp:
    print(f"WARNING: ANTIALIAS patch error: {e_mp}")

# --- SERVICE CLIENT IMPORTS ---
ELEVENLABS_CLIENT_IMPORTED = False
ElevenLabsAPIClient = None
Voice = None
VoiceSettings = None
try:
    from elevenlabs.client import ElevenLabs as ImportedElevenLabsClient
    from elevenlabs import Voice as ImportedVoice, VoiceSettings as ImportedVoiceSettings
    ElevenLabsAPIClient = ImportedElevenLabsClient
    Voice = ImportedVoice
    VoiceSettings = ImportedVoiceSettings
    ELEVENLABS_CLIENT_IMPORTED = True
    logger.info("ElevenLabs client components imported.")
except Exception as e_eleven:
    logger.warning(f"ElevenLabs client import failed: {e_eleven}. Audio disabled.")

RUNWAYML_SDK_IMPORTED = False
RunwayMLAPIClient = None
try:
    from runwayml import RunwayML as ImportedRunwayMLClient
    RunwayMLAPIClient = ImportedRunwayMLClient
    RUNWAYML_SDK_IMPORTED = True
    logger.info("RunwayML SDK imported successfully.")
except ImportError:
    logger.warning("RunwayML SDK not found (pip install runwayml). RunwayML video generation will be disabled.")
except Exception as e_runway_sdk:
    logger.warning(f"Error importing RunwayML SDK: {e_runway_sdk}. RunwayML features disabled.")


class VisualEngine:
    def __init__(self, output_dir="temp_cinegen_media", default_elevenlabs_voice_id="Rachel"):
        self.output_dir = output_dir
        os.makedirs(self.output_dir, exist_ok=True)
        self.font_filename = "DejaVuSans-Bold.ttf"
        font_paths_to_try = [
            self.font_filename,
            "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf",
            "/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf",
            "/System/Library/Fonts/Supplemental/Arial.ttf",
            "C:/Windows/Fonts/arial.ttf",
            "/usr/local/share/fonts/truetype/mycustomfonts/arial.ttf"
        ]
        self.font_path_pil = next((p for p in font_paths_to_try if os.path.exists(p)), None)
        self.font_size_pil = 20
        self.video_overlay_font_size = 30
        self.video_overlay_font_color = 'white'
        self.video_overlay_font = 'DejaVu-Sans-Bold'
        try:
            if self.font_path_pil:
                self.font = ImageFont.truetype(self.font_path_pil, self.font_size_pil)
                logger.info(f"Pillow font: {self.font_path_pil}.")
            else:
                self.font = ImageFont.load_default()
                logger.warning("Default Pillow font.")
                self.font_size_pil = 10
        except IOError as e_font:
            logger.error(f"Pillow font IOError: {e_font}. Default.")
            self.font = ImageFont.load_default()
            self.font_size_pil = 10

        self.openai_api_key = None
        self.USE_AI_IMAGE_GENERATION = False
        self.dalle_model = "dall-e-3"
        self.image_size_dalle3 = "1792x1024"
        self.video_frame_size = (1280, 720)

        self.elevenlabs_api_key = None
        self.USE_ELEVENLABS = False
        self.elevenlabs_client = None
        self.elevenlabs_voice_id = default_elevenlabs_voice_id
        if VoiceSettings and ELEVENLABS_CLIENT_IMPORTED:
            self.elevenlabs_voice_settings = VoiceSettings(
                stability=0.60,
                similarity_boost=0.80,
                style=0.15,
                use_speaker_boost=True
            )
        else:
            self.elevenlabs_voice_settings = None

        self.pexels_api_key = None
        self.USE_PEXELS = False

        self.runway_api_key = None
        self.USE_RUNWAYML = False
        self.runway_client = None
        if RUNWAYML_SDK_IMPORTED and RunwayMLAPIClient:
            try:
                if os.getenv("RUNWAYML_API_SECRET"):
                    self.runway_client = RunwayMLAPIClient()
                    logger.info("RunwayML Client initialized using RUNWAYML_API_SECRET env var.")
            except Exception as e_runway_init:
                logger.error(f"Failed to initialize RunwayML client during __init__: {e_runway_init}", exc_info=True)

        logger.info("VisualEngine initialized.")

    def set_openai_api_key(self, k):
        self.openai_api_key = k
        self.USE_AI_IMAGE_GENERATION = bool(k)
        logger.info(f"DALL-E ({self.dalle_model}) {'Ready.' if k else 'Disabled.'}")

    def set_elevenlabs_api_key(self, api_key, voice_id_from_secret=None):
        self.elevenlabs_api_key = api_key
        if voice_id_from_secret:
            self.elevenlabs_voice_id = voice_id_from_secret
        if api_key and ELEVENLABS_CLIENT_IMPORTED and ElevenLabsAPIClient:
            try:
                self.elevenlabs_client = ElevenLabsAPIClient(api_key=api_key)
                self.USE_ELEVENLABS = bool(self.elevenlabs_client)
                logger.info(f"ElevenLabs Client {'Ready' if self.USE_ELEVENLABS else 'Failed Init'} (Voice ID: {self.elevenlabs_voice_id}).")
            except Exception as e:
                logger.error(f"ElevenLabs client init error: {e}. Disabled.", exc_info=True)
                self.USE_ELEVENLABS = False
        else:
            self.USE_ELEVENLABS = False
            logger.info("ElevenLabs Disabled (no key or SDK issue).")

    def set_pexels_api_key(self, k):
        self.pexels_api_key = k
        self.USE_PEXELS = bool(k)
        logger.info(f"Pexels Search {'Ready.' if k else 'Disabled.'}")

    def set_runway_api_key(self, k):
        self.runway_api_key = k
        if k:
            if RUNWAYML_SDK_IMPORTED and RunwayMLAPIClient:
                if not self.runway_client:
                    try:
                        if not os.getenv("RUNWAYML_API_SECRET"):
                            os.environ["RUNWAYML_API_SECRET"] = k
                            logger.info("Setting RUNWAYML_API_SECRET env var from provided key.")
                        self.runway_client = RunwayMLAPIClient()
                        self.USE_RUNWAYML = True
                        logger.info("RunwayML Client initialized successfully via set_runway_api_key.")
                    except Exception as e_client_init:
                        logger.error(f"RunwayML Client init failed in set_runway_api_key: {e_client_init}", exc_info=True)
                        self.USE_RUNWAYML = False
                else:
                    self.USE_RUNWAYML = True
                    logger.info("RunwayML Client was already initialized.")
            else:
                logger.warning("RunwayML SDK not imported. API key set, but integration requires SDK.")
                self.USE_RUNWAYML = False
        else:
            self.USE_RUNWAYML = False
            logger.info("RunwayML Disabled (no API key).")

    def _image_to_data_uri(self, image_path):
        try:
            mime_type, _ = mimetypes.guess_type(image_path)
            if not mime_type:
                ext = os.path.splitext(image_path)[1].lower()
                if ext == ".png":
                    mime_type = "image/png"
                elif ext in [".jpg", ".jpeg"]:
                    mime_type = "image/jpeg"
                else:
                    mime_type = "application/octet-stream"
                    logger.warning(f"Unknown MIME for {image_path}, using {mime_type}.")
            with open(image_path, "rb") as image_file:
                encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
            data_uri = f"data:{mime_type};base64,{encoded_string}"
            logger.debug(f"Data URI for {image_path} (first 100): {data_uri[:100]}")
            return data_uri
        except Exception as e:
            logger.error(f"Error converting {image_path} to data URI: {e}", exc_info=True)
            return None

    def _map_resolution_to_runway_ratio(self, width, height):
        ratio_str = f"{width}:{height}"
        supported_ratios = ["1280:720", "720:1280", "1104:832", "832:1104", "960:960", "1584:672"]
        if ratio_str in supported_ratios:
            return ratio_str
        logger.warning(f"Res {ratio_str} not directly Gen-4 supported. Default 1280:720.")
        return "1280:720"

    def _get_text_dimensions(self, text_content, font_obj):
        default_char_height = getattr(font_obj, 'size', self.font_size_pil)
        if not text_content:
            return 0, default_char_height
        try:
            if hasattr(font_obj, 'getbbox'):
                bbox = font_obj.getbbox(text_content)
                w = bbox[2] - bbox[0]
                h = bbox[3] - bbox[1]
                return w, h if h > 0 else default_char_height
            elif hasattr(font_obj, 'getsize'):
                w, h = font_obj.getsize(text_content)
                return w, h if h > 0 else default_char_height
            else:
                return int(len(text_content) * default_char_height * 0.6), int(default_char_height * 1.2)
        except Exception as e:
            logger.warning(f"Error in _get_text_dimensions: {e}")
            return int(len(text_content) * self.font_size_pil * 0.6), int(self.font_size_pil * 1.2)

    def _create_placeholder_image_content(self, text_description, filename, size=None):
        if size is None:
            size = self.video_frame_size
        img = Image.new('RGB', size, color=(20, 20, 40))
        d = ImageDraw.Draw(img)
        padding = 25
        max_w = size[0] - (2 * padding)
        lines = []
        if not text_description:
            text_description = "(Placeholder Image)"
        words = text_description.split()
        current_line = ""
        for word_idx, word in enumerate(words):
            prospective_line_addition = word + (" " if word_idx < len(words) - 1 else "")
            test_line = current_line + prospective_line_addition
            current_line_width, _ = self._get_text_dimensions(test_line, self.font)
            if current_line_width == 0 and test_line.strip():
                current_line_width = len(test_line) * (self.font_size_pil * 0.6)
            if current_line_width <= max_w:
                current_line = test_line
            else:
                if current_line.strip():
                    lines.append(current_line.strip())
                current_line = prospective_line_addition
        if current_line.strip():
            lines.append(current_line.strip())
        if not lines and text_description:
            avg_char_width, _ = self._get_text_dimensions("W", self.font)
            if avg_char_width == 0:
                avg_char_width = self.font_size_pil * 0.6
            chars_per_line = int(max_w / avg_char_width) if avg_char_width > 0 else 20
            lines.append(text_description[:chars_per_line] + ("..." if len(text_description) > chars_per_line else ""))
        elif not lines:
            lines.append("(Placeholder Error)")
        _, single_line_h = self._get_text_dimensions("Ay", self.font)
        single_line_h = single_line_h if single_line_h > 0 else self.font_size_pil + 2
        max_lines_to_display = min(len(lines), (size[1] - (2 * padding)) // (single_line_h + 2)) if single_line_h > 0 else 1
        if max_lines_to_display <= 0:
            max_lines_to_display = 1
        y_text_start = padding + (size[1] - (2 * padding) - max_lines_to_display * (single_line_h + 2)) / 2.0
        y_text = y_text_start
        for i in range(max_lines_to_display):
            line_content = lines[i]
            line_w, _ = self._get_text_dimensions(line_content, self.font)
            if line_w == 0 and line_content.strip():
                line_w = len(line_content) * (self.font_size_pil * 0.6)
            x_text = (size[0] - line_w) / 2.0
            try:
                d.text((x_text, y_text), line_content, font=self.font, fill=(200, 200, 180))
            except Exception as e_draw:
                logger.error(f"Pillow d.text error: {e_draw} for line '{line_content}'")
            y_text += single_line_h + 2
            if i == 6 and max_lines_to_display > 7:
                try:
                    d.text((x_text, y_text), "...", font=self.font, fill=(200, 200, 180))
                except Exception as e_ellipsis:
                    logger.error(f"Pillow d.text ellipsis error: {e_ellipsis}")
                break
        filepath = os.path.join(self.output_dir, filename)
        try:
            img.save(filepath)
            return filepath
        except Exception as e:
            logger.error(f"Saving placeholder image {filepath}: {e}", exc_info=True)
            return None

    def _search_pexels_image(self, query, output_filename_base):
        # <<< CORRECTED METHOD >>>
        if not self.USE_PEXELS or not self.pexels_api_key:
            return None
        headers = {"Authorization": self.pexels_api_key}
        params = {"query": query, "per_page": 1, "orientation": "landscape", "size": "large2x"}
        pexels_filename = output_filename_base.replace(".png", f"_pexels_{random.randint(1000,9999)}.jpg")\
                                             .replace(".mp4", f"_pexels_{random.randint(1000,9999)}.jpg")
        filepath = os.path.join(self.output_dir, pexels_filename)
        try:
            logger.info(f"Pexels search: '{query}'")
            effective_query = " ".join(query.split()[:5])
            params["query"] = effective_query
            response = requests.get("https://api.pexels.com/v1/search", headers=headers, params=params, timeout=20)
            response.raise_for_status()
            data = response.json()
            if data.get("photos") and len(data["photos"]) > 0:
                photo_url = data["photos"][0]["src"]["large2x"]
                image_response = requests.get(photo_url, timeout=60)
                image_response.raise_for_status()
                img_data = Image.open(io.BytesIO(image_response.content))
                if img_data.mode != 'RGB':
                    img_data = img_data.convert('RGB')
                img_data.save(filepath)
                logger.info(f"Pexels image saved: {filepath}")
                return filepath
            else:
                logger.info(f"No photos found on Pexels for query: '{effective_query}'")
                return None
        except requests.exceptions.RequestException as e_req:
            logger.error(f"Pexels request error for query '{query}': {e_req}", exc_info=True)
        except json.JSONDecodeError as e_json:
            logger.error(f"Pexels JSON decode error for query '{query}': {e_json}", exc_info=True)
        except IOError as e_io:
            logger.error(f"Pexels image save error for query '{query}': {e_io}", exc_info=True)
        except Exception as e:
            logger.error(f"Unexpected Pexels error for query '{query}': {e}", exc_info=True)
        return None

    def _generate_video_clip_with_runwayml(self, text_prompt_for_motion, input_image_path,
                                           scene_identifier_filename_base, target_duration_seconds=5):
        if not self.USE_RUNWAYML or not self.runway_client:
            logger.warning("RunwayML not enabled/client not init. Skip video.")
            return None
        if not input_image_path or not os.path.exists(input_image_path):
            logger.error(f"Runway Gen-4 needs input image. Path invalid: {input_image_path}")
            return None
        image_data_uri = self._image_to_data_uri(input_image_path)
        if not image_data_uri:
            return None
        runway_duration = 10 if target_duration_seconds > 7 else 5
        runway_ratio_str = self._map_resolution_to_runway_ratio(
            self.video_frame_size[0], self.video_frame_size[1]
        )
        output_video_filename = scene_identifier_filename_base.replace(
            ".png", f"_runway_gen4_d{runway_duration}s.mp4"
        )
        output_video_filepath = os.path.join(self.output_dir, output_video_filename)
        logger.info(f"Runway Gen-4 task: motion='{text_prompt_for_motion[:100]}...', "
                    f"img='{os.path.basename(input_image_path)}', dur={runway_duration}s, ratio='{runway_ratio_str}'")
        try:
            task = self.runway_client.image_to_video.create(
                model='gen4_turbo',
                prompt_image=image_data_uri,
                prompt_text=text_prompt_for_motion,
                duration=runway_duration,
                ratio=runway_ratio_str
            )
            logger.info(f"Runway Gen-4 task ID: {task.id}. Polling...")
            poll_interval = 10
            max_polls = 36
            for _ in range(max_polls):
                time.sleep(poll_interval)
                task_details = self.runway_client.tasks.retrieve(id=task.id)
                logger.info(f"Runway task {task.id} status: {task_details.status}")
                if task_details.status == 'SUCCEEDED':
                    output_url = (
                        getattr(getattr(task_details, 'output', None), 'url', None)
                        or (
                            getattr(task_details, 'artifacts', None)
                            and task_details.artifacts[0].url
                            if task_details.artifacts and hasattr(task_details.artifacts[0], 'url')
                            else None
                        )
                        or (
                            getattr(task_details, 'artifacts', None)
                            and task_details.artifacts[0].download_url
                            if task_details.artifacts and hasattr(task_details.artifacts[0], 'download_url')
                            else None
                        )
                    )
                    if not output_url:
                        logger.error(
                            f"Runway task {task.id} SUCCEEDED, but no output URL in details: "
                            f"{vars(task_details) if hasattr(task_details, '__dict__') else task_details}"
                        )
                        return None
                    logger.info(f"Runway task {task.id} SUCCEEDED. Downloading from: {output_url}")
                    video_response = requests.get(output_url, stream=True, timeout=300)
                    video_response.raise_for_status()
                    with open(output_video_filepath, 'wb') as f:
                        for chunk in video_response.iter_content(chunk_size=8192):
                            f.write(chunk)
                    logger.info(f"Runway Gen-4 video saved: {output_video_filepath}")
                    return output_video_filepath
                elif task_details.status in ['FAILED', 'ABORTED']:
                    em = (
                        getattr(task_details, 'error_message', None)
                        or getattr(getattr(task_details, 'output', None), 'error', "Unknown error")
                    )
                    logger.error(f"Runway task {task.id} status: {task_details.status}. Error: {em}")
                    return None
            logger.warning(f"Runway task {task.id} timed out.")
            return None
        except AttributeError as ae:
            logger.error(f"RunwayML SDK AttributeError: {ae}. SDK/methods might differ.", exc_info=True)
            return None
        except Exception as e:
            logger.error(f"Runway Gen-4 API error: {e}", exc_info=True)
            return None

    def _create_placeholder_video_content(self, td, fn, dur=4, sz=None):
        if sz is None:
            sz = self.video_frame_size
        fp = os.path.join(self.output_dir, fn)
        tc = None
        try:
            tc = TextClip(
                td,
                fontsize=50,
                color='white',
                font=self.video_overlay_font,
                bg_color='black',
                size=sz,
                method='caption'
            ).set_duration(dur)
            tc.write_videofile(fp, fps=24, codec='libx264', preset='ultrafast', logger=None, threads=2)
            logger.info(f"Generic placeholder video: {fp}")
            return fp
        except Exception as e:
            logger.error(f"Generic placeholder video error {fp}: {e}", exc_info=True)
            return None
        finally:
            if tc and hasattr(tc, 'close'):
                tc.close()

    def generate_scene_asset(
        self,
        image_generation_prompt_text,
        motion_prompt_text_for_video,
        scene_data,
        scene_identifier_filename_base,
        generate_as_video_clip=False,
        runway_target_duration=5
    ):
        base_name, _ = os.path.splitext(scene_identifier_filename_base)
        asset_info = {
            'path': None,
            'type': 'none',
            'error': True,
            'prompt_used': image_generation_prompt_text,
            'error_message': 'Asset generation init failed'
        }
        input_image_for_runway_path = None
        base_image_filename = base_name + ("_base_for_video.png" if generate_as_video_clip else ".png")
        base_image_filepath = os.path.join(self.output_dir, base_image_filename)

        if self.USE_AI_IMAGE_GENERATION and self.openai_api_key:
            max_r = 2
            for att_n in range(max_r):
                try:
                    logger.info(f"Att {att_n+1} DALL-E (base img): {image_generation_prompt_text[:70]}...")
                    cl = openai.OpenAI(api_key=self.openai_api_key, timeout=90.0)
                    r = cl.images.generate(
                        model=self.dalle_model,
                        prompt=image_generation_prompt_text,
                        n=1,
                        size=self.image_size_dalle3,
                        quality="hd",
                        response_format="url",
                        style="vivid"
                    )
                    iu = r.data[0].url
                    rp = getattr(r.data[0], 'revised_prompt', None)
                    if rp:
                        logger.info(f"DALL-E revised: {rp[:70]}...")
                    ir = requests.get(iu, timeout=120)
                    ir.raise_for_status()
                    id_img = Image.open(io.BytesIO(ir.content))
                    if id_img.mode != 'RGB':
                        id_img = id_img.convert('RGB')
                    id_img.save(base_image_filepath)
                    logger.info(f"DALL-E base img saved: {base_image_filepath}")
                    input_image_for_runway_path = base_image_filepath
                    asset_info = {
                        'path': base_image_filepath,
                        'type': 'image',
                        'error': False,
                        'prompt_used': image_generation_prompt_text,
                        'revised_prompt': rp
                    }
                    break
                except openai.RateLimitError as e:
                    logger.warning(f"OpenAI RateLimit {att_n+1}:{e}. Retry...")
                    time.sleep(5 * (att_n + 1))
                    asset_info['error_message'] = str(e)
                except Exception as e:
                    logger.error(f"DALL-E base img error: {e}", exc_info=True)
                    asset_info['error_message'] = str(e)
                    break
            if asset_info['error']:
                logger.warning(f"DALL-E failed after {att_n+1} attempts for base img.")

        if asset_info['error'] and self.USE_PEXELS:
            logger.info("Trying Pexels for base img.")
            pqt = scene_data.get('pexels_search_query_감독', f"{scene_data.get('emotional_beat','')} {scene_data.get('setting_description','')}")
            pp = self._search_pexels_image(pqt, base_image_filename)
            if pp:
                input_image_for_runway_path = pp
                asset_info = {
                    'path': pp,
                    'type': 'image',
                    'error': False,
                    'prompt_used': f"Pexels:{pqt}"
                }
            else:
                current_em = asset_info.get('error_message', "")
                asset_info['error_message'] = (current_em + " Pexels failed for base.").strip()

        if asset_info['error']:
            logger.warning("Base img (DALL-E/Pexels) failed. Using placeholder.")
            ppt = asset_info.get('prompt_used', image_generation_prompt_text)
            php = self._create_placeholder_image_content(f"[Base Placeholder]{ppt[:70]}...", base_image_filename)
            if php:
                input_image_for_runway_path = php
                asset_info = {
                    'path': php,
                    'type': 'image',
                    'error': False,
                    'prompt_used': ppt
                }
            else:
                current_em = asset_info.get('error_message', "")
                asset_info['error_message'] = (current_em + " Base placeholder failed.").strip()

        if generate_as_video_clip:
            if not input_image_for_runway_path:
                logger.error("RunwayML video: base img failed.")
                asset_info['error'] = True
                asset_info['error_message'] = (asset_info.get('error_message', "") + " Base img miss, Runway abort.").strip()
                asset_info['type'] = 'none'
                return asset_info
            if self.USE_RUNWAYML:
                logger.info(f"Runway Gen-4 video for {base_name} using base: {input_image_for_runway_path}")
                video_path = self._generate_video_clip_with_runwayml(
                    motion_prompt_text_for_video,
                    input_image_for_runway_path,
                    base_name,
                    runway_target_duration
                )
                if video_path and os.path.exists(video_path):
                    asset_info = {
                        'path': video_path,
                        'type': 'video',
                        'error': False,
                        'prompt_used': motion_prompt_text_for_video,
                        'base_image_path': input_image_for_runway_path
                    }
                else:
                    logger.warning(f"RunwayML video failed for {base_name}. Fallback to base img.")
                    asset_info['error'] = True
                    asset_info['error_message'] = (
                        asset_info.get('error_message', "Base img ok.") +
                        " RunwayML video fail; use base img."
                    ).strip()
                    asset_info['path'] = input_image_for_runway_path
                    asset_info['type'] = 'image'
                    asset_info['prompt_used'] = image_generation_prompt_text
            else:
                logger.warning("RunwayML selected but disabled. Use base img.")
                asset_info['error'] = True
                asset_info['error_message'] = (
                    asset_info.get('error_message', "Base img ok.") +
                    " RunwayML disabled; use base img."
                ).strip()
                asset_info['path'] = input_image_for_runway_path
                asset_info['type'] = 'image'
                asset_info['prompt_used'] = image_generation_prompt_text

        return asset_info

    def generate_narration_audio(self, text_to_narrate, output_filename="narration_overall.mp3"):
        if not self.USE_ELEVENLABS or not self.elevenlabs_client or not text_to_narrate:
            logger.info("11L skip.")
            return None

        afp = os.path.join(self.output_dir, output_filename)

        try:
            logger.info(f"11L audio (Voice:{self.elevenlabs_voice_id}): {text_to_narrate[:70]}...")
            asm = None

            # Determine which ElevenLabs streaming/non-streaming method to use
            if hasattr(self.elevenlabs_client, 'text_to_speech') and \
               hasattr(self.elevenlabs_client.text_to_speech, 'stream'):
                asm = self.elevenlabs_client.text_to_speech.stream
                logger.info("Using 11L .text_to_speech.stream()")

            elif hasattr(self.elevenlabs_client, 'generate_stream'):
                asm = self.elevenlabs_client.generate_stream
                logger.info("Using 11L .generate_stream()")

            elif hasattr(self.elevenlabs_client, 'generate'):
                logger.info("Using 11L .generate()")
                if Voice and self.elevenlabs_voice_settings:
                    vp = Voice(
                        voice_id=str(self.elevenlabs_voice_id),
                        settings=self.elevenlabs_voice_settings
                    )
                else:
                    vp = str(self.elevenlabs_voice_id)

                ab = self.elevenlabs_client.generate(
                    text=text_to_narrate,
                    voice=vp,
                    model="eleven_multilingual_v2"
                )

                with open(afp, "wb") as f:
                    f.write(ab)

                logger.info(f"11L audio (non-stream): {afp}")
                return afp

            else:
                logger.error("No 11L audio method.")
                return None

            # If a streaming method is available:
            if asm:
                vps = {"voice_id": str(self.elevenlabs_voice_id)}
                if self.elevenlabs_voice_settings:
                    if hasattr(self.elevenlabs_voice_settings, 'model_dump'):
                        vps["voice_settings"] = self.elevenlabs_voice_settings.model_dump()
                    elif hasattr(self.elevenlabs_voice_settings, 'dict'):
                        vps["voice_settings"] = self.elevenlabs_voice_settings.dict()
                    else:
                        vps["voice_settings"] = self.elevenlabs_voice_settings

                adi = asm(
                    text=text_to_narrate,
                    model_id="eleven_multilingual_v2",
                    **vps
                )

                with open(afp, "wb") as f:
                    for c in adi:
                        if c:
                            f.write(c)

                logger.info(f"11L audio (stream): {afp}")
                return afp

        except Exception as e:
            logger.error(f"11L audio error: {e}", exc_info=True)
            return None

    def assemble_animatic_from_assets(self, asset_data_list, overall_narration_path=None, output_filename="final_video.mp4", fps=24):
        if not asset_data_list:
            logger.warning("No assets for animatic.")
            return None

        processed_clips = []
        narration_clip = None
        final_clip = None
        logger.info(f"Assembling from {len(asset_data_list)} assets. Frame: {self.video_frame_size}.")

        for i, asset_info in enumerate(asset_data_list):
            asset_path = asset_info.get('path')
            asset_type = asset_info.get('type')
            scene_dur = asset_info.get('duration', 4.5)
            scene_num = asset_info.get('scene_num', i + 1)
            key_action = asset_info.get('key_action', '')
            logger.info(f"S{scene_num}: Path='{asset_path}', Type='{asset_type}', Dur='{scene_dur}'s")

            if not (asset_path and os.path.exists(asset_path)):
                logger.warning(f"S{scene_num}: Not found '{asset_path}'. Skip.")
                continue
            if scene_dur <= 0:
                logger.warning(f"S{scene_num}: Invalid duration ({scene_dur}s). Skip.")
                continue

            current_scene_mvpy_clip = None
            try:
                if asset_type == 'image':
                    pil_img = Image.open(asset_path)
                    logger.debug(f"S{scene_num}: Loaded img. Mode:{pil_img.mode}, Size:{pil_img.size}")
                    img_rgba = pil_img.convert('RGBA') if pil_img.mode != 'RGBA' else pil_img.copy()
                    thumb = img_rgba.copy()
                    rf = Image.Resampling.LANCZOS if hasattr(Image.Resampling, 'LANCZOS') else Image.BILINEAR
                    thumb.thumbnail(self.video_frame_size, rf)

                    cv_rgba = Image.new('RGBA', self.video_frame_size, (0, 0, 0, 0))
                    xo = (self.video_frame_size[0] - thumb.width) // 2
                    yo = (self.video_frame_size[1] - thumb.height) // 2
                    cv_rgba.paste(thumb, (xo, yo), thumb)
                    final_rgb_pil = Image.new("RGB", self.video_frame_size, (0, 0, 0))
                    final_rgb_pil.paste(cv_rgba, mask=cv_rgba.split()[3])

                    dbg_path = os.path.join(self.output_dir, f"debug_PRE_NUMPY_S{scene_num}.png")
                    final_rgb_pil.save(dbg_path)
                    logger.info(f"DEBUG: Saved PRE_NUMPY_S{scene_num} to {dbg_path}")

                    frame_np = np.array(final_rgb_pil, dtype=np.uint8)
                    if not frame_np.flags['C_CONTIGUOUS']:
                        frame_np = np.ascontiguousarray(frame_np, dtype=np.uint8)
                    logger.debug(f"S{scene_num}: NumPy for MoviePy. Shape:{frame_np.shape}, DType:{frame_np.dtype}, C-Contig:{frame_np.flags['C_CONTIGUOUS']}")

                    if frame_np.size == 0 or frame_np.ndim != 3 or frame_np.shape[2] != 3:
                        logger.error(f"S{scene_num}: Invalid NumPy. Skip.")
                        continue

                    clip_base = ImageClip(frame_np, transparent=False).set_duration(scene_dur)
                    mvpy_dbg_path = os.path.join(self.output_dir, f"debug_MOVIEPY_FRAME_S{scene_num}.png")
                    clip_base.save_frame(mvpy_dbg_path, t=0.1)
                    logger.info(f"DEBUG: Saved MOVIEPY_FRAME_S{scene_num} to {mvpy_dbg_path}")

                    clip_fx = clip_base
                    try:
                        es = random.uniform(1.03, 1.08)
                        clip_fx = clip_base.fx(
                            vfx.resize,
                            lambda t: 1 + (es - 1) * (t / scene_dur) if scene_dur > 0 else 1
                        ).set_position('center')
                    except Exception as e:
                        logger.error(f"S{scene_num} Ken Burns error: {e}", exc_info=False)

                    current_scene_mvpy_clip = clip_fx

                elif asset_type == 'video':
                    src_clip = None
                    try:
                        src_clip = VideoFileClip(
                            asset_path,
                            target_resolution=(self.video_frame_size[1], self.video_frame_size[0]) if self.video_frame_size else None,
                            audio=False
                        )
                        tmp_clip = src_clip
                        if src_clip.duration != scene_dur:
                            if src_clip.duration > scene_dur:
                                tmp_clip = src_clip.subclip(0, scene_dur)
                            else:
                                if scene_dur / src_clip.duration > 1.5 and src_clip.duration > 0.1:
                                    tmp_clip = src_clip.loop(duration=scene_dur)
                                else:
                                    tmp_clip = src_clip.set_duration(src_clip.duration)
                                    logger.info(f"S{scene_num} Video clip ({src_clip.duration:.2f}s) shorter than target ({scene_dur:.2f}s).")
                        current_scene_mvpy_clip = tmp_clip.set_duration(scene_dur)
                        if current_scene_mvpy_clip.size != list(self.video_frame_size):
                            current_scene_mvpy_clip = current_scene_mvpy_clip.resize(self.video_frame_size)
                    except Exception as e:
                        logger.error(f"S{scene_num} Video load error '{asset_path}': {e}", exc_info=True)
                        continue
                    finally:
                        if src_clip and src_clip is not current_scene_mvpy_clip and hasattr(src_clip, 'close'):
                            src_clip.close()
                else:
                    logger.warning(f"S{scene_num} Unknown asset type '{asset_type}'. Skip.")
                    continue

                if current_scene_mvpy_clip and key_action:
                    try:
                        to_dur = min(current_scene_mvpy_clip.duration - 0.5,
                                     current_scene_mvpy_clip.duration * 0.8) if current_scene_mvpy_clip.duration > 0.5 else current_scene_mvpy_clip.duration
                        to_start = 0.25
                        if to_dur > 0:
                            txt_c = TextClip(
                                f"Scene {scene_num}\n{key_action}",
                                fontsize=self.video_overlay_font_size,
                                color=self.video_overlay_font_color,
                                font=self.video_overlay_font,
                                bg_color='rgba(10,10,20,0.7)',
                                method='caption',
                                align='West',
                                size=(self.video_frame_size[0] * 0.9, None),
                                kerning=-1,
                                stroke_color='black',
                                stroke_width=1.5
                            ).set_duration(to_dur).set_start(to_start).set_position(('center', 0.92), relative=True)
                            current_scene_mvpy_clip = CompositeVideoClip(
                                [current_scene_mvpy_clip, txt_c],
                                size=self.video_frame_size,
                                use_bgclip=True
                            )
                        else:
                            logger.warning(f"S{scene_num}: Text overlay duration is zero. Skip text.")
                    except Exception as e:
                        logger.error(f"S{scene_num} TextClip error: {e}. No text.", exc_info=True)

                if current_scene_mvpy_clip:
                    processed_clips.append(current_scene_mvpy_clip)
                    logger.info(f"S{scene_num} Processed. Dur:{current_scene_mvpy_clip.duration:.2f}s.")
            except Exception as e:
                logger.error(f"MAJOR Error S{scene_num} ({asset_path}): {e}", exc_info=True)
            finally:
                if current_scene_mvpy_clip and hasattr(current_scene_mvpy_clip, 'close'):
                    try:
                        current_scene_mvpy_clip.close()
                    except Exception:
                        pass

        if not processed_clips:
            logger.warning("No clips processed. Abort.")
            return None

        td = 0.75
        try:
            logger.info(f"Concatenating {len(processed_clips)} clips.")
            if len(processed_clips) > 1:
                final_clip = concatenate_videoclips(processed_clips, padding=-td if td > 0 else 0, method="compose")
            elif processed_clips:
                final_clip = processed_clips[0]
            if not final_clip:
                logger.error("Concatenation failed.")
                return None
            logger.info(f"Concatenated dur:{final_clip.duration:.2f}s")
            if td > 0 and final_clip.duration > 0:
                if final_clip.duration > td * 2:
                    final_clip = final_clip.fx(vfx.fadein, td).fx(vfx.fadeout, td)
                else:
                    final_clip = final_clip.fx(vfx.fadein, min(td, final_clip.duration / 2.0))
            if overall_narration_path and os.path.exists(overall_narration_path) and final_clip.duration > 0:
                try:
                    narration_clip = AudioFileClip(overall_narration_path)
                    final_clip = final_clip.set_audio(narration_clip)
                    logger.info("Narration added.")
                except Exception as e:
                    logger.error(f"Narration add error: {e}", exc_info=True)
            elif final_clip.duration <= 0:
                logger.warning("Video no duration. No audio.")
            if final_clip and final_clip.duration > 0:
                op = os.path.join(self.output_dir, output_filename)
                logger.info(f"Writing video:{op} (Dur:{final_clip.duration:.2f}s)")
                final_clip.write_videofile(
                    op,
                    fps=fps,
                    codec='libx264',
                    preset='medium',
                    audio_codec='aac',
                    temp_audiofile=os.path.join(
                        self.output_dir, f'temp-audio-{os.urandom(4).hex()}.m4a'
                    ),
                    remove_temp=True,
                    threads=os.cpu_count() or 2,
                    logger='bar',
                    bitrate="5000k",
                    ffmpeg_params=["-pix_fmt", "yuv420p"]
                )
                logger.info(f"Video created:{op}")
                return op
            else:
                logger.error("Final clip invalid. No write.")
                return None
        except Exception as e:
            logger.error(f"Video write error: {e}", exc_info=True)
            return None
        finally:
            logger.debug("Closing all MoviePy clips in `assemble_animatic_from_assets` finally block.")
            all_clips_to_close = processed_clips + ([narration_clip] if narration_clip else []) + ([final_clip] if final_clip else [])
            for clip_obj_to_close in all_clips_to_close:
                if clip_obj_to_close and hasattr(clip_obj_to_close, 'close'):
                    try:
                        clip_obj_to_close.close()
                    except Exception as e_close:
                        logger.warning(f"Ignoring error while closing a clip: {type(clip_obj_to_close).__name__} - {e_close}")