core/visual_engine.py

# core/visual_engine.py
from PIL import Image, ImageDraw, ImageFont, ImageOps
import base64
import mimetypes
import numpy as np
import os
import openai
import requests
import io
import time
import random
import logging

# --- MoviePy Imports ---
from moviepy.editor import (
    ImageClip,
    VideoFileClip,
    concatenate_videoclips,
    TextClip,
    CompositeVideoClip,
    AudioFileClip,
)
import moviepy.video.fx.all as vfx

# --- MONKEY PATCH for Pillow/MoviePy compatibility ---
try:
    if hasattr(Image, "Resampling") and hasattr(Image.Resampling, "LANCZOS"):  # Pillow 9+
        if not hasattr(Image, "ANTIALIAS"):
            Image.ANTIALIAS = Image.Resampling.LANCZOS
    elif hasattr(Image, "LANCZOS"):  # Pillow 8
        if not hasattr(Image, "ANTIALIAS"):
            Image.ANTIALIAS = Image.LANCZOS
    elif not hasattr(Image, "ANTIALIAS"):  # Fallback if no common resampling attributes found
        print(
            "WARNING: Pillow version lacks common Resampling attributes or ANTIALIAS. MoviePy effects might fail or look different."
        )
except Exception as e_monkey_patch:
    print(
        f"WARNING: An unexpected error occurred during Pillow ANTIALIAS monkey-patch: {e_monkey_patch}"
    )

logger = logging.getLogger(__name__)
# Consider setting level in main app if not already configured:
# logger.setLevel(logging.DEBUG) # For very verbose output during debugging

# --- External 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 successfully.")
except ImportError:
    logger.warning(
        "ElevenLabs SDK not found (pip install elevenlabs). Audio generation will be disabled."
    )
except Exception as e_eleven_import:
    logger.warning(
        f"Error importing ElevenLabs client components: {e_eleven_import}. Audio generation disabled."
    )

RUNWAYML_SDK_IMPORTED = False
RunwayMLAPIClient = None  # Using a more specific name for the client class
try:
    from runwayml import RunwayML as ImportedRunwayMLClient  # Actual SDK import

    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_import:
    logger.warning(
        f"Error importing RunwayML SDK: {e_runway_sdk_import}. RunwayML features disabled."
    )


class VisualEngine:
    DEFAULT_FONT_SIZE_PIL = 10  # For default Pillow font
    PREFERRED_FONT_SIZE_PIL = 20  # For custom font
    VIDEO_OVERLAY_FONT_SIZE = 30
    VIDEO_OVERLAY_FONT_COLOR = "white"
    # Standard font names ImageMagick (used by TextClip) is likely to find in Linux containers
    DEFAULT_MOVIEPY_FONT = "DejaVu-Sans-Bold"
    PREFERRED_MOVIEPY_FONT = "Liberation-Sans-Bold"  # Often available

    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_pil = "DejaVuSans-Bold.ttf"  # A more standard Linux font
        font_paths_to_try = [
            self.font_filename_pil,  # If in working dir or PATH
            f"/usr/share/fonts/truetype/dejavu/{self.font_filename_pil}",
            f"/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf",  # Alternative
            f"/System/Library/Fonts/Supplemental/Arial.ttf",  # macOS fallback
            f"C:/Windows/Fonts/arial.ttf",  # Windows fallback
            f"/usr/local/share/fonts/truetype/mycustomfonts/arial.ttf",  # User's previous custom path
        ]
        self.font_path_pil_resolved = next(
            (p for p in font_paths_to_try if os.path.exists(p)), None
        )

        self.font_pil = ImageFont.load_default()  # Default
        self.current_font_size_pil = self.DEFAULT_FONT_SIZE_PIL

        if self.font_path_pil_resolved:
            try:
                self.font_pil = ImageFont.truetype(
                    self.font_path_pil_resolved, self.PREFERRED_FONT_SIZE_PIL
                )
                self.current_font_size_pil = self.PREFERRED_FONT_SIZE_PIL
                logger.info(
                    f"Pillow font loaded: {self.font_path_pil_resolved} at size {self.current_font_size_pil}."
                )
                # Determine MoviePy font based on loaded PIL font
                if "dejavu" in self.font_path_pil_resolved.lower():
                    self.video_overlay_font = "DejaVu-Sans-Bold"
                elif "liberation" in self.font_path_pil_resolved.lower():
                    self.video_overlay_font = "Liberation-Sans-Bold"
                else:  # Fallback if custom font doesn't have an obvious ImageMagick name
                    self.video_overlay_font = self.DEFAULT_MOVIEPY_FONT
            except IOError as e_font_load:
                logger.error(
                    f"Pillow font loading IOError for '{self.font_path_pil_resolved}': {e_font_load}. Using default."
                )
        else:
            logger.warning("Custom Pillow font not found. Using default.")

        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_ml_client_instance = None  # More specific name

        # Attempt to initialize Runway client if SDK is present and env var might be set
        if (
            RUNWAYML_SDK_IMPORTED
            and RunwayMLAPIClient
            and os.getenv("RUNWAYML_API_SECRET")
        ):
            try:
                self.runway_ml_client_instance = RunwayMLAPIClient()  # SDK uses env var
                self.USE_RUNWAYML = True  # Assume enabled if client initializes
                logger.info(
                    "RunwayML Client initialized from RUNWAYML_API_SECRET env var at startup."
                )
            except Exception as e_runway_init_startup:
                logger.error(
                    f"Initial RunwayML client init failed (env var RUNWAYML_API_SECRET might be invalid): {e_runway_init_startup}"
                )
                self.USE_RUNWAYML = False

        logger.info("VisualEngine initialized.")

    # --- API Key Setters ---
    def set_openai_api_key(self, api_key):
        self.openai_api_key = api_key
        self.USE_AI_IMAGE_GENERATION = bool(api_key)
        logger.info(
            f"DALL-E ({self.dalle_model}) status: {'Ready' if self.USE_AI_IMAGE_GENERATION 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 status: {'Ready' if self.USE_ELEVENLABS else 'Failed Initialization'} (Using Voice ID: {self.elevenlabs_voice_id})"
                )
            except Exception as e:
                logger.error(
                    f"ElevenLabs client initialization error: {e}. Service Disabled.",
                    exc_info=True,
                )
                self.USE_ELEVENLABS = False
                self.elevenlabs_client = None
        else:
            self.USE_ELEVENLABS = False
            logger.info(
                f"ElevenLabs Service Disabled (API key not provided or SDK import issue)."
            )

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

    def set_runway_api_key(self, api_key):
        self.runway_api_key = api_key  # Store key regardless for potential direct HTTP use
        if api_key:
            if RUNWAYML_SDK_IMPORTED and RunwayMLAPIClient:
                if not self.runway_ml_client_instance:  # If not already initialized by env var
                    try:
                        # The RunwayML Python SDK expects the API key via the RUNWAYML_API_SECRET env var.
                        # If it's not set, we set it temporarily for client initialization.
                        original_env_secret = os.getenv("RUNWAYML_API_SECRET")
                        if not original_env_secret:
                            logger.info(
                                "Temporarily setting RUNWAYML_API_SECRET from provided key for SDK client init."
                            )
                            os.environ["RUNWAYML_API_SECRET"] = api_key

                        self.runway_ml_client_instance = RunwayMLAPIClient()
                        self.USE_RUNWAYML = True  # SDK client successfully initialized
                        logger.info(
                            "RunwayML Client initialized successfully using provided API key."
                        )

                        if not original_env_secret:  # Clean up if we set it
                            del os.environ["RUNWAYML_API_SECRET"]
                            logger.info(
                                "Cleared temporary RUNWAYML_API_SECRET env var."
                            )

                    except Exception as e_client_init:
                        logger.error(
                            f"RunwayML Client initialization via set_runway_api_key failed: {e_client_init}",
                            exc_info=True,
                        )
                        self.USE_RUNWAYML = False
                        self.runway_ml_client_instance = None
                else:  # Client was already initialized (likely via env var during __init__)
                    self.USE_RUNWAYML = True
                    logger.info(
                        "RunwayML Client was already initialized (likely from env var). API key stored."
                    )
            else:  # SDK not imported
                logger.warning(
                    "RunwayML SDK not imported. API key stored, but integration requires SDK. Service effectively disabled."
                )
                self.USE_RUNWAYML = False
        else:  # No API key provided
            self.USE_RUNWAYML = False
            self.runway_ml_client_instance = None
            logger.info("RunwayML Service Disabled (no API key provided).")

    # --- Helper Methods ---
    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()
                mime_map = {".png": "image/png", ".jpg": "image/jpeg", ".jpeg": "image/jpeg"}
                mime_type = mime_map.get(ext, "application/octet-stream")
                if mime_type == "application/octet-stream":
                    logger.warning(
                        f"Could not determine MIME type for {image_path}, using default."
                    )

            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"Generated data URI for {os.path.basename(image_path)} (first 100 chars): {data_uri[:100]}..."
            )
            return data_uri
        except FileNotFoundError:
            logger.error(f"Image file not found at {image_path} for data URI conversion.")
            return None
        except Exception as e:
            logger.error(
                f"Error converting image {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}"
        # Gen-4 supports: "1280:720", "720:1280", "1104:832", "832:1104", "960:960", "1584:672"
        supported_ratios_gen4 = [
            "1280:720",
            "720:1280",
            "1104:832",
            "832:1104",
            "960:960",
            "1584:672",
        ]
        if ratio_str in supported_ratios_gen4:
            return ratio_str
        # Fallback or find closest - for now, strict matching or default
        logger.warning(
            f"Resolution {ratio_str} not directly in Gen-4 supported list. Defaulting to 1280:720."
        )
        return "1280:720"

    def _get_text_dimensions(self, text_content, font_object):
        # (Robust version from before)
        default_char_height = getattr(font_object, "size", self.current_font_size_pil)
        if not text_content:
            return 0, default_char_height
        try:
            if hasattr(font_object, "getbbox"):
                bbox = font_object.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_object, "getsize"):
                w, h = font_object.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.current_font_size_pil * 0.6),
                int(self.current_font_size_pil * 1.2),
            )

    def _create_placeholder_image_content(self, text_description, filename, size=None):
        # (Corrected version from previous response)
        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_text = ""
        for word_idx, word in enumerate(words):
            prospective_addition = word + (" " if word_idx < len(words) - 1 else "")
            test_line_text = current_line_text + prospective_addition
            current_w, _ = self._get_text_dimensions(test_line_text, self.font_pil)
            if current_w == 0 and test_line_text.strip():
                current_w = len(test_line_text) * (self.current_font_size_pil * 0.6)  # Estimate

            if current_w <= max_w:
                current_line_text = test_line_text
            else:
                if current_line_text.strip():
                    lines.append(current_line_text.strip())
                current_line_text = prospective_addition  # Start new line
        if current_line_text.strip():
            lines.append(current_line_text.strip())

        if not lines and text_description:
            avg_char_w, _ = self._get_text_dimensions("W", self.font_pil)
            avg_char_w = avg_char_w or (self.current_font_size_pil * 0.6)
            chars_per_line = int(max_w / avg_char_w) if avg_char_w > 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_pil)
        single_line_h = single_line_h if single_line_h > 0 else self.current_font_size_pil + 2
        max_lines = (
            min(len(lines), (size[1] - (2 * padding)) // (single_line_h + 2))
            if single_line_h > 0
            else 1
        )
        max_lines = max(1, max_lines)  # Ensure at least one line

        y_pos = padding + (size[1] - (2 * padding) - max_lines * (single_line_h + 2)) / 2.0
        for i in range(max_lines):
            line_text = lines[i]
            line_w, _ = self._get_text_dimensions(line_text, self.font_pil)
            if line_w == 0 and line_text.strip():
                line_w = len(line_text) * (self.current_font_size_pil * 0.6)
            x_pos = (size[0] - line_w) / 2.0
            try:
                d.text((x_pos, y_pos), line_text, font=self.font_pil, fill=(200, 200, 180))
            except Exception as e_draw:
                logger.error(f"Pillow d.text error: {e_draw} for '{line_text}'")
            y_pos += single_line_h + 2
            if i == 6 and max_lines > 7:
                try:
                    d.text((x_pos, y_pos), "...", font=self.font_pil, fill=(200, 200, 180))
                except Exception as e_elip:
                    logger.error(f"Pillow d.text ellipsis error: {e_elip}")
                    break

        filepath = os.path.join(self.output_dir, filename)
        try:
            img.save(filepath)
            return filepath
        except Exception as e_save:
            logger.error(
                f"Saving placeholder image '{filepath}' error: {e_save}", exc_info=True
            )
            return None

    def _search_pexels_image(self, query, output_filename_base):
        # <<< THIS IS THE 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"}
        base_name_for_pexels, _ = os.path.splitext(output_filename_base)
        pexels_filename = base_name_for_pexels + f"_pexels_{random.randint(1000,9999)}.jpg"
        filepath = os.path.join(self.output_dir, pexels_filename)
        try:
            logger.info(f"Pexels: Searching for '{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_details = data["photos"][0]
                photo_url = photo_details.get("src", {}).get("large2x")
                if not photo_url:
                    logger.warning(
                        f"Pexels: 'large2x' URL missing for '{effective_query}'. Details: {photo_details}"
                    )
                    return None
                image_response = requests.get(photo_url, timeout=60)
                image_response.raise_for_status()
                img_data_pil = Image.open(io.BytesIO(image_response.content))
                if img_data_pil.mode != "RGB":
                    img_data_pil = img_data_pil.convert("RGB")
                img_data_pil.save(filepath)
                logger.info(f"Pexels: Image saved to {filepath}")
                return filepath
            else:
                logger.info(f"Pexels: No photos for '{effective_query}'.")
                return None
        except requests.exceptions.RequestException as e_req:
            logger.error(f"Pexels: RequestException for '{query}': {e_req}", exc_info=False)
            return None  # Less verbose for network
        except Exception as e:
            logger.error(f"Pexels: General error for '{query}': {e}", exc_info=True)
            return None

    # --- RunwayML Video Generation (Gen-4 Aligned with SDK) ---
    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_ml_client_instance:
            logger.warning("RunwayML not enabled or client not initialized. Cannot generate video clip.")
            return None
        if not input_image_path or not os.path.exists(input_image_path):
            logger.error(
                f"Runway Gen-4 requires an input image. Path not provided or 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 >= 8 else 5  # Map to 5s or 10s for Gen-4
        runway_ratio_str = self._map_resolution_to_runway_ratio(
            self.video_frame_size[0], self.video_frame_size[1]
        )

        # Use a more descriptive output filename for Runway videos
        base_name_for_runway, _ = os.path.splitext(scene_identifier_filename_base)
        output_video_filename = base_name_for_runway + f"_runway_gen4_d{runway_duration}s.mp4"
        output_video_filepath = os.path.join(self.output_dir, output_video_filename)

        logger.info(
            f"Initiating Runway Gen-4 task: motion='{text_prompt_for_motion[:100]}...', image='{os.path.basename(input_image_path)}', dur={runway_duration}s, ratio='{runway_ratio_str}'"
        )
        try:
            # Using the RunwayML Python SDK structure
            task_submission = self.runway_ml_client_instance.image_to_video.create(
                model="gen4_turbo",
                prompt_image=image_data_uri,
                prompt_text=text_prompt_for_motion,  # This is the motion prompt
                duration=runway_duration,
                ratio=runway_ratio_str,
                # seed=random.randint(0, 4294967295), # Optional: for reproducibility
                # Other Gen-4 params (motion_score, upscale, watermark etc. can be added here if available in SDK)
            )
            task_id = task_submission.id
            logger.info(f"Runway Gen-4 task created with ID: {task_id}. Polling for completion...")

            poll_interval_seconds = 10
            max_polling_duration_seconds = 6 * 60  # 6 minutes
            start_time = time.time()

            while time.time() - start_time < max_polling_duration_seconds:
                time.sleep(poll_interval_seconds)
                task_details = self.runway_ml_client_instance.tasks.retrieve(id=task_id)
                logger.info(f"Runway task {task_id} status: {task_details.status}")

                if task_details.status == "SUCCEEDED":
                    # Determine output URL (this structure might vary based on SDK version)
                    output_url = None
                    if hasattr(task_details, "output") and task_details.output and hasattr(
                        task_details.output, "url"
                    ):
                        output_url = task_details.output.url
                    elif (
                        hasattr(task_details, "artifacts")
                        and task_details.artifacts
                        and isinstance(task_details.artifacts, list)
                        and len(task_details.artifacts) > 0
                    ):
                        first_artifact = task_details.artifacts[0]
                        if hasattr(first_artifact, "url"):
                            output_url = first_artifact.url
                        elif hasattr(first_artifact, "download_url"):
                            output_url = first_artifact.download_url

                    if not output_url:
                        logger.error(
                            f"Runway task {task_id} SUCCEEDED, but no output URL found. Details: {vars(task_details) if hasattr(task_details,'__dict__') else str(task_details)}"
                        )
                        return None

                    logger.info(f"Runway task {task_id} SUCCEEDED. Downloading video 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 successfully downloaded to: {output_video_filepath}"
                    )
                    return output_video_filepath

                elif task_details.status in ["FAILED", "ABORTED", "ERROR"]:  # Added ERROR
                    error_msg = (
                        getattr(task_details, "error_message", None)
                        or getattr(getattr(task_details, "output", None), "error", "Unknown error from Runway task.")
                    )
                    logger.error(
                        f"Runway task {task_id} final status: {task_details.status}. Error: {error_msg}"
                    )
                    return None

            logger.warning(
                f"Runway task {task_id} timed out polling after {max_polling_duration_seconds} seconds."
            )
            return None

        except AttributeError as ae:  # If SDK methods are not as expected
            logger.error(
                f"AttributeError with RunwayML SDK: {ae}. Ensure SDK is up to date and methods/attributes match documentation.",
                exc_info=True,
            )
            return None
        except Exception as e_runway_call:
            logger.error(
                f"General error during Runway Gen-4 API call or processing: {e_runway_call}",
                exc_info=True,
            )
            return None

    def _create_placeholder_video_content(self, text_description, filename, duration=4, size=None):
        # (Keeping as before)
        if size is None:
            size = self.video_frame_size
        fp = os.path.join(self.output_dir, filename)
        tc = None
        try:
            tc = TextClip(
                text_description,
                fontsize=50,
                color="white",
                font=self.video_overlay_font,
                bg_color="black",
                size=size,
                method="caption",
            ).set_duration(duration)
            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()

    # --- generate_scene_asset (Main asset generation logic using Runway Gen-4 workflow) ---
    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,
    ):
        # (Logic updated for improved DALL·E and RunwayML fallback)
        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
        # Use a distinct name for the base image if it's only an intermediate step for video
        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)

        # STEP 1: Generate/acquire the base image via DALL·E
        if self.USE_AI_IMAGE_GENERATION and self.openai_api_key:
            try:
                logger.info(f"Calling DALL·E with prompt: {image_generation_prompt_text[:70]}...")
                response = openai.Image.create(
                    prompt=image_generation_prompt_text,
                    n=1,
                    size=self.image_size_dalle3,
                    model=self.dalle_model,
                )
                image_url = response["data"][0]["url"]
                ir = requests.get(image_url, 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 image 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,
                }
            except openai.error.OpenAIError as e:
                logger.warning(f"DALL·E error: {e}. Falling back to Pexels or placeholder.")
                asset_info["error_message"] = str(e)
            except Exception as e:
                logger.error(f"Unexpected DALL·E error: {e}", exc_info=True)
                asset_info["error_message"] = str(e)

        # STEP 2: If DALL·E failed, try Pexels
        if asset_info["error"] and self.USE_PEXELS:
            logger.info("Attempting Pexels fallback for base image.")
            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 fallback failed.").strip()

        # STEP 3: If both DALL·E and Pexels failed, create placeholder
        if asset_info["error"]:
            logger.warning("Both DALL·E and Pexels failed. Creating placeholder image.")
            ppt = asset_info.get("prompt_used", image_generation_prompt_text)
            php = self._create_placeholder_image_content(
                f"[Placeholder for] {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 + " Placeholder creation failed.").strip()

        # STEP 4: If a video clip is requested, attempt RunwayML
        if generate_as_video_clip:
            if not input_image_for_runway_path or not os.path.exists(input_image_for_runway_path):
                logger.error("No valid base image for RunwayML. Skipping video generation.")
                asset_info["error"] = True
                asset_info["error_message"] = (asset_info.get("error_message", "") + " No base image.").strip()
                asset_info["type"] = "none"
                return asset_info

            if self.USE_RUNWAYML and self.runway_ml_client_instance:
                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("RunwayML video generation failed. Returning base image instead.")
                    asset_info = {
                        "path": input_image_for_runway_path,
                        "type": "image",
                        "error": True,
                        "prompt_used": image_generation_prompt_text,
                        "error_message": (asset_info.get("error_message", "") + " RunwayML failed.").strip(),
                    }
            else:
                logger.warning("RunwayML not enabled or client not initialized. Skipping video generation.")
                asset_info = {
                    "path": input_image_for_runway_path,
                    "type": "image",
                    "error": True,
                    "prompt_used": image_generation_prompt_text,
                    "error_message": (asset_info.get("error_message", "") + " RunwayML disabled.").strip(),
                }

        return asset_info

    def generate_narration_audio(self, text_to_narrate, output_filename="narration_overall.mp3"):
        # (Keep as before - robust enough)
        if not self.USE_ELEVENLABS or not self.elevenlabs_client or not text_to_narrate:
            logger.info("ElevenLabs audio skipped.")
            return None

        afp = os.path.join(self.output_dir, output_filename)
        try:
            logger.info(f"ElevenLabs audio (Voice:{self.elevenlabs_voice_id}): {text_to_narrate[:70]}...")
            asm = None

            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 ElevenLabs .text_to_speech.stream()")
            elif hasattr(self.elevenlabs_client, "generate_stream"):
                asm = self.elevenlabs_client.generate_stream
                logger.info("Using ElevenLabs .generate_stream()")
            elif hasattr(self.elevenlabs_client, "generate"):
                logger.info("Using ElevenLabs .generate()")
                vp = (
                    Voice(voice_id=str(self.elevenlabs_voice_id), settings=self.elevenlabs_voice_settings)
                    if Voice and self.elevenlabs_voice_settings
                    else 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"ElevenLabs audio (non-stream) saved: {afp}")
                return afp
            else:
                logger.error("No ElevenLabs audio method available.")
                return None

            # If we have a streaming method (asm), use it
            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"ElevenLabs audio (stream) saved: {afp}")
                return afp

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

    # --- assemble_animatic_from_assets (Still contains crucial debug saves for blank video issue) ---
    def assemble_animatic_from_assets(
        self, asset_data_list, overall_narration_path=None, output_filename="final_video.mp4", fps=24
    ):
        # (Keep the version with robust image processing, C-contiguous arrays, debug saves, and pix_fmt)
        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, yo = (
                        (self.video_frame_size[0] - thumb.width) // 2,
                        (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:
                        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}"
                        )