Update core/visual_engine.py

core/visual_engine.py

@@ -1,19 +1,7 @@
 # core/visual_engine.py
 from PIL import Image, ImageDraw, ImageFont, ImageOps
-# --- MONKEY PATCH FOR Image.ANTIALIAS ---
-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'):
-             print("WARNING: Pillow version lacks common Resampling attributes or ANTIALIAS. Video effects might fail.")
-except Exception as e_mp: print(f"WARNING: ANTIALIAS monkey-patch error: {e_mp}")
-# --- END MONKEY PATCH ---
-
-from moviepy.editor import (ImageClip, VideoFileClip, concatenate_videoclips, TextClip,
-                            CompositeVideoClip, AudioFileClip)
-import moviepy.video.fx.all as vfx
+import base64
+import mimetypes
 import numpy as np
 import os
 import openai
@@ -23,348 +11,1029 @@ 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__)
-logger.setLevel(logging.INFO)
+# Consider setting level in main app if not already configured:
+# logger.setLevel(logging.DEBUG) # For very verbose output during debugging
 
-# --- ElevenLabs Client Import ---
-ELEVENLABS_CLIENT_IMPORTED = False; ElevenLabsAPIClient = None; Voice = None; VoiceSettings = None
+# --- 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.")
-except Exception as e_eleven: logger.warning(f"ElevenLabs client import failed: {e_eleven}. Audio disabled.")
 
-# --- RunwayML Client Import (Placeholder) ---
-RUNWAYML_SDK_IMPORTED = False; RunwayMLClient = None
+    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:
-    logger.info("RunwayML SDK import is a placeholder.")
-except ImportError: logger.warning("RunwayML SDK (placeholder) not found. RunwayML disabled.")
-except Exception as e_runway_sdk: logger.warning(f"Error importing RunwayML SDK (placeholder): {e_runway_sdk}. RunwayML disabled.")
+    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:
-    def __init__(self, output_dir="temp_cinegen_media", default_elevenlabs_voice_id="Rachel"):
+    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 = "DejaVuSans-Bold.ttf"
+
+        self.font_filename_pil = "DejaVuSans-Bold.ttf"  # A more standard Linux font
         font_paths_to_try = [
-            self.font_filename,
-            f"/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf",
-            f"/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf",
-            f"/System/Library/Fonts/Supplemental/Arial.ttf", f"C:/Windows/Fonts/arial.ttf",
-            f"/usr/local/share/fonts/truetype/mycustomfonts/arial.ttf"
+            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 = 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'
+        self.font_path_pil_resolved = next(
+            (p for p in font_paths_to_try if os.path.exists(p)), None
+        )
 
-        try:
-            self.font = ImageFont.truetype(self.font_path_pil, self.font_size_pil) if self.font_path_pil else ImageFont.load_default()
-            if self.font_path_pil: logger.info(f"Pillow font loaded: {self.font_path_pil}.")
-            else: logger.warning("Using default Pillow font."); self.font_size_pil = 10
-        except IOError as e_font: logger.error(f"Pillow font loading IOError: {e_font}. Using default."); self.font = ImageFont.load_default(); self.font_size_pil = 10
+        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.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_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 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.")
 
-    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
+    # --- 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 {'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).")
-    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 and RUNWAYML_SDK_IMPORTED and RunwayMLClient:
-            try: self.USE_RUNWAYML = True; logger.info(f"RunwayML Client (Placeholder SDK) {'Ready.' if self.USE_RUNWAYML else 'Failed Init.'}")
-            except Exception as e: logger.error(f"RunwayML client (Placeholder SDK) init error: {e}. Disabled.", exc_info=True); self.USE_RUNWAYML = False
-        elif k: self.USE_RUNWAYML = True; logger.info("RunwayML API Key set (direct API or placeholder).")
-        else: self.USE_RUNWAYML = False; logger.info("RunwayML Disabled (no API key).")
-
-    def _get_text_dimensions(self, text_content, font_obj):
-        default_line_height = getattr(font_obj, 'size', self.font_size_pil)
-        if not text_content: return 0, default_line_height
+            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_obj, 'getbbox'):
-                bbox = font_obj.getbbox(text_content); width = bbox[2] - bbox[0]; height = bbox[3] - bbox[1]
-                return width, height if height > 0 else default_line_height
-            elif hasattr(font_obj, 'getsize'):
-                width, height = font_obj.getsize(text_content)
-                return width, height if height > 0 else default_line_height
-            else: return int(len(text_content) * default_line_height * 0.6), int(default_line_height * 1.2)
-        except Exception as e: logger.warning(f"Error in _get_text_dimensions for '{text_content[:20]}...': {e}"); return int(len(text_content) * self.font_size_pil * 0.6),int(self.font_size_pil * 1.2)
+            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):
-        if size is None: size = self.video_frame_size
-        img = Image.new('RGB', size, color=(20, 20, 40)); draw = ImageDraw.Draw(img)
-        padding = 25; max_text_width = size[0] - (2 * padding); lines = []
-        if not text_description: text_description = "(Placeholder: No text description provided)"
-        words = text_description.split(); current_line = ""
-        for word in words:
-            test_line = current_line + word + " "; line_width_test, _ = self._get_text_dimensions(test_line.strip(), self.font)
-            if line_width_test <= max_text_width: current_line = test_line
+        # (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.strip(): lines.append(current_line.strip())
-                word_width, _ = self._get_text_dimensions(word, self.font)
-                if word_width > max_text_width:
-                    avg_char_w = self._get_text_dimensions("A", self.font)[0] or 10
-                    chars_that_fit = int(max_text_width / avg_char_w)
-                    lines.append(word[:chars_that_fit-3] + "...") if len(word) > chars_that_fit else lines.append(word)
-                    current_line = ""
-                else: current_line = word + " "
-        if current_line.strip(): lines.append(current_line.strip())
+                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("A", self.font)[0] or 10; chars_that_fit = int(max_text_width / avg_char_w)
-            lines.append(text_description[:chars_that_fit-3] + "..." if len(text_description) > chars_that_fit else text_description)
-        elif not lines: lines.append("(Placeholder Text Error)")
-        _, single_line_height = self._get_text_dimensions("Ay", self.font); single_line_height = single_line_height if single_line_height > 0 else (self.font_size_pil + 2)
-        line_spacing = 2; max_lines_to_display = min(len(lines), (size[1]-(2*padding))//(single_line_height+line_spacing)) if single_line_height > 0 else 1
-        if max_lines_to_display <= 0: max_lines_to_display = 1
-        total_text_block_height = max_lines_to_display * single_line_height + (max_lines_to_display-1)*line_spacing
-        y_text_start = padding + (size[1]-(2*padding)-total_text_block_height)/2.0; current_y = y_text_start
-        for i in range(max_lines_to_display):
-            line_content = lines[i]; line_width_actual, _ = self._get_text_dimensions(line_content, self.font)
-            x_text = max(padding, (size[0]-line_width_actual)/2.0)
-            draw.text((x_text, current_y), line_content, font=self.font, fill=(200,200,180)); current_y += single_line_height + line_spacing
-            if i==6 and max_lines_to_display > 7 and len(lines) > max_lines_to_display:
-                ellipsis_width, _ = self._get_text_dimensions("...",self.font); x_ellipsis = max(padding, (size[0]-ellipsis_width)/2.0)
-                draw.text((x_ellipsis, current_y), "...", font=self.font, fill=(200,200,180)); break
+            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: logger.error(f"Error saving placeholder image {filepath}: {e}", exc_info=True); return None
-
-    def _search_pexels_image(self, q, ofnb):
-        if not self.USE_PEXELS or not self.pexels_api_key: return None; h={"Authorization":self.pexels_api_key};p={"query":q,"per_page":1,"orientation":"landscape","size":"large2x"}
-        pfn=ofnb.replace(".png",f"_pexels_{random.randint(1000,9999)}.jpg").replace(".mp4",f"_pexels_{random.randint(1000,9999)}.jpg");fp=os.path.join(self.output_dir,pfn)
-        try: logger.info(f"Pexels search: '{q}'");eq=" ".join(q.split()[:5]);p["query"]=eq;r=requests.get("https://api.pexels.com/v1/search",headers=h,params=p,timeout=20)
-        r.raise_for_status();d=r.json()
-        if d.get("photos") and len(d["photos"])>0:pu=d["photos"][0]["src"]["large2x"];ir=requests.get(pu,timeout=60);ir.raise_for_status();id_img=Image.open(io.BytesIO(ir.content)) # Renamed id to id_img
-        if id_img.mode!='RGB':id_img=id_img.convert('RGB');id_img.save(fp);logger.info(f"Pexels saved: {fp}");return fp
-        else: logger.info(f"No Pexels for: '{eq}'")
-        except Exception as e:logger.error(f"Pexels error ('{q}'): {e}",exc_info=True);return None
-
-    def _generate_video_clip_with_runwayml(self, pt, sifnb, tds=5, iip=None): # Default tds to 5s for Gen-4
-        if not self.USE_RUNWAYML or not self.runway_api_key: logger.warning("RunwayML disabled."); return None
-        if not iip or not os.path.exists(iip): logger.error(f"Runway Gen-4 needs input image. Path invalid: {iip}"); return None
-        runway_dur = 10 if tds > 7 else 5 # Map to 5s or 10s
-        ovfn = sifnb.replace(".png", f"_runway_gen4_d{runway_dur}s.mp4")
-        ovfp = os.path.join(self.output_dir, ovfn)
-        logger.info(f"Runway Gen-4 (Placeholder) img: {os.path.basename(iip)}, motion: '{pt[:100]}...', dur: {runway_dur}s")
-        # --- ACTUAL RUNWAYML API CALL (NEEDS IMPLEMENTATION) ---
-        logger.warning("Using PLACEHOLDER video for Runway Gen-4.")
-        img_clip=None; txt_c=None; final_ph_clip=None # Initialize for finally block
         try:
-            img_clip = ImageClip(iip).set_duration(runway_dur)
-            txt = f"Runway Gen-4 Placeholder\nInput: {os.path.basename(iip)}\nMotion: {pt[:50]}..."
-            txt_c = TextClip(txt, fontsize=24,color='white',font=self.video_overlay_font,bg_color='rgba(0,0,0,0.5)',size=(self.video_frame_size[0]*0.8,None),method='caption').set_duration(runway_dur).set_position('center')
-            final_ph_clip = CompositeVideoClip([img_clip, txt_c], size=img_clip.size)
-            final_ph_clip.write_videofile(ovfp,fps=24,codec='libx264',preset='ultrafast',logger=None,threads=2)
-            logger.info(f"Runway Gen-4 placeholder video: {ovfp}"); return ovfp
-        except Exception as e: logger.error(f"Runway Gen-4 placeholder error: {e}",exc_info=True); return None
-        finally:
-            if img_clip and hasattr(img_clip,'close'): img_clip.close()
-            if txt_c and hasattr(txt_c,'close'): txt_c.close()
-            if final_ph_clip and hasattr(final_ph_clip,'close'): final_ph_clip.close()
-
-    def _create_placeholder_video_content(self, td, fn, dur=4, sz=None): # Generic placeholder
-        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 error {fp}: {e}",exc_info=True); return None
+            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()
+            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):
+    # --- 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': 'Generation not attempted'}
+        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
-        image_filename_base = base_name + "_base_image" # For base image files
-        temp_image_asset_info = {'error': True, 'prompt_used': image_generation_prompt_text, 'error_message': 'Base image generation not attempted'}
+        # 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 base image (DALL-E -> Pexels -> Placeholder)
+        # STEP 1: Generate/acquire the base image via DALL·E
         if self.USE_AI_IMAGE_GENERATION and self.openai_api_key:
-            # ... (DALL-E logic as before, saving to image_filename_base + ".png") ...
-            max_r, att_n = 2, 0
-            for att_n in range(max_r):
-                try:
-                    img_fp_dalle = os.path.join(self.output_dir, image_filename_base + ".png")
-                    logger.info(f"Attempt {att_n+1} DALL-E (base img): {image_generation_prompt_text[:100]}...")
-                    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[:100]}...")
-                    ir = requests.get(iu, timeout=120); ir.raise_for_status()
-                    id_img = Image.open(io.BytesIO(ir.content)); # Renamed
-                    if id_img.mode != 'RGB': id_img = id_img.convert('RGB')
-                    id_img.save(img_fp_dalle); logger.info(f"DALL-E base image: {img_fp_dalle}");
-                    input_image_for_runway_path = img_fp_dalle
-                    temp_image_asset_info = {'path': img_fp_dalle, 'type': 'image', 'error': False, 'prompt_used': image_generation_prompt_text, 'revised_prompt': rp}
-                    break
-                except openai.RateLimitError as e: logger.warning(f"OpenAI Rate Limit {att_n+1}: {e}. Retry..."); time.sleep(5*(att_n+1)); temp_image_asset_info['error_message']=str(e)
-                except Exception as e: logger.error(f"DALL-E error: {e}", exc_info=True); temp_image_asset_info['error_message']=str(e); break
-            if temp_image_asset_info['error']: logger.warning(f"DALL-E failed after {att_n+1} attempts for base image.")
-        
-        if temp_image_asset_info['error'] and self.USE_PEXELS:
-            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, image_filename_base + ".png") # Use png in base name for consistency
-            if pp: input_image_for_runway_path = pp; temp_image_asset_info = {'path': pp, 'type': 'image', 'error': False, 'prompt_used': f"Pexels: {pqt}"}
-            else: current_em = temp_image_asset_info.get('error_message',""); temp_image_asset_info['error_message']=(current_em + " Pexels failed.").strip()
-
-        if temp_image_asset_info['error']:
-            logger.warning("Base image (DALL-E/Pexels) failed. Placeholder base image.")
-            ppt = temp_image_asset_info.get('prompt_used', image_generation_prompt_text)
-            php = self._create_placeholder_image_content(f"[Base Img Placeholder] {ppt[:100]}...", image_filename_base + ".png")
-            if php: input_image_for_runway_path = php; temp_image_asset_info = {'path': php, 'type': 'image', 'error': False, 'prompt_used': ppt}
-            else: current_em=temp_image_asset_info.get('error_message',"");temp_image_asset_info['error_message']=(current_em + " Base placeholder failed.").strip()
-        
-        # Step 2: Generate video with RunwayML if requested and base image exists
+            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 self.USE_RUNWAYML and 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):
-                    return {'path': video_path, 'type': 'video', 'error': False, 'prompt_used': motion_prompt_text_for_video, 'base_image_path': input_image_for_runway_path}
-                else: # Runway failed, return base image with error for video step
-                    asset_info = temp_image_asset_info; asset_info['error'] = True; asset_info['error_message'] = "RunwayML video gen failed; using base image."; asset_info['type'] = 'image'
-                    return asset_info
-            elif not self.USE_RUNWAYML: # Video requested, but RunwayML disabled
-                asset_info = temp_image_asset_info; asset_info['error_message'] = "RunwayML disabled; using base image."; asset_info['type'] = 'image'
-                return asset_info
-            else: # Video requested, but base image failed
-                asset_info = temp_image_asset_info; asset_info['error_message'] = (asset_info.get('error_message',"") + " Base image failed, Runway video not attempted.").strip(); asset_info['type'] = 'image'
+            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
-        else: # Image was requested directly
-            return temp_image_asset_info # Return result of base image generation
-
-    def generate_narration_audio(self, ttn, ofn="narration_overall.mp3"):
-        if not self.USE_ELEVENLABS or not self.elevenlabs_client or not ttn: logger.info("11L skip."); return None; afp=os.path.join(self.output_dir,ofn)
-        try: logger.info(f"11L audio (Voice:{self.elevenlabs_voice_id}): {ttn[: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 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()");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=ttn,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 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=ttn,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
+
+            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_type, scene_dur = asset_info.get('path'), asset_info.get('type'), asset_info.get('duration', 4.5)
-            scene_num, key_action = asset_info.get('scene_num', i + 1), asset_info.get('key_action', '')
+            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
+            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}")
-                    
+                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)
+                    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
+                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) # Added 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)
+                        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
+                                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 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
-                        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)
-                    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)
+                        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 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
 
-        if not processed_clips:logger.warning("No clips processed. Abort.");return None
-        td=0.75
+        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"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
+            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.")
-            clips_to_close = processed_clips + ([narration_clip] if narration_clip else []) + ([final_clip] if final_clip else [])
-            for clip_obj in clips_to_close:
-                if clip_obj and hasattr(clip_obj, 'close'):
-                    try: clip_obj.close()
-                    except Exception as e_close: logger.warning(f"Ignoring error while closing a clip: {e_close}")
+            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}"
+                        )