Update core/visual_engine.py

core/visual_engine.py

@@ -1,39 +1,40 @@
 # 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
+import openai # Ensure this is OpenAI v1.x.x+
 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'):
+             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)
+# logger.setLevel(logging.DEBUG) # Uncomment for verbose debugging
 
-# --- ElevenLabs Client Import ---
+# --- External Service Client Imports ---
 ELEVENLABS_CLIENT_IMPORTED = False
-ElevenLabsAPIClient = None
-Voice = None
-VoiceSettings = None
+ElevenLabsAPIClient = None 
+Voice = None              
+VoiceSettings = None      
 try:
     from elevenlabs.client import ElevenLabs as ImportedElevenLabsClient
     from elevenlabs import Voice as ImportedVoice, VoiceSettings as ImportedVoiceSettings
@@ -41,694 +42,392 @@ try:
     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.")
+    logger.info("ElevenLabs client components (SDK v1.x.x pattern) imported successfully.")
+except ImportError:
+    logger.warning("ElevenLabs SDK not found (expected 'pip install elevenlabs>=1.0.0'). Audio generation will be disabled.")
+except Exception as e_eleven_import_general:
+    logger.warning(f"General error importing ElevenLabs client components: {e_eleven_import_general}. Audio generation disabled.")
 
-# --- RunwayML Client Import (Placeholder) ---
 RUNWAYML_SDK_IMPORTED = False
-RunwayMLClient = None
+RunwayMLAPIClientClass = None 
 try:
-    logger.info("RunwayML SDK import is a placeholder.")
+    from runwayml import RunwayML as ImportedRunwayMLAPIClientClass
+    RunwayMLAPIClientClass = ImportedRunwayMLAPIClientClass
+    RUNWAYML_SDK_IMPORTED = True
+    logger.info("RunwayML SDK (runwayml) imported successfully.")
 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.")
+    logger.warning("RunwayML SDK not found (pip install runwayml). RunwayML video generation will be disabled.")
+except Exception as e_runway_sdk_import_general:
+    logger.warning(f"General error importing RunwayML SDK: {e_runway_sdk_import_general}. RunwayML features disabled.")
 
 
 class VisualEngine:
+    DEFAULT_FONT_SIZE_PIL = 10
+    PREFERRED_FONT_SIZE_PIL = 20
+    VIDEO_OVERLAY_FONT_SIZE = 30
+    VIDEO_OVERLAY_FONT_COLOR = 'white'
+    DEFAULT_MOVIEPY_FONT = 'DejaVu-Sans-Bold'
+    PREFERRED_MOVIEPY_FONT = 'Liberation-Sans-Bold'
+
     def __init__(self, output_dir="temp_cinegen_media", default_elevenlabs_voice_id="Rachel"):
         self.output_dir = output_dir
         os.makedirs(self.output_dir, exist_ok=True)
-        self.font_filename = "DejaVuSans-Bold.ttf"
-        font_paths_to_try = [
-            self.font_filename,
-            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_path_pil = next((p for p in font_paths_to_try if os.path.exists(p)), None)
-        self.font_size_pil = 20
-        self.video_overlay_font_size = 30
-        self.video_overlay_font_color = 'white'
-        self.video_overlay_font = 'DejaVu-Sans-Bold'
-
-        try:
-            if self.font_path_pil:
-                self.font = ImageFont.truetype(self.font_path_pil, self.font_size_pil)
-                logger.info(f"Pillow font loaded: {self.font_path_pil}.")
-            else:
-                self.font = ImageFont.load_default()
-                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.openai_api_key = None
-        self.USE_AI_IMAGE_GENERATION = False
-        self.dalle_model = "dall-e-3"
-        self.image_size_dalle3 = "1792x1024"
+        self.font_filename_pil_preference = "DejaVuSans-Bold.ttf"
+        font_paths_to_try = [ self.font_filename_pil_preference, f"/usr/share/fonts/truetype/dejavu/{self.font_filename_pil_preference}", 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.resolved_font_path_pil = next((p for p in font_paths_to_try if os.path.exists(p)), None)
+        self.active_font_pil = ImageFont.load_default(); self.active_font_size_pil = self.DEFAULT_FONT_SIZE_PIL
+        self.active_moviepy_font_name = self.DEFAULT_MOVIEPY_FONT
+        if self.resolved_font_path_pil:
+            try: self.active_font_pil = ImageFont.truetype(self.resolved_font_path_pil, self.PREFERRED_FONT_SIZE_PIL); self.active_font_size_pil = self.PREFERRED_FONT_SIZE_PIL; logger.info(f"Pillow font: {self.resolved_font_path_pil} sz {self.active_font_size_pil}."); self.active_moviepy_font_name = 'DejaVu-Sans-Bold' if "dejavu" in self.resolved_font_path_pil.lower() else ('Liberation-Sans-Bold' if "liberation" in self.resolved_font_path_pil.lower() else self.DEFAULT_MOVIEPY_FONT)
+            except IOError as e_font_load_io: logger.error(f"Pillow font IOError '{self.resolved_font_path_pil}': {e_font_load_io}. Default.")
+        else: logger.warning("Preferred Pillow font not found. 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_api_key = None; self.USE_ELEVENLABS = False; self.elevenlabs_client_instance = 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_obj = VoiceSettings(stability=0.60, similarity_boost=0.80, style=0.15, use_speaker_boost=True)
+        else: self.elevenlabs_voice_settings_obj = None
+        self.pexels_api_key = None; self.USE_PEXELS = False
+        self.runway_api_key = None; self.USE_RUNWAYML = False; self.runway_ml_sdk_client_instance = None
+        if RUNWAYML_SDK_IMPORTED and RunwayMLAPIClientClass and os.getenv("RUNWAYML_API_SECRET"):
+            try: self.runway_ml_sdk_client_instance = RunwayMLAPIClientClass(); self.USE_RUNWAYML = True; logger.info("RunwayML Client init from env var at startup.")
+            except Exception as e_runway_init_startup: logger.error(f"Initial RunwayML client init failed: {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
-        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_openai_api_key(self, api_key_value): self.openai_api_key = api_key_value; self.USE_AI_IMAGE_GENERATION = bool(api_key_value); logger.info(f"DALL-E status: {'Ready' if self.USE_AI_IMAGE_GENERATION else 'Disabled'}")
+    def set_elevenlabs_api_key(self, api_key_value, voice_id_from_secret=None):
+        self.elevenlabs_api_key = api_key_value
+        if voice_id_from_secret: self.elevenlabs_voice_id = voice_id_from_secret
+        if api_key_value and ELEVENLABS_CLIENT_IMPORTED and ElevenLabsAPIClient:
+            try: self.elevenlabs_client_instance = ElevenLabsAPIClient(api_key=api_key_value); self.USE_ELEVENLABS = bool(self.elevenlabs_client_instance); logger.info(f"11L Client: {'Ready' if self.USE_ELEVENLABS else 'Failed'} (Voice: {self.elevenlabs_voice_id})")
+            except Exception as e_11l_setkey_init: logger.error(f"11L client init error: {e_11l_setkey_init}. Disabled.", exc_info=True); self.USE_ELEVENLABS=False; self.elevenlabs_client_instance=None
+        else: self.USE_ELEVENLABS = False; logger.info(f"11L Disabled (key/SDK).")
+    def set_pexels_api_key(self, api_key_value): self.pexels_api_key = api_key_value; self.USE_PEXELS = bool(api_key_value); logger.info(f"Pexels status: {'Ready' if self.USE_PEXELS else 'Disabled'}")
+    def set_runway_api_key(self, api_key_value):
+        self.runway_api_key = api_key_value
+        if api_key_value:
+            if RUNWAYML_SDK_IMPORTED and RunwayMLAPIClientClass:
+                if not self.runway_ml_sdk_client_instance:
+                    try:
+                        original_env_secret = os.getenv("RUNWAYML_API_SECRET")
+                        if not original_env_secret: os.environ["RUNWAYML_API_SECRET"] = api_key_value; logger.info("Temp set RUNWAYML_API_SECRET for SDK.")
+                        self.runway_ml_sdk_client_instance = RunwayMLAPIClientClass(); self.USE_RUNWAYML = True; logger.info("RunwayML Client init via set_runway_api_key.")
+                        if not original_env_secret: del os.environ["RUNWAYML_API_SECRET"]; logger.info("Cleared temp RUNWAYML_API_SECRET.")
+                    except Exception as e_runway_setkey_init: logger.error(f"RunwayML Client init in set_runway_api_key fail: {e_runway_setkey_init}", exc_info=True); self.USE_RUNWAYML=False;self.runway_ml_sdk_client_instance=None
+                else: self.USE_RUNWAYML = True; logger.info("RunwayML Client already init.")
+            else: logger.warning("RunwayML SDK not imported. Service disabled."); self.USE_RUNWAYML = False
+        else: self.USE_RUNWAYML = False; self.runway_ml_sdk_client_instance = None; logger.info("RunwayML Disabled (no API key).")
 
-    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 _image_to_data_uri(self, image_path):
+        # (Implementation from before)
+        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",".webp":"image/webp"};mime_type=mime_map.get(ext,"application/octet-stream");
+        if mime_type=="application/octet-stream":logger.warning(f"Unknown MIME for {image_path}, using {mime_type}.")
+        with open(image_path,"rb")as image_file:encoded_string=base64.b64encode(image_file.read()).decode('utf-8')
+        data_uri=f"data:{mime_type};base64,{encoded_string}";logger.debug(f"Data URI for {os.path.basename(image_path)} (start): {data_uri[:100]}...");return data_uri
+        except FileNotFoundError:logger.error(f"Img not found {image_path} for data URI.");return None
+        except Exception as e:logger.error(f"Error converting {image_path} to data URI:{e}",exc_info=True);return None
 
-    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 _map_resolution_to_runway_ratio(self, width, height):
+        # (Implementation from before)
+        ratio_str=f"{width}:{height}";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
+        logger.warning(f"Res {ratio_str} not in Gen-4 list. Default 1280:720.");return "1280:720"
 
-    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
+    def _get_text_dimensions(self, text_content, font_object_pil):
+        # (Implementation from before)
+        dch=getattr(font_object_pil,'size',self.active_font_size_pil);
+        if not text_content:return 0,dch
         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_pil,'getbbox'):bb=font_object_pil.getbbox(text_content);w=bb[2]-bb[0];h=bb[3]-bb[1];return w,h if h>0 else dch
+            elif hasattr(font_object_pil,'getsize'):w,h=font_object_pil.getsize(text_content);return w,h if h>0 else dch
+            else:return int(len(text_content)*dch*0.6),int(dch*1.2)
+        except Exception as e_getdim_inner:logger.warning(f"Error in _get_text_dimensions:{e_getdim_inner}");return int(len(text_content)*self.active_font_size_pil*0.6),int(self.active_font_size_pil*1.2) # Renamed e
 
-    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
-            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) if avg_char_w > 0 else 10
-                    if len(word) > chars_that_fit:
-                        lines.append(word[:chars_that_fit-3] + "...")
-                    else:
-                        lines.append(word)
-                    current_line = ""
-                else:
-                    current_line = word + " "
-        if current_line.strip():
-            lines.append(current_line.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) if avg_char_w > 0 else 10
-            if len(text_description) > chars_that_fit:
-                lines.append(text_description[:chars_that_fit-3] + "...")
+    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_for_placeholder = []
+        if not text_description: text_description = "(Placeholder Image)"
+        words_list = text_description.split(); current_line_buffer = ""
+        for word_idx, word_item in enumerate(words_list):
+            prospective_addition = word_item + (" " if word_idx < len(words_list) - 1 else "")
+            test_line_candidate = current_line_buffer + prospective_addition
+            current_w_text, _ = self._get_text_dimensions(test_line_candidate, self.active_font_pil)
+            if current_w_text == 0 and test_line_candidate.strip(): current_w_text = len(test_line_candidate) * (self.active_font_size_pil * 0.6)
+            if current_w_text <= max_w: current_line_buffer = test_line_candidate
             else:
-                lines.append(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
-        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
+                if current_line_buffer.strip(): lines_for_placeholder.append(current_line_buffer.strip())
+                current_line_buffer = prospective_addition
+        if current_line_buffer.strip(): lines_for_placeholder.append(current_line_buffer.strip())
+        if not lines_for_placeholder and text_description:
+            avg_char_w_est, _ = self._get_text_dimensions("W", self.active_font_pil); avg_char_w_est = avg_char_w_est or (self.active_font_size_pil * 0.6)
+            chars_per_line_est = int(max_w / avg_char_w_est) if avg_char_w_est > 0 else 20
+            lines_for_placeholder.append(text_description[:chars_per_line_est] + ("..." if len(text_description) > chars_per_line_est else ""))
+        elif not lines_for_placeholder: lines_for_placeholder.append("(Placeholder Error)")
+        _, single_h = self._get_text_dimensions("Ay", self.active_font_pil); single_h = single_h if single_h > 0 else self.active_font_size_pil + 2
+        max_l = min(len(lines_for_placeholder), (size[1] - (2 * padding)) // (single_h + 2)) if single_h > 0 else 1; max_l = max(1, max_l)
+        y_p = padding + (size[1] - (2 * padding) - max_l * (single_h + 2)) / 2.0
+        for i_line in range(max_l):
+            line_txt_content = lines_for_placeholder[i_line]; line_w_val, _ = self._get_text_dimensions(line_txt_content, self.active_font_pil)
+            if line_w_val == 0 and line_txt_content.strip(): line_w_val = len(line_txt_content) * (self.active_font_size_pil * 0.6)
+            x_p = (size[0] - line_w_val) / 2.0
+            try: d.text((x_p, y_p), line_txt_content, font=self.active_font_pil, fill=(200, 200, 180))
+            except Exception as e_draw: logger.error(f"Pillow d.text error: {e_draw} for '{line_txt_content}'")
+            y_p += single_h + 2
+            if i_line == 6 and max_l > 7:
+                try: d.text((x_p, y_p), "...", font=self.active_font_pil, fill=(200, 200, 180))
+                except Exception as e_elip: logger.error(f"Pillow d.text ellipsis error: {e_elip}"); break
+        filepath_placeholder = os.path.join(self.output_dir, filename)
+        try: img.save(filepath_placeholder); return filepath_placeholder
+        except Exception as e_save: logger.error(f"Saving placeholder image '{filepath_placeholder}' error: {e_save}", exc_info=True); return None
 
-    def _search_pexels_image(self, query, output_filename_base):
-        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, _ = os.path.splitext(output_filename_base)
-        pexels_filename = base_name + f"_pexels_{random.randint(1000,9999)}.jpg"
-        filepath = os.path.join(self.output_dir, pexels_filename)
+    def _search_pexels_image(self, query_str, output_fn_base):
+        # (Corrected version from previous response)
+        if not self.USE_PEXELS or not self.pexels_api_key: return None
+        http_headers = {"Authorization": self.pexels_api_key}
+        http_params = {"query": query_str, "per_page": 1, "orientation": "landscape", "size": "large2x"}
+        base_name_px, _ = os.path.splitext(output_fn_base)
+        pexels_fn_str = base_name_px + f"_pexels_{random.randint(1000,9999)}.jpg"
+        file_path_px = os.path.join(self.output_dir, pexels_fn_str)
         try:
-            logger.info(f"Pexels search: '{query}'")
-            effective_query = " ".join(query.split()[:5])
-            params["query"] = effective_query
-            response = requests.get("https://api.pexels.com/v1/search", headers=headers, params=params, timeout=20)
-            response.raise_for_status()
-            data = response.json()
-            if data.get("photos") and len(data["photos"]) > 0:
-                photo_details = data["photos"][0]
-                photo_url = photo_details["src"]["large2x"]
-                logger.info(f"Downloading Pexels image from: {photo_url}")
-                image_response = requests.get(photo_url, timeout=60)
-                image_response.raise_for_status()
-                img_data = Image.open(io.BytesIO(image_response.content))
-                if img_data.mode != 'RGB':
-                    logger.debug(f"Pexels image mode is {img_data.mode}, converting to RGB.")
-                    img_data = img_data.convert('RGB')
-                img_data.save(filepath)
-                logger.info(f"Pexels image saved successfully: {filepath}")
-                return filepath
-            else:
-                logger.info(f"No photos found on Pexels for query: '{effective_query}'")
-                return None
-        except requests.exceptions.RequestException as e_req:
-            logger.error(f"Pexels request error for query '{query}': {e_req}", exc_info=True)
-        except json.JSONDecodeError as e_json:
-            logger.error(f"Pexels JSON decode error for query '{query}': {e_json}", exc_info=True)
-        except Exception as e:
-            logger.error(f"General Pexels error for query '{query}': {e}", exc_info=True)
-        return None
+            logger.info(f"Pexels: Searching for '{query_str}'")
+            eff_query_px = " ".join(query_str.split()[:5])
+            http_params["query"] = eff_query_px
+            response_px = requests.get("https://api.pexels.com/v1/search", headers=http_headers, params=http_params, timeout=20)
+            response_px.raise_for_status()
+            data_px = response_px.json()
+            if data_px.get("photos") and len(data_px["photos"]) > 0:
+                photo_details_px = data_px["photos"][0]
+                photo_url_px = photo_details_px.get("src", {}).get("large2x")
+                if not photo_url_px: logger.warning(f"Pexels: 'large2x' URL missing for '{eff_query_px}'. Details: {photo_details_px}"); return None
+                image_response_px = requests.get(photo_url_px, timeout=60); image_response_px.raise_for_status()
+                img_pil_data_px = Image.open(io.BytesIO(image_response_px.content))
+                if img_pil_data_px.mode != 'RGB': img_pil_data_px = img_pil_data_px.convert('RGB')
+                img_pil_data_px.save(file_path_px); logger.info(f"Pexels: Image saved to {file_path_px}"); return file_path_px
+            else: logger.info(f"Pexels: No photos for '{eff_query_px}'."); return None
+        except requests.exceptions.RequestException as e_req_px: logger.error(f"Pexels: RequestException for '{query_str}': {e_req_px}", exc_info=False); return None
+        except Exception as e_px_gen: logger.error(f"Pexels: General error for '{query_str}': {e_px_gen}", exc_info=True); return None
 
-    def _generate_video_clip_with_runwayml(self, pt, iip, sifnb, tds=5):
-        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
-        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")
-        logger.warning("Using PLACEHOLDER video for Runway Gen-4.")
-        img_clip = None
-        txt_c = None
-        final_ph_clip = None
+    def _generate_video_clip_with_runwayml(self, text_prompt_for_motion, input_image_path, scene_identifier_filename_base, target_duration_seconds=5):
+        # (Updated RunwayML integration from before)
+        if not self.USE_RUNWAYML or not self.runway_ml_sdk_client_instance: logger.warning("RunwayML not enabled/client not init. Skip video."); return None
+        if not input_image_path or not os.path.exists(input_image_path): logger.error(f"Runway Gen-4 needs input image. Path invalid: {input_image_path}"); return None
+        image_data_uri_str = self._image_to_data_uri(input_image_path)
+        if not image_data_uri_str: return None
+        runway_dur = 10 if target_duration_seconds >= 8 else 5
+        runway_ratio = self._map_resolution_to_runway_ratio(self.video_frame_size[0], self.video_frame_size[1])
+        base_name_for_runway_vid, _ = os.path.splitext(scene_identifier_filename_base); output_vid_fn = base_name_for_runway_vid + f"_runway_gen4_d{runway_dur}s.mp4"
+        output_vid_fp = os.path.join(self.output_dir, output_vid_fn)
+        logger.info(f"Runway Gen-4 task: motion='{text_prompt_for_motion[:100]}...', img='{os.path.basename(input_image_path)}', dur={runway_dur}s, ratio='{runway_ratio}'")
         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()
+            task_submitted_runway = self.runway_ml_sdk_client_instance.image_to_video.create(model='gen4_turbo', prompt_image=image_data_uri_str, prompt_text=text_prompt_for_motion, duration=runway_dur, ratio=runway_ratio)
+            task_id_runway = task_submitted_runway.id; logger.info(f"Runway Gen-4 task ID: {task_id_runway}. Polling...")
+            poll_sec=10; max_poll_count=36; poll_start_time = time.time()
+            while time.time() - poll_start_time < max_poll_count * poll_sec:
+                time.sleep(poll_sec); task_details_runway = self.runway_ml_sdk_client_instance.tasks.retrieve(id=task_id_runway)
+                logger.info(f"Runway task {task_id_runway} status: {task_details_runway.status}")
+                if task_details_runway.status == 'SUCCEEDED':
+                    output_url_runway = getattr(getattr(task_details_runway,'output',None),'url',None) or \
+                                        (getattr(task_details_runway,'artifacts',None) and task_details_runway.artifacts[0].url if task_details_runway.artifacts and hasattr(task_details_runway.artifacts[0],'url') else None) or \
+                                        (getattr(task_details_runway,'artifacts',None) and task_details_runway.artifacts[0].download_url if task_details_runway.artifacts and hasattr(task_details_runway.artifacts[0],'download_url') else None)
+                    if not output_url_runway: logger.error(f"Runway task {task_id_runway} SUCCEEDED, but no output URL. Details: {vars(task_details_runway) if hasattr(task_details_runway,'__dict__') else task_details_runway}"); return None
+                    logger.info(f"Runway task {task_id_runway} SUCCEEDED. Downloading: {output_url_runway}")
+                    video_resp_get = requests.get(output_url_runway, stream=True, timeout=300); video_resp_get.raise_for_status()
+                    with open(output_vid_fp,'wb') as f_vid:
+                        for chunk_data in video_resp_get.iter_content(chunk_size=8192): f_vid.write(chunk_data)
+                    logger.info(f"Runway Gen-4 video saved: {output_vid_fp}"); return output_vid_fp
+                elif task_details_runway.status in ['FAILED','ABORTED','ERROR']:
+                    err_msg_runway = getattr(task_details_runway,'error_message',None) or getattr(getattr(task_details_runway,'output',None),'error',"Unknown Runway error.")
+                    logger.error(f"Runway task {task_id_runway} status: {task_details_runway.status}. Error: {err_msg_runway}"); return None
+            logger.warning(f"Runway task {task_id_runway} timed out."); return None
+        except AttributeError as ae_sdk: logger.error(f"RunwayML SDK AttrError: {ae_sdk}. SDK/methods changed?", exc_info=True); return None
+        except Exception as e_runway_gen: logger.error(f"Runway Gen-4 API error: {e_runway_gen}", exc_info=True); return None
 
-    def _create_placeholder_video_content(self, text_description, filename, duration=4, size=None):
-        if size is None:
-            size = self.video_frame_size
-        filepath = os.path.join(self.output_dir, filename)
-        txt_clip = None
-        try:
-            txt_clip = TextClip(
-                text_description,
-                fontsize=50,
-                color='white',
-                font=self.video_overlay_font,
-                bg_color='black',
-                size=size,
-                method='caption'
-            ).set_duration(duration)
-            txt_clip.write_videofile(
-                filepath,
-                fps=24,
-                codec='libx264',
-                preset='ultrafast',
-                logger=None,
-                threads=2
-            )
-            logger.info(f"Generic placeholder video created successfully: {filepath}")
-            return filepath
-        except Exception as e:
-            logger.error(f"Failed to create generic placeholder video {filepath}: {e}", exc_info=True)
+    def _create_placeholder_video_content(self, text_desc_ph, filename_ph, duration_ph=4, size_ph=None):
+        # <<< THIS IS THE CORRECTED METHOD >>>
+        if size_ph is None: size_ph = self.video_frame_size
+        filepath_ph = os.path.join(self.output_dir, filename_ph)
+        text_clip_ph = None 
+        try: # Ensure try block is here
+            text_clip_ph = TextClip(text_desc_ph, fontsize=50, color='white', font=self.video_overlay_font,
+                                bg_color='black', size=size_ph, method='caption').set_duration(duration_ph)
+            text_clip_ph.write_videofile(filepath_ph, fps=24, codec='libx264', preset='ultrafast', logger=None, threads=2)
+            logger.info(f"Generic placeholder video created: {filepath_ph}")
+            return filepath_ph
+        except Exception as e_ph_vid: 
+            logger.error(f"Failed to create generic placeholder video '{filepath_ph}': {e_ph_vid}", exc_info=True)
             return None
-        finally:
-            if txt_clip and hasattr(txt_clip, 'close'):
-                try:
-                    txt_clip.close()
-                except Exception as e_close:
-                    logger.warning(f"Error closing TextClip in _create_placeholder_video_content: {e_close}")
-
+        finally: # Ensure finally block is here
+            if text_clip_ph and hasattr(text_clip_ph, 'close'):
+                text_clip_ph.close()
+    
     def generate_scene_asset(self, image_generation_prompt_text, motion_prompt_text_for_video,
-                             scene_data, scene_identifier_filename_base,
-                             generate_as_video_clip=False, runway_target_duration=5):
-        base_name = scene_identifier_filename_base
-        asset_info = {
-            'path': None,
-            'type': 'none',
-            'error': True,
-            'prompt_used': image_generation_prompt_text,
-            'error_message': 'Generation not attempted'
-        }
-        input_image_for_runway_path = None
-        image_filename_for_base = base_name + "_base_image.png"
-        temp_image_asset_info = {
-            'error': True,
-            'prompt_used': image_generation_prompt_text,
-            'error_message': 'Base image generation not attempted'
-        }
-
+                             scene_data_dict, scene_identifier_fn_base,
+                             generate_as_video_clip_flag=False, runway_target_dur_val=5):
+        # (Corrected DALL-E loop from previous response)
+        base_name_asset, _ = os.path.splitext(scene_identifier_fn_base)
+        asset_info_result = {'path': None, 'type': 'none', 'error': True, 'prompt_used': image_generation_prompt_text, 'error_message': 'Asset generation init failed'}
+        path_for_input_image_runway = None
+        fn_for_base_image = base_name_asset + ("_base_for_video.png" if generate_as_video_clip_flag else ".png")
+        fp_for_base_image = os.path.join(self.output_dir, fn_for_base_image)
         if self.USE_AI_IMAGE_GENERATION and self.openai_api_key:
-            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_for_base)
-                    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))
-                    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.")
+            max_r_dalle, attempt_count_dalle = 2,0;
+            for att_n_dalle in range(max_r_dalle):
+                attempt_count_dalle = att_n_dalle + 1
+                try: 
+                    logger.info(f"Att {attempt_count_dalle} DALL-E (base img): {image_generation_prompt_text[:70]}..."); oai_cl = openai.OpenAI(api_key=self.openai_api_key,timeout=90.0); oai_r = oai_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"); oai_iu = oai_r.data[0].url; oai_rp = getattr(oai_r.data[0],'revised_prompt',None);
+                    if oai_rp: logger.info(f"DALL-E revised: {oai_rp[:70]}...")
+                    oai_ir = requests.get(oai_iu,timeout=120); oai_ir.raise_for_status(); oai_id = Image.open(io.BytesIO(oai_ir.content));
+                    if oai_id.mode!='RGB': oai_id=oai_id.convert('RGB')
+                    oai_id.save(fp_for_base_image); logger.info(f"DALL-E base img saved: {fp_for_base_image}"); path_for_input_image_runway=fp_for_base_image; asset_info_result={'path':fp_for_base_image,'type':'image','error':False,'prompt_used':image_generation_prompt_text,'revised_prompt':oai_rp}; break 
+                except openai.RateLimitError as e_oai_rl: logger.warning(f"OpenAI RateLimit Att {attempt_count_dalle}:{e_oai_rl}.Retry...");time.sleep(5*attempt_count_dalle);asset_info_result['error_message']=str(e_oai_rl)
+                except openai.APIError as e_oai_api: logger.error(f"OpenAI APIError Att {attempt_count_dalle}:{e_oai_api}");asset_info_result['error_message']=str(e_oai_api);break
+                except requests.exceptions.RequestException as e_oai_req: logger.error(f"Requests Err DALL-E Att {attempt_count_dalle}:{e_oai_req}");asset_info_result['error_message']=str(e_oai_req);break
+                except Exception as e_oai_gen: logger.error(f"General DALL-E Err Att {attempt_count_dalle}:{e_oai_gen}",exc_info=True);asset_info_result['error_message']=str(e_oai_gen);break
+            if asset_info_result['error']: logger.warning(f"DALL-E failed after {attempt_count_dalle} attempts for base img.")
+        if asset_info_result['error'] and self.USE_PEXELS:
+            logger.info("Trying Pexels for base img.");px_qt=scene_data_dict.get('pexels_search_query_감독',f"{scene_data_dict.get('emotional_beat','')} {scene_data_dict.get('setting_description','')}");px_pp=self._search_pexels_image(px_qt,fn_for_base_image);
+            if px_pp:path_for_input_image_runway=px_pp;asset_info_result={'path':px_pp,'type':'image','error':False,'prompt_used':f"Pexels:{px_qt}"}
+            else:current_em_px=asset_info_result.get('error_message',"");asset_info_result['error_message']=(current_em_px+" Pexels failed for base.").strip()
+        if asset_info_result['error']:
+            logger.warning("Base img (DALL-E/Pexels) failed. Using placeholder.");ph_ppt=asset_info_result.get('prompt_used',image_generation_prompt_text);php=self._create_placeholder_image_content(f"[Base Placeholder]{ph_ppt[:70]}...",fn_for_base_image);
+            if php:path_for_input_image_runway=php;asset_info_result={'path':php,'type':'image','error':False,'prompt_used':ph_ppt}
+            else:current_em_ph=asset_info_result.get('error_message',"");asset_info_result['error_message']=(current_em_ph+" Base placeholder failed.").strip()
+        if generate_as_video_clip_flag:
+            if not path_for_input_image_runway:logger.error("RunwayML video: base img failed.");asset_info_result['error']=True;asset_info_result['error_message']=(asset_info_result.get('error_message',"")+" Base img miss, Runway abort.").strip();asset_info_result['type']='none';return asset_info_result
+            if self.USE_RUNWAYML:
+                runway_video_p=self._generate_video_clip_with_runwayml(motion_prompt_text_for_video,path_for_input_image_runway,base_name_asset,runway_target_dur_val)
+                if runway_video_p and os.path.exists(runway_video_p):asset_info_result={'path':runway_video_p,'type':'video','error':False,'prompt_used':motion_prompt_text_for_video,'base_image_path':path_for_input_image_runway}
+                else:logger.warning(f"RunwayML video failed for {base_name_asset}. Fallback to base img.");asset_info_result['error']=True;asset_info_result['error_message']=(asset_info_result.get('error_message',"Base img ok.")+" RunwayML video fail; use base img.").strip();asset_info_result['path']=path_for_input_image_runway;asset_info_result['type']='image';asset_info_result['prompt_used']=image_generation_prompt_text
+            else:logger.warning("RunwayML selected but disabled. Use base img.");asset_info_result['error']=True;asset_info_result['error_message']=(asset_info_result.get('error_message',"Base img ok.")+" RunwayML disabled; use base img.").strip();asset_info_result['path']=path_for_input_image_runway;asset_info_result['type']='image';asset_info_result['prompt_used']=image_generation_prompt_text
+        return asset_info_result
 
-        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_for_base)
-            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_for_base)
-            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()
-
-        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:
-                    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:
-                asset_info = temp_image_asset_info
-                asset_info['error_message'] = "RunwayML disabled; using base image."
-                asset_info['type'] = 'image'
-                return asset_info
-            else:
-                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'
-                return asset_info
-        else:
-            return temp_image_asset_info
-
-    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.")
+    def generate_narration_audio(self, text_to_narrate, output_filename="narration_overall.mp3"):
+        # <<< CORRECTED VERSION OF THIS METHOD >>>
+        if not self.USE_ELEVENLABS or not self.elevenlabs_client_instance or not text_to_narrate:
+            logger.info("ElevenLabs conditions not met (service disabled, client not init, or no text). Skipping audio generation.")
             return None
-        afp = os.path.join(self.output_dir, ofn)
+        audio_filepath_narration = os.path.join(self.output_dir, output_filename)
         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
+            logger.info(f"Generating ElevenLabs audio (Voice ID: {self.elevenlabs_voice_id}) for text: \"{text_to_narrate[:70]}...\"")
+            audio_stream_method_11l = None
+            if hasattr(self.elevenlabs_client_instance, 'text_to_speech') and hasattr(self.elevenlabs_client_instance.text_to_speech, 'stream'):
+                audio_stream_method_11l = self.elevenlabs_client_instance.text_to_speech.stream; logger.info("Using ElevenLabs SDK method: client.text_to_speech.stream()")
+            elif hasattr(self.elevenlabs_client_instance, 'generate_stream'):
+                audio_stream_method_11l = self.elevenlabs_client_instance.generate_stream; logger.info("Using ElevenLabs SDK method: client.generate_stream()")
+            elif hasattr(self.elevenlabs_client_instance, 'generate'):
+                logger.info("Using ElevenLabs SDK method: client.generate() (non-streaming).")
+                voice_param_11l = str(self.elevenlabs_voice_id)
+                if Voice and self.elevenlabs_voice_settings_obj: voice_param_11l = Voice(voice_id=str(self.elevenlabs_voice_id), settings=self.elevenlabs_voice_settings_obj)
+                audio_bytes_data = self.elevenlabs_client_instance.generate(text=text_to_narrate, voice=voice_param_11l, model="eleven_multilingual_v2")
+                with open(audio_filepath_narration, "wb") as audio_file_out: audio_file_out.write(audio_bytes_data)
+                logger.info(f"ElevenLabs audio (non-streamed) saved successfully to: {audio_filepath_narration}"); return audio_filepath_narration
+            else: logger.error("No recognized audio generation method found on the ElevenLabs client instance."); 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
+            if audio_stream_method_11l: # If a streaming method was identified
+                params_for_voice_stream = {"voice_id": str(self.elevenlabs_voice_id)}
+                if self.elevenlabs_voice_settings_obj:
+                    if hasattr(self.elevenlabs_voice_settings_obj, 'model_dump'): params_for_voice_stream["voice_settings"] = self.elevenlabs_voice_settings_obj.model_dump()
+                    elif hasattr(self.elevenlabs_voice_settings_obj, 'dict'): params_for_voice_stream["voice_settings"] = self.elevenlabs_voice_settings_obj.dict()
+                    else: params_for_voice_stream["voice_settings"] = self.elevenlabs_voice_settings_obj
+                audio_data_iterator_11l = audio_stream_method_11l(text=text_to_narrate, model_id="eleven_multilingual_v2", **params_for_voice_stream)
+                with open(audio_filepath_narration, "wb") as audio_file_out_stream:
+                    for audio_chunk_data in audio_data_iterator_11l:
+                        if audio_chunk_data: audio_file_out_stream.write(audio_chunk_data)
+                logger.info(f"ElevenLabs audio (streamed) saved successfully to: {audio_filepath_narration}"); return audio_filepath_narration
+        except AttributeError as ae_11l_sdk: logger.error(f"AttributeError with ElevenLabs SDK client: {ae_11l_sdk}. SDK version/methods might differ.", exc_info=True); return None
+        except Exception as e_11l_general_audio: logger.error(f"General error during ElevenLabs audio generation: {e_11l_general_audio}", exc_info=True); return None
 
-    def assemble_animatic_from_assets(self, asset_data_list, overall_narration_path=None, output_filename="final_video.mp4", fps=24):
-        if not asset_data_list:
-            logger.warning("No assets for animatic.")
-            return None
-        processed_clips = []
-        narration_clip = None
-        final_clip = None
-        logger.info(f"Assembling from {len(asset_data_list)} assets. Frame: {self.video_frame_size}.")
-
-        for i, asset_info in enumerate(asset_data_list):
-            asset_path = asset_info.get('path')
-            asset_type = asset_info.get('type')
-            scene_dur = asset_info.get('duration', 4.5)
-            scene_num = asset_info.get('scene_num', i + 1)
-            key_action = asset_info.get('key_action', '')
-            logger.info(f"S{scene_num}: Path='{asset_path}', Type='{asset_type}', Dur='{scene_dur}'s")
-
-            if not (asset_path and os.path.exists(asset_path)):
-                logger.warning(f"S{scene_num}: Not found '{asset_path}'. Skip.")
-                continue
-            if scene_dur <= 0:
-                logger.warning(f"S{scene_num}: Invalid duration ({scene_dur}s). Skip.")
-                continue
 
-            current_scene_mvpy_clip = None
+    def assemble_animatic_from_assets(self, asset_data_list, overall_narration_path=None, output_filename="final_video.mp4", fps=24):
+        # (Keep as in the version with robust image processing, C-contiguous array, debug saves, and pix_fmt)
+        # ... (This extensive method is assumed to be largely correct from the previous iteration focusing on blank video issues)
+        if not asset_data_list: logger.warning("No assets for animatic."); return None
+        processed_moviepy_clips_list = []; narration_audio_clip_mvpy = None; final_video_output_clip = None
+        logger.info(f"Assembling from {len(asset_data_list)} assets. Target Frame: {self.video_frame_size}.")
+        for i_asset, asset_info_item_loop in enumerate(asset_data_list):
+            path_of_asset, type_of_asset, duration_for_scene = asset_info_item_loop.get('path'), asset_info_item_loop.get('type'), asset_info_item_loop.get('duration', 4.5)
+            num_of_scene, action_in_key = asset_info_item_loop.get('scene_num', i_asset + 1), asset_info_item_loop.get('key_action', '')
+            logger.info(f"S{num_of_scene}: Path='{path_of_asset}', Type='{type_of_asset}', Dur='{duration_for_scene}'s")
+            if not (path_of_asset and os.path.exists(path_of_asset)): logger.warning(f"S{num_of_scene}: Not found '{path_of_asset}'. Skip."); continue
+            if duration_for_scene <= 0: logger.warning(f"S{num_of_scene}: Invalid duration ({duration_for_scene}s). Skip."); continue
+            active_scene_clip = None
             try:
-                if asset_type == 'image':
-                    pil_img = Image.open(asset_path)
-                    logger.debug(f"S{scene_num}: Loaded img. Mode:{pil_img.mode}, Size:{pil_img.size}")
-                    img_rgba = pil_img.convert('RGBA') if pil_img.mode != 'RGBA' else pil_img.copy()
-                    thumb = img_rgba.copy()
-                    rf = Image.Resampling.LANCZOS if hasattr(Image.Resampling, 'LANCZOS') else Image.BILINEAR
-                    thumb.thumbnail(self.video_frame_size, rf)
-                    cv_rgba = Image.new('RGBA', self.video_frame_size, (0, 0, 0, 0))
-                    xo = (self.video_frame_size[0] - thumb.width) // 2
-                    yo = (self.video_frame_size[1] - thumb.height) // 2
-                    cv_rgba.paste(thumb, (xo, yo), thumb)
-                    final_rgb_pil = Image.new("RGB", self.video_frame_size, (0, 0, 0))
-                    final_rgb_pil.paste(cv_rgba, mask=cv_rgba.split()[3])
-                    dbg_path = os.path.join(self.output_dir, f"debug_PRE_NUMPY_S{scene_num}.png")
-                    final_rgb_pil.save(dbg_path)
-                    logger.info(f"DEBUG: Saved PRE_NUMPY_S{scene_num} to {dbg_path}")
-                    frame_np = np.array(final_rgb_pil, dtype=np.uint8)
-                    if not frame_np.flags['C_CONTIGUOUS']:
-                        frame_np = np.ascontiguousarray(frame_np, dtype=np.uint8)
-                    logger.debug(f"S{scene_num}: NumPy for MoviePy. Shape:{frame_np.shape}, DType:{frame_np.dtype}, C-Contig:{frame_np.flags['C_CONTIGUOUS']}")
-                    if frame_np.size == 0 or frame_np.ndim != 3 or frame_np.shape[2] != 3:
-                        logger.error(f"S{scene_num}: Invalid NumPy. Skip.")
-                        continue
-                    clip_base = ImageClip(frame_np, transparent=False).set_duration(scene_dur)
-                    mvpy_dbg_path = os.path.join(self.output_dir, f"debug_MOVIEPY_FRAME_S{scene_num}.png")
-                    clip_base.save_frame(mvpy_dbg_path, t=0.1)
-                    logger.info(f"DEBUG: Saved MOVIEPY_FRAME_S{scene_num} to {mvpy_dbg_path}")
-                    clip_fx = clip_base
-                    try:
-                        es = random.uniform(1.03, 1.08)
-                        clip_fx = clip_base.fx(
-                            vfx.resize,
-                            lambda t: 1 + (es - 1) * (t / scene_dur) if scene_dur > 0 else 1
-                        ).set_position('center')
-                    except Exception as e:
-                        logger.error(f"S{scene_num} Ken Burns error: {e}", exc_info=False)
-                    current_scene_mvpy_clip = clip_fx
-                elif asset_type == 'video':
-                    src_clip = None
+                if type_of_asset == 'image':
+                    opened_pil_img = Image.open(path_of_asset); logger.debug(f"S{num_of_scene}: Loaded img. Mode:{opened_pil_img.mode}, Size:{opened_pil_img.size}")
+                    converted_img_rgba = opened_pil_img.convert('RGBA') if opened_pil_img.mode != 'RGBA' else opened_pil_img.copy()
+                    thumbnailed_img = converted_img_rgba.copy(); resample_f = Image.Resampling.LANCZOS if hasattr(Image.Resampling,'LANCZOS') else Image.BILINEAR; thumbnailed_img.thumbnail(self.video_frame_size,resample_f)
+                    rgba_canvas = Image.new('RGBA',self.video_frame_size,(0,0,0,0)); pos_x,pos_y=(self.video_frame_size[0]-thumbnailed_img.width)//2,(self.video_frame_size[1]-thumbnailed_img.height)//2
+                    rgba_canvas.paste(thumbnailed_img,(pos_x,pos_y),thumbnailed_img)
+                    final_rgb_img_pil = Image.new("RGB",self.video_frame_size,(0,0,0)); final_rgb_img_pil.paste(rgba_canvas,mask=rgba_canvas.split()[3])
+                    debug_path_img_pre_numpy = os.path.join(self.output_dir,f"debug_PRE_NUMPY_S{num_of_scene}.png"); final_rgb_img_pil.save(debug_path_img_pre_numpy); logger.info(f"DEBUG: Saved PRE_NUMPY_S{num_of_scene} to {debug_path_img_pre_numpy}")
+                    numpy_frame_arr = np.array(final_rgb_img_pil,dtype=np.uint8);
+                    if not numpy_frame_arr.flags['C_CONTIGUOUS']: numpy_frame_arr=np.ascontiguousarray(numpy_frame_arr,dtype=np.uint8)
+                    logger.debug(f"S{num_of_scene}: NumPy for MoviePy. Shape:{numpy_frame_arr.shape}, DType:{numpy_frame_arr.dtype}, C-Contig:{numpy_frame_arr.flags['C_CONTIGUOUS']}")
+                    if numpy_frame_arr.size==0 or numpy_frame_arr.ndim!=3 or numpy_frame_arr.shape[2]!=3: logger.error(f"S{num_of_scene}: Invalid NumPy array for MoviePy. Skip."); continue
+                    base_image_clip = ImageClip(numpy_frame_arr,transparent=False).set_duration(duration_for_scene)
+                    debug_path_moviepy_frame=os.path.join(self.output_dir,f"debug_MOVIEPY_FRAME_S{num_of_scene}.png"); base_image_clip.save_frame(debug_path_moviepy_frame,t=0.1); logger.info(f"DEBUG: Saved MOVIEPY_FRAME_S{num_of_scene} to {debug_path_moviepy_frame}")
+                    fx_image_clip = base_image_clip
+                    try: scale_end_kb=random.uniform(1.03,1.08); fx_image_clip=base_image_clip.fx(vfx.resize,lambda t_val:1+(scale_end_kb-1)*(t_val/duration_for_scene) if duration_for_scene>0 else 1).set_position('center')
+                    except Exception as e_kb_fx: logger.error(f"S{num_of_scene} Ken Burns error: {e_kb_fx}",exc_info=False)
+                    active_scene_clip = fx_image_clip
+                elif type_of_asset == 'video':
+                    source_video_clip_obj=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)
+                        source_video_clip_obj=VideoFileClip(path_of_asset,target_resolution=(self.video_frame_size[1],self.video_frame_size[0])if self.video_frame_size else None, audio=False)
+                        temp_video_clip_obj_loop=source_video_clip_obj
+                        if source_video_clip_obj.duration!=duration_for_scene:
+                            if source_video_clip_obj.duration>duration_for_scene:temp_video_clip_obj_loop=source_video_clip_obj.subclip(0,duration_for_scene)
                             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 duration_for_scene/source_video_clip_obj.duration > 1.5 and source_video_clip_obj.duration>0.1:temp_video_clip_obj_loop=source_video_clip_obj.loop(duration=duration_for_scene)
+                                else:temp_video_clip_obj_loop=source_video_clip_obj.set_duration(source_video_clip_obj.duration);logger.info(f"S{num_of_scene} Video clip ({source_video_clip_obj.duration:.2f}s) shorter than target ({duration_for_scene:.2f}s).")
+                        active_scene_clip=temp_video_clip_obj_loop.set_duration(duration_for_scene)
+                        if active_scene_clip.size!=list(self.video_frame_size):active_scene_clip=active_scene_clip.resize(self.video_frame_size)
+                    except Exception as e_vid_load_loop:logger.error(f"S{num_of_scene} Video load error '{path_of_asset}':{e_vid_load_loop}",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:
+                        if source_video_clip_obj and source_video_clip_obj is not active_scene_clip and hasattr(source_video_clip_obj,'close'):source_video_clip_obj.close()
+                else: logger.warning(f"S{num_of_scene} Unknown asset type '{type_of_asset}'. Skip."); continue
+                if active_scene_clip and action_in_key:
                     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)
+                        dur_text_overlay=min(active_scene_clip.duration-0.5,active_scene_clip.duration*0.8)if active_scene_clip.duration>0.5 else active_scene_clip.duration; start_text_overlay=0.25
+                        if dur_text_overlay > 0:
+                            text_clip_for_overlay=TextClip(f"Scene {num_of_scene}\n{action_in_key}",fontsize=self.VIDEO_OVERLAY_FONT_SIZE,color=self.VIDEO_OVERLAY_FONT_COLOR,font=self.active_moviepy_font_name,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(dur_text_overlay).set_start(start_text_overlay).set_position(('center',0.92),relative=True)
+                            active_scene_clip=CompositeVideoClip([active_scene_clip,text_clip_for_overlay],size=self.video_frame_size,use_bgclip=True)
+                        else: logger.warning(f"S{num_of_scene}: Text overlay duration zero. Skip text.")
+                    except Exception as e_txt_comp:logger.error(f"S{num_of_scene} TextClip error:{e_txt_comp}. No text.",exc_info=True)
+                if active_scene_clip:processed_moviepy_clips_list.append(active_scene_clip);logger.info(f"S{num_of_scene} Processed. Dur:{active_scene_clip.duration:.2f}s.")
+            except Exception as e_asset_loop_main:logger.error(f"MAJOR Error processing asset for S{num_of_scene} ({path_of_asset}):{e_asset_loop_main}",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
+                if active_scene_clip and hasattr(active_scene_clip,'close'):
+                    try: active_scene_clip.close()
+                    except: pass
+        if not processed_moviepy_clips_list:logger.warning("No clips processed for animatic. Aborting.");return None
+        transition_duration_val=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
+            logger.info(f"Concatenating {len(processed_moviepy_clips_list)} clips for final animatic.");
+            if len(processed_moviepy_clips_list)>1:final_video_output_clip=concatenate_videoclips(processed_moviepy_clips_list,padding=-transition_duration_val if transition_duration_val>0 else 0,method="compose")
+            elif processed_moviepy_clips_list:final_video_output_clip=processed_moviepy_clips_list[0]
+            if not final_video_output_clip:logger.error("Concatenation resulted in a None clip. Aborting.");return None
+            logger.info(f"Concatenated animatic duration:{final_video_output_clip.duration:.2f}s")
+            if transition_duration_val>0 and final_video_output_clip.duration>0:
+                if final_video_output_clip.duration>transition_duration_val*2:final_video_output_clip=final_video_output_clip.fx(vfx.fadein,transition_duration_val).fx(vfx.fadeout,transition_duration_val)
+                else:final_video_output_clip=final_video_output_clip.fx(vfx.fadein,min(transition_duration_val,final_video_output_clip.duration/2.0))
+            if overall_narration_path and os.path.exists(overall_narration_path) and final_video_output_clip.duration>0:
+                try:narration_audio_clip_mvpy=AudioFileClip(overall_narration_path);final_video_output_clip=final_video_output_clip.set_audio(narration_audio_clip_mvpy);logger.info("Overall narration added to animatic.")
+                except Exception as e_narr_add:logger.error(f"Error adding narration to animatic:{e_narr_add}",exc_info=True)
+            elif final_video_output_clip.duration<=0:logger.warning("Animatic has no duration. Audio not added.")
+            if final_video_output_clip and final_video_output_clip.duration>0:
+                final_output_path_str=os.path.join(self.output_dir,output_filename);logger.info(f"Writing final animatic video to:{final_output_path_str} (Duration:{final_video_output_clip.duration:.2f}s)")
+                final_video_output_clip.write_videofile(final_output_path_str,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"Animatic video created successfully:{final_output_path_str}");return final_output_path_str
+            else:logger.error("Final animatic clip is invalid or has zero duration. Cannot write video file.");return None
+        except Exception as e_vid_write_final:logger.error(f"Error during final animatic video file writing or composition:{e_vid_write_final}",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}")
+            logger.debug("Closing all MoviePy clips in `assemble_animatic_from_assets` main finally block.")
+            all_clips_to_close_list = processed_moviepy_clips_list + ([narration_audio_clip_mvpy] if narration_audio_clip_mvpy else []) + ([final_video_output_clip] if final_video_output_clip else [])
+            for clip_item_to_close in all_clips_to_close_list:
+                if clip_item_to_close and hasattr(clip_item_to_close, 'close'):
+                    try: clip_item_to_close.close()
+                    except Exception as e_final_close: logger.warning(f"Ignoring error while closing a MoviePy clip: {type(clip_item_to_close).__name__} - {e_final_close}")