Update core/visual_engine.py

core/visual_engine.py

@@ -1,41 +1,39 @@
 # core/visual_engine.py
 from PIL import Image, ImageDraw, ImageFont, ImageOps
-import base64
-import mimetypes
+# --- 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 numpy as np
 import os
-import openai # Ensure this is OpenAI v1.x.x+
+import openai
 import requests
 import io
 import time
 import random
 import logging
 
-# --- MoviePy Imports ---
-from moviepy.editor import (ImageClip, VideoFileClip, concatenate_videoclips, TextClip,
-                            CompositeVideoClip, AudioFileClip)
-import moviepy.video.fx.all as vfx
-
-# --- MONKEY PATCH for Pillow/MoviePy compatibility ---
-try:
-    if hasattr(Image, 'Resampling') and hasattr(Image.Resampling, 'LANCZOS'): # Pillow 9+
-        if not hasattr(Image, 'ANTIALIAS'): Image.ANTIALIAS = Image.Resampling.LANCZOS
-    elif hasattr(Image, 'LANCZOS'): # Pillow 8
-         if not hasattr(Image, 'ANTIALIAS'): Image.ANTIALIAS = Image.LANCZOS
-    elif not hasattr(Image, 'ANTIALIAS'):
-             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__)
-# Set a default level; can be overridden by the main app's logging config
-# logger.setLevel(logging.DEBUG) # Uncomment for very verbose output during development
+logger.setLevel(logging.INFO)
 
-# --- External Service Client Imports ---
+# --- ElevenLabs Client Import ---
 ELEVENLABS_CLIENT_IMPORTED = False
-ElevenLabsAPIClient = None # Class placeholder
-Voice = None               # Class placeholder
-VoiceSettings = None       # Class placeholder
+ElevenLabsAPIClient = None
+Voice = None
+VoiceSettings = None
 try:
     from elevenlabs.client import ElevenLabs as ImportedElevenLabsClient
     from elevenlabs import Voice as ImportedVoice, VoiceSettings as ImportedVoiceSettings
@@ -43,476 +41,694 @@ try:
     Voice = ImportedVoice
     VoiceSettings = ImportedVoiceSettings
     ELEVENLABS_CLIENT_IMPORTED = True
-    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.")
+    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
-RunwayMLAPIClientClass = None # Storing the class itself
+RunwayMLClient = None
 try:
-    from runwayml import RunwayML as ImportedRunwayMLAPIClientClass # Actual SDK import
-    RunwayMLAPIClientClass = ImportedRunwayMLAPIClientClass
-    RUNWAYML_SDK_IMPORTED = True
-    logger.info("RunwayML SDK (runwayml) imported successfully.")
+    logger.info("RunwayML SDK import is a placeholder.")
 except ImportError:
-    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.")
+    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.")
 
 
 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' # Common ImageMagick font name
-    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_pil_preference = "DejaVuSans-Bold.ttf" # More standard Linux font
+        self.font_filename = "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", # Alternative
+            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" # Previous custom
+            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() # Fallback default
-        self.active_font_size_pil = self.DEFAULT_FONT_SIZE_PIL
-        self.active_moviepy_font_name = self.DEFAULT_MOVIEPY_FONT
+        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'
 
-        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 loaded: {self.resolved_font_path_pil} at size {self.active_font_size_pil}.")
-                # Determine MoviePy font based on loaded PIL font for consistency
-                if "dejavu" in self.resolved_font_path_pil.lower(): self.active_moviepy_font_name = 'DejaVu-Sans-Bold'
-                elif "liberation" in self.resolved_font_path_pil.lower(): self.active_moviepy_font_name = 'Liberation-Sans-Bold'
-            except IOError as e_font_load_io:
-                logger.error(f"Pillow font loading IOError for '{self.resolved_font_path_pil}': {e_font_load_io}. Using default font.")
-        else:
-            logger.warning("Preferred Pillow font not found in predefined paths. Using default font.")
-
-        # Service API keys and flags
-        self.openai_api_key = None; self.USE_AI_IMAGE_GENERATION = False
-        self.dalle_model = "dall-e-3"; self.image_size_dalle3 = "1792x1024" # DALL-E 3 landscape
-        self.video_frame_size = (1280, 720) # HD 16:9
-
-        self.elevenlabs_api_key = None; self.USE_ELEVENLABS = False; self.elevenlabs_client_instance = None
+        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.video_frame_size = (1280, 720)
+        self.elevenlabs_api_key = None
+        self.USE_ELEVENLABS = False
+        self.elevenlabs_client = None
         self.elevenlabs_voice_id = default_elevenlabs_voice_id
         if VoiceSettings and ELEVENLABS_CLIENT_IMPORTED:
-            self.elevenlabs_voice_settings_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 # Instance of RunwayML()
-
-        # Attempt to initialize RunwayML client if SDK is present and RUNWAYML_API_SECRET env var might be set
-        if RUNWAYML_SDK_IMPORTED and RunwayMLAPIClientClass and os.getenv("RUNWAYML_API_SECRET"):
-            try:
-                self.runway_ml_sdk_client_instance = RunwayMLAPIClientClass() # SDK uses env var by default
-                self.USE_RUNWAYML = True # Assume enabled if client initializes without error
-                logger.info("RunwayML Client initialized using RUNWAYML_API_SECRET environment variable at startup.")
-            except Exception as e_runway_init_at_startup:
-                logger.error(f"Initial RunwayML client initialization failed (env var RUNWAYML_API_SECRET might be invalid or SDK issue): {e_runway_init_at_startup}")
-                self.USE_RUNWAYML = False # Ensure it's disabled if init fails
-        
+            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
         logger.info("VisualEngine initialized.")
 
-    # --- API Key Setter Methods ---
-    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 ({self.dalle_model}) service status: {'Ready' if self.USE_AI_IMAGE_GENERATION else 'Disabled (no API key)'}")
+    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_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:
+    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_instance = ElevenLabsAPIClient(api_key=api_key_value) # Pass key directly
-                self.USE_ELEVENLABS = bool(self.elevenlabs_client_instance)
-                logger.info(f"ElevenLabs Client service status: {'Ready' if self.USE_ELEVENLABS else 'Failed Initialization'} (Using Voice ID: {self.elevenlabs_voice_id})")
-            except Exception as e_11l_init:
-                logger.error(f"ElevenLabs client initialization error: {e_11l_init}. Service Disabled.", exc_info=True)
-                self.USE_ELEVENLABS = False; self.elevenlabs_client_instance = None
+                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(f"ElevenLabs Service Disabled (API key not provided or SDK import issue).")
+            logger.info("ElevenLabs Disabled (no key or 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 Search service status: {'Ready' if self.USE_PEXELS else '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 set_runway_api_key(self, api_key_value):
-        self.runway_api_key = api_key_value # Store the key itself
-        if api_key_value:
-            if RUNWAYML_SDK_IMPORTED and RunwayMLAPIClientClass:
-                if not self.runway_ml_sdk_client_instance: # If not already initialized (e.g., by env var at startup)
-                    try:
-                        # The RunwayML Python SDK expects the API key via the RUNWAYML_API_SECRET env var.
-                        original_env_secret = os.getenv("RUNWAYML_API_SECRET")
-                        if not original_env_secret: # If env var not set, set it temporarily from passed key
-                            logger.info("Temporarily setting RUNWAYML_API_SECRET from provided key for SDK client initialization.")
-                            os.environ["RUNWAYML_API_SECRET"] = api_key_value
-                        
-                        self.runway_ml_sdk_client_instance = RunwayMLAPIClientClass()
-                        self.USE_RUNWAYML = True # Mark service as usable if client initializes
-                        logger.info("RunwayML Client initialized successfully using provided API key (via env var).")
-
-                        if not original_env_secret: # Clean up: remove env var if we set it
-                            del os.environ["RUNWAYML_API_SECRET"]
-                            logger.info("Cleared temporary RUNWAYML_API_SECRET environment variable.")
-                    except Exception as e_runway_client_setkey_init:
-                        logger.error(f"RunwayML Client initialization via set_runway_api_key failed: {e_runway_client_setkey_init}", exc_info=True)
-                        self.USE_RUNWAYML = False; self.runway_ml_sdk_client_instance = None # Ensure it's disabled
-                else: # Client was already initialized
-                    self.USE_RUNWAYML = True # Service is usable
-                    logger.info("RunwayML Client was already initialized (likely from environment variable). API key stored.")
-            else: # SDK not imported
-                logger.warning("RunwayML SDK not imported. API key has been stored, but the current integration relies on the SDK. Service effectively disabled.")
-                self.USE_RUNWAYML = False # Can't use without SDK if that's the implemented path
-        else: # No API key provided
-            self.USE_RUNWAYML = False; self.runway_ml_sdk_client_instance = None
-            logger.info("RunwayML Service Disabled (no API key provided to set_runway_api_key).")
-
-    # --- Helper Methods (_image_to_data_uri, _map_resolution_to_runway_ratio, etc.) ---
-    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", ".webp": "image/webp"}
-                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}"
-        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"Resolution {ratio_str} not directly in Gen-4 supported list. Defaulting to 1280:720 for RunwayML.")
-        return "1280:720"
+    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_object_pil):
-        default_h = getattr(font_object_pil, 'size', self.active_font_size_pil)
-        if not text_content: return 0, default_h
+    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:
-            if hasattr(font_object_pil,'getbbox'): bbox=font_object_pil.getbbox(text_content);w=bbox[2]-bbox[0];h=bbox[3]-bbox[1]; return w, h if h > 0 else default_h
-            elif hasattr(font_object_pil,'getsize'): w,h=font_object_pil.getsize(text_content); return w, h if h > 0 else default_h
-            else: return int(len(text_content)*default_h*0.6),int(default_h*1.2) # Basic estimate
-        except Exception as e_getdim: logger.warning(f"Error in _get_text_dimensions: {e_getdim}"); return int(len(text_content)*self.active_font_size_pil*0.6),int(self.active_font_size_pil*1.2)
+            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)
 
     def _create_placeholder_image_content(self, text_description, filename, size=None):
-        # <<< THIS IS THE CORRECTED VERSION OF THIS METHOD >>>
-        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 = [] # Renamed to avoid conflict
-        if not text_description: text_description = "(Placeholder Image)"
-        words_list = text_description.split(); current_line_buffer = "" # Renamed
-        for word_idx, word_item in enumerate(words_list): # Renamed
-            prospective_add = word_item + (" " if word_idx < len(words_list) - 1 else "")
-            test_line_candidate = current_line_buffer + prospective_add
-            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
+        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_buffer.strip(): lines_for_placeholder.append(current_line_buffer.strip())
-                current_line_buffer = prospective_add
-        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): # Renamed
-            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_txt: logger.error(f"Pillow d.text error: {e_draw_txt} 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_elps: logger.error(f"Pillow d.text ellipsis error: {e_elps}"); break
-        filepath_placeholder = os.path.join(self.output_dir, filename) # Renamed
-        try: img.save(filepath_placeholder); return filepath_placeholder
-        except Exception as e_save_ph: logger.error(f"Saving placeholder image '{filepath_placeholder}' error: {e_save_ph}", exc_info=True); return None
-
-    def _search_pexels_image(self, query_str, output_fn_base):
-        # (Corrected 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)
+                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] + "...")
+            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:
-            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
+            img.save(filepath)
+            return filepath
+        except Exception as e:
+            logger.error(f"Error saving placeholder image {filepath}: {e}", exc_info=True)
+            return None
 
-    def _generate_video_clip_with_runwayml(self, text_prompt_for_motion, input_image_path, scene_identifier_filename_base, target_duration_seconds=5):
-        # (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) # Renamed
-        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" # Renamed
-        output_vid_fp = os.path.join(self.output_dir, output_vid_fn) # Renamed
-        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}'")
+    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)
         try:
-            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) # Renamed
-            task_id_runway = task_submitted_runway.id; logger.info(f"Runway Gen-4 task ID: {task_id_runway}. Polling...") # Renamed
-            poll_sec=10; max_poll_count=36; poll_start_time = time.time() # Renamed
-            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) # Renamed
-                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) # Renamed
-                    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() # Renamed
-                    with open(output_vid_fp,'wb') as f_vid: # Renamed
-                        for chunk_data in video_resp_get.iter_content(chunk_size=8192): f_vid.write(chunk_data) # Renamed
-                    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.") # Renamed
-                    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 # Renamed
-        except Exception as e_runway_gen: logger.error(f"Runway Gen-4 API error: {e_runway_gen}", exc_info=True); return None # Renamed
-
-    def _create_placeholder_video_content(self, text_desc_ph, filename_ph, duration_ph=4, size_ph=None): # Renamed variables
-        # <<< 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 # Initialize
+            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
+
+    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
         try:
-            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: # Specific exception variable
-            logger.error(f"Failed to create generic placeholder video '{filepath_ph}': {e_ph_vid}", exc_info=True)
+            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 text_clip_ph and hasattr(text_clip_ph, 'close'):
-                text_clip_ph.close()
-    
-    # --- generate_scene_asset (Main asset generation logic) ---
-    def generate_scene_asset(self, image_generation_prompt_text, motion_prompt_text_for_video,
-                             scene_data_dict, scene_identifier_fn_base, # Renamed
-                             generate_as_video_clip_flag=False, runway_target_dur_val=5): # Renamed
-        # (Logic mostly as before, ensuring base image is robustly generated first)
-        base_name_asset, _ = os.path.splitext(scene_identifier_fn_base) # Renamed
-        asset_info_result = {'path': None, 'type': 'none', 'error': True, 'prompt_used': image_generation_prompt_text, 'error_message': 'Asset generation init failed'} # Renamed
-        path_for_input_image_runway = None # Renamed
-        fn_for_base_image = base_name_asset + ("_base_for_video.png" if generate_as_video_clip_flag else ".png") # Renamed
-        fp_for_base_image = os.path.join(self.output_dir, fn_for_base_image) # Renamed
-        
-        if self.USE_AI_IMAGE_GENERATION and self.openai_api_key:
-            # (DALL-E logic with corrected try/except from previous response)
-            max_r_dalle, att_n_dalle = 2,0;
-            for att_n_dalle in range(max_r_dalle):
-                att_c_dalle = att_n_dalle + 1 # Renamed
-                try: 
-                    logger.info(f"Att {att_c_dalle} DALL-E (base img): {image_generation_prompt_text[:70]}...");
-                    oai_cl = openai.OpenAI(api_key=self.openai_api_key,timeout=90.0) # Renamed
-                    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") # Renamed
-                    oai_iu = oai_r.data[0].url; oai_rp = getattr(oai_r.data[0],'revised_prompt',None) # Renamed
-                    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() # Renamed
-                    oai_id = Image.open(io.BytesIO(oai_ir.content)); # Renamed
-                    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 {att_c_dalle}:{e_oai_rl}.Retry...");time.sleep(5*att_c_dalle);asset_info_result['error_message']=str(e_oai_rl) # Renamed
-                except openai.APIError as e_oai_api: logger.error(f"OpenAI APIError Att {att_c_dalle}:{e_oai_api}");asset_info_result['error_message']=str(e_oai_api);break # Renamed
-                except requests.exceptions.RequestException as e_oai_req: logger.error(f"Requests Err DALL-E Att {att_c_dalle}:{e_oai_req}");asset_info_result['error_message']=str(e_oai_req);break # Renamed
-                except Exception as e_oai_gen: logger.error(f"General DALL-E Err Att {att_c_dalle}:{e_oai_gen}",exc_info=True);asset_info_result['error_message']=str(e_oai_gen);break # Renamed
-            if asset_info_result['error']: logger.warning(f"DALL-E failed after {att_c_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); # Renamed variables
-            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() # Renamed
+            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, 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)
+            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}")
 
-        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);ph_p=self._create_placeholder_image_content(f"[Base Placeholder]{ph_ppt[:70]}...",fn_for_base_image); # Renamed variables
-            if ph_p:path_for_input_image_runway=ph_p;asset_info_result={'path':ph_p,'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() # Renamed
+    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'
+        }
 
-        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) # Renamed
-                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 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.")
+
+        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, text_to_narrate, output_filename="narration_overall.mp3"):
-        # (Keep as before)
-        if not self.USE_ELEVENLABS or not self.elevenlabs_client_instance or not text_to_narrate: logger.info("11L skip."); return None # Check instance
-        audio_fp_11l=os.path.join(self.output_dir,output_filename) # Renamed
-        try: logger.info(f"11L audio (Voice:{self.elevenlabs_voice_id}): {text_to_narrate[:70]}..."); audio_sm_11l=None # Renamed
-        if hasattr(self.elevenlabs_client_instance,'text_to_speech')and hasattr(self.elevenlabs_client_instance.text_to_speech,'stream'):audio_sm_11l=self.elevenlabs_client_instance.text_to_speech.stream;logger.info("Using 11L .text_to_speech.stream()")
-        elif hasattr(self.elevenlabs_client_instance,'generate_stream'):audio_sm_11l=self.elevenlabs_client_instance.generate_stream;logger.info("Using 11L .generate_stream()")
-        elif hasattr(self.elevenlabs_client_instance,'generate'):logger.info("Using 11L .generate()");eleven_vp=Voice(voice_id=str(self.elevenlabs_voice_id),settings=self.elevenlabs_voice_settings_obj)if Voice and self.elevenlabs_voice_settings_obj else str(self.elevenlabs_voice_id);eleven_ab=self.elevenlabs_client_instance.generate(text=text_to_narrate,voice=eleven_vp,model="eleven_multilingual_v2"); # Renamed
-        with open(audio_fp_11l,"wb")as f_11l:f_11l.write(eleven_ab);logger.info(f"11L audio (non-stream): {audio_fp_11l}");return audio_fp_11l # Renamed
-        else:logger.error("No 11L audio method.");return None
-        if audio_sm_11l:eleven_vps={"voice_id":str(self.elevenlabs_voice_id)} # Renamed
-        if self.elevenlabs_voice_settings_obj:
-            if hasattr(self.elevenlabs_voice_settings_obj,'model_dump'):eleven_vps["voice_settings"]=self.elevenlabs_voice_settings_obj.model_dump()
-            elif hasattr(self.elevenlabs_voice_settings_obj,'dict'):eleven_vps["voice_settings"]=self.elevenlabs_voice_settings_obj.dict()
-            else:eleven_vps["voice_settings"]=self.elevenlabs_voice_settings_obj
-        eleven_adi=audio_sm_11l(text=text_to_narrate,model_id="eleven_multilingual_v2",**eleven_vps) # Renamed
-        with open(audio_fp_11l,"wb")as f_11l_stream: # Renamed
-            for chunk_11l in eleven_adi: # Renamed
-                if chunk_11l:f_11l_stream.write(chunk_11l)
-        logger.info(f"11L audio (stream): {audio_fp_11l}");return audio_fp_11l
-        except Exception as e_11labs_audio:logger.error(f"11L audio error: {e_11labs_audio}",exc_info=True);return None # Renamed
+    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):
-        # (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)
-        # ... (The core of that robust version should be retained here) ...
-        # For brevity, I'm not re-pasting the entire assemble_animatic_from_assets if it was correct before this syntax error hunt.
-        # If it also needs review, let me know. The key is that the *input* (asset_data_list)
-        # from the corrected generate_scene_asset is now more reliable.
-        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 # Renamed variables
-        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): # Renamed loop variables
-            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 # Clip for this iteration
+        if not asset_data_list:
+            logger.warning("No assets for animatic.")
+            return None
+        processed_clips = []
+        narration_clip = None
+        final_clip = None
+        logger.info(f"Assembling from {len(asset_data_list)} assets. Frame: {self.video_frame_size}.")
+
+        for i, asset_info in enumerate(asset_data_list):
+            asset_path = asset_info.get('path')
+            asset_type = asset_info.get('type')
+            scene_dur = asset_info.get('duration', 4.5)
+            scene_num = asset_info.get('scene_num', i + 1)
+            key_action = asset_info.get('key_action', '')
+            logger.info(f"S{scene_num}: Path='{asset_path}', Type='{asset_type}', Dur='{scene_dur}'s")
+
+            if not (asset_path and os.path.exists(asset_path)):
+                logger.warning(f"S{scene_num}: Not found '{asset_path}'. Skip.")
+                continue
+            if scene_dur <= 0:
+                logger.warning(f"S{scene_num}: Invalid duration ({scene_dur}s). Skip.")
+                continue
+
+            current_scene_mvpy_clip = None
             try:
-                if type_of_asset == 'image':
-                    # (Robust image processing from previous version, including debug saves)
-                    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':
-                    # (Video processing logic from previous version)
-                    source_video_clip_obj=None
+                if asset_type == 'image':
+                    pil_img = Image.open(asset_path)
+                    logger.debug(f"S{scene_num}: Loaded img. Mode:{pil_img.mode}, Size:{pil_img.size}")
+                    img_rgba = pil_img.convert('RGBA') if pil_img.mode != 'RGBA' else pil_img.copy()
+                    thumb = img_rgba.copy()
+                    rf = Image.Resampling.LANCZOS if hasattr(Image.Resampling, 'LANCZOS') else Image.BILINEAR
+                    thumb.thumbnail(self.video_frame_size, rf)
+                    cv_rgba = Image.new('RGBA', self.video_frame_size, (0, 0, 0, 0))
+                    xo = (self.video_frame_size[0] - thumb.width) // 2
+                    yo = (self.video_frame_size[1] - thumb.height) // 2
+                    cv_rgba.paste(thumb, (xo, yo), thumb)
+                    final_rgb_pil = Image.new("RGB", self.video_frame_size, (0, 0, 0))
+                    final_rgb_pil.paste(cv_rgba, mask=cv_rgba.split()[3])
+                    dbg_path = os.path.join(self.output_dir, f"debug_PRE_NUMPY_S{scene_num}.png")
+                    final_rgb_pil.save(dbg_path)
+                    logger.info(f"DEBUG: Saved PRE_NUMPY_S{scene_num} to {dbg_path}")
+                    frame_np = np.array(final_rgb_pil, dtype=np.uint8)
+                    if not frame_np.flags['C_CONTIGUOUS']:
+                        frame_np = np.ascontiguousarray(frame_np, dtype=np.uint8)
+                    logger.debug(f"S{scene_num}: NumPy for MoviePy. Shape:{frame_np.shape}, DType:{frame_np.dtype}, C-Contig:{frame_np.flags['C_CONTIGUOUS']}")
+                    if frame_np.size == 0 or frame_np.ndim != 3 or frame_np.shape[2] != 3:
+                        logger.error(f"S{scene_num}: Invalid NumPy. Skip.")
+                        continue
+                    clip_base = ImageClip(frame_np, transparent=False).set_duration(scene_dur)
+                    mvpy_dbg_path = os.path.join(self.output_dir, f"debug_MOVIEPY_FRAME_S{scene_num}.png")
+                    clip_base.save_frame(mvpy_dbg_path, t=0.1)
+                    logger.info(f"DEBUG: Saved MOVIEPY_FRAME_S{scene_num} to {mvpy_dbg_path}")
+                    clip_fx = clip_base
+                    try:
+                        es = random.uniform(1.03, 1.08)
+                        clip_fx = clip_base.fx(
+                            vfx.resize,
+                            lambda t: 1 + (es - 1) * (t / scene_dur) if scene_dur > 0 else 1
+                        ).set_position('center')
+                    except Exception as e:
+                        logger.error(f"S{scene_num} Ken Burns error: {e}", exc_info=False)
+                    current_scene_mvpy_clip = clip_fx
+                elif asset_type == 'video':
+                    src_clip = None
                     try:
-                        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)
+                        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 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
+                                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 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: # Text Overlay
+                        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:
-                        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)
+                        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)
             finally:
-                if active_scene_clip and hasattr(active_scene_clip,'close'):
-                    try: active_scene_clip.close()
-                    except: pass # Ignore errors during cleanup
+                if current_scene_mvpy_clip and hasattr(current_scene_mvpy_clip, 'close'):
+                    try:
+                        current_scene_mvpy_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 # Renamed
+        if not processed_clips:
+            logger.warning("No clips processed. Abort.")
+            return None
+        td = 0.75
         try:
-            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)") # Renamed
-                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 # Renamed
+            logger.info(f"Concatenating {len(processed_clips)} clips.")
+            if len(processed_clips) > 1:
+                final_clip = concatenate_videoclips(processed_clips, padding=-td if td > 0 else 0, method="compose")
+            elif processed_clips:
+                final_clip = processed_clips[0]
+            if not final_clip:
+                logger.error("Concatenation failed.")
+                return None
+            logger.info(f"Concatenated dur:{final_clip.duration:.2f}s")
+            if td > 0 and final_clip.duration > 0:
+                if final_clip.duration > td * 2:
+                    final_clip = final_clip.fx(vfx.fadein, td).fx(vfx.fadeout, td)
+                else:
+                    final_clip = final_clip.fx(vfx.fadein, min(td, final_clip.duration / 2.0))
+            if overall_narration_path and os.path.exists(overall_narration_path) and final_clip.duration > 0:
+                try:
+                    narration_clip = AudioFileClip(overall_narration_path)
+                    final_clip = final_clip.set_audio(narration_clip)
+                    logger.info("Narration added.")
+                except Exception as e:
+                    logger.error(f"Narration add error:{e}", exc_info=True)
+            elif final_clip.duration <= 0:
+                logger.warning("Video no duration. No audio.")
+            if final_clip and final_clip.duration > 0:
+                op = os.path.join(self.output_dir, output_filename)
+                logger.info(f"Writing video:{op} (Dur:{final_clip.duration:.2f}s)")
+                final_clip.write_videofile(
+                    op,
+                    fps=fps,
+                    codec='libx264',
+                    preset='medium',
+                    audio_codec='aac',
+                    temp_audiofile=os.path.join(self.output_dir, f'temp-audio-{os.urandom(4).hex()}.m4a'),
+                    remove_temp=True,
+                    threads=os.cpu_count() or 2,
+                    logger='bar',
+                    bitrate="5000k",
+                    ffmpeg_params=["-pix_fmt", "yuv420p"]
+                )
+                logger.info(f"Video created:{op}")
+                return op
+            else:
+                logger.error("Final clip invalid. No write.")
+                return None
+        except Exception as e:
+            logger.error(f"Video write error:{e}", exc_info=True)
+            return None
         finally:
-            logger.debug("Closing all MoviePy clips in `assemble_animatic_from_assets` main finally block.")
-            clips_for_final_closure = processed_moviepy_clips_list + ([narration_audio_clip_mvpy] if narration_audio_clip_mvpy else []) + ([final_video_output_clip] if final_video_output_clip else []) # Renamed
-            for clip_item_to_close in clips_for_final_closure: # Renamed
-                if clip_item_to_close and hasattr(clip_item_to_close, 'close'):
-                    try: clip_item_to_close.close()
-                    except Exception as e_final_clip_close: logger.warning(f"Ignoring error while closing a MoviePy clip: {type(clip_item_to_close).__name__} - {e_final_clip_close}")
+            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}")