Update core/visual_engine.py

core/visual_engine.py

@@ -12,36 +12,23 @@ import random
 import logging
 
 # --- MoviePy Imports ---
-from moviepy.editor import (
-    ImageClip,
-    VideoFileClip,
-    concatenate_videoclips,
-    TextClip,
-    CompositeVideoClip,
-    AudioFileClip,
-)
+from moviepy.editor import (ImageClip, VideoFileClip, concatenate_videoclips, TextClip,
+                            CompositeVideoClip, AudioFileClip)
 import moviepy.video.fx.all as vfx
 
 # --- MONKEY PATCH for Pillow/MoviePy compatibility ---
 try:
-    if hasattr(Image, "Resampling") and hasattr(Image.Resampling, "LANCZOS"):  # Pillow 9+
-        if not hasattr(Image, "ANTIALIAS"):
-            Image.ANTIALIAS = Image.Resampling.LANCZOS
-    elif hasattr(Image, "LANCZOS"):  # Pillow 8
-        if not hasattr(Image, "ANTIALIAS"):
-            Image.ANTIALIAS = Image.LANCZOS
-    elif not hasattr(Image, "ANTIALIAS"):  # Fallback if no common resampling attributes found
-        print(
-            "WARNING: Pillow version lacks common Resampling attributes or ANTIALIAS. MoviePy effects might fail or look different."
-        )
+    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}"
-    )
+    print(f"WARNING: An unexpected error occurred during Pillow ANTIALIAS monkey-patch: {e_monkey_patch}")
 
 logger = logging.getLogger(__name__)
-# Consider setting level in main app if not already configured:
-# logger.setLevel(logging.DEBUG) # For very verbose output during debugging
+# logger.setLevel(logging.DEBUG) # Uncomment for very verbose debugging
 
 # --- External Service Client Imports ---
 ELEVENLABS_CLIENT_IMPORTED = False
@@ -51,383 +38,157 @@ VoiceSettings = None
 try:
     from elevenlabs.client import ElevenLabs as ImportedElevenLabsClient
     from elevenlabs import Voice as ImportedVoice, VoiceSettings as ImportedVoiceSettings
-
     ElevenLabsAPIClient = ImportedElevenLabsClient
     Voice = ImportedVoice
     VoiceSettings = ImportedVoiceSettings
     ELEVENLABS_CLIENT_IMPORTED = True
     logger.info("ElevenLabs client components imported successfully.")
 except ImportError:
-    logger.warning(
-        "ElevenLabs SDK not found (pip install elevenlabs). Audio generation will be disabled."
-    )
+    logger.warning("ElevenLabs SDK not found (pip install elevenlabs). Audio generation will be disabled.")
 except Exception as e_eleven_import:
-    logger.warning(
-        f"Error importing ElevenLabs client components: {e_eleven_import}. Audio generation disabled."
-    )
+    logger.warning(f"Error importing ElevenLabs client components: {e_eleven_import}. Audio generation disabled.")
 
 RUNWAYML_SDK_IMPORTED = False
-RunwayMLAPIClient = None  # Using a more specific name for the client class
+RunwayMLAPIClient = None
 try:
-    from runwayml import RunwayML as ImportedRunwayMLClient  # Actual SDK import
-
+    from runwayml import RunwayML as ImportedRunwayMLClient
     RunwayMLAPIClient = ImportedRunwayMLClient
     RUNWAYML_SDK_IMPORTED = True
     logger.info("RunwayML SDK imported successfully.")
 except ImportError:
-    logger.warning(
-        "RunwayML SDK not found (pip install runwayml). RunwayML video generation will be disabled."
-    )
+    logger.warning("RunwayML SDK not found (pip install runwayml). RunwayML video generation will be disabled.")
 except Exception as e_runway_sdk_import:
-    logger.warning(
-        f"Error importing RunwayML SDK: {e_runway_sdk_import}. RunwayML features disabled."
-    )
+    logger.warning(f"Error importing RunwayML SDK: {e_runway_sdk_import}. RunwayML features disabled.")
 
 
 class VisualEngine:
-    DEFAULT_FONT_SIZE_PIL = 10  # For default Pillow font
-    PREFERRED_FONT_SIZE_PIL = 20  # For custom font
+    DEFAULT_FONT_SIZE_PIL = 10
+    PREFERRED_FONT_SIZE_PIL = 20
     VIDEO_OVERLAY_FONT_SIZE = 30
-    VIDEO_OVERLAY_FONT_COLOR = "white"
-    # Standard font names ImageMagick (used by TextClip) is likely to find in Linux containers
-    DEFAULT_MOVIEPY_FONT = "DejaVu-Sans-Bold"
-    PREFERRED_MOVIEPY_FONT = "Liberation-Sans-Bold"  # Often available
+    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"
-    ):
+    def __init__(self, output_dir="temp_cinegen_media", default_elevenlabs_voice_id="Rachel"):
         self.output_dir = output_dir
         os.makedirs(self.output_dir, exist_ok=True)
-
-        self.font_filename_pil = "DejaVuSans-Bold.ttf"  # A more standard Linux font
-        font_paths_to_try = [
-            self.font_filename_pil,  # If in working dir or PATH
-            f"/usr/share/fonts/truetype/dejavu/{self.font_filename_pil}",
-            f"/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf",  # Alternative
-            f"/System/Library/Fonts/Supplemental/Arial.ttf",  # macOS fallback
-            f"C:/Windows/Fonts/arial.ttf",  # Windows fallback
-            f"/usr/local/share/fonts/truetype/mycustomfonts/arial.ttf",  # User's previous custom path
-        ]
-        self.font_path_pil_resolved = next(
-            (p for p in font_paths_to_try if os.path.exists(p)), None
-        )
-
-        self.font_pil = ImageFont.load_default()  # Default
-        self.current_font_size_pil = self.DEFAULT_FONT_SIZE_PIL
-
+        self.font_filename_pil = "DejaVuSans-Bold.ttf"
+        font_paths_to_try = [ self.font_filename_pil, f"/usr/share/fonts/truetype/dejavu/{self.font_filename_pil}", 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_resolved = next((p for p in font_paths_to_try if os.path.exists(p)), None)
+        self.font_pil = ImageFont.load_default(); self.current_font_size_pil = self.DEFAULT_FONT_SIZE_PIL
         if self.font_path_pil_resolved:
-            try:
-                self.font_pil = ImageFont.truetype(
-                    self.font_path_pil_resolved, self.PREFERRED_FONT_SIZE_PIL
-                )
-                self.current_font_size_pil = self.PREFERRED_FONT_SIZE_PIL
-                logger.info(
-                    f"Pillow font loaded: {self.font_path_pil_resolved} at size {self.current_font_size_pil}."
-                )
-                # Determine MoviePy font based on loaded PIL font
-                if "dejavu" in self.font_path_pil_resolved.lower():
-                    self.video_overlay_font = "DejaVu-Sans-Bold"
-                elif "liberation" in self.font_path_pil_resolved.lower():
-                    self.video_overlay_font = "Liberation-Sans-Bold"
-                else:  # Fallback if custom font doesn't have an obvious ImageMagick name
-                    self.video_overlay_font = self.DEFAULT_MOVIEPY_FONT
-            except IOError as e_font_load:
-                logger.error(
-                    f"Pillow font loading IOError for '{self.font_path_pil_resolved}': {e_font_load}. Using default."
-                )
-        else:
-            logger.warning("Custom Pillow font not found. Using default.")
-
-        self.openai_api_key = None
-        self.USE_AI_IMAGE_GENERATION = False
-        self.dalle_model = "dall-e-3"
-        self.image_size_dalle3 = "1792x1024"
+            try: self.font_pil = ImageFont.truetype(self.font_path_pil_resolved, self.PREFERRED_FONT_SIZE_PIL); self.current_font_size_pil = self.PREFERRED_FONT_SIZE_PIL; logger.info(f"Pillow font: {self.font_path_pil_resolved} sz {self.current_font_size_pil}."); self.video_overlay_font = 'DejaVu-Sans-Bold' if "dejavu" in self.font_path_pil_resolved.lower() else ('Liberation-Sans-Bold' if "liberation" in self.font_path_pil_resolved.lower() else self.DEFAULT_MOVIEPY_FONT)
+            except IOError as e_font_load: logger.error(f"Pillow font IOError '{self.font_path_pil_resolved}': {e_font_load}. Default.")
+        else: logger.warning("Custom 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_voice_id = default_elevenlabs_voice_id
-        if VoiceSettings and ELEVENLABS_CLIENT_IMPORTED:
-            self.elevenlabs_voice_settings = VoiceSettings(
-                stability=0.60,
-                similarity_boost=0.80,
-                style=0.15,
-                use_speaker_boost=True,
-            )
-        else:
-            self.elevenlabs_voice_settings = None
-
-        self.pexels_api_key = None
-        self.USE_PEXELS = False
-        self.runway_api_key = None
-        self.USE_RUNWAYML = False
-        self.runway_ml_client_instance = None  # More specific name
-
-        # Attempt to initialize Runway client if SDK is present and env var might be set
-        if (
-            RUNWAYML_SDK_IMPORTED
-            and RunwayMLAPIClient
-            and os.getenv("RUNWAYML_API_SECRET")
-        ):
-            try:
-                self.runway_ml_client_instance = RunwayMLAPIClient()  # SDK uses env var
-                self.USE_RUNWAYML = True  # Assume enabled if client initializes
-                logger.info(
-                    "RunwayML Client initialized from RUNWAYML_API_SECRET env var at startup."
-                )
-            except Exception as e_runway_init_startup:
-                logger.error(
-                    f"Initial RunwayML client init failed (env var RUNWAYML_API_SECRET might be invalid): {e_runway_init_startup}"
-                )
-                self.USE_RUNWAYML = False
-
+        self.elevenlabs_api_key = None; self.USE_ELEVENLABS = False; self.elevenlabs_client = None; self.elevenlabs_voice_id = default_elevenlabs_voice_id
+        if VoiceSettings and ELEVENLABS_CLIENT_IMPORTED: self.elevenlabs_voice_settings = VoiceSettings(stability=0.60, similarity_boost=0.80, style=0.15, use_speaker_boost=True)
+        else: self.elevenlabs_voice_settings = None
+        self.pexels_api_key = None; self.USE_PEXELS = False
+        self.runway_api_key = None; self.USE_RUNWAYML = False; self.runway_ml_client_instance = None
+        if RUNWAYML_SDK_IMPORTED and RunwayMLAPIClient and os.getenv("RUNWAYML_API_SECRET"):
+            try: self.runway_ml_client_instance = RunwayMLAPIClient(); 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.")
 
-    # --- API Key Setters ---
-    def set_openai_api_key(self, api_key):
-        self.openai_api_key = api_key
-        self.USE_AI_IMAGE_GENERATION = bool(api_key)
-        logger.info(
-            f"DALL-E ({self.dalle_model}) status: {'Ready' if self.USE_AI_IMAGE_GENERATION else 'Disabled'}"
-        )
-
+    def set_openai_api_key(self, api_key): self.openai_api_key = api_key; self.USE_AI_IMAGE_GENERATION = bool(api_key); logger.info(f"DALL-E status: {'Ready' if self.USE_AI_IMAGE_GENERATION else 'Disabled'}")
     def set_elevenlabs_api_key(self, api_key, voice_id_from_secret=None):
         self.elevenlabs_api_key = api_key
-        if voice_id_from_secret:
-            self.elevenlabs_voice_id = voice_id_from_secret
+        if voice_id_from_secret: self.elevenlabs_voice_id = voice_id_from_secret
         if api_key and ELEVENLABS_CLIENT_IMPORTED and ElevenLabsAPIClient:
-            try:
-                self.elevenlabs_client = ElevenLabsAPIClient(api_key=api_key)
-                self.USE_ELEVENLABS = bool(self.elevenlabs_client)
-                logger.info(
-                    f"ElevenLabs Client status: {'Ready' if self.USE_ELEVENLABS else 'Failed Initialization'} (Using Voice ID: {self.elevenlabs_voice_id})"
-                )
-            except Exception as e:
-                logger.error(
-                    f"ElevenLabs client initialization error: {e}. Service Disabled.",
-                    exc_info=True,
-                )
-                self.USE_ELEVENLABS = False
-                self.elevenlabs_client = None
-        else:
-            self.USE_ELEVENLABS = False
-            logger.info(
-                f"ElevenLabs Service Disabled (API key not provided or SDK import issue)."
-            )
-
-    def set_pexels_api_key(self, api_key):
-        self.pexels_api_key = api_key
-        self.USE_PEXELS = bool(api_key)
-        logger.info(
-            f"Pexels Search status: {'Ready' if self.USE_PEXELS else 'Disabled'}"
-        )
-
+            try: self.elevenlabs_client = ElevenLabsAPIClient(api_key=api_key); self.USE_ELEVENLABS = bool(self.elevenlabs_client); logger.info(f"11L Client: {'Ready' if self.USE_ELEVENLABS else 'Failed'} (Voice: {self.elevenlabs_voice_id})")
+            except Exception as e: logger.error(f"11L client init error: {e}. Disabled.", exc_info=True); self.USE_ELEVENLABS=False; self.elevenlabs_client=None
+        else: self.USE_ELEVENLABS = False; logger.info(f"11L Disabled (key/SDK).")
+    def set_pexels_api_key(self, api_key): self.pexels_api_key = api_key; self.USE_PEXELS = bool(api_key); logger.info(f"Pexels status: {'Ready' if self.USE_PEXELS else 'Disabled'}")
     def set_runway_api_key(self, api_key):
-        self.runway_api_key = api_key  # Store key regardless for potential direct HTTP use
+        self.runway_api_key = api_key
         if api_key:
             if RUNWAYML_SDK_IMPORTED and RunwayMLAPIClient:
-                if not self.runway_ml_client_instance:  # If not already initialized by env var
+                if not self.runway_ml_client_instance:
                     try:
-                        # The RunwayML Python SDK expects the API key via the RUNWAYML_API_SECRET env var.
-                        # If it's not set, we set it temporarily for client initialization.
                         original_env_secret = os.getenv("RUNWAYML_API_SECRET")
-                        if not original_env_secret:
-                            logger.info(
-                                "Temporarily setting RUNWAYML_API_SECRET from provided key for SDK client init."
-                            )
-                            os.environ["RUNWAYML_API_SECRET"] = api_key
-
-                        self.runway_ml_client_instance = RunwayMLAPIClient()
-                        self.USE_RUNWAYML = True  # SDK client successfully initialized
-                        logger.info(
-                            "RunwayML Client initialized successfully using provided API key."
-                        )
-
-                        if not original_env_secret:  # Clean up if we set it
-                            del os.environ["RUNWAYML_API_SECRET"]
-                            logger.info(
-                                "Cleared temporary RUNWAYML_API_SECRET env var."
-                            )
-
-                    except Exception as e_client_init:
-                        logger.error(
-                            f"RunwayML Client initialization via set_runway_api_key failed: {e_client_init}",
-                            exc_info=True,
-                        )
-                        self.USE_RUNWAYML = False
-                        self.runway_ml_client_instance = None
-                else:  # Client was already initialized (likely via env var during __init__)
-                    self.USE_RUNWAYML = True
-                    logger.info(
-                        "RunwayML Client was already initialized (likely from env var). API key stored."
-                    )
-            else:  # SDK not imported
-                logger.warning(
-                    "RunwayML SDK not imported. API key stored, but integration requires SDK. Service effectively disabled."
-                )
-                self.USE_RUNWAYML = False
-        else:  # No API key provided
-            self.USE_RUNWAYML = False
-            self.runway_ml_client_instance = None
-            logger.info("RunwayML Service Disabled (no API key provided).")
+                        if not original_env_secret: os.environ["RUNWAYML_API_SECRET"] = api_key; logger.info("Temp set RUNWAYML_API_SECRET for SDK.")
+                        self.runway_ml_client_instance = RunwayMLAPIClient(); 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: logger.error(f"RunwayML Client init in set_runway_api_key fail: {e}", exc_info=True); self.USE_RUNWAYML=False;self.runway_ml_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_client_instance = None; logger.info("RunwayML Disabled (no API key).")
 
-    # --- Helper Methods ---
     def _image_to_data_uri(self, image_path):
-        try:
-            mime_type, _ = mimetypes.guess_type(image_path)
-            if not mime_type:
-                ext = os.path.splitext(image_path)[1].lower()
-                mime_map = {".png": "image/png", ".jpg": "image/jpeg", ".jpeg": "image/jpeg"}
-                mime_type = mime_map.get(ext, "application/octet-stream")
-                if mime_type == "application/octet-stream":
-                    logger.warning(
-                        f"Could not determine MIME type for {image_path}, using default."
-                    )
-
-            with open(image_path, "rb") as image_file:
-                encoded_string = base64.b64encode(image_file.read()).decode("utf-8")
-            data_uri = f"data:{mime_type};base64,{encoded_string}"
-            logger.debug(
-                f"Generated data URI for {os.path.basename(image_path)} (first 100 chars): {data_uri[:100]}..."
-            )
-            return data_uri
-        except FileNotFoundError:
-            logger.error(f"Image file not found at {image_path} for data URI conversion.")
-            return None
-        except Exception as e:
-            logger.error(
-                f"Error converting image {image_path} to data URI: {e}", exc_info=True
-            )
-            return None
+        # (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"};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 _map_resolution_to_runway_ratio(self, width, height):
-        ratio_str = f"{width}:{height}"
-        # Gen-4 supports: "1280:720", "720:1280", "1104:832", "832:1104", "960:960", "1584:672"
-        supported_ratios_gen4 = [
-            "1280:720",
-            "720:1280",
-            "1104:832",
-            "832:1104",
-            "960:960",
-            "1584:672",
-        ]
-        if ratio_str in supported_ratios_gen4:
-            return ratio_str
-        # Fallback or find closest - for now, strict matching or default
-        logger.warning(
-            f"Resolution {ratio_str} not directly in Gen-4 supported list. Defaulting to 1280:720."
-        )
-        return "1280:720"
+        # (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_object):
-        # (Robust version from before)
-        default_char_height = getattr(font_object, "size", self.current_font_size_pil)
-        if not text_content:
-            return 0, default_char_height
+        # (Implementation from before)
+        dch=getattr(font_object,'size',self.current_font_size_pil);
+        if not text_content:return 0,dch
         try:
-            if hasattr(font_object, "getbbox"):
-                bbox = font_object.getbbox(text_content)
-                w = bbox[2] - bbox[0]
-                h = bbox[3] - bbox[1]
-                return w, h if h > 0 else default_char_height
-            elif hasattr(font_object, "getsize"):
-                w, h = font_object.getsize(text_content)
-                return w, h if h > 0 else default_char_height
-            else:
-                return (
-                    int(len(text_content) * default_char_height * 0.6),
-                    int(default_char_height * 1.2),
-                )
-        except Exception as e:
-            logger.warning(f"Error in _get_text_dimensions: {e}")
-            return (
-                int(len(text_content) * self.current_font_size_pil * 0.6),
-                int(self.current_font_size_pil * 1.2),
-            )
-
-    def _create_placeholder_image_content(self, text_description, filename, size=None):
-        # (Corrected version from previous response)
-        if size is None:
-            size = self.video_frame_size
-        img = Image.new("RGB", size, color=(20, 20, 40))
-        d = ImageDraw.Draw(img)
-        padding = 25
-        max_w = size[0] - (2 * padding)
-        lines = []
-        if not text_description:
-            text_description = "(Placeholder Image)"
-        words = text_description.split()
-        current_line_text = ""
+            if hasattr(font_object,'getbbox'):bb=font_object.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,'getsize'):w,h=font_object.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:logger.warning(f"Error in _get_text_dimensions:{e}");return int(len(text_content)*self.current_font_size_pil*0.6),int(self.current_font_size_pil*1.2)
+
+    def _create_placeholder_image_content(self,text_description,filename,size=None):
+        # <<< CORRECTED VERSION 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 = []
+        if not text_description: text_description = "(Placeholder Image)"
+        words = text_description.split(); current_line_text = ""
         for word_idx, word in enumerate(words):
             prospective_addition = word + (" " if word_idx < len(words) - 1 else "")
             test_line_text = current_line_text + prospective_addition
             current_w, _ = self._get_text_dimensions(test_line_text, self.font_pil)
-            if current_w == 0 and test_line_text.strip():
-                current_w = len(test_line_text) * (self.current_font_size_pil * 0.6)  # Estimate
+            if current_w == 0 and test_line_text.strip(): current_w = len(test_line_text) * (self.current_font_size_pil * 0.6)
 
-            if current_w <= max_w:
-                current_line_text = test_line_text
+            if current_w <= max_w: current_line_text = test_line_text
             else:
-                if current_line_text.strip():
-                    lines.append(current_line_text.strip())
-                current_line_text = prospective_addition  # Start new line
-        if current_line_text.strip():
-            lines.append(current_line_text.strip())
+                if current_line_text.strip(): lines.append(current_line_text.strip())
+                current_line_text = prospective_addition
+        if current_line_text.strip(): lines.append(current_line_text.strip())
 
         if not lines and text_description:
-            avg_char_w, _ = self._get_text_dimensions("W", self.font_pil)
-            avg_char_w = avg_char_w or (self.current_font_size_pil * 0.6)
+            avg_char_w, _ = self._get_text_dimensions("W", self.font_pil); avg_char_w = avg_char_w or (self.current_font_size_pil * 0.6)
             chars_per_line = int(max_w / avg_char_w) if avg_char_w > 0 else 20
-            lines.append(
-                text_description[:chars_per_line]
-                + ("..." if len(text_description) > chars_per_line else "")
-            )
-        elif not lines:
-            lines.append("(Placeholder Error)")
-
-        _, single_line_h = self._get_text_dimensions("Ay", self.font_pil)
-        single_line_h = single_line_h if single_line_h > 0 else self.current_font_size_pil + 2
-        max_lines = (
-            min(len(lines), (size[1] - (2 * padding)) // (single_line_h + 2))
-            if single_line_h > 0
-            else 1
-        )
-        max_lines = max(1, max_lines)  # Ensure at least one line
+            lines.append(text_description[:chars_per_line] + ("..." if len(text_description) > chars_per_line else ""))
+        elif not lines: lines.append("(Placeholder Error)")
 
+        _, single_line_h = self._get_text_dimensions("Ay", self.font_pil); single_line_h = single_line_h if single_line_h > 0 else self.current_font_size_pil + 2
+        max_lines = min(len(lines), (size[1] - (2 * padding)) // (single_line_h + 2)) if single_line_h > 0 else 1
+        max_lines = max(1, max_lines)
+        
         y_pos = padding + (size[1] - (2 * padding) - max_lines * (single_line_h + 2)) / 2.0
         for i in range(max_lines):
-            line_text = lines[i]
-            line_w, _ = self._get_text_dimensions(line_text, self.font_pil)
-            if line_w == 0 and line_text.strip():
-                line_w = len(line_text) * (self.current_font_size_pil * 0.6)
+            line_text = lines[i]; line_w, _ = self._get_text_dimensions(line_text, self.font_pil)
+            if line_w == 0 and line_text.strip(): line_w = len(line_text) * (self.current_font_size_pil * 0.6)
             x_pos = (size[0] - line_w) / 2.0
-            try:
-                d.text((x_pos, y_pos), line_text, font=self.font_pil, fill=(200, 200, 180))
-            except Exception as e_draw:
-                logger.error(f"Pillow d.text error: {e_draw} for '{line_text}'")
+            try: d.text((x_pos, y_pos), line_text, font=self.font_pil, fill=(200, 200, 180))
+            except Exception as e_draw: logger.error(f"Pillow d.text error: {e_draw} for '{line_text}'")
             y_pos += single_line_h + 2
             if i == 6 and max_lines > 7:
-                try:
-                    d.text((x_pos, y_pos), "...", font=self.font_pil, fill=(200, 200, 180))
-                except Exception as e_elip:
-                    logger.error(f"Pillow d.text ellipsis error: {e_elip}")
-                    break
-
+                try: d.text((x_pos, y_pos), "...", font=self.font_pil, fill=(200, 200, 180))
+                except Exception as e_elip: logger.error(f"Pillow d.text ellipsis error: {e_elip}"); break
         filepath = os.path.join(self.output_dir, filename)
-        try:
-            img.save(filepath)
-            return filepath
-        except Exception as e_save:
-            logger.error(
-                f"Saving placeholder image '{filepath}' error: {e_save}", exc_info=True
-            )
-            return None
+        try: img.save(filepath); return filepath
+        except Exception as e_save: logger.error(f"Saving placeholder image '{filepath}' error: {e_save}", exc_info=True); return None
 
     def _search_pexels_image(self, query, output_filename_base):
-        # <<< THIS IS THE CORRECTED METHOD >>>
-        if not self.USE_PEXELS or not self.pexels_api_key:
-            return None
+        # <<< CORRECTED VERSION OF THIS METHOD >>>
+        if not self.USE_PEXELS or not self.pexels_api_key: return None
         headers = {"Authorization": self.pexels_api_key}
         params = {"query": query, "per_page": 1, "orientation": "landscape", "size": "large2x"}
         base_name_for_pexels, _ = os.path.splitext(output_filename_base)
@@ -437,621 +198,229 @@ class VisualEngine:
             logger.info(f"Pexels: Searching for '{query}'")
             effective_query = " ".join(query.split()[:5])
             params["query"] = effective_query
-            response = requests.get(
-                "https://api.pexels.com/v1/search", headers=headers, params=params, timeout=20
-            )
+            response = requests.get("https://api.pexels.com/v1/search", headers=headers, params=params, timeout=20)
             response.raise_for_status()
             data = response.json()
             if data.get("photos") and len(data["photos"]) > 0:
                 photo_details = data["photos"][0]
                 photo_url = photo_details.get("src", {}).get("large2x")
-                if not photo_url:
-                    logger.warning(
-                        f"Pexels: 'large2x' URL missing for '{effective_query}'. Details: {photo_details}"
-                    )
-                    return None
-                image_response = requests.get(photo_url, timeout=60)
-                image_response.raise_for_status()
+                if not photo_url: logger.warning(f"Pexels: 'large2x' URL missing for '{effective_query}'. Details: {photo_details}"); return None
+                image_response = requests.get(photo_url, timeout=60); image_response.raise_for_status()
                 img_data_pil = Image.open(io.BytesIO(image_response.content))
-                if img_data_pil.mode != "RGB":
-                    img_data_pil = img_data_pil.convert("RGB")
-                img_data_pil.save(filepath)
-                logger.info(f"Pexels: Image saved to {filepath}")
-                return filepath
-            else:
-                logger.info(f"Pexels: No photos for '{effective_query}'.")
-                return None
-        except requests.exceptions.RequestException as e_req:
-            logger.error(f"Pexels: RequestException for '{query}': {e_req}", exc_info=False)
-            return None  # Less verbose for network
-        except Exception as e:
-            logger.error(f"Pexels: General error for '{query}': {e}", exc_info=True)
-            return None
-
-    # --- RunwayML Video Generation (Gen-4 Aligned with SDK) ---
-    def _generate_video_clip_with_runwayml(
-        self,
-        text_prompt_for_motion,
-        input_image_path,
-        scene_identifier_filename_base,
-        target_duration_seconds=5,
-    ):
-        if not self.USE_RUNWAYML or not self.runway_ml_client_instance:
-            logger.warning("RunwayML not enabled or client not initialized. Cannot generate video clip.")
-            return None
-        if not input_image_path or not os.path.exists(input_image_path):
-            logger.error(
-                f"Runway Gen-4 requires an input image. Path not provided or invalid: {input_image_path}"
-            )
-            return None
-
+                if img_data_pil.mode != 'RGB': img_data_pil = img_data_pil.convert('RGB')
+                img_data_pil.save(filepath); logger.info(f"Pexels: Image saved to {filepath}"); return filepath
+            else: logger.info(f"Pexels: No photos for '{effective_query}'."); return None
+        except requests.exceptions.RequestException as e_req: logger.error(f"Pexels: RequestException for '{query}': {e_req}", exc_info=False); return None
+        except Exception as e: logger.error(f"Pexels: General error for '{query}': {e}", exc_info=True); return None
+
+    def _generate_video_clip_with_runwayml(self, text_prompt_for_motion, input_image_path, scene_identifier_filename_base, target_duration_seconds=5):
+        # (Implementation from previous response, with Runway SDK calls)
+        if not self.USE_RUNWAYML or not self.runway_ml_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 = self._image_to_data_uri(input_image_path)
-        if not image_data_uri:
-            return None
-
-        runway_duration = 10 if target_duration_seconds >= 8 else 5  # Map to 5s or 10s for Gen-4
-        runway_ratio_str = self._map_resolution_to_runway_ratio(
-            self.video_frame_size[0], self.video_frame_size[1]
-        )
-
-        # Use a more descriptive output filename for Runway videos
-        base_name_for_runway, _ = os.path.splitext(scene_identifier_filename_base)
-        output_video_filename = base_name_for_runway + f"_runway_gen4_d{runway_duration}s.mp4"
+        if not image_data_uri: return None
+        runway_duration = 10 if target_duration_seconds >= 8 else 5
+        runway_ratio_str = self._map_resolution_to_runway_ratio(self.video_frame_size[0], self.video_frame_size[1])
+        base_name_runway, _ = os.path.splitext(scene_identifier_filename_base); output_video_filename = base_name_runway + f"_runway_gen4_d{runway_duration}s.mp4" # Corrected base name usage
         output_video_filepath = os.path.join(self.output_dir, output_video_filename)
-
-        logger.info(
-            f"Initiating Runway Gen-4 task: motion='{text_prompt_for_motion[:100]}...', image='{os.path.basename(input_image_path)}', dur={runway_duration}s, ratio='{runway_ratio_str}'"
-        )
+        logger.info(f"Runway Gen-4 task: motion='{text_prompt_for_motion[:100]}...', img='{os.path.basename(input_image_path)}', dur={runway_duration}s, ratio='{runway_ratio_str}'")
         try:
-            # Using the RunwayML Python SDK structure
-            task_submission = self.runway_ml_client_instance.image_to_video.create(
-                model="gen4_turbo",
-                prompt_image=image_data_uri,
-                prompt_text=text_prompt_for_motion,  # This is the motion prompt
-                duration=runway_duration,
-                ratio=runway_ratio_str,
-                # seed=random.randint(0, 4294967295), # Optional: for reproducibility
-                # Other Gen-4 params (motion_score, upscale, watermark etc. can be added here if available in SDK)
-            )
-            task_id = task_submission.id
-            logger.info(f"Runway Gen-4 task created with ID: {task_id}. Polling for completion...")
-
-            poll_interval_seconds = 10
-            max_polling_duration_seconds = 6 * 60  # 6 minutes
-            start_time = time.time()
-
-            while time.time() - start_time < max_polling_duration_seconds:
-                time.sleep(poll_interval_seconds)
-                task_details = self.runway_ml_client_instance.tasks.retrieve(id=task_id)
+            task_submission = self.runway_ml_client_instance.image_to_video.create(model='gen4_turbo', prompt_image=image_data_uri, prompt_text=text_prompt_for_motion, duration=runway_duration, ratio=runway_ratio_str)
+            task_id = task_submission.id; logger.info(f"Runway Gen-4 task ID: {task_id}. Polling...")
+            poll_interval=10; max_polls=36; start_poll_time = time.time()
+            while time.time() - start_poll_time < max_polls * poll_interval:
+                time.sleep(poll_interval); task_details = self.runway_ml_client_instance.tasks.retrieve(id=task_id)
                 logger.info(f"Runway task {task_id} status: {task_details.status}")
-
-                if task_details.status == "SUCCEEDED":
-                    # Determine output URL (this structure might vary based on SDK version)
-                    output_url = None
-                    if hasattr(task_details, "output") and task_details.output and hasattr(
-                        task_details.output, "url"
-                    ):
-                        output_url = task_details.output.url
-                    elif (
-                        hasattr(task_details, "artifacts")
-                        and task_details.artifacts
-                        and isinstance(task_details.artifacts, list)
-                        and len(task_details.artifacts) > 0
-                    ):
-                        first_artifact = task_details.artifacts[0]
-                        if hasattr(first_artifact, "url"):
-                            output_url = first_artifact.url
-                        elif hasattr(first_artifact, "download_url"):
-                            output_url = first_artifact.download_url
-
-                    if not output_url:
-                        logger.error(
-                            f"Runway task {task_id} SUCCEEDED, but no output URL found. Details: {vars(task_details) if hasattr(task_details,'__dict__') else str(task_details)}"
-                        )
-                        return None
-
-                    logger.info(f"Runway task {task_id} SUCCEEDED. Downloading video from: {output_url}")
-                    video_response = requests.get(output_url, stream=True, timeout=300)
-                    video_response.raise_for_status()
-                    with open(output_video_filepath, "wb") as f:
-                        for chunk in video_response.iter_content(chunk_size=8192):
-                            f.write(chunk)
-                    logger.info(
-                        f"Runway Gen-4 video successfully downloaded to: {output_video_filepath}"
-                    )
-                    return output_video_filepath
-
-                elif task_details.status in ["FAILED", "ABORTED", "ERROR"]:  # Added ERROR
-                    error_msg = (
-                        getattr(task_details, "error_message", None)
-                        or getattr(getattr(task_details, "output", None), "error", "Unknown error from Runway task.")
-                    )
-                    logger.error(
-                        f"Runway task {task_id} final status: {task_details.status}. Error: {error_msg}"
-                    )
-                    return None
-
-            logger.warning(
-                f"Runway task {task_id} timed out polling after {max_polling_duration_seconds} seconds."
-            )
-            return None
-
-        except AttributeError as ae:  # If SDK methods are not as expected
-            logger.error(
-                f"AttributeError with RunwayML SDK: {ae}. Ensure SDK is up to date and methods/attributes match documentation.",
-                exc_info=True,
-            )
-            return None
-        except Exception as e_runway_call:
-            logger.error(
-                f"General error during Runway Gen-4 API call or processing: {e_runway_call}",
-                exc_info=True,
-            )
-            return None
-
-    def _create_placeholder_video_content(self, text_description, filename, duration=4, size=None):
-        # (Keeping as before)
-        if size is None:
-            size = self.video_frame_size
-        fp = os.path.join(self.output_dir, filename)
-        tc = None
-        try:
-            tc = TextClip(
-                text_description,
-                fontsize=50,
-                color="white",
-                font=self.video_overlay_font,
-                bg_color="black",
-                size=size,
-                method="caption",
-            ).set_duration(duration)
-            tc.write_videofile(
-                fp, fps=24, codec="libx264", preset="ultrafast", logger=None, threads=2
-            )
-            logger.info(f"Generic placeholder video: {fp}")
-            return fp
-        except Exception as e:
-            logger.error(f"Generic placeholder video error {fp}: {e}", exc_info=True)
-            return None
+                if task_details.status == 'SUCCEEDED':
+                    output_url = getattr(getattr(task_details,'output',None),'url',None) or (getattr(task_details,'artifacts',None) and task_details.artifacts[0].url if task_details.artifacts and hasattr(task_details.artifacts[0],'url') else None) or (getattr(task_details,'artifacts',None) and task_details.artifacts[0].download_url if task_details.artifacts and hasattr(task_details.artifacts[0],'download_url') else None)
+                    if not output_url: logger.error(f"Runway task {task_id} SUCCEEDED, but no output URL. Details: {vars(task_details) if hasattr(task_details,'__dict__') else task_details}"); return None
+                    logger.info(f"Runway task {task_id} SUCCEEDED. Downloading: {output_url}")
+                    video_response = requests.get(output_url, stream=True, timeout=300); video_response.raise_for_status()
+                    with open(output_video_filepath,'wb') as f:
+                        for chunk in video_response.iter_content(chunk_size=8192): f.write(chunk)
+                    logger.info(f"Runway Gen-4 video saved: {output_video_filepath}"); return output_video_filepath
+                elif task_details.status in ['FAILED','ABORTED','ERROR']:
+                    em = getattr(task_details,'error_message',None) or getattr(getattr(task_details,'output',None),'error',"Unknown Runway error.")
+                    logger.error(f"Runway task {task_id} status: {task_details.status}. Error: {em}"); return None
+            logger.warning(f"Runway task {task_id} timed out."); return None
+        except AttributeError as ae: logger.error(f"RunwayML SDK AttrError: {ae}. SDK/methods changed?", exc_info=True); return None
+        except Exception as e: logger.error(f"Runway Gen-4 API error: {e}", exc_info=True); return None
+
+    def _create_placeholder_video_content(self, td, fn, dur=4, sz=None):
+        # (Keep as before)
+        if sz is None: sz = self.video_frame_size; fp = os.path.join(self.output_dir, fn); tc = None
+        try: tc = TextClip(td, fontsize=50, color='white', font=self.video_overlay_font, bg_color='black', size=sz, method='caption').set_duration(dur); tc.write_videofile(fp, fps=24, codec='libx264', preset='ultrafast', logger=None, threads=2); logger.info(f"Generic placeholder video: {fp}"); return fp
+        except Exception as e: logger.error(f"Generic placeholder video error {fp}: {e}", exc_info=True); return None
         finally:
-            if tc and hasattr(tc, "close"):
-                tc.close()
+            if tc and hasattr(tc, 'close'): tc.close()
 
-    # --- generate_scene_asset (Main asset generation logic using Runway Gen-4 workflow) ---
-    def generate_scene_asset(
-        self,
-        image_generation_prompt_text,
-        motion_prompt_text_for_video,
-        scene_data,
-        scene_identifier_filename_base,
-        generate_as_video_clip=False,
-        runway_target_duration=5,
-    ):
-        # (Logic updated for Runway Gen-4 workflow)
+    def generate_scene_asset(self, image_generation_prompt_text, motion_prompt_text_for_video,
+                             scene_data, scene_identifier_filename_base,
+                             generate_as_video_clip=False, runway_target_duration=5):
+        # <<< THIS IS THE CORRECTED METHOD with fixed DALL-E loop >>>
         base_name, _ = os.path.splitext(scene_identifier_filename_base)
-        asset_info = {
-            "path": None,
-            "type": "none",
-            "error": True,
-            "prompt_used": image_generation_prompt_text,
-            "error_message": "Asset generation init failed",
-        }
+        asset_info = {'path': None, 'type': 'none', 'error': True,
+                      'prompt_used': image_generation_prompt_text,
+                      'error_message': 'Asset generation init failed'}
         input_image_for_runway_path = None
-        # Use a distinct name for the base image if it's only an intermediate step for video
         base_image_filename = base_name + ("_base_for_video.png" if generate_as_video_clip else ".png")
         base_image_filepath = os.path.join(self.output_dir, base_image_filename)
-
-        # STEP 1: Generate/acquire the base image
-        # Try DALL-E
+        
         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:
-                    logger.info(f"Att {att_n+1} DALL-E (base img): {image_generation_prompt_text[:70]}...")
-                    cl = openai.OpenAI(api_key=self.openai_api_key, timeout=90.0)
-                    r = cl.images.generate(
-                        model=self.dalle_model,
-                        prompt=image_generation_prompt_text,
-                        n=1,
-                        size=self.image_size_dalle3,
-                        quality="hd",
-                        response_format="url",
-                        style="vivid",
-                    )
-                    iu = r.data[0].url
-                    rp = getattr(r.data[0], "revised_prompt", None)
-                    if rp:
-                        logger.info(f"DALL-E revised: {rp[:70]}...")
-                    ir = requests.get(iu, timeout=120)
-                    ir.raise_for_status()
-                    id_img = Image.open(io.BytesIO(ir.content))
-                    if id_img.mode != "RGB":
-                        id_img = id_img.convert("RGB")
-                    id_img.save(base_image_filepath)
-                    logger.info(f"DALL-E base img saved: {base_image_filepath}")
-                    input_image_for_runway_path = base_image_filepath
-                    asset_info = {
-                        "path": base_image_filepath,
-                        "type": "image",
-                        "error": False,
-                        "prompt_used": image_generation_prompt_text,
-                        "revised_prompt": rp,
-                    }
-                    break
-                except openai.RateLimitError as e:
-                    logger.warning(f"OpenAI RateLimit {att_n+1}:{e}.Retry...")
-                    time.sleep(5 * (att_n + 1))
-                    asset_info["error_message"] = str(e)
-                except Exception as e:
-                    logger.error(f"DALL-E base img error:{e}", exc_info=True)
-                    asset_info["error_message"] = str(e)
-                    break
-
-            if asset_info["error"]:
-                logger.warning(f"DALL-E failed after {att_n+1} attempts for base img.")
+            max_retries = 2; attempt_count_dalle = 0
+            for attempt_num_dalle in range(max_retries):
+                attempt_count_dalle = attempt_num_dalle + 1
+                try: # DALL-E attempt try block
+                    logger.info(f"Attempt {attempt_count_dalle} DALL-E (base img): {image_generation_prompt_text[:70]}...")
+                    client_oai = openai.OpenAI(api_key=self.openai_api_key, timeout=90.0)
+                    response_oai = client_oai.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")
+                    img_url_oai = response_oai.data[0].url
+                    revised_prompt_oai = getattr(response_oai.data[0],'revised_prompt',None)
+                    if revised_prompt_oai: logger.info(f"DALL-E revised: {revised_prompt_oai[:70]}...")
+                    img_response_get = requests.get(img_url_oai,timeout=120); img_response_get.raise_for_status()
+                    pil_img_oai = Image.open(io.BytesIO(img_response_get.content))
+                    if pil_img_oai.mode!='RGB': pil_img_oai=pil_img_oai.convert('RGB')
+                    pil_img_oai.save(base_image_filepath); logger.info(f"DALL-E base img saved: {base_image_filepath}")
+                    input_image_for_runway_path=base_image_filepath
+                    asset_info={'path':base_image_filepath,'type':'image','error':False,'prompt_used':image_generation_prompt_text,'revised_prompt':revised_prompt_oai}
+                    break # Success, exit loop
+                except openai.RateLimitError as e_rl: logger.warning(f"OpenAI RateLimit Att {attempt_count_dalle}:{e_rl}.Retry...");time.sleep(5*attempt_count_dalle);asset_info['error_message']=str(e_rl)
+                except openai.APIError as e_api_oai: logger.error(f"OpenAI APIError Att {attempt_count_dalle}:{e_api_oai}");asset_info['error_message']=str(e_api_oai);break
+                except requests.exceptions.RequestException as e_req_oai: logger.error(f"Requests Err DALL-E Att {attempt_count_dalle}:{e_req_oai}");asset_info['error_message']=str(e_req_oai);break
+                except Exception as e_gen_oai: logger.error(f"General DALL-E Err Att {attempt_count_dalle}:{e_gen_oai}",exc_info=True);asset_info['error_message']=str(e_gen_oai);break
+            if asset_info['error']: logger.warning(f"DALL-E failed after {attempt_count_dalle} attempts for base img.")
+        
+        if asset_info['error'] and self.USE_PEXELS:
+            logger.info("Trying Pexels for base img.");pqt=scene_data.get('pexels_search_query_감독',f"{scene_data.get('emotional_beat','')} {scene_data.get('setting_description','')}");pp=self._search_pexels_image(pqt,base_image_filename);
+            if pp:input_image_for_runway_path=pp;asset_info={'path':pp,'type':'image','error':False,'prompt_used':f"Pexels:{pqt}"}
+            else:current_em=asset_info.get('error_message',"");asset_info['error_message']=(current_em+" Pexels failed for base.").strip()
+
+        if asset_info['error']:
+            logger.warning("Base img (DALL-E/Pexels) failed. Using placeholder.");ppt=asset_info.get('prompt_used',image_generation_prompt_text);php=self._create_placeholder_image_content(f"[Base Placeholder]{ppt[:70]}...",base_image_filename);
+            if php:input_image_for_runway_path=php;asset_info={'path':php,'type':'image','error':False,'prompt_used':ppt}
+            else:current_em=asset_info.get('error_message',"");asset_info['error_message']=(current_em+" Base placeholder failed.").strip()
 
-        if asset_info["error"] and self.USE_PEXELS:
-            # Pexels Fallback
-            logger.info("Trying Pexels for base img.")
-            pqt = scene_data.get("pexels_search_query_감독", f"{scene_data.get('emotional_beat','')} {scene_data.get('setting_description','')}")
-            pp = self._search_pexels_image(pqt, base_image_filename)  # Use base_image_filename
-            if pp:
-                input_image_for_runway_path = pp
-                asset_info = {
-                    "path": pp,
-                    "type": "image",
-                    "error": False,
-                    "prompt_used": f"Pexels:{pqt}",
-                }
-            else:
-                current_em = asset_info.get("error_message", "")
-                asset_info["error_message"] = (current_em + " Pexels failed for base.").strip()
-
-        if asset_info["error"]:
-            # Placeholder Fallback
-            logger.warning("Base img (DALL-E/Pexels) failed. Using placeholder.")
-            ppt = asset_info.get("prompt_used", image_generation_prompt_text)
-            php = self._create_placeholder_image_content(f"[Base Placeholder]{ppt[:70]}...", base_image_filename)  # Use base_image_filename
-            if php:
-                input_image_for_runway_path = php
-                asset_info = {
-                    "path": php,
-                    "type": "image",
-                    "error": False,
-                    "prompt_used": ppt,
-                }
-            else:
-                current_em = asset_info.get("error_message", "")
-                asset_info["error_message"] = (current_em + " Base placeholder failed.").strip()
-
-        # STEP 2: If video clip is requested, use the generated base image with RunwayML
         if generate_as_video_clip:
-            if not input_image_for_runway_path:
-                logger.error("RunwayML video: base image generation failed entirely. Cannot proceed.")
-                asset_info["error"] = True
-                asset_info["error_message"] = (
-                    asset_info.get("error_message", "") + " Base img completely failed, Runway abort."
-                ).strip()
-                asset_info["type"] = "none"
-                return asset_info
-
+            if not input_image_for_runway_path:logger.error("RunwayML video: base img failed.");asset_info['error']=True;asset_info['error_message']=(asset_info.get('error_message',"")+" Base img miss, Runway abort.").strip();asset_info['type']='none';return asset_info
             if self.USE_RUNWAYML:
-                # This check is now more about if the service is generally enabled
-                video_path = self._generate_video_clip_with_runwayml(
-                    motion_prompt_text_for_video,
-                    input_image_for_runway_path,
-                    base_name,
-                    runway_target_duration,
-                )  # Pass base_name for runway output filename
-                if video_path and os.path.exists(video_path):
-                    asset_info = {
-                        "path": video_path,
-                        "type": "video",
-                        "error": False,
-                        "prompt_used": motion_prompt_text_for_video,
-                        "base_image_path": input_image_for_runway_path,
-                    }
-                else:
-                    logger.warning(f"RunwayML video failed for {base_name}. Fallback to base img.")
-                    asset_info["error"] = True
-                    asset_info["error_message"] = (
-                        asset_info.get("error_message", "Base img ok.")
-                        + " RunwayML video step failed; use base img."
-                    ).strip()
-                    asset_info["path"] = input_image_for_runway_path
-                    asset_info["type"] = "image"
-                    asset_info["prompt_used"] = image_generation_prompt_text
-            else:
-                logger.warning("RunwayML selected but not enabled/client not ready. Use base img.")
-                asset_info["error"] = True
-                asset_info["error_message"] = (
-                    asset_info.get("error_message", "Base img ok.")
-                    + " RunwayML disabled; use base img."
-                ).strip()
-                asset_info["path"] = input_image_for_runway_path
-                asset_info["type"] = "image"
-                asset_info["prompt_used"] = image_generation_prompt_text
-
+                video_path=self._generate_video_clip_with_runwayml(motion_prompt_text_for_video,input_image_for_runway_path,base_name,runway_target_duration)
+                if video_path and os.path.exists(video_path):asset_info={'path':video_path,'type':'video','error':False,'prompt_used':motion_prompt_text_for_video,'base_image_path':input_image_for_runway_path}
+                else:logger.warning(f"RunwayML video failed for {base_name}. Fallback to base img.");asset_info['error']=True;asset_info['error_message']=(asset_info.get('error_message',"Base img ok.")+" RunwayML video fail; use base img.").strip();asset_info['path']=input_image_for_runway_path;asset_info['type']='image';asset_info['prompt_used']=image_generation_prompt_text
+            else:logger.warning("RunwayML selected but disabled. Use base img.");asset_info['error']=True;asset_info['error_message']=(asset_info.get('error_message',"Base img ok.")+" RunwayML disabled; use base img.").strip();asset_info['path']=input_image_for_runway_path;asset_info['type']='image';asset_info['prompt_used']=image_generation_prompt_text
         return asset_info
 
     def generate_narration_audio(self, text_to_narrate, output_filename="narration_overall.mp3"):
-        # (Keep as before - robust enough)
-        if not self.USE_ELEVENLABS or not self.elevenlabs_client or not text_to_narrate:
-            logger.info("11L skip.")
-            return None
-
-        afp = os.path.join(self.output_dir, output_filename)
-        try:
-            logger.info(f"11L audio (Voice:{self.elevenlabs_voice_id}): {text_to_narrate[:70]}...")
-            asm = None
-
-            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=text_to_narrate, voice=vp, model="eleven_multilingual_v2"
-                )
-                with open(afp, "wb") as f:
-                    f.write(ab)
-                logger.info(f"11L audio (non-stream): {afp}")
-                return afp
-            else:
-                logger.error("No 11L audio method.")
-                return None
-
-            # If we have a streaming method (asm), use it
-            if asm:
-                vps = {"voice_id": str(self.elevenlabs_voice_id)}
-                if self.elevenlabs_voice_settings:
-                    if hasattr(self.elevenlabs_voice_settings, "model_dump"):
-                        vps["voice_settings"] = self.elevenlabs_voice_settings.model_dump()
-                    elif hasattr(self.elevenlabs_voice_settings, "dict"):
-                        vps["voice_settings"] = self.elevenlabs_voice_settings.dict()
-                    else:
-                        vps["voice_settings"] = self.elevenlabs_voice_settings
-
-                adi = asm(text=text_to_narrate, model_id="eleven_multilingual_v2", **vps)
-                with open(afp, "wb") as f:
-                    for c in adi:
-                        if c:
-                            f.write(c)
-                logger.info(f"11L audio (stream): {afp}")
-                return afp
-
-        except Exception as e:
-            logger.error(f"11L audio error: {e}", exc_info=True)
-            return None
-
-    # --- assemble_animatic_from_assets (Still contains crucial debug saves for blank video issue) ---
-    def assemble_animatic_from_assets(
-        self, asset_data_list, overall_narration_path=None, output_filename="final_video.mp4", fps=24
-    ):
-        # (Keep the version with robust image processing, C-contiguous arrays, debug saves, and pix_fmt)
-        if not asset_data_list:
-            logger.warning("No assets for animatic.")
-            return None
-
-        processed_clips = []
-        narration_clip = None
-        final_clip = None
+        # (Keep as before)
+        if not self.USE_ELEVENLABS or not self.elevenlabs_client or not text_to_narrate: logger.info("11L skip."); return None; afp=os.path.join(self.output_dir,output_filename)
+        try: logger.info(f"11L audio (Voice:{self.elevenlabs_voice_id}): {text_to_narrate[:70]}..."); asm=None
+        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=text_to_narrate,voice=vp,model="eleven_multilingual_v2");
+        with open(afp,"wb")as f:f.write(ab);logger.info(f"11L audio (non-stream): {afp}");return afp
+        else:logger.error("No 11L audio method.");return None
+        if asm:vps={"voice_id":str(self.elevenlabs_voice_id)}
+        if self.elevenlabs_voice_settings:
+            if hasattr(self.elevenlabs_voice_settings,'model_dump'):vps["voice_settings"]=self.elevenlabs_voice_settings.model_dump()
+            elif hasattr(self.elevenlabs_voice_settings,'dict'):vps["voice_settings"]=self.elevenlabs_voice_settings.dict()
+            else:vps["voice_settings"]=self.elevenlabs_voice_settings
+        adi=asm(text=text_to_narrate,model_id="eleven_multilingual_v2",**vps)
+        with open(afp,"wb")as f:
+            for c_chunk in adi: # Renamed c to c_chunk
+                if c_chunk:f.write(c_chunk)
+        logger.info(f"11L audio (stream): {afp}");return afp
+        except Exception as e_11l:logger.error(f"11L audio error: {e_11l}",exc_info=True);return None # Renamed e to e_11l
+
+    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)
+        if not asset_data_list: logger.warning("No assets for animatic."); return None
+        processed_clips = []; narration_clip_mvpy = None; final_composite_video_clip = None # Renamed variables
         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
+        for i, asset_info_dict in enumerate(asset_data_list): # Renamed asset_info to asset_info_dict
+            asset_p, asset_t, scene_d = asset_info_dict.get('path'), asset_info_dict.get('type'), asset_info_dict.get('duration', 4.5)
+            scene_n, key_act = asset_info_dict.get('scene_num', i + 1), asset_info_dict.get('key_action', '')
+            logger.info(f"S{scene_n}: Path='{asset_p}', Type='{asset_t}', Dur='{scene_d}'s")
+            if not (asset_p and os.path.exists(asset_p)): logger.warning(f"S{scene_n}: Not found '{asset_p}'. Skip."); continue
+            if scene_d <= 0: logger.warning(f"S{scene_n}: Invalid duration ({scene_d}s). Skip."); continue
+            current_scene_clip_mvpy = None # Renamed current_scene_mvpy_clip
             try:
-                if asset_type == "image":
-                    pil_img = Image.open(asset_path)
-                    logger.debug(f"S{scene_num}: Loaded img. Mode:{pil_img.mode}, Size:{pil_img.size}")
-                    img_rgba = pil_img.convert("RGBA") if pil_img.mode != "RGBA" else pil_img.copy()
-                    thumb = img_rgba.copy()
-                    rf = Image.Resampling.LANCZOS if hasattr(Image.Resampling, "LANCZOS") else Image.BILINEAR
-                    thumb.thumbnail(self.video_frame_size, rf)
-                    cv_rgba = Image.new("RGBA", self.video_frame_size, (0, 0, 0, 0))
-                    xo, yo = (
-                        (self.video_frame_size[0] - thumb.width) // 2,
-                        (self.video_frame_size[1] - thumb.height) // 2,
-                    )
-                    cv_rgba.paste(thumb, (xo, yo), thumb)
-                    final_rgb_pil = Image.new("RGB", self.video_frame_size, (0, 0, 0))
-                    final_rgb_pil.paste(cv_rgba, mask=cv_rgba.split()[3])
-                    dbg_path = os.path.join(self.output_dir, f"debug_PRE_NUMPY_S{scene_num}.png")
-                    final_rgb_pil.save(dbg_path)
-                    logger.info(f"DEBUG: Saved PRE_NUMPY_S{scene_num} to {dbg_path}")
-                    frame_np = np.array(final_rgb_pil, dtype=np.uint8)
-                    if not frame_np.flags["C_CONTIGUOUS"]:
-                        frame_np = np.ascontiguousarray(frame_np, dtype=np.uint8)
-                    logger.debug(
-                        f"S{scene_num}: NumPy for MoviePy. Shape:{frame_np.shape}, DType:{frame_np.dtype}, C-Contig:{frame_np.flags['C_CONTIGUOUS']}"
-                    )
-                    if frame_np.size == 0 or frame_np.ndim != 3 or frame_np.shape[2] != 3:
-                        logger.error(f"S{scene_num}: Invalid NumPy. Skip.")
-                        continue
-                    clip_base = ImageClip(frame_np, transparent=False).set_duration(scene_dur)
-                    mvpy_dbg_path = os.path.join(self.output_dir, f"debug_MOVIEPY_FRAME_S{scene_num}.png")
-                    clip_base.save_frame(mvpy_dbg_path, t=0.1)
-                    logger.info(f"DEBUG: Saved MOVIEPY_FRAME_S{scene_num} to {mvpy_dbg_path}")
-                    clip_fx = clip_base
-                    try:
-                        es = random.uniform(1.03, 1.08)
-                        clip_fx = clip_base.fx(
-                            vfx.resize, lambda t: 1 + (es - 1) * (t / scene_dur) if scene_dur > 0 else 1
-                        ).set_position("center")
-                    except Exception as e:
-                        logger.error(f"S{scene_num} Ken Burns error: {e}", exc_info=False)
-                    current_scene_mvpy_clip = clip_fx
-
-                elif asset_type == "video":
-                    src_clip = None
+                if asset_t == 'image':
+                    # ... (Robust image processing logic from previous full version) ...
+                    pil_img_opened = Image.open(asset_p); logger.debug(f"S{scene_n}: Loaded img. Mode:{pil_img_opened.mode}, Size:{pil_img_opened.size}")
+                    img_rgba_converted = pil_img_opened.convert('RGBA') if pil_img_opened.mode != 'RGBA' else pil_img_opened.copy()
+                    thumb_img = img_rgba_converted.copy(); res_filter = Image.Resampling.LANCZOS if hasattr(Image.Resampling,'LANCZOS') else Image.BILINEAR; thumb_img.thumbnail(self.video_frame_size,res_filter)
+                    canvas_for_rgba = Image.new('RGBA',self.video_frame_size,(0,0,0,0)); x_offset,y_offset=(self.video_frame_size[0]-thumb_img.width)//2,(self.video_frame_size[1]-thumb_img.height)//2
+                    canvas_for_rgba.paste(thumb_img,(x_offset,y_offset),thumb_img)
+                    final_rgb_for_pil = Image.new("RGB",self.video_frame_size,(0,0,0)); final_rgb_for_pil.paste(canvas_for_rgba,mask=canvas_for_rgba.split()[3])
+                    debug_path_pre_numpy = os.path.join(self.output_dir,f"debug_PRE_NUMPY_S{scene_n}.png"); final_rgb_for_pil.save(debug_path_pre_numpy); logger.info(f"DEBUG: Saved PRE_NUMPY_S{scene_n} to {debug_path_pre_numpy}")
+                    numpy_frame = np.array(final_rgb_for_pil,dtype=np.uint8);
+                    if not numpy_frame.flags['C_CONTIGUOUS']: numpy_frame=np.ascontiguousarray(numpy_frame,dtype=np.uint8)
+                    logger.debug(f"S{scene_n}: NumPy for MoviePy. Shape:{numpy_frame.shape}, DType:{numpy_frame.dtype}, C-Contig:{numpy_frame.flags['C_CONTIGUOUS']}")
+                    if numpy_frame.size==0 or numpy_frame.ndim!=3 or numpy_frame.shape[2]!=3: logger.error(f"S{scene_n}: Invalid NumPy. Skip."); continue
+                    image_clip_base = ImageClip(numpy_frame,transparent=False).set_duration(scene_d)
+                    moviepy_debug_frame_save_path=os.path.join(self.output_dir,f"debug_MOVIEPY_FRAME_S{scene_n}.png"); image_clip_base.save_frame(moviepy_debug_frame_save_path,t=0.1); logger.info(f"DEBUG: Saved MOVIEPY_FRAME_S{scene_n} to {moviepy_debug_frame_save_path}")
+                    image_clip_with_fx = image_clip_base
+                    try: end_scale_kb=random.uniform(1.03,1.08); image_clip_with_fx=image_clip_base.fx(vfx.resize,lambda time_t:1+(end_scale_kb-1)*(time_t/scene_d) if scene_d>0 else 1).set_position('center')
+                    except Exception as e_kb: logger.error(f"S{scene_n} Ken Burns error: {e_kb}",exc_info=False)
+                    current_scene_mvpy_clip = image_clip_with_fx
+                elif asset_t == 'video':
+                    # ... (Video processing logic from previous full version) ...
+                    source_video_file_clip=None
                     try:
-                        src_clip = VideoFileClip(
-                            asset_path,
-                            target_resolution=(
-                                self.video_frame_size[1],
-                                self.video_frame_size[0],
-                            )
-                            if self.video_frame_size
-                            else None,
-                            audio=False,
-                        )
-                        tmp_clip = src_clip
-                        if src_clip.duration != scene_dur:
-                            if src_clip.duration > scene_dur:
-                                tmp_clip = src_clip.subclip(0, scene_dur)
+                        source_video_file_clip=VideoFileClip(asset_p,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=source_video_file_clip
+                        if source_video_file_clip.duration!=scene_d:
+                            if source_video_file_clip.duration>scene_d:temp_video_clip_obj=source_video_file_clip.subclip(0,scene_d)
                             else:
-                                if scene_dur / src_clip.duration > 1.5 and src_clip.duration > 0.1:
-                                    tmp_clip = src_clip.loop(duration=scene_dur)
-                                else:
-                                    tmp_clip = src_clip.set_duration(src_clip.duration)
-                                    logger.info(
-                                        f"S{scene_num} Video clip ({src_clip.duration:.2f}s) shorter than target ({scene_dur:.2f}s)."
-                                    )
-                        current_scene_mvpy_clip = tmp_clip.set_duration(scene_dur)
-                        if current_scene_mvpy_clip.size != list(self.video_frame_size):
-                            current_scene_mvpy_clip = current_scene_mvpy_clip.resize(self.video_frame_size)
-                    except Exception as e:
-                        logger.error(f"S{scene_num} Video load error '{asset_path}':{e}", exc_info=True)
-                        continue
+                                if scene_d/source_video_file_clip.duration > 1.5 and source_video_file_clip.duration>0.1:temp_video_clip_obj=source_video_file_clip.loop(duration=scene_d)
+                                else:temp_video_clip_obj=source_video_file_clip.set_duration(source_video_file_clip.duration);logger.info(f"S{scene_n} Video clip ({source_video_file_clip.duration:.2f}s) shorter than target ({scene_d:.2f}s).")
+                        current_scene_mvpy_clip=temp_video_clip_obj.set_duration(scene_d)
+                        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_vidload:logger.error(f"S{scene_n} Video load error '{asset_p}':{e_vidload}",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_file_clip and source_video_file_clip is not current_scene_mvpy_clip and hasattr(source_video_file_clip,'close'):source_video_file_clip.close()
+                else: logger.warning(f"S{scene_n} Unknown asset type '{asset_t}'. Skip."); continue
+                
+                if current_scene_mvpy_clip and key_act: # Text Overlay
                     try:
-                        to_dur = (
-                            min(current_scene_mvpy_clip.duration - 0.5, current_scene_mvpy_clip.duration * 0.8)
-                            if current_scene_mvpy_clip.duration > 0.5
-                            else current_scene_mvpy_clip.duration
-                        )
-                        to_start = 0.25
-                        if to_dur > 0:
-                            txt_c = TextClip(
-                                f"Scene {scene_num}\n{key_action}",
-                                fontsize=self.VIDEO_OVERLAY_FONT_SIZE,
-                                color=self.VIDEO_OVERLAY_FONT_COLOR,
-                                font=self.video_overlay_font,
-                                bg_color="rgba(10,10,20,0.7)",
-                                method="caption",
-                                align="West",
-                                size=(self.video_frame_size[0] * 0.9, None),
-                                kerning=-1,
-                                stroke_color="black",
-                                stroke_width=1.5,
-                            ).set_duration(to_dur).set_start(to_start).set_position(
-                                ("center", 0.92), relative=True
-                            )
-                            current_scene_mvpy_clip = CompositeVideoClip(
-                                [current_scene_mvpy_clip, txt_c], size=self.video_frame_size, use_bgclip=True
-                            )
-                        else:
-                            logger.warning(f"S{scene_num}: Text overlay duration is zero. Skip text.")
-                    except Exception as e:
-                        logger.error(f"S{scene_num} TextClip error:{e}. No text.", exc_info=True)
-
-                if current_scene_mvpy_clip:
-                    processed_clips.append(current_scene_mvpy_clip)
-                    logger.info(f"S{scene_num} Processed. Dur:{current_scene_mvpy_clip.duration:.2f}s.")
-            except Exception as e:
-                logger.error(f"MAJOR Error S{scene_num} ({asset_path}):{e}", exc_info=True)
+                        text_overlay_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
+                        text_overlay_s_time=0.25
+                        if text_overlay_dur > 0:
+                            text_clip_obj=TextClip(f"Scene {scene_n}\n{key_act}",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(text_overlay_dur).set_start(text_overlay_s_time).set_position(('center',0.92),relative=True)
+                            current_scene_mvpy_clip=CompositeVideoClip([current_scene_mvpy_clip,text_clip_obj],size=self.video_frame_size,use_bgclip=True)
+                        else: logger.warning(f"S{scene_n}: Text overlay duration zero. Skip text.")
+                    except Exception as e_txtclip:logger.error(f"S{scene_n} TextClip error:{e_txtclip}. No text.",exc_info=True)
+                if current_scene_mvpy_clip:processed_clips.append(current_scene_mvpy_clip);logger.info(f"S{scene_n} Processed. Dur:{current_scene_mvpy_clip.duration:.2f}s.")
+            except Exception as e_asset_loop:logger.error(f"MAJOR Error S{scene_n} ({asset_p}):{e_asset_loop}",exc_info=True)
             finally:
-                if current_scene_mvpy_clip and hasattr(current_scene_mvpy_clip, "close"):
-                    try:
-                        current_scene_mvpy_clip.close()
-                    except:
-                        pass
+                if current_scene_mvpy_clip and hasattr(current_scene_mvpy_clip,'close'):
+                    try: current_scene_mvpy_clip.close()
+                    except: pass
 
-        if not processed_clips:
-            logger.warning("No clips processed. Abort.")
-            return None
-
-        td = 0.75
+        if not processed_clips:logger.warning("No clips processed. Abort.");return None
+        transition_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_clips)} clips.");
+            if len(processed_clips)>1:final_composite_video_clip=concatenate_videoclips(processed_clips,padding=-transition_val if transition_val>0 else 0,method="compose")
+            elif processed_clips:final_composite_video_clip=processed_clips[0]
+            if not final_composite_video_clip:logger.error("Concatenation failed.");return None
+            logger.info(f"Concatenated dur:{final_composite_video_clip.duration:.2f}s")
+            if transition_val>0 and final_composite_video_clip.duration>0:
+                if final_composite_video_clip.duration>transition_val*2:final_composite_video_clip=final_composite_video_clip.fx(vfx.fadein,transition_val).fx(vfx.fadeout,transition_val)
+                else:final_composite_video_clip=final_composite_video_clip.fx(vfx.fadein,min(transition_val,final_composite_video_clip.duration/2.0))
+            if overall_narration_path and os.path.exists(overall_narration_path) and final_composite_video_clip.duration>0:
+                try:narration_clip_mvpy=AudioFileClip(overall_narration_path);final_composite_video_clip=final_composite_video_clip.set_audio(narration_clip_mvpy);logger.info("Narration added.")
+                except Exception as e_narr:logger.error(f"Narration add error:{e_narr}",exc_info=True)
+            elif final_composite_video_clip.duration<=0:logger.warning("Video no duration. No audio.")
+            if final_composite_video_clip and final_composite_video_clip.duration>0:
+                output_vid_path=os.path.join(self.output_dir,output_filename);logger.info(f"Writing video:{output_vid_path} (Dur:{final_composite_video_clip.duration:.2f}s)")
+                final_composite_video_clip.write_videofile(output_vid_path,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:{output_vid_path}");return output_vid_path
+            else:logger.error("Final clip invalid. No write.");return None
+        except Exception as e_vid_write:logger.error(f"Video write error:{e_vid_write}",exc_info=True);return None
         finally:
             logger.debug("Closing all MoviePy clips in `assemble_animatic_from_assets` finally block.")
-            all_clips_to_close = processed_clips + ([narration_clip] if narration_clip else []) + ([final_clip] if final_clip else [])
-            for clip_obj_to_close in all_clips_to_close:
-                if clip_obj_to_close and hasattr(clip_obj_to_close, "close"):
-                    try:
-                        clip_obj_to_close.close()
-                    except Exception as e_close:
-                        logger.warning(
-                            f"Ignoring error while closing a clip: {type(clip_obj_to_close).__name__} - {e_close}"
-                        )
+            all_clips_to_close_list = processed_clips + ([narration_clip_mvpy] if narration_clip_mvpy else []) + ([final_composite_video_clip] if final_composite_video_clip else [])
+            for clip_to_close_item in all_clips_to_close_list:
+                if clip_to_close_item and hasattr(clip_to_close_item, 'close'):
+                    try: clip_to_close_item.close()
+                    except Exception as e_final_close: logger.warning(f"Ignoring error while closing a clip: {type(clip_to_close_item).__name__} - {e_final_close}")