Update core/visual_engine.py

core/visual_engine.py

@@ -1,49 +1,42 @@
 # core/visual_engine.py
 from PIL import Image, ImageDraw, ImageFont, ImageOps
-import base64
-import mimetypes
-import numpy as np
-import os
-import openai
-import requests
-import io
-import time
-import random
-import logging
 
-# --- MoviePy Imports ---
+# --- 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,
+    AudioFileClip
 )
 import moviepy.video.fx.all as vfx
-
-# --- MONKEY PATCH for Pillow/MoviePy compatibility ---
-try:
-    if hasattr(Image, "Resampling") and hasattr(Image.Resampling, "LANCZOS"):  # Pillow 9+
-        if not hasattr(Image, "ANTIALIAS"):
-            Image.ANTIALIAS = Image.Resampling.LANCZOS
-    elif hasattr(Image, "LANCZOS"):  # Pillow 8
-        if not hasattr(Image, "ANTIALIAS"):
-            Image.ANTIALIAS = Image.LANCZOS
-    elif not hasattr(Image, "ANTIALIAS"):  # Fallback if no common resampling attributes found
-        print(
-            "WARNING: Pillow version lacks common Resampling attributes or ANTIALIAS. MoviePy effects might fail or look different."
-        )
-except Exception as e_monkey_patch:
-    print(
-        f"WARNING: An unexpected error occurred during Pillow ANTIALIAS monkey-patch: {e_monkey_patch}"
-    )
+import numpy as np
+import os
+import openai
+import requests
+import io
+import time
+import random
+import logging
 
 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.INFO)
 
-# --- External Service Client Imports ---
+# --- ElevenLabs Client Import ---
 ELEVENLABS_CLIENT_IMPORTED = False
 ElevenLabsAPIClient = None
 Voice = None
@@ -51,92 +44,57 @@ VoiceSettings = None
 try:
     from elevenlabs.client import ElevenLabs as ImportedElevenLabsClient
     from elevenlabs import Voice as ImportedVoice, VoiceSettings as ImportedVoiceSettings
-
     ElevenLabsAPIClient = ImportedElevenLabsClient
     Voice = ImportedVoice
     VoiceSettings = ImportedVoiceSettings
     ELEVENLABS_CLIENT_IMPORTED = True
-    logger.info("ElevenLabs client components imported successfully.")
-except ImportError:
-    logger.warning(
-        "ElevenLabs SDK not found (pip install elevenlabs). Audio generation will be disabled."
-    )
-except Exception as e_eleven_import:
-    logger.warning(
-        f"Error importing ElevenLabs client components: {e_eleven_import}. Audio generation disabled."
-    )
+    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
-RunwayMLAPIClient = None  # Using a more specific name for the client class
+RunwayMLClient = None
 try:
-    from runwayml import RunwayML as ImportedRunwayMLClient  # Actual SDK import
-
-    RunwayMLAPIClient = ImportedRunwayMLClient
-    RUNWAYML_SDK_IMPORTED = True
-    logger.info("RunwayML SDK 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:
-    logger.warning(
-        f"Error importing RunwayML SDK: {e_runway_sdk_import}. 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  # For default Pillow font
-    PREFERRED_FONT_SIZE_PIL = 20  # For custom font
-    VIDEO_OVERLAY_FONT_SIZE = 30
-    VIDEO_OVERLAY_FONT_COLOR = "white"
-    # Standard font names ImageMagick (used by TextClip) is likely to find in Linux containers
-    DEFAULT_MOVIEPY_FONT = "DejaVu-Sans-Bold"
-    PREFERRED_MOVIEPY_FONT = "Liberation-Sans-Bold"  # Often available
-
-    def __init__(
-        self, output_dir="temp_cinegen_media", default_elevenlabs_voice_id="Rachel"
-    ):
+    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
+        self.font_filename = "DejaVuSans-Bold.ttf"
         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_filename,
+            "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf",
+            "/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf",
+            "/System/Library/Fonts/Supplemental/Arial.ttf",
+            "C:/Windows/Fonts/arial.ttf",
+            "/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()  # Default
-        self.current_font_size_pil = self.DEFAULT_FONT_SIZE_PIL
+        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.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.")
+        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
@@ -153,44 +111,24 @@ class VisualEngine:
                 stability=0.60,
                 similarity_boost=0.80,
                 style=0.15,
-                use_speaker_boost=True,
+                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.runway_client = None
 
         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, k):
+        self.openai_api_key = k
+        self.USE_AI_IMAGE_GENERATION = bool(k)
+        logger.info(f"DALL-E ({self.dalle_model}) {'Ready.' if k else 'Disabled.'}")
 
     def set_elevenlabs_api_key(self, api_key, voice_id_from_secret=None):
         self.elevenlabs_api_key = api_key
@@ -201,423 +139,285 @@ class VisualEngine:
                 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})"
+                    f"ElevenLabs Client {'Ready' if self.USE_ELEVENLABS else 'Failed Init'} "
+                    f"(Voice ID: {self.elevenlabs_voice_id})."
                 )
             except Exception as e:
-                logger.error(
-                    f"ElevenLabs client initialization error: {e}. Service Disabled.",
-                    exc_info=True,
-                )
+                logger.error(f"ElevenLabs client init error: {e}. Disabled.", exc_info=True)
                 self.USE_ELEVENLABS = False
-                self.elevenlabs_client = None
         else:
             self.USE_ELEVENLABS = False
-            logger.info(
-                f"ElevenLabs Service Disabled (API key not provided or SDK import issue)."
-            )
-
-    def set_pexels_api_key(self, api_key):
-        self.pexels_api_key = api_key
-        self.USE_PEXELS = bool(api_key)
-        logger.info(
-            f"Pexels Search status: {'Ready' if self.USE_PEXELS else 'Disabled'}"
-        )
-
-    def set_runway_api_key(self, api_key):
-        self.runway_api_key = api_key  # Store key regardless for potential direct HTTP use
-        if api_key:
-            if RUNWAYML_SDK_IMPORTED and RunwayMLAPIClient:
-                if not self.runway_ml_client_instance:  # If not already initialized by env var
-                    try:
-                        # The RunwayML Python SDK expects the API key via the RUNWAYML_API_SECRET env var.
-                        # If it's not set, we set it temporarily for client initialization.
-                        original_env_secret = os.getenv("RUNWAYML_API_SECRET")
-                        if not original_env_secret:
-                            logger.info(
-                                "Temporarily setting RUNWAYML_API_SECRET from provided key for SDK client init."
-                            )
-                            os.environ["RUNWAYML_API_SECRET"] = api_key
-
-                        self.runway_ml_client_instance = RunwayMLAPIClient()
-                        self.USE_RUNWAYML = True  # SDK client successfully initialized
-                        logger.info(
-                            "RunwayML Client initialized successfully using provided API key."
-                        )
+            logger.info("ElevenLabs Disabled (no key or SDK).")
 
-                        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."
-                            )
+    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.'}")
 
-                    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."
+    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
-        else:  # No API key provided
+        elif k:
+            self.USE_RUNWAYML = True
+            logger.info("RunwayML API Key set (direct API or placeholder).")
+        else:
             self.USE_RUNWAYML = False
-            self.runway_ml_client_instance = None
-            logger.info("RunwayML Service Disabled (no API key provided).")
-
-    # --- Helper Methods ---
-    def _image_to_data_uri(self, image_path):
-        try:
-            mime_type, _ = mimetypes.guess_type(image_path)
-            if not mime_type:
-                ext = os.path.splitext(image_path)[1].lower()
-                mime_map = {".png": "image/png", ".jpg": "image/jpeg", ".jpeg": "image/jpeg"}
-                mime_type = mime_map.get(ext, "application/octet-stream")
-                if mime_type == "application/octet-stream":
-                    logger.warning(
-                        f"Could not determine MIME type for {image_path}, using default."
-                    )
-
-            with open(image_path, "rb") as image_file:
-                encoded_string = base64.b64encode(image_file.read()).decode("utf-8")
-            data_uri = f"data:{mime_type};base64,{encoded_string}"
-            logger.debug(
-                f"Generated data URI for {os.path.basename(image_path)} (first 100 chars): {data_uri[:100]}..."
-            )
-            return data_uri
-        except FileNotFoundError:
-            logger.error(f"Image file not found at {image_path} for data URI conversion.")
-            return None
-        except Exception as e:
-            logger.error(
-                f"Error converting image {image_path} to data URI: {e}", exc_info=True
-            )
-            return None
+            logger.info("RunwayML Disabled (no API key).")
 
-    def _map_resolution_to_runway_ratio(self, width, height):
-        ratio_str = f"{width}:{height}"
-        # Gen-4 supports: "1280:720", "720:1280", "1104:832", "832:1104", "960:960", "1584:672"
-        supported_ratios_gen4 = [
-            "1280:720",
-            "720:1280",
-            "1104:832",
-            "832:1104",
-            "960:960",
-            "1584:672",
-        ]
-        if ratio_str in supported_ratios_gen4:
-            return ratio_str
-        # Fallback or find closest - for now, strict matching or default
-        logger.warning(
-            f"Resolution {ratio_str} not directly in Gen-4 supported list. Defaulting to 1280:720."
-        )
-        return "1280:720"
-
-    def _get_text_dimensions(self, text_content, font_object):
-        # (Robust version from before)
-        default_char_height = getattr(font_object, "size", self.current_font_size_pil)
+    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_char_height
+            return 0, default_line_height
         try:
-            if hasattr(font_object, "getbbox"):
-                bbox = font_object.getbbox(text_content)
-                w = bbox[2] - bbox[0]
-                h = bbox[3] - bbox[1]
-                return w, h if h > 0 else default_char_height
-            elif hasattr(font_object, "getsize"):
-                w, h = font_object.getsize(text_content)
-                return w, h if h > 0 else default_char_height
+            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_char_height * 0.6),
-                    int(default_char_height * 1.2),
-                )
+                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: {e}")
-            return (
-                int(len(text_content) * self.current_font_size_pil * 0.6),
-                int(self.current_font_size_pil * 1.2),
-            )
+            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):
-        # (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)
+
+        img = Image.new('RGB', size, color=(20, 20, 40))
+        draw = ImageDraw.Draw(img)
         padding = 25
-        max_w = size[0] - (2 * padding)
+        max_text_width = 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
+            text_description = "(Placeholder: No text description provided)"
 
-            if current_w <= max_w:
-                current_line_text = test_line_text
+        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_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.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)
+                    truncated = (
+                        word[:chars_that_fit-3] + "..."
+                        if len(word) > chars_that_fit else word
+                    )
+                    lines.append(truncated)
+                    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("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 "")
+            avg_char_w = self._get_text_dimensions("A", self.font)[0] or 10
+            chars_that_fit = int(max_text_width / avg_char_w)
+            truncated = (
+                text_description[:chars_that_fit-3] + "..."
+                if len(text_description) > chars_that_fit else text_description
             )
+            lines.append(truncated)
         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
+            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
         )
-        max_lines = max(1, max_lines)  # Ensure at least one line
-
-        y_pos = padding + (size[1] - (2 * padding) - max_lines * (single_line_h + 2)) / 2.0
-        for i in range(max_lines):
-            line_text = lines[i]
-            line_w, _ = self._get_text_dimensions(line_text, self.font_pil)
-            if line_w == 0 and line_text.strip():
-                line_w = len(line_text) * (self.current_font_size_pil * 0.6)
-            x_pos = (size[0] - line_w) / 2.0
-            try:
-                d.text((x_pos, y_pos), line_text, font=self.font_pil, fill=(200, 200, 180))
-            except Exception as e_draw:
-                logger.error(f"Pillow d.text error: {e_draw} for '{line_text}'")
-            y_pos += single_line_h + 2
-            if i == 6 and max_lines > 7:
-                try:
-                    d.text((x_pos, y_pos), "...", font=self.font_pil, fill=(200, 200, 180))
-                except Exception as e_elip:
-                    logger.error(f"Pillow d.text ellipsis error: {e_elip}")
-                    break
+        y_text_start = padding + (size[1] - (2 * padding) - total_text_block_height) / 2.0
+        current_y = y_text_start
+
+        for i in range(max_lines_to_display):
+            line_content = lines[i]
+            line_width_actual, _ = self._get_text_dimensions(line_content, self.font)
+            x_text = max(padding, (size[0] - line_width_actual) / 2.0)
+            draw.text((x_text, current_y), line_content, font=self.font, fill=(200, 200, 180))
+            current_y += single_line_height + line_spacing
+
+            if i == 6 and max_lines_to_display > 7 and len(lines) > max_lines_to_display:
+                ellipsis_width, _ = self._get_text_dimensions("...", self.font)
+                x_ellipsis = max(padding, (size[0] - ellipsis_width) / 2.0)
+                draw.text((x_ellipsis, current_y), "...", font=self.font, fill=(200, 200, 180))
+                break
 
         filepath = os.path.join(self.output_dir, filename)
         try:
             img.save(filepath)
             return filepath
-        except Exception as e_save:
-            logger.error(
-                f"Saving placeholder image '{filepath}' error: {e_save}", exc_info=True
-            )
+        except Exception as e:
+            logger.error(f"Error saving placeholder image {filepath}: {e}", exc_info=True)
             return None
 
     def _search_pexels_image(self, query, output_filename_base):
-        # <<< THIS IS THE CORRECTED METHOD >>>
         if not self.USE_PEXELS or not self.pexels_api_key:
             return None
+
         headers = {"Authorization": self.pexels_api_key}
         params = {"query": query, "per_page": 1, "orientation": "landscape", "size": "large2x"}
-        base_name_for_pexels, _ = os.path.splitext(output_filename_base)
-        pexels_filename = base_name_for_pexels + f"_pexels_{random.randint(1000,9999)}.jpg"
+        base_name, _ = os.path.splitext(output_filename_base)
+        pexels_filename = f"{base_name}_pexels_{random.randint(1000, 9999)}.jpg"
         filepath = os.path.join(self.output_dir, pexels_filename)
+
         try:
-            logger.info(f"Pexels: Searching for '{query}'")
+            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
+                "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
+                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_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}")
+                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"Pexels: No photos for '{effective_query}'.")
+                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: RequestException for '{query}': {e_req}", exc_info=False)
-            return None  # Less verbose for network
+            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"Pexels: General error for '{query}': {e}", exc_info=True)
-            return None
+            logger.error(f"General Pexels error for query '{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}"
-            )
+    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
-
-        image_data_uri = self._image_to_data_uri(input_image_path)
-        if not image_data_uri:
+        if not iip or not os.path.exists(iip):
+            logger.error(f"Runway Gen-4 needs input image. Path invalid: {iip}")
             return None
 
-        runway_duration = 10 if target_duration_seconds >= 8 else 5  # Map to 5s or 10s for Gen-4
-        runway_ratio_str = self._map_resolution_to_runway_ratio(
-            self.video_frame_size[0], self.video_frame_size[1]
-        )
-
-        # Use a more descriptive output filename for Runway videos
-        base_name_for_runway, _ = os.path.splitext(scene_identifier_filename_base)
-        output_video_filename = base_name_for_runway + f"_runway_gen4_d{runway_duration}s.mp4"
-        output_video_filepath = os.path.join(self.output_dir, output_video_filename)
-
+        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"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}'"
+            f"Runway Gen-4 (Placeholder) img: {os.path.basename(iip)}, "
+            f"motion: '{pt[:100]}...', dur: {runway_dur}s"
         )
-        try:
-            # Using the RunwayML Python SDK structure
-            task_submission = self.runway_ml_client_instance.image_to_video.create(
-                model="gen4_turbo",
-                prompt_image=image_data_uri,
-                prompt_text=text_prompt_for_motion,  # This is the motion prompt
-                duration=runway_duration,
-                ratio=runway_ratio_str,
-                # seed=random.randint(0, 4294967295), # Optional: for reproducibility
-                # Other Gen-4 params (motion_score, upscale, watermark etc. can be added here if available in SDK)
-            )
-            task_id = task_submission.id
-            logger.info(f"Runway Gen-4 task created with ID: {task_id}. Polling for completion...")
-
-            poll_interval_seconds = 10
-            max_polling_duration_seconds = 6 * 60  # 6 minutes
-            start_time = time.time()
-
-            while time.time() - start_time < max_polling_duration_seconds:
-                time.sleep(poll_interval_seconds)
-                task_details = self.runway_ml_client_instance.tasks.retrieve(id=task_id)
-                logger.info(f"Runway task {task_id} status: {task_details.status}")
-
-                if task_details.status == "SUCCEEDED":
-                    # Determine output URL (this structure might vary based on SDK version)
-                    output_url = None
-                    if hasattr(task_details, "output") and task_details.output and hasattr(
-                        task_details.output, "url"
-                    ):
-                        output_url = task_details.output.url
-                    elif (
-                        hasattr(task_details, "artifacts")
-                        and task_details.artifacts
-                        and isinstance(task_details.artifacts, list)
-                        and len(task_details.artifacts) > 0
-                    ):
-                        first_artifact = task_details.artifacts[0]
-                        if hasattr(first_artifact, "url"):
-                            output_url = first_artifact.url
-                        elif hasattr(first_artifact, "download_url"):
-                            output_url = first_artifact.download_url
+        logger.warning("Using PLACEHOLDER video for Runway Gen-4.")
 
-                    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,
+        img_clip = None
+        txt_c = None
+        final_ph_clip = None
+        try:
+            img_clip = ImageClip(iip).set_duration(runway_dur)
+            txt = (
+                f"Runway Gen-4 Placeholder\n"
+                f"Input: {os.path.basename(iip)}\n"
+                f"Motion: {pt[:50]}..."
             )
-            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,
+            txt_c = TextClip(
+                txt,
+                fontsize=24,
+                color='white',
+                font=self.video_overlay_font,
+                bg_color='rgba(0,0,0,0.5)',
+                size=(int(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
-
-    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)
+        finally:
+            if img_clip and hasattr(img_clip, 'close'):
+                img_clip.close()
+            if txt_c and hasattr(txt_c, 'close'):
+                txt_c.close()
+            if final_ph_clip and hasattr(final_ph_clip, 'close'):
+                final_ph_clip.close()
+
+    def _create_placeholder_video_content(self, td, fn, dur=4, sz=None):
+        if sz is None:
+            sz = self.video_frame_size
+        fp = os.path.join(self.output_dir, fn)
         tc = None
         try:
             tc = TextClip(
-                text_description,
+                td,
                 fontsize=50,
-                color="white",
+                color='white',
                 font=self.video_overlay_font,
-                bg_color="black",
-                size=size,
-                method="caption",
-            ).set_duration(duration)
+                bg_color='black',
+                size=sz,
+                method='caption'
+            ).set_duration(dur)
             tc.write_videofile(
-                fp, fps=24, codec="libx264", preset="ultrafast", logger=None, threads=2
+                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)
+            logger.error(f"Generic placeholder error {fp}: {e}", exc_info=True)
             return None
         finally:
-            if tc and hasattr(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,
@@ -625,202 +425,210 @@ class VisualEngine:
         scene_data,
         scene_identifier_filename_base,
         generate_as_video_clip=False,
-        runway_target_duration=5,
+        runway_target_duration=5
     ):
-        # (Logic updated for improved DALL·E and RunwayML fallback)
-        base_name, _ = os.path.splitext(scene_identifier_filename_base)
+        base_name = scene_identifier_filename_base
         asset_info = {
-            "path": None,
-            "type": "none",
-            "error": True,
-            "prompt_used": image_generation_prompt_text,
-            "error_message": "Asset generation init failed",
+            'path': None,
+            'type': 'none',
+            'error': True,
+            'prompt_used': image_generation_prompt_text,
+            'error_message': 'Generation not attempted'
         }
         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)
+        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'
+        }
 
-        # STEP 1: Generate/acquire the base image via DALL·E
         if self.USE_AI_IMAGE_GENERATION and self.openai_api_key:
-            try:
-                logger.info(f"Calling DALL·E with prompt: {image_generation_prompt_text[:70]}...")
-                response = openai.Image.create(
-                    prompt=image_generation_prompt_text,
-                    n=1,
-                    size=self.image_size_dalle3,
-                    model=self.dalle_model,
-                )
-                image_url = response["data"][0]["url"]
-                ir = requests.get(image_url, timeout=120)
-                ir.raise_for_status()
-                id_img = Image.open(io.BytesIO(ir.content))
-                if id_img.mode != "RGB":
-                    id_img = id_img.convert("RGB")
-                id_img.save(base_image_filepath)
-                logger.info(f"DALL·E base image saved: {base_image_filepath}")
-                input_image_for_runway_path = base_image_filepath
-                asset_info = {
-                    "path": base_image_filepath,
-                    "type": "image",
-                    "error": False,
-                    "prompt_used": image_generation_prompt_text,
-                }
-            except openai.error.OpenAIError as e:
-                logger.warning(f"DALL·E error: {e}. Falling back to Pexels or placeholder.")
-                asset_info["error_message"] = str(e)
-            except Exception as e:
-                logger.error(f"Unexpected DALL·E error: {e}", exc_info=True)
-                asset_info["error_message"] = str(e)
+            max_r = 2
+            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): "
+                        f"{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.")
 
-        # STEP 2: If DALL·E failed, try Pexels
-        if asset_info["error"] and self.USE_PEXELS:
-            logger.info("Attempting Pexels fallback 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','')}"
+                'pexels_search_query_감독',
+                f"{scene_data.get('emotional_beat', '')} {scene_data.get('setting_description', '')}"
             )
-            pp = self._search_pexels_image(pqt, base_image_filename)
+            pp = self._search_pexels_image(pqt, image_filename_for_base)
             if pp:
                 input_image_for_runway_path = pp
-                asset_info = {
-                    "path": pp,
-                    "type": "image",
-                    "error": False,
-                    "prompt_used": f"Pexels:{pqt}",
+                temp_image_asset_info = {
+                    'path': pp,
+                    'type': 'image',
+                    'error': False,
+                    'prompt_used': f"Pexels: {pqt}"
                 }
             else:
-                current_em = asset_info.get("error_message", "")
-                asset_info["error_message"] = (current_em + " Pexels fallback failed.").strip()
+                current_em = temp_image_asset_info.get('error_message', "")
+                temp_image_asset_info['error_message'] = (current_em + " Pexels failed.").strip()
 
-        # STEP 3: If both DALL·E and Pexels failed, create placeholder
-        if asset_info["error"]:
-            logger.warning("Both DALL·E and Pexels failed. Creating placeholder image.")
-            ppt = asset_info.get("prompt_used", image_generation_prompt_text)
+        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"[Placeholder for] {ppt[:70]}...", base_image_filename
+                f"[Base Img Placeholder] {ppt[:100]}...",
+                image_filename_for_base
             )
             if php:
                 input_image_for_runway_path = php
-                asset_info = {
-                    "path": php,
-                    "type": "image",
-                    "error": False,
-                    "prompt_used": ppt,
+                temp_image_asset_info = {
+                    'path': php,
+                    'type': 'image',
+                    'error': False,
+                    'prompt_used': ppt
                 }
             else:
-                current_em = asset_info.get("error_message", "")
-                asset_info["error_message"] = (current_em + " Placeholder creation failed.").strip()
+                current_em = temp_image_asset_info.get('error_message', "")
+                temp_image_asset_info['error_message'] = (current_em + " Base placeholder failed.").strip()
 
-        # STEP 4: If a video clip is requested, attempt RunwayML
         if generate_as_video_clip:
-            if not input_image_for_runway_path or not os.path.exists(input_image_for_runway_path):
-                logger.error("No valid base image for RunwayML. Skipping video generation.")
-                asset_info["error"] = True
-                asset_info["error_message"] = (asset_info.get("error_message", "") + " No base image.").strip()
-                asset_info["type"] = "none"
-                return asset_info
-
-            if self.USE_RUNWAYML and self.runway_ml_client_instance:
+            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,
+                    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,
+                    return {
+                        'path': video_path,
+                        'type': 'video',
+                        'error': False,
+                        'prompt_used': motion_prompt_text_for_video,
+                        'base_image_path': input_image_for_runway_path
                     }
                 else:
-                    logger.warning("RunwayML video generation failed. Returning base image instead.")
-                    asset_info = {
-                        "path": input_image_for_runway_path,
-                        "type": "image",
-                        "error": True,
-                        "prompt_used": image_generation_prompt_text,
-                        "error_message": (asset_info.get("error_message", "") + " RunwayML failed.").strip(),
-                    }
+                    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:
-                logger.warning("RunwayML not enabled or client not initialized. Skipping video generation.")
-                asset_info = {
-                    "path": input_image_for_runway_path,
-                    "type": "image",
-                    "error": True,
-                    "prompt_used": image_generation_prompt_text,
-                    "error_message": (asset_info.get("error_message", "") + " RunwayML disabled.").strip(),
-                }
-
-        return asset_info
+                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 - robust enough)
-        if not self.USE_ELEVENLABS or not self.elevenlabs_client or not text_to_narrate:
-            logger.info("ElevenLabs audio skipped.")
+    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, output_filename)
+        afp = os.path.join(self.output_dir, ofn)
         try:
-            logger.info(f"ElevenLabs audio (Voice:{self.elevenlabs_voice_id}): {text_to_narrate[:70]}...")
+            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"
+            if (
+                hasattr(self.elevenlabs_client, 'text_to_speech') and
+                hasattr(self.elevenlabs_client.text_to_speech, 'stream')
             ):
                 asm = self.elevenlabs_client.text_to_speech.stream
-                logger.info("Using ElevenLabs .text_to_speech.stream()")
-            elif hasattr(self.elevenlabs_client, "generate_stream"):
+                logger.info("Using 11L .text_to_speech.stream()")
+            elif hasattr(self.elevenlabs_client, 'generate_stream'):
                 asm = self.elevenlabs_client.generate_stream
-                logger.info("Using ElevenLabs .generate_stream()")
-            elif hasattr(self.elevenlabs_client, "generate"):
-                logger.info("Using ElevenLabs .generate()")
+                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"
+                    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"ElevenLabs audio (non-stream) saved: {afp}")
+                logger.info(f"11L audio (non-stream): {afp}")
                 return afp
             else:
-                logger.error("No ElevenLabs audio method available.")
+                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"ElevenLabs audio (stream) saved: {afp}")
-                return afp
-
+            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"ElevenLabs audio error: {e}", exc_info=True)
+            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
+        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
@@ -831,12 +639,14 @@ class VisualEngine:
         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")
+            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.")
@@ -847,59 +657,61 @@ class VisualEngine:
 
             current_scene_mvpy_clip = None
             try:
-                if asset_type == "image":
+                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()
+                    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
+                    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 = 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")
+                    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"]:
+                    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']}"
+                        f"S{scene_num}: NumPy for MoviePy. "
+                        f"Shape:{frame_np.shape}, DType:{frame_np.dtype}, "
+                        f"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")
+                    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")
+                            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":
+                elif asset_type == 'video':
                     src_clip = None
                     try:
                         src_clip = VideoFileClip(
                             asset_path,
                             target_resolution=(
-                                self.video_frame_size[1],
-                                self.video_frame_size[0],
-                            )
-                            if self.video_frame_size
-                            else None,
-                            audio=False,
+                                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:
@@ -911,16 +723,23 @@ class VisualEngine:
                                 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)."
+                                        f"S{scene_num} Video clip ({src_clip.duration:.2f}s) "
+                                        f"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)
+                        logger.error(
+                            f"S{scene_num} Video load error '{asset_path}':{e}",
+                            exc_info=True
+                        )
                         continue
                     finally:
-                        if src_clip and src_clip is not current_scene_mvpy_clip and hasattr(src_clip, "close"):
+                        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.")
@@ -929,32 +748,32 @@ class VisualEngine:
                 if current_scene_mvpy_clip and key_action:
                     try:
                         to_dur = (
-                            min(current_scene_mvpy_clip.duration - 0.5, current_scene_mvpy_clip.duration * 0.8)
-                            if current_scene_mvpy_clip.duration > 0.5
-                            else current_scene_mvpy_clip.duration
+                            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.")
+                        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=(int(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)
 
@@ -964,7 +783,7 @@ class VisualEngine:
             except Exception as e:
                 logger.error(f"MAJOR Error S{scene_num} ({asset_path}):{e}", exc_info=True)
             finally:
-                if current_scene_mvpy_clip and hasattr(current_scene_mvpy_clip, "close"):
+                if current_scene_mvpy_clip and hasattr(current_scene_mvpy_clip, 'close'):
                     try:
                         current_scene_mvpy_clip.close()
                     except:
@@ -978,21 +797,32 @@ class VisualEngine:
         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")
+                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))
+                    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:
+            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)
@@ -1008,15 +838,18 @@ class VisualEngine:
                 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"),
+                    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",
+                    logger='bar',
                     bitrate="5000k",
-                    ffmpeg_params=["-pix_fmt", "yuv420p"],
+                    ffmpeg_params=["-pix_fmt", "yuv420p"]
                 )
                 logger.info(f"Video created:{op}")
                 return op
@@ -1027,13 +860,17 @@ class VisualEngine:
             logger.error(f"Video write error:{e}", exc_info=True)
             return None
         finally:
-            logger.debug("Closing all MoviePy clips in `assemble_animatic_from_assets` finally block.")
-            all_clips_to_close = processed_clips + ([narration_clip] if narration_clip else []) + ([final_clip] if final_clip else [])
-            for clip_obj_to_close in all_clips_to_close:
-                if clip_obj_to_close and hasattr(clip_obj_to_close, "close"):
+            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_to_close.close()
+                        clip_obj.close()
                     except Exception as e_close:
-                        logger.warning(
-                            f"Ignoring error while closing a clip: {type(clip_obj_to_close).__name__} - {e_close}"
-                        )
+                        logger.warning(f"Ignoring error while closing a clip: {e_close}")