core/visual_engine.py

# core/visual_engine.py
from PIL import Image, ImageDraw, ImageFont, ImageOps
# --- MONKEY PATCH FOR Image.ANTIALIAS ---
try:
    if hasattr(Image, 'Resampling') and hasattr(Image.Resampling, 'LANCZOS'): # Pillow 9+
        if not hasattr(Image, 'ANTIALIAS'): Image.ANTIALIAS = Image.Resampling.LANCZOS
    elif hasattr(Image, 'LANCZOS'): # Pillow 8
         if not hasattr(Image, 'ANTIALIAS'): Image.ANTIALIAS = Image.LANCZOS
    elif not hasattr(Image, 'ANTIALIAS'):
             print("WARNING: Pillow version lacks common Resampling attributes or ANTIALIAS. Video effects might fail.")
except Exception as e_mp: print(f"WARNING: ANTIALIAS monkey-patch error: {e_mp}")
# --- END MONKEY PATCH ---

from moviepy.editor import (ImageClip, VideoFileClip, concatenate_videoclips, TextClip,
                            CompositeVideoClip, AudioFileClip)
import moviepy.video.fx.all as vfx
import numpy as np
import os
import openai
import requests
import io
import time
import random
import logging

logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)

# --- ElevenLabs Client Import ---
ELEVENLABS_CLIENT_IMPORTED = False
ElevenLabsAPIClient = None
Voice = None
VoiceSettings = None
try:
    from elevenlabs.client import ElevenLabs as ImportedElevenLabsClient
    from elevenlabs import Voice as ImportedVoice, VoiceSettings as ImportedVoiceSettings
    ElevenLabsAPIClient = ImportedElevenLabsClient
    Voice = ImportedVoice
    VoiceSettings = ImportedVoiceSettings
    ELEVENLABS_CLIENT_IMPORTED = True
    logger.info("ElevenLabs client components imported.")
except Exception as e_eleven:
    logger.warning(f"ElevenLabs client import failed: {e_eleven}. Audio generation disabled.")

# --- RunwayML Client Import (Placeholder) ---
RUNWAYML_SDK_IMPORTED = False
RunwayMLClient = None # Placeholder for the actual RunwayML client class
try:
    # This is a hypothetical import. Replace with actual RunwayML SDK import if available.
    # Example: from runwayml import RunwayClient as ImportedRunwayMLClient
    # RunwayMLClient = ImportedRunwayMLClient
    # RUNWAYML_SDK_IMPORTED = True
    # logger.info("RunwayML SDK (placeholder) imported.")
    logger.info("RunwayML SDK import is a placeholder. Actual SDK needed for Runway features.")
except ImportError:
    logger.warning("RunwayML SDK (placeholder) not found. RunwayML video generation will be disabled.")
except Exception as e_runway_sdk:
    logger.warning(f"Error importing RunwayML SDK (placeholder): {e_runway_sdk}. RunwayML features disabled.")


class VisualEngine:
    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 = "arial.ttf"
        font_paths_to_try = [
            self.font_filename,
            f"/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf",
            f"/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf",
            f"/System/Library/Fonts/Supplemental/Arial.ttf",
            f"C:/Windows/Fonts/arial.ttf",
            f"/usr/local/share/fonts/truetype/mycustomfonts/{self.font_filename}"
        ]
        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 = 'Liberation-Sans-Bold' # For MoviePy TextClip

        try:
            self.font = ImageFont.truetype(self.font_path_pil, self.font_size_pil) if self.font_path_pil else ImageFont.load_default()
            if self.font_path_pil: logger.info(f"Pillow font loaded: {self.font_path_pil}.")
            else: logger.warning("Using default Pillow font."); self.font_size_pil = 10
        except IOError:
            logger.warning("Pillow font error. Using default."); self.font = ImageFont.load_default(); self.font_size_pil = 10

        self.openai_api_key = None; self.USE_AI_IMAGE_GENERATION = False
        self.dalle_model = "dall-e-3"; self.image_size_dalle3 = "1792x1024"
        self.video_frame_size = (1280, 720)

        self.elevenlabs_api_key = None; self.USE_ELEVENLABS = False
        self.elevenlabs_client = None
        self.elevenlabs_voice_id = default_elevenlabs_voice_id
        if VoiceSettings and ELEVENLABS_CLIENT_IMPORTED:
            self.elevenlabs_voice_settings = 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

        # <<< RUNWAYML START >>>
        self.runway_api_key = None; self.USE_RUNWAYML = False
        self.runway_client = None # Placeholder for the actual RunwayML client instance
        # <<< RUNWAYML END >>>

        logger.info("VisualEngine initialized.")

    def set_openai_api_key(self,k):
        self.openai_api_key=k; self.USE_AI_IMAGE_GENERATION=bool(k)
        logger.info(f"DALL-E ({self.dalle_model}) {'Ready.' if k else 'Disabled.'}")

    def set_elevenlabs_api_key(self,api_key, voice_id_from_secret=None):
        self.elevenlabs_api_key=api_key
        if voice_id_from_secret: self.elevenlabs_voice_id = voice_id_from_secret
        if api_key and ELEVENLABS_CLIENT_IMPORTED and ElevenLabsAPIClient:
            try:
                self.elevenlabs_client = ElevenLabsAPIClient(api_key=api_key)
                self.USE_ELEVENLABS=bool(self.elevenlabs_client)
                logger.info(f"ElevenLabs Client {'Ready' if self.USE_ELEVENLABS else 'Failed Init'} (Voice ID: {self.elevenlabs_voice_id}).")
            except Exception as e: logger.error(f"ElevenLabs client init error: {e}. Disabled.", exc_info=True); self.USE_ELEVENLABS=False
        else: self.USE_ELEVENLABS=False; logger.info("ElevenLabs Disabled (no key or SDK).")

    def set_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.'}")

    # <<< RUNWAYML START >>>
    def set_runway_api_key(self, k):
        self.runway_api_key = k
        if k and RUNWAYML_SDK_IMPORTED and RunwayMLClient: # Assuming RunwayMLClient is the SDK's client class
            try:
                # self.runway_client = RunwayMLClient(api_key=k) # Actual initialization
                self.USE_RUNWAYML = True # Assume success for placeholder
                logger.info(f"RunwayML Client (Placeholder) {'Ready.' if self.USE_RUNWAYML else 'Failed Init.'}")
            except Exception as e:
                logger.error(f"RunwayML client (Placeholder) init error: {e}. Disabled.", exc_info=True)
                self.USE_RUNWAYML = False
        elif k and not (RUNWAYML_SDK_IMPORTED and RunwayMLClient):
            self.USE_RUNWAYML = True # Allow use with direct HTTP requests if SDK isn't used/available
            logger.info("RunwayML API Key set. SDK (Placeholder) not imported/used. Direct API calls would be needed.")
        else:
            self.USE_RUNWAYML = False
            logger.info("RunwayML Disabled (no API key or SDK issue).")
    # <<< RUNWAYML END >>>

    def _get_text_dimensions(self,text_content,font_obj):
        # ... (no changes from your previous version)
        if not text_content: return 0,self.font_size_pil
        try:
            if hasattr(font_obj,'getbbox'): # Pillow 8.0.0+
                bbox=font_obj.getbbox(text_content);w=bbox[2]-bbox[0];h=bbox[3]-bbox[1]
                return w, h if h > 0 else self.font_size_pil
            elif hasattr(font_obj,'getsize'): # Older Pillow
                w,h=font_obj.getsize(text_content)
                return w, h if h > 0 else self.font_size_pil
            else: # Should not happen with standard ImageFont objects
                return int(len(text_content)*self.font_size_pil*0.6),int(self.font_size_pil*1.2 if self.font_size_pil*1.2>0 else self.font_size_pil)
        except Exception as e:
            logger.warning(f"Error in _get_text_dimensions for '{text_content[:20]}...': {e}")
            return int(len(text_content)*self.font_size_pil*0.6),int(self.font_size_pil*1.2) # Fallback


    def _create_placeholder_image_content(self,text_description,filename,size=None):
        # ... (no changes from your previous version, ensure filename includes extension e.g. .png)
        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: No prompt text)"
        words=text_description.split();current_line=""
        for word in words:
            test_line=current_line+word+" ";
            if self._get_text_dimensions(test_line,self.font)[0] <= max_w: current_line=test_line
            else:
                if current_line: lines.append(current_line.strip());
                current_line=word+" "
        if current_line.strip(): lines.append(current_line.strip()) # Add last line
        if not lines and text_description: lines.append(text_description[:int(max_w//(self.font_size_pil*0.6 +1))]+"..." if text_description else "(Text too long)") # Handle single very long word
        elif not lines: lines.append("(Placeholder Text Error)")

        _,single_line_h=self._get_text_dimensions("Ay",self.font); single_line_h = single_line_h if single_line_h > 0 else self.font_size_pil + 2

        max_lines_to_display=min(len(lines),(size[1]-(2*padding))//(single_line_h+2)) if single_line_h > 0 else 1
        if max_lines_to_display <=0: max_lines_to_display = 1 # Ensure at least one line can be attempted

        y_text_start = padding + (size[1]-(2*padding) - max_lines_to_display*(single_line_h+2))/2.0
        y_text = y_text_start

        for i in range(max_lines_to_display):
            line_content=lines[i];line_w,_=self._get_text_dimensions(line_content,self.font);x_text=(size[0]-line_w)/2.0
            d.text((x_text,y_text),line_content,font=self.font,fill=(200,200,180));y_text+=single_line_h+2
            if i==6 and max_lines_to_display > 7: d.text((x_text,y_text),"...",font=self.font,fill=(200,200,180));break
        filepath=os.path.join(self.output_dir,filename);
        try:img.save(filepath);return filepath
        except Exception as e:logger.error(f"Saving placeholder image {filepath}: {e}", exc_info=True);return None


    def _search_pexels_image(self, query, output_filename_base):
        # ... (no changes from your previous version, ensure output_filename_base has .png for consistency, it will be replaced)
        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": "large"}
        # Use a more unique filename for Pexels images to avoid clashes if query is similar
        pexels_filename = output_filename_base.replace(".png", f"_pexels_{random.randint(1000,9999)}.jpg").replace(".mp4", f"_pexels_{random.randint(1000,9999)}.jpg")
        filepath = os.path.join(self.output_dir, pexels_filename)
        try:
            logger.info(f"Searching Pexels for: '{query}'"); effective_query = " ".join(query.split()[:5]); params["query"] = effective_query
            response = requests.get("https://api.pexels.com/v1/search", headers=headers, params=params, timeout=20)
            response.raise_for_status(); data = response.json()
            if data.get("photos") and len(data["photos"]) > 0:
                photo_url = data["photos"][0]["src"]["large2x"] # Using large2x for better quality
                image_response = requests.get(photo_url, timeout=60); image_response.raise_for_status()
                img_data = Image.open(io.BytesIO(image_response.content))
                if img_data.mode != 'RGB': img_data = img_data.convert('RGB')
                img_data.save(filepath); logger.info(f"Pexels image saved: {filepath}"); return filepath
            else: logger.info(f"No photos found on Pexels for query: '{effective_query}'")
        except Exception as e: logger.error(f"Pexels search/download for query '{query}': {e}", exc_info=True)
        return None


    # <<< RUNWAYML START >>>
    def _generate_video_clip_with_runwayml(self, prompt_text, scene_identifier_filename_base, target_duration_seconds=4, input_image_path=None):
        """
        Placeholder for generating a video clip using RunwayML.
        This needs to be implemented with the actual RunwayML SDK or API.
        """
        if not self.USE_RUNWAYML or not self.runway_api_key:
            logger.warning("RunwayML not enabled or API key missing. Cannot generate video clip.")
            return None

        output_video_filename = scene_identifier_filename_base.replace(".png", ".mp4") # Ensure .mp4 extension
        output_video_filepath = os.path.join(self.output_dir, output_video_filename)

        logger.info(f"Attempting RunwayML video generation for: {prompt_text[:100]}... (Target duration: {target_duration_seconds}s)")
        logger.info(f"RunwayML Output (Placeholder): {output_video_filepath}")

        # --- START ACTUAL RUNWAYML API INTERACTION (HYPOTHETICAL) ---
        # This section is highly dependent on RunwayML's specific API/SDK.
        # Example using a hypothetical SDK:
        # try:
        #     if not self.runway_client:
        #         # self.runway_client = RunwayMLClient(api_key=self.runway_api_key) # Or however it's initialized
        #         logger.warning("RunwayML client not initialized (Placeholder).")
        #         # For placeholder, simulate creating a dummy video file
        #         return self._create_placeholder_video_content(prompt_text, output_video_filename, duration=target_duration_seconds)


        #     generation_params = {
        #         "text_prompt": prompt_text,
        #         "duration_seconds": target_duration_seconds,
        #         "width": self.video_frame_size[0], # Or Runway's supported sizes
        #         "height": self.video_frame_size[1],
        #         # Add other params like seed, motion scale, etc.
        #     }
        #     if input_image_path and os.path.exists(input_image_path):
        #         generation_params["input_image_path"] = input_image_path # For image-to-video
        #         logger.info(f"Using input image for RunwayML: {input_image_path}")

        #     task_id = self.runway_client.submit_video_generation_task(**generation_params) # Hypothetical
        #     logger.info(f"RunwayML task submitted: {task_id}. Polling for completion...")

        #     while True:
        #         status = self.runway_client.get_task_status(task_id) # Hypothetical
        #         if status == "completed":
        #             video_url = self.runway_client.get_video_url(task_id) # Hypothetical
        #             video_response = requests.get(video_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"RunwayML video downloaded and saved: {output_video_filepath}")
        #             return output_video_filepath
        #         elif status in ["failed", "error"]:
        #             logger.error(f"RunwayML task {task_id} failed.")
        #             return None
        #         time.sleep(10) # Poll interval

        # except Exception as e:
        #     logger.error(f"Error during RunwayML video generation: {e}", exc_info=True)
        #     return None
        # --- END ACTUAL RUNWAYML API INTERACTION (HYPOTHETICAL) ---

        # For now, as a placeholder, create a dummy MP4 file with MoviePy
        # This allows the rest of the pipeline to be tested.
        # **REPLACE THIS WITH ACTUAL RUNWAYML CALLS**
        logger.warning("Using PLACEHOLDER video generation for RunwayML.")
        return self._create_placeholder_video_content(f"[RunwayML Placeholder] {prompt_text}", output_video_filename, duration=target_duration_seconds)

    def _create_placeholder_video_content(self, text_description, filename, duration=4, size=None):
        """Creates a short video clip with text as a placeholder."""
        if size is None: size = self.video_frame_size
        filepath = os.path.join(self.output_dir, filename)
        
        # Create a simple text clip
        txt_clip = TextClip(text_description, fontsize=50, color='white', font=self.video_overlay_font,
                            bg_color='black', size=size, method='caption').set_duration(duration)
        
        try:
            txt_clip.write_videofile(filepath, fps=24, codec='libx264', preset='ultrafast', logger=None)
            logger.info(f"Placeholder video saved: {filepath}")
            return filepath
        except Exception as e:
            logger.error(f"Failed to create placeholder video {filepath}: {e}", exc_info=True)
            return None
        finally:
            if hasattr(txt_clip, 'close'): txt_clip.close()
    # <<< RUNWAYML END >>>


    def generate_scene_asset(self, image_prompt_text, scene_data, scene_identifier_filename_base,
                             generate_as_video_clip=False, runway_target_duration=4, input_image_for_runway=None):
        """
        Generates either an image or a video clip for a scene.
        Returns a dictionary: {'path': asset_path, 'type': 'image'/'video', 'error': bool}
        """
        # Ensure scene_identifier_filename_base does not have an extension yet, or handle it
        base_name, _ = os.path.splitext(scene_identifier_filename_base)

        if generate_as_video_clip and self.USE_RUNWAYML:
            logger.info(f"Attempting RunwayML video clip generation for {base_name}")
            video_path = self._generate_video_clip_with_runwayml(
                image_prompt_text, # Use DALL-E prompt also for Runway text-to-video
                base_name, # Pass base name, function will add .mp4
                target_duration_seconds=runway_target_duration,
                input_image_path=input_image_for_runway
            )
            if video_path and os.path.exists(video_path):
                return {'path': video_path, 'type': 'video', 'error': False, 'prompt_used': image_prompt_text}
            else:
                logger.warning(f"RunwayML video clip generation failed for {base_name}. Falling back to image.")
                # Fall through to image generation

        # Image Generation (DALL-E, Pexels, Placeholder)
        # Ensure image filename has .png
        image_filename_with_ext = base_name + ".png"
        filepath = os.path.join(self.output_dir, image_filename_with_ext)

        if self.USE_AI_IMAGE_GENERATION and self.openai_api_key:
            max_retries = 2
            for attempt in range(max_retries):
                try:
                    # ... (DALL-E generation logic - no changes from your previous version) ...
                    logger.info(f"Attempt {attempt+1}: DALL-E ({self.dalle_model}) for: {image_prompt_text[:100]}...")
                    client = openai.OpenAI(api_key=self.openai_api_key, timeout=90.0)
                    response = client.images.generate(model=self.dalle_model, prompt=image_prompt_text, n=1, size=self.image_size_dalle3, quality="hd", response_format="url", style="vivid")
                    image_url = response.data[0].url; revised_prompt = getattr(response.data[0], 'revised_prompt', None)
                    if revised_prompt: logger.info(f"DALL-E 3 revised_prompt: {revised_prompt[:100]}...")
                    image_response = requests.get(image_url, timeout=120); image_response.raise_for_status()
                    img_data = Image.open(io.BytesIO(image_response.content));
                    if img_data.mode != 'RGB': img_data = img_data.convert('RGB')
                    img_data.save(filepath); logger.info(f"AI Image (DALL-E) saved: {filepath}");
                    return {'path': filepath, 'type': 'image', 'error': False, 'prompt_used': image_prompt_text, 'revised_prompt': revised_prompt}
                except openai.RateLimitError as e:
                    logger.warning(f"OpenAI Rate Limit: {e}. Retrying after {5*(attempt+1)}s..."); time.sleep(5 * (attempt + 1))
                    if attempt == max_retries - 1: logger.error("Max retries for RateLimitError."); break
                except openai.APIError as e: logger.error(f"OpenAI API Error: {e}"); break
                except requests.exceptions.RequestException as e: logger.error(f"Requests Error (DALL-E download): {e}"); break
                except Exception as e: logger.error(f"Generic error (DALL-E gen): {e}", exc_info=True); break
            logger.warning("DALL-E generation failed. Trying Pexels fallback...")
        
        # Pexels or Placeholder if DALL-E failed or disabled
        if self.USE_PEXELS:
            pexels_query_text = scene_data.get('pexels_search_query_감독', f"{scene_data.get('emotional_beat','')} {scene_data.get('setting_description','')}")
            pexels_path = self._search_pexels_image(pexels_query_text, image_filename_with_ext) # Pass filename with extension
            if pexels_path:
                return {'path': pexels_path, 'type': 'image', 'error': False, 'prompt_used': f"Pexels: {pexels_query_text}"}
            logger.warning("Pexels also failed/disabled. Using placeholder image.")
        
        placeholder_path = self._create_placeholder_image_content(
            f"[AI/Pexels Failed] {image_prompt_text[:100]}...", image_filename_with_ext
        )
        if placeholder_path:
            return {'path': placeholder_path, 'type': 'image', 'error': False, 'prompt_used': image_prompt_text}
        else:
            return {'path': None, 'type': 'none', 'error': True, 'prompt_used': image_prompt_text}


    def generate_narration_audio(self, text_to_narrate, output_filename="narration_overall.mp3"):
        # ... (no changes from your previous version) ...
        if not self.USE_ELEVENLABS or not self.elevenlabs_client or not text_to_narrate:
            logger.info("ElevenLabs conditions not met (API key, client init, or text). Skipping audio.")
            return None

        audio_filepath = os.path.join(self.output_dir, output_filename)
        try:
            logger.info(f"Generating ElevenLabs audio (Voice ID: {self.elevenlabs_voice_id}) for: {text_to_narrate[:70]}...")

            audio_stream_method = None
            if hasattr(self.elevenlabs_client, 'text_to_speech') and hasattr(self.elevenlabs_client.text_to_speech, 'stream'):
                audio_stream_method = self.elevenlabs_client.text_to_speech.stream
                logger.info("Using elevenlabs_client.text_to_speech.stream()")
            elif hasattr(self.elevenlabs_client, 'generate_stream') : # Older SDK might have this
                audio_stream_method = self.elevenlabs_client.generate_stream
                logger.info("Using elevenlabs_client.generate_stream()")
            elif hasattr(self.elevenlabs_client, 'generate'): # Fallback to non-streaming
                logger.info("Using elevenlabs_client.generate() (non-streaming).")
                # This one doesn't return a stream, it returns bytes directly
                voice_param = 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)
                audio_bytes = self.elevenlabs_client.generate(
                    text=text_to_narrate,
                    voice=voice_param,
                    model="eleven_multilingual_v2" # or other suitable model
                )
                with open(audio_filepath, "wb") as f:
                    f.write(audio_bytes)
                logger.info(f"ElevenLabs audio (non-streamed) saved: {audio_filepath}")
                return audio_filepath
            else:
                logger.error("No recognized audio generation method found on ElevenLabs client.")
                return None

            # If we have a streaming method
            if audio_stream_method:
                voice_param_for_stream = {"voice_id": str(self.elevenlabs_voice_id)}
                # For Pydantic v1 style for elevenlabs sdk <1.0
                # if self.elevenlabs_voice_settings and hasattr(self.elevenlabs_voice_settings, 'dict'):
                #    voice_param_for_stream["voice_settings"] = self.elevenlabs_voice_settings.dict()
                # For Pydantic v2 style for elevenlabs skd >=1.0
                if self.elevenlabs_voice_settings and hasattr(self.elevenlabs_voice_settings, 'model_dump'):
                     voice_param_for_stream["voice_settings"] = self.elevenlabs_voice_settings.model_dump()
                elif self.elevenlabs_voice_settings : # If not a pydantic model, pass as is if supported
                     voice_param_for_stream["voice_settings"] = self.elevenlabs_voice_settings

                audio_data_iterator = audio_stream_method(
                    text=text_to_narrate,
                    model_id="eleven_multilingual_v2",
                    **voice_param_for_stream
                )
                with open(audio_filepath, "wb") as f:
                    for chunk in audio_data_iterator:
                        if chunk: f.write(chunk)
                logger.info(f"ElevenLabs audio (streamed) saved: {audio_filepath}")
                return audio_filepath

        except AttributeError as ae:
             logger.error(f"AttributeError with ElevenLabs client: {ae}. SDK method/params might be different.", exc_info=True)
        except Exception as e:
            logger.error(f"Error generating ElevenLabs audio: {e}", exc_info=True)
        return None

    def assemble_animatic_from_assets(self, asset_data_list, overall_narration_path=None, output_filename="final_video.mp4", fps=24):
        """
        Assembles the final video from a list of assets (images or video clips).
        Each item in asset_data_list should be a dict like:
        {'path': 'path/to/asset', 'type': 'image'|'video', 'duration': desired_scene_duration_in_animatic,
         'scene_num': num, 'key_action': 'text'}
        """
        if not asset_data_list:
            logger.warning("No asset data provided for animatic assembly.")
            return None

        processed_moviepy_clips = []
        narration_audio_clip = None
        final_composite_clip = None
        total_video_duration_from_assets = sum(item.get('duration', 4.5) for item in asset_data_list)
        logger.info(f"Assembling animatic from {len(asset_data_list)} assets. Target frame: {self.video_frame_size}. Approx total duration: {total_video_duration_from_assets:.2f}s.")

        for i, asset_info in enumerate(asset_data_list):
            asset_path = asset_info.get('path')
            asset_type = asset_info.get('type')
            # This 'duration' is the desired display duration of THIS scene in the final animatic
            target_scene_duration = asset_info.get('duration', 4.5) # Default if not specified
            scene_num = asset_info.get('scene_num', i + 1)
            key_action = asset_info.get('key_action', '')

            if not (asset_path and os.path.exists(asset_path)):
                logger.warning(f"Asset not found for Scene {scene_num}: {asset_path}. Skipping.")
                continue
            if target_scene_duration <= 0:
                logger.warning(f"Scene {scene_num} has invalid duration ({target_scene_duration}s). Skipping.")
                continue

            current_clip = None
            try:
                if asset_type == 'image':
                    pil_img = Image.open(asset_path)
                    if pil_img.mode != 'RGB': pil_img = pil_img.convert('RGB')
                    img_copy = pil_img.copy()
                    resample_filter = Image.Resampling.LANCZOS if hasattr(Image.Resampling, 'LANCZOS') else (Image.ANTIALIAS if hasattr(Image, 'ANTIALIAS') else Image.BILINEAR)
                    img_copy.thumbnail(self.video_frame_size, resample_filter)
                    canvas = Image.new('RGB', self.video_frame_size, (random.randint(0,10), random.randint(0,10), random.randint(0,10)))
                    xo, yo = (self.video_frame_size[0] - img_copy.width) // 2, (self.video_frame_size[1] - img_copy.height) // 2
                    canvas.paste(img_copy, (xo, yo))
                    frame_np = np.array(canvas)
                    current_clip_base = ImageClip(frame_np).set_duration(target_scene_duration)

                    # Ken Burns for ImageClips
                    try:
                        end_scale = random.uniform(1.03, 1.08)
                        current_clip = current_clip_base.fx(vfx.resize, lambda t: 1 + (end_scale - 1) * (t / target_scene_duration)).set_position('center')
                    except Exception as e_fx:
                        logger.error(f"Ken Burns error for image {asset_path}: {e_fx}. Using static image.")
                        current_clip = current_clip_base

                elif asset_type == 'video':
                    source_video_clip = VideoFileClip(asset_path, target_resolution=(self.video_frame_size[1], self.video_frame_size[0]))
                    # Fit video into target_scene_duration:
                    # If source is shorter, it will play once. If longer, it will be cut.
                    # For more complex looping/speed adjustments, more logic is needed.
                    if source_video_clip.duration > target_scene_duration:
                        current_clip = source_video_clip.subclip(0, target_scene_duration)
                    elif source_video_clip.duration < target_scene_duration:
                         # Simple loop if significantly shorter, or just play once if close
                        if target_scene_duration / source_video_clip.duration > 1.5 and source_video_clip.duration > 0.1 : # Loop if target is >150% of source
                             current_clip = source_video_clip.loop(duration=target_scene_duration)
                        else: # Play once, duration will be its own, MoviePy handles concatenation padding
                             current_clip = source_video_clip.set_duration(source_video_clip.duration) # Explicitly set
                             logger.info(f"Runway clip for S{scene_num} ({source_video_clip.duration:.2f}s) shorter than target ({target_scene_duration:.2f}s), will play once.")
                    else: # Durations match
                        current_clip = source_video_clip
                    
                    # Ensure the clip has the target duration for consistent concatenation
                    if current_clip.duration != target_scene_duration:
                         current_clip = current_clip.set_duration(target_scene_duration)


                    # Resize if necessary (MoviePy does this on CompositeVideoClip too, but explicit can be good)
                    if current_clip.size != list(self.video_frame_size):
                        current_clip = current_clip.resize(self.video_frame_size)
                    
                    # Close the original source_video_clip if it's different from current_clip (e.g., after subclip)
                    if current_clip != source_video_clip and hasattr(source_video_clip, 'close'):
                        source_video_clip.close()


                else:
                    logger.warning(f"Unknown asset type '{asset_type}' for Scene {scene_num}. Skipping.")
                    continue

                # Add text overlay
                if current_clip and key_action:
                    text_overlay_duration = min(target_scene_duration - 0.5, target_scene_duration * 0.8) if target_scene_duration > 0.5 else target_scene_duration
                    text_overlay_start = (target_scene_duration - text_overlay_duration) / 2.0
                    if text_overlay_duration > 0:
                        txt_clip = 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(text_overlay_duration).set_start(text_overlay_start).set_position(('center', 0.92), relative=True)
                        current_clip = CompositeVideoClip([current_clip, txt_clip], size=self.video_frame_size, use_bgclip=True, bg_color=(0,0,0))
                
                if current_clip:
                    processed_moviepy_clips.append(current_clip)

            except Exception as e:
                logger.error(f"Error processing asset for Scene {scene_num} ({asset_path}): {e}", exc_info=True)
                if current_clip and hasattr(current_clip, 'close'): current_clip.close() # Ensure closure on error

        if not processed_moviepy_clips:
            logger.warning("No MoviePy clips successfully processed. Aborting animatic assembly.")
            return None

        transition_duration = 0.75
        try:
            if len(processed_moviepy_clips) > 1:
                final_composite_clip = concatenate_videoclips(processed_moviepy_clips, padding=-transition_duration, method="compose")
            elif processed_moviepy_clips:
                final_composite_clip = processed_moviepy_clips[0]
            else: # Should have been caught above, but defensive
                logger.error("No clips available for final concatenation.")
                return None


            if final_composite_clip.duration > transition_duration * 2:
                final_composite_clip = final_composite_clip.fx(vfx.fadein, transition_duration).fx(vfx.fadeout, transition_duration)
            elif final_composite_clip.duration > 0:
                 final_composite_clip = final_composite_clip.fx(vfx.fadein, min(transition_duration, final_composite_clip.duration/2.0))

            if overall_narration_path and os.path.exists(overall_narration_path):
                try:
                    narration_audio_clip = AudioFileClip(overall_narration_path)
                    if final_composite_clip.duration > 0 and narration_audio_clip.duration < final_composite_clip.duration:
                        logger.info(f"Narration ({narration_audio_clip.duration:.2f}s) shorter than visuals ({final_composite_clip.duration:.2f}s). Trimming video.")
                        final_composite_clip = final_composite_clip.subclip(0, narration_audio_clip.duration)
                    elif final_composite_clip.duration <= 0: logger.warning("Video has no duration. Audio not added.")
                    
                    if narration_audio_clip and final_composite_clip.duration > 0: # Check again
                        final_composite_clip = final_composite_clip.set_audio(narration_audio_clip)
                        logger.info("Overall narration added.")
                except Exception as e: logger.error(f"Adding narration error: {e}", exc_info=True)
            
            if final_composite_clip and final_composite_clip.duration > 0:
                output_path = os.path.join(self.output_dir, output_filename)
                logger.info(f"Writing final animatic: {output_path} (Duration: {final_composite_clip.duration:.2f}s)")
                final_composite_clip.write_videofile(
                    output_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"
                )
                logger.info(f"Animatic created: {output_path}"); return output_path
            else: logger.error("Final animatic clip invalid or has no duration. Not writing file."); return None
        except Exception as e: logger.error(f"Animatic writing error: {e}", exc_info=True); return None
        finally:
            for clip in processed_moviepy_clips:
                if hasattr(clip, 'close'): clip.close()
            if narration_audio_clip and hasattr(narration_audio_clip, 'close'): narration_audio_clip.close()
            if final_composite_clip and hasattr(final_composite_clip, 'close'): final_composite_clip.close()