core/visual_engine.py

# core/visual_engine.py
from PIL import Image, ImageDraw, ImageFont, ImageOps
import base64 # For Data URI conversion

# --- MONKEY PATCH ---
try:
    if hasattr(Image, 'Resampling') and hasattr(Image.Resampling, 'LANCZOS'):
        if not hasattr(Image, 'ANTIALIAS'): Image.ANTIALIAS = Image.Resampling.LANCZOS
    elif hasattr(Image, 'LANCZOS'):
         if not hasattr(Image, 'ANTIALIAS'): Image.ANTIALIAS = Image.LANCZOS
    elif not hasattr(Image, 'ANTIALIAS'): print("WARNING: Pillow ANTIALIAS/Resampling issue.")
except Exception as e_mp: print(f"WARNING: ANTIALIAS patch error: {e_mp}")

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
import mimetypes # For Data URI

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

# --- SERVICE CLIENT IMPORTS ---
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 disabled.")

RUNWAYML_SDK_IMPORTED = False; RunwayMLAPIClient = None # Renamed for clarity
try:
    from runwayml import RunwayML as ImportedRunwayMLClient # Actual SDK import
    RunwayMLAPIClient = ImportedRunwayMLClient
    RUNWAYML_SDK_IMPORTED = True
    logger.info("RunwayML SDK imported successfully.")
except ImportError:
    logger.warning("RunwayML SDK not found (pip install runwayml). RunwayML video generation will be disabled.")
except Exception as e_runway_sdk:
    logger.warning(f"Error importing RunwayML SDK: {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 = "DejaVuSans-Bold.ttf"
        font_paths_to_try = [ 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", f"/usr/local/share/fonts/truetype/mycustomfonts/arial.ttf"]
        self.font_path_pil = next((p for p in font_paths_to_try if os.path.exists(p)), None)
        self.font_size_pil = 20; self.video_overlay_font_size = 30; self.video_overlay_font_color = 'white'
        self.video_overlay_font = 'DejaVu-Sans-Bold'
        try:
            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: {self.font_path_pil}.")
            else: logger.warning("Default Pillow font."); self.font_size_pil = 10
        except IOError as e_font: logger.error(f"Pillow font IOError: {e_font}. 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) # Default, will be mapped to Runway ratio
        
        self.elevenlabs_api_key = None; self.USE_ELEVENLABS = False; self.elevenlabs_client = None; self.elevenlabs_voice_id = default_elevenlabs_voice_id
        if VoiceSettings and ELEVENLABS_CLIENT_IMPORTED: self.elevenlabs_voice_settings = VoiceSettings(stability=0.60, similarity_boost=0.80, style=0.15, use_speaker_boost=True)
        else: self.elevenlabs_voice_settings = None
        
        self.pexels_api_key = None; self.USE_PEXELS = False
        
        self.runway_api_key = None; self.USE_RUNWAYML = False; self.runway_client = None
        if RUNWAYML_SDK_IMPORTED and RunwayMLAPIClient: # Initialize if SDK is available
            try:
                # The SDK expects RUNWAYML_API_SECRET env var.
                # If your key is passed directly, you might need to initialize differently or set the env var.
                if os.getenv("RUNWAYML_API_SECRET"):
                    self.runway_client = RunwayMLAPIClient()
                    logger.info("RunwayML Client initialized using RUNWAYML_API_SECRET env var.")
                else:
                    logger.warning("RUNWAYML_API_SECRET env var not set. RunwayML client not initialized here (will try in set_runway_api_key).")
            except Exception as e_runway_init:
                logger.error(f"Failed to initialize RunwayML client during __init__: {e_runway_init}", exc_info=True)
        
        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: # This API key is for the client
            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 issue).")
    def set_pexels_api_key(self,k): self.pexels_api_key=k; self.USE_PEXELS=bool(k); logger.info(f"Pexels Search {'Ready.' if k else 'Disabled.'}")
    
    def set_runway_api_key(self, k): # For RunwayML
        self.runway_api_key = k # Store the key
        if k:
            if RUNWAYML_SDK_IMPORTED and RunwayMLAPIClient:
                if not self.runway_client: # If not initialized in __init__
                    try:
                        # Ensure RUNWAYML_API_SECRET is set if SDK relies on it
                        if not os.getenv("RUNWAYML_API_SECRET") and k:
                            logger.info("Setting RUNWAYML_API_SECRET environment variable from provided key for SDK.")
                            os.environ["RUNWAYML_API_SECRET"] = k # Make key available to SDK
                        
                        self.runway_client = RunwayMLAPIClient()
                        self.USE_RUNWAYML = True
                        logger.info("RunwayML Client initialized successfully via set_runway_api_key.")
                    except Exception as e_client_init:
                        logger.error(f"RunwayML Client initialization failed in set_runway_api_key: {e_client_init}", exc_info=True)
                        self.USE_RUNWAYML = False
                else: # Client was already initialized (e.g., from env var in __init__)
                    self.USE_RUNWAYML = True
                    logger.info("RunwayML Client already initialized.")
            else: # SDK not imported
                logger.warning("RunwayML SDK not imported. API key set, but direct HTTP calls would be needed (not implemented).")
                self.USE_RUNWAYML = False # Can't use if SDK is the only implemented path
        else:
            self.USE_RUNWAYML = False
            logger.info("RunwayML Disabled (no API key provided to set_runway_api_key).")


    def _image_to_data_uri(self, image_path):
        try:
            mime_type, _ = mimetypes.guess_type(image_path)
            if not mime_type:
                # Fallback for common image types if mimetypes fails (e.g., on some systems)
                ext = os.path.splitext(image_path)[1].lower()
                if ext == ".png": mime_type = "image/png"
                elif ext in [".jpg", ".jpeg"]: mime_type = "image/jpeg"
                else:
                    logger.warning(f"Could not determine MIME type for {image_path}. Defaulting to application/octet-stream.")
                    mime_type = "application/octet-stream" # Fallback, Runway might reject this

            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 {image_path} (first 100 chars): {data_uri[:100]}")
            return data_uri
        except Exception as e:
            logger.error(f"Error converting image {image_path} to data URI: {e}", exc_info=True)
            return None

    def _map_resolution_to_runway_ratio(self, width, height):
        # Gen-4 supports specific ratios. Find the closest supported or default.
        # Example: 1280x720 -> "1280:720"
        # This needs to be robust. For now, we'll assume app.py sends a valid W:H string
        # or we use a default that matches self.video_frame_size if it's standard.
        if width == 1280 and height == 720: return "1280:720"
        if width == 720 and height == 1280: return "720:1280"
        # Add more mappings based on Gen-4 supported ratios if your self.video_frame_size can vary
        logger.warning(f"Unsupported resolution {width}x{height} for Runway Gen-4 mapping. Defaulting to 1280:720.")
        return "1280:720" # Default

    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_client: # Check for initialized client
            logger.warning("RunwayML not enabled or client not initialized. Cannot generate video clip.")
            return None
        if not input_image_path or not os.path.exists(input_image_path):
            logger.error(f"Runway Gen-4 requires an input image. Path not provided or invalid: {input_image_path}")
            return None

        image_data_uri = self._image_to_data_uri(input_image_path)
        if not image_data_uri:
            return None

        runway_duration = 10 if target_duration_seconds > 7 else 5 # Map to 5s or 10s
        runway_ratio_str = self._map_resolution_to_runway_ratio(self.video_frame_size[0], self.video_frame_size[1])

        output_video_filename = scene_identifier_filename_base.replace(".png", f"_runway_gen4_d{runway_duration}s.mp4")
        output_video_filepath = os.path.join(self.output_dir, output_video_filename)

        logger.info(f"Initiating Runway Gen-4 task: motion='{text_prompt_for_motion[:100]}...', image='{os.path.basename(input_image_path)}', dur={runway_duration}s, ratio='{runway_ratio_str}'")

        try:
            task = self.runway_client.image_to_video.create(
                model='gen4_turbo',
                prompt_image=image_data_uri,
                prompt_text=text_prompt_for_motion,
                duration=runway_duration,
                ratio=runway_ratio_str, # e.g., "1280:720"
                # seed=random.randint(0, 4294967295), # Optional
                # Other Gen-4 params can be added here: motion_score, upscale etc.
            )
            logger.info(f"Runway Gen-4 task created with ID: {task.id}. Polling for completion...")

            poll_interval = 10 # seconds
            max_polls = 36 # Max 6 minutes (36 * 10s)
            for _ in range(max_polls):
                time.sleep(poll_interval)
                task_details = self.runway_client.tasks.retrieve(id=task.id)
                logger.info(f"Runway task {task.id} status: {task_details.status}")
                if task_details.status == 'SUCCEEDED':
                    # The SDK docs don't explicitly show how to get the output URL from `task_details`.
                    # Common patterns are `task_details.output.url` or `task_details.artifacts[0].url`.
                    # This is a GUESS based on typical API structures. You MUST verify this.
                    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:
                         # Assuming the first artifact is the video and has a URL
                        if hasattr(task_details.artifacts[0], 'url'):
                            output_url = task_details.artifacts[0].url
                        elif hasattr(task_details.artifacts[0], 'download_url'): # Another common name
                            output_url = task_details.artifacts[0].download_url


                    if not output_url:
                        logger.error(f"Runway task {task.id} SUCCEEDED, but no output URL found in task details: {task_details}")
                        # Attempt to log the full task_details object for inspection
                        try: logger.error(f"Full task details: {vars(task_details)}")
                        except: pass
                        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) # 5 min timeout for download
                    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 and saved to: {output_video_filepath}")
                    return output_video_filepath
                
                elif task_details.status in ['FAILED', 'ABORTED']:
                    error_message = "Unknown error"
                    if hasattr(task_details, 'error_message') and task_details.error_message:
                        error_message = task_details.error_message
                    elif hasattr(task_details, 'output') and hasattr(task_details.output, 'error') and task_details.output.error:
                        error_message = task_details.output.error
                    logger.error(f"Runway task {task.id} status: {task_details.status}. Error: {error_message}")
                    return None
            
            logger.warning(f"Runway task {task.id} timed out after {max_polls * poll_interval} 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 match.", exc_info=True)
            return None
        except Exception as e_runway:
            logger.error(f"Error during Runway Gen-4 API call or processing: {e_runway}", exc_info=True)
            return None

    # --- Other helper methods (_get_text_dimensions, _create_placeholder_image_content, _search_pexels_image, _create_placeholder_video_content) ---
    # --- Keep these as they were in the previous full rewrite unless they need minor adjustments for the Gen-4 workflow ---
    def _get_text_dimensions(self,tc,fo): di=fo.size if hasattr(fo,'size') else self.font_size_pil; return (0,di) if not tc else (lambda b:(b[2]-b[0],b[3]-b[1] if b[3]-b[1]>0 else di))(fo.getbbox(tc)) if hasattr(fo,'getbbox') else (lambda s:(s[0],s[1] if s[1]>0 else di))(fo.getsize(tc)) if hasattr(fo,'getsize') else (int(len(tc)*di*0.6),int(di*1.2))
    def _create_placeholder_image_content(self,td,fn,sz=None):
        if sz is None: sz = self.video_frame_size; img=Image.new('RGB',sz,color=(20,20,40));d=ImageDraw.Draw(img);pd=25;mw=sz[0]-(2*pd);ls=[];
        if not td: td="(Placeholder Image)"
        ws=td.split();cl=""
        for w in ws: tl=cl+w+" ";raw_w,_=self._get_text_dimensions(tl,self.font);check_w=raw_w if raw_w > 0 else len(tl)*(self.font_size_pil*0.6); # Corrected w to check_w
        if check_w<=mw:cl=tl;else: # Corrected w to check_w
            if cl:ls.append(cl.strip());cl=w+" "
        if cl.strip():ls.append(cl.strip())
        if not ls and td:ls.append(td[:int(mw//(self._get_text_dimensions("A",self.font)[0]or 10))]+"..." if td else "(Text too long)");elif not ls:ls.append("(Placeholder Error)")
        _,slh=self._get_text_dimensions("Ay",self.font);slh=slh if slh>0 else self.font_size_pil+2;mld=min(len(ls),(sz[1]-(2*pd))//(slh+2)) if slh>0 else 1;
        if mld<=0:mld=1;yts=pd+(sz[1]-(2*pd)-mld*(slh+2))/2.0;yt=yts
        for i in range(mld):lc=ls[i];lw,_=self._get_text_dimensions(lc,self.font);xt=(sz[0]-lw)/2.0;d.text((xt,yt),lc,font=self.font,fill=(200,200,180));yt+=slh+2
        if i==6 and mld>7:d.text((xt,yt),"...",font=self.font,fill=(200,200,180));break
        fp=os.path.join(self.output_dir,fn);
        try:img.save(fp);return fp
        except Exception as e:logger.error(f"Save placeholder img {fp}: {e}",exc_info=True);return None
    def _search_pexels_image(self, q, ofnb):
        if not self.USE_PEXELS or not self.pexels_api_key: return None; h={"Authorization":self.pexels_api_key};p={"query":q,"per_page":1,"orientation":"landscape","size":"large2x"}
        pfn=ofnb.replace(".png",f"_pexels_{random.randint(1000,9999)}.jpg").replace(".mp4",f"_pexels_{random.randint(1000,9999)}.jpg");fp=os.path.join(self.output_dir,pfn)
        try: logger.info(f"Pexels search: '{q}'");eq=" ".join(q.split()[:5]);p["query"]=eq;r=requests.get("https://api.pexels.com/v1/search",headers=h,params=p,timeout=20)
        r.raise_for_status();d=r.json()
        if d.get("photos") and len(d["photos"])>0:pu=d["photos"][0]["src"]["large2x"];ir=requests.get(pu,timeout=60);ir.raise_for_status();id_img=Image.open(io.BytesIO(ir.content)) # Renamed id to id_img
        if id_img.mode!='RGB':id_img=id_img.convert('RGB');id_img.save(fp);logger.info(f"Pexels saved: {fp}");return fp
        else: logger.info(f"No Pexels for: '{eq}'")
        except Exception as e:logger.error(f"Pexels error ('{q}'): {e}",exc_info=True);return None
    def _create_placeholder_video_content(self, td, fn, dur=4, sz=None): # Generic placeholder
        if sz is None: sz = self.video_frame_size; fp = os.path.join(self.output_dir, fn); tc = None
        try:
            tc = TextClip(td, fontsize=50, color='white', font=self.video_overlay_font, bg_color='black', size=sz, method='caption').set_duration(dur)
            tc.write_videofile(fp, fps=24, codec='libx264', preset='ultrafast', logger=None, threads=2)
            logger.info(f"Generic placeholder video: {fp}"); return fp
        except Exception as e: logger.error(f"Generic placeholder video error {fp}: {e}", exc_info=True); return None
        finally:
            if tc and hasattr(tc, 'close'): tc.close()


    # --- generate_scene_asset (Main asset generation logic) ---
    def generate_scene_asset(self, image_generation_prompt_text, motion_prompt_text_for_video,
                             scene_data, scene_identifier_filename_base,
                             generate_as_video_clip=False, runway_target_duration=5):
        base_name, _ = os.path.splitext(scene_identifier_filename_base)
        # Default asset_info for error state
        asset_info = {'path': None, 'type': 'none', 'error': True,
                      'prompt_used': image_generation_prompt_text, # Default to image prompt
                      'error_message': 'Asset generation not fully attempted'}

        # STEP 1: Generate/acquire the base image for Runway Gen-4 or for direct image output
        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)
        
        # Try DALL-E for base image
        if self.USE_AI_IMAGE_GENERATION and self.openai_api_key:
            max_r, att_n = 2, 0
            for att_n in range(max_r):
                try:
                    logger.info(f"Attempt {att_n+1} DALL-E (base image): {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(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, 'revised_prompt': rp}
                    break # DALL-E success
                except openai.RateLimitError as e: logger.warning(f"OpenAI Rate Limit {att_n+1}: {e}. Retry..."); time.sleep(5*(att_n+1)); asset_info['error_message']=str(e)
                except Exception as e: logger.error(f"DALL-E base image error: {e}", exc_info=True); asset_info['error_message']=str(e); break
            if asset_info['error']: logger.warning(f"DALL-E failed after {att_n+1} attempts for base image.")
        
        # Try Pexels if DALL-E failed or not used
        if asset_info['error'] and self.USE_PEXELS:
            logger.info("Attempting Pexels for base image.")
            pqt = scene_data.get('pexels_search_query_감독', f"{scene_data.get('emotional_beat','')} {scene_data.get('setting_description','')}")
            pp = self._search_pexels_image(pqt, base_image_filename) # Pass base image filename
            if pp: input_image_for_runway_path = pp; asset_info = {'path': pp, 'type': 'image', 'error': False, 'prompt_used': f"Pexels: {pqt}"}
            else: current_em = asset_info.get('error_message',""); asset_info['error_message']=(current_em + " Pexels failed for base image.").strip()

        # Fallback to placeholder for base image if all above failed
        if asset_info['error']:
            logger.warning("Base image (DALL-E/Pexels) failed. Using placeholder for base image.")
            ppt = asset_info.get('prompt_used', image_generation_prompt_text) # Use the original image prompt
            php = self._create_placeholder_image_content(f"[Base Img Placeholder] {ppt[:100]}...", base_image_filename)
            if php: input_image_for_runway_path = php; asset_info = {'path': php, 'type': 'image', 'error': False, 'prompt_used': ppt}
            else: current_em=asset_info.get('error_message',"");asset_info['error_message']=(current_em + " Base placeholder failed.").strip()

        # STEP 2: If video clip is requested, use the generated base image with RunwayML
        if generate_as_video_clip:
            if not input_image_for_runway_path: # If base image generation totally failed
                logger.error("Cannot generate RunwayML video: base image path is missing or generation failed.")
                asset_info['error'] = True # Ensure error state is propagated
                asset_info['error_message'] = (asset_info.get('error_message',"") + " Base image missing, Runway video aborted.").strip()
                asset_info['type'] = 'none' # No valid asset produced
                return asset_info

            if self.USE_RUNWAYML:
                logger.info(f"Proceeding to Runway Gen-4 video for {base_name} using base image: {input_image_for_runway_path}")
                video_path = self._generate_video_clip_with_runwayml(
                    text_prompt_for_motion=motion_prompt_text_for_video,
                    input_image_path=input_image_for_runway_path,
                    scene_identifier_filename_base=base_name, # _runway_gen4.mp4 will be appended
                    target_duration_seconds=runway_target_duration
                )
                if video_path and os.path.exists(video_path):
                    # Success generating video
                    asset_info = {'path': video_path, 'type': 'video', 'error': False,
                                  'prompt_used': motion_prompt_text_for_video, # This is the prompt for Runway
                                  'base_image_path': input_image_for_runway_path}
                else:
                    # RunwayML failed, return the base image info but mark video as failed
                    logger.warning(f"RunwayML video generation failed for {base_name}. Using the base image as fallback.")
                    asset_info['error'] = True # Video step specifically failed
                    asset_info['error_message'] = (asset_info.get('error_message', "Base image generated.") + " RunwayML video step failed; using base image instead.").strip()
                    asset_info['path'] = input_image_for_runway_path # Path of the base image
                    asset_info['type'] = 'image' # Fallback asset type is image
                    asset_info['prompt_used'] = image_generation_prompt_text # Prompt for the base image
            else: # RunwayML not enabled, use base image
                logger.warning("RunwayML selected but not enabled/configured. Using base image.")
                asset_info['error'] = True # Mark that video wasn't generated
                asset_info['error_message'] = (asset_info.get('error_message', "Base image generated.") + " RunwayML disabled; using base image.").strip()
                asset_info['path'] = input_image_for_runway_path
                asset_info['type'] = 'image'
                asset_info['prompt_used'] = image_generation_prompt_text
        # If not generate_as_video_clip, asset_info already holds the result of image generation
        return asset_info


    # --- generate_narration_audio (Keep as before) ---
    def generate_narration_audio(self, text_to_narrate, output_filename="narration_overall.mp3"):
        if not self.USE_ELEVENLABS or not self.elevenlabs_client or not text_to_narrate: logger.info("11L skip."); return None; afp=os.path.join(self.output_dir,output_filename)
        try: logger.info(f"11L audio (Voice:{self.elevenlabs_voice_id}): {text_to_narrate[:70]}..."); asm=None
        if hasattr(self.elevenlabs_client,'text_to_speech')and hasattr(self.elevenlabs_client.text_to_speech,'stream'):asm=self.elevenlabs_client.text_to_speech.stream;logger.info("Using 11L .text_to_speech.stream()")
        elif hasattr(self.elevenlabs_client,'generate_stream'):asm=self.elevenlabs_client.generate_stream;logger.info("Using 11L .generate_stream()")
        elif hasattr(self.elevenlabs_client,'generate'):logger.info("Using 11L .generate()");vp=Voice(voice_id=str(self.elevenlabs_voice_id),settings=self.elevenlabs_voice_settings)if Voice and self.elevenlabs_voice_settings else str(self.elevenlabs_voice_id);ab=self.elevenlabs_client.generate(text=text_to_narrate,voice=vp,model="eleven_multilingual_v2");
        with open(afp,"wb")as f:f.write(ab);logger.info(f"11L audio (non-stream): {afp}");return afp
        else:logger.error("No 11L audio method.");return None
        if asm:vps={"voice_id":str(self.elevenlabs_voice_id)}
        if self.elevenlabs_voice_settings:
            if hasattr(self.elevenlabs_voice_settings,'model_dump'):vps["voice_settings"]=self.elevenlabs_voice_settings.model_dump()
            elif hasattr(self.elevenlabs_voice_settings,'dict'):vps["voice_settings"]=self.elevenlabs_voice_settings.dict()
            else:vps["voice_settings"]=self.elevenlabs_voice_settings
        adi=asm(text=text_to_narrate,model_id="eleven_multilingual_v2",**vps)
        with open(afp,"wb")as f:
            for c in adi:
                if c:f.write(c)
        logger.info(f"11L audio (stream): {afp}");return afp
        except Exception as e:logger.error(f"11L audio error: {e}",exc_info=True);return None


    # --- assemble_animatic_from_assets (Keep robust image processing, C-contiguous, debug saves, pix_fmt) ---
    def assemble_animatic_from_assets(self, asset_data_list, overall_narration_path=None, output_filename="final_video.mp4", fps=24):
        if not asset_data_list: logger.warning("No assets for animatic."); return None
        processed_clips = []; narration_clip = None; final_clip = None # final_composite_clip_obj renamed to final_clip
        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_type, scene_dur = asset_info.get('path'), asset_info.get('type'), asset_info.get('duration', 4.5)
            scene_num, key_action = asset_info.get('scene_num', i + 1), asset_info.get('key_action', '')
            logger.info(f"S{scene_num}: Path='{asset_path}', Type='{asset_type}', Dur='{scene_dur}'s")

            if not (asset_path and os.path.exists(asset_path)): logger.warning(f"S{scene_num}: Not found '{asset_path}'. Skip."); continue
            if scene_dur <= 0: logger.warning(f"S{scene_num}: Invalid duration ({scene_dur}s). Skip."); continue

            current_scene_mvpy_clip = None
            try:
                if asset_type == 'image':
                    pil_img = Image.open(asset_path); logger.debug(f"S{scene_num}: Loaded img. Mode:{pil_img.mode}, Size:{pil_img.size}")
                    img_rgba = pil_img.convert('RGBA') if pil_img.mode != 'RGBA' else pil_img.copy()
                    thumb = img_rgba.copy(); rf = Image.Resampling.LANCZOS if hasattr(Image.Resampling,'LANCZOS') else Image.BILINEAR; thumb.thumbnail(self.video_frame_size,rf)
                    cv_rgba = Image.new('RGBA',self.video_frame_size,(0,0,0,0)); xo,yo=(self.video_frame_size[0]-thumb.width)//2,(self.video_frame_size[1]-thumb.height)//2
                    cv_rgba.paste(thumb,(xo,yo),thumb)
                    final_rgb_pil = Image.new("RGB",self.video_frame_size,(0,0,0)); final_rgb_pil.paste(cv_rgba,mask=cv_rgba.split()[3])
                    dbg_path = os.path.join(self.output_dir,f"debug_PRE_NUMPY_S{scene_num}.png"); final_rgb_pil.save(dbg_path); logger.info(f"DEBUG: Saved PRE_NUMPY_S{scene_num} to {dbg_path}")
                    frame_np = np.array(final_rgb_pil,dtype=np.uint8);
                    if not frame_np.flags['C_CONTIGUOUS']: frame_np=np.ascontiguousarray(frame_np,dtype=np.uint8)
                    logger.debug(f"S{scene_num}: NumPy for MoviePy. Shape:{frame_np.shape}, DType:{frame_np.dtype}, C-Contig:{frame_np.flags['C_CONTIGUOUS']}")
                    if frame_np.size==0 or frame_np.ndim!=3 or frame_np.shape[2]!=3: logger.error(f"S{scene_num}: Invalid NumPy. Skip."); continue
                    clip_base = ImageClip(frame_np,transparent=False).set_duration(scene_dur)
                    mvpy_dbg_path=os.path.join(self.output_dir,f"debug_MOVIEPY_FRAME_S{scene_num}.png"); clip_base.save_frame(mvpy_dbg_path,t=0.1); logger.info(f"DEBUG: Saved MOVIEPY_FRAME_S{scene_num} to {mvpy_dbg_path}")
                    clip_fx = clip_base
                    try: es=random.uniform(1.03,1.08); clip_fx=clip_base.fx(vfx.resize,lambda t:1+(es-1)*(t/scene_dur) if scene_dur>0 else 1).set_position('center')
                    except Exception as e: logger.error(f"S{scene_num} Ken Burns error: {e}",exc_info=False)
                    current_scene_mvpy_clip = clip_fx
                elif asset_type == 'video':
                    src_clip=None
                    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) # Explicitly no audio from source video clips
                        tmp_clip=src_clip
                        if src_clip.duration!=scene_dur:
                            if src_clip.duration>scene_dur:tmp_clip=src_clip.subclip(0,scene_dur)
                            else:
                                if scene_dur/src_clip.duration > 1.5 and src_clip.duration>0.1:tmp_clip=src_clip.loop(duration=scene_dur)
                                else:tmp_clip=src_clip.set_duration(src_clip.duration);logger.info(f"S{scene_num} Video clip ({src_clip.duration:.2f}s) shorter than target ({scene_dur:.2f}s).")
                        current_scene_mvpy_clip=tmp_clip.set_duration(scene_dur)
                        if current_scene_mvpy_clip.size!=list(self.video_frame_size):current_scene_mvpy_clip=current_scene_mvpy_clip.resize(self.video_frame_size)
                    except Exception as e:logger.error(f"S{scene_num} Video load error '{asset_path}':{e}",exc_info=True);continue
                    finally:
                        if src_clip and src_clip is not current_scene_mvpy_clip and hasattr(src_clip,'close'):src_clip.close()
                else: logger.warning(f"S{scene_num} Unknown asset type '{asset_type}'. Skip."); continue
                
                if current_scene_mvpy_clip and key_action:
                    try:
                        to_dur=min(current_scene_mvpy_clip.duration-0.5,current_scene_mvpy_clip.duration*0.8)if current_scene_mvpy_clip.duration>0.5 else current_scene_mvpy_clip.duration
                        to_start=0.25
                        if to_dur > 0 : # Only add text if duration is positive
                            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 or negative. Skipping text overlay.")
                    except Exception as e:logger.error(f"S{scene_num} TextClip error:{e}. No text.",exc_info=True)
                if current_scene_mvpy_clip:processed_clips.append(current_scene_mvpy_clip);logger.info(f"S{scene_num} Processed. Dur:{current_scene_mvpy_clip.duration:.2f}s.")
            except Exception as e:logger.error(f"MAJOR Error S{scene_num} ({asset_path}):{e}",exc_info=True)
            finally:
                if current_scene_mvpy_clip and hasattr(current_scene_mvpy_clip,'close'): # Check if it's a VideoFileClip instance that needs closing
                    if hasattr(current_scene_mvpy_clip, 'reader') and current_scene_mvpy_clip.reader: current_scene_mvpy_clip.close()
                    elif not hasattr(current_scene_mvpy_clip, 'reader'): current_scene_mvpy_clip.close() # For ImageClip if close() is added

        if not processed_clips:logger.warning("No clips processed. Abort.");return None
        td=0.75
        try:
            logger.info(f"Concatenating {len(processed_clips)} clips.");
            if len(processed_clips)>1:final_clip=concatenate_videoclips(processed_clips,padding=-td if td>0 else 0,method="compose")
            elif processed_clips:final_clip=processed_clips[0]
            if not final_clip:logger.error("Concatenation failed.");return None
            logger.info(f"Concatenated dur:{final_clip.duration:.2f}s")
            if td>0 and final_clip.duration>0:
                if final_clip.duration>td*2:final_clip=final_clip.fx(vfx.fadein,td).fx(vfx.fadeout,td)
                else:final_clip=final_clip.fx(vfx.fadein,min(td,final_clip.duration/2.0))
            if overall_narration_path and os.path.exists(overall_narration_path) and final_clip.duration>0:
                try:narration_clip=AudioFileClip(overall_narration_path);final_clip=final_clip.set_audio(narration_clip);logger.info("Narration added.")
                except Exception as e:logger.error(f"Narration add error:{e}",exc_info=True)
            elif final_clip.duration<=0:logger.warning("Video no duration. No audio.")
            if final_clip and final_clip.duration>0:
                op=os.path.join(self.output_dir,output_filename);logger.info(f"Writing video:{op} (Dur:{final_clip.duration:.2f}s)")
                final_clip.write_videofile(op,fps=fps,codec='libx264',preset='medium',audio_codec='aac',temp_audiofile=os.path.join(self.output_dir,f'temp-audio-{os.urandom(4).hex()}.m4a'),remove_temp=True,threads=os.cpu_count()or 2,logger='bar',bitrate="5000k",ffmpeg_params=["-pix_fmt", "yuv420p"])
                logger.info(f"Video created:{op}");return op
            else:logger.error("Final clip invalid. No write.");return None
        except Exception as e:logger.error(f"Video write error:{e}",exc_info=True);return None
        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 in all_clips_to_close: # Use a different name to avoid scope issues
                if clip_obj and hasattr(clip_obj, 'close'):
                    try: clip_obj.close()
                    except Exception as e_close: logger.warning(f"Ignoring error while closing a clip: {type(clip_obj).__name__} - {e_close}")