core/visual_engine.py

# 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 ---
from moviepy.editor import (ImageClip, VideoFileClip, concatenate_videoclips, TextClip,
                            CompositeVideoClip, AudioFileClip)
import moviepy.video.fx.all as vfx

# --- MONKEY PATCH for Pillow/MoviePy compatibility ---
try:
    if hasattr(Image, 'Resampling') and hasattr(Image.Resampling, 'LANCZOS'): # Pillow 9+
        if not hasattr(Image, 'ANTIALIAS'): Image.ANTIALIAS = Image.Resampling.LANCZOS
    elif hasattr(Image, 'LANCZOS'): # Pillow 8
         if not hasattr(Image, 'ANTIALIAS'): Image.ANTIALIAS = Image.LANCZOS
    elif not hasattr(Image, 'ANTIALIAS'): # Fallback if no common resampling attributes found
             print("WARNING: Pillow version lacks common Resampling attributes or ANTIALIAS. MoviePy effects might fail or look different.")
except Exception as e_monkey_patch:
    print(f"WARNING: An unexpected error occurred during Pillow ANTIALIAS monkey-patch: {e_monkey_patch}")

logger = logging.getLogger(__name__)
# Consider setting level in main app if not already configured:
# logger.setLevel(logging.DEBUG) # For very verbose output during debugging

# --- External 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 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.")

RUNWAYML_SDK_IMPORTED = False
RunwayMLAPIClient = None # Using a more specific name for the client class
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_import:
    logger.warning(f"Error importing RunwayML SDK: {e_runway_sdk_import}. RunwayML features disabled.")


class VisualEngine:
    DEFAULT_FONT_SIZE_PIL = 10 # For default Pillow font
    PREFERRED_FONT_SIZE_PIL = 20 # For custom font
    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"):
        self.output_dir = output_dir
        os.makedirs(self.output_dir, exist_ok=True)

        self.font_filename_pil = "DejaVuSans-Bold.ttf" # A more standard Linux font
        font_paths_to_try = [
            self.font_filename_pil, # If in working dir or PATH
            f"/usr/share/fonts/truetype/dejavu/{self.font_filename_pil}",
            f"/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf", # Alternative
            f"/System/Library/Fonts/Supplemental/Arial.ttf", # macOS fallback
            f"C:/Windows/Fonts/arial.ttf", # Windows fallback
            f"/usr/local/share/fonts/truetype/mycustomfonts/arial.ttf" # User's previous custom path
        ]
        self.font_path_pil_resolved = next((p for p in font_paths_to_try if os.path.exists(p)), None)
        
        self.font_pil = ImageFont.load_default() # Default
        self.current_font_size_pil = self.DEFAULT_FONT_SIZE_PIL

        if self.font_path_pil_resolved:
            try:
                self.font_pil = ImageFont.truetype(self.font_path_pil_resolved, self.PREFERRED_FONT_SIZE_PIL)
                self.current_font_size_pil = self.PREFERRED_FONT_SIZE_PIL
                logger.info(f"Pillow font loaded: {self.font_path_pil_resolved} at size {self.current_font_size_pil}.")
                # Determine MoviePy font based on loaded PIL font
                if "dejavu" in self.font_path_pil_resolved.lower():
                    self.video_overlay_font = 'DejaVu-Sans-Bold'
                elif "liberation" in self.font_path_pil_resolved.lower():
                    self.video_overlay_font = 'Liberation-Sans-Bold'
                else: # Fallback if custom font doesn't have an obvious ImageMagick name
                    self.video_overlay_font = self.DEFAULT_MOVIEPY_FONT
            except IOError as e_font_load:
                logger.error(f"Pillow font loading IOError for '{self.font_path_pil_resolved}': {e_font_load}. Using default.")
        else:
            logger.warning("Custom Pillow font not found. Using default.")

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

        self.elevenlabs_api_key = None; self.USE_ELEVENLABS = False; self.elevenlabs_client = None
        self.elevenlabs_voice_id = default_elevenlabs_voice_id
        if VoiceSettings and ELEVENLABS_CLIENT_IMPORTED:
            self.elevenlabs_voice_settings = VoiceSettings(stability=0.60, similarity_boost=0.80, style=0.15, use_speaker_boost=True)
        else: self.elevenlabs_voice_settings = None

        self.pexels_api_key = None; self.USE_PEXELS = False
        self.runway_api_key = None; self.USE_RUNWAYML = False; self.runway_ml_client_instance = None # More specific name

        # Attempt to initialize Runway client if SDK is present and env var might be set
        if RUNWAYML_SDK_IMPORTED and RunwayMLAPIClient and os.getenv("RUNWAYML_API_SECRET"):
            try:
                self.runway_ml_client_instance = RunwayMLAPIClient() # SDK uses env var
                self.USE_RUNWAYML = True # Assume enabled if client initializes
                logger.info("RunwayML Client initialized from RUNWAYML_API_SECRET env var at startup.")
            except Exception as e_runway_init_startup:
                logger.error(f"Initial RunwayML client init failed (env var RUNWAYML_API_SECRET might be invalid): {e_runway_init_startup}")
                self.USE_RUNWAYML = False
        
        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_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 status: {'Ready' if self.USE_ELEVENLABS else 'Failed Initialization'} (Using Voice ID: {self.elevenlabs_voice_id})")
            except Exception as e:
                logger.error(f"ElevenLabs client initialization error: {e}. Service Disabled.", exc_info=True)
                self.USE_ELEVENLABS = False; self.elevenlabs_client = None
        else:
            self.USE_ELEVENLABS = False
            logger.info(f"ElevenLabs Service Disabled (API key not provided or SDK import issue).")

    def set_pexels_api_key(self, api_key):
        self.pexels_api_key = api_key; self.USE_PEXELS = bool(api_key)
        logger.info(f"Pexels Search status: {'Ready' if self.USE_PEXELS else 'Disabled'}")

    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.")

                        if not original_env_secret: # Clean up if we set it
                            del os.environ["RUNWAYML_API_SECRET"]
                            logger.info("Cleared temporary RUNWAYML_API_SECRET env var.")

                    except Exception as e_client_init:
                        logger.error(f"RunwayML Client initialization via set_runway_api_key failed: {e_client_init}", exc_info=True)
                        self.USE_RUNWAYML = False; self.runway_ml_client_instance = None
                else: # Client was already initialized (likely via env var during __init__)
                    self.USE_RUNWAYML = True
                    logger.info("RunwayML Client was already initialized (likely from env var). API key stored.")
            else: # SDK not imported
                logger.warning("RunwayML SDK not imported. API key stored, but integration requires SDK. Service effectively disabled.")
                self.USE_RUNWAYML = False
        else: # No API key provided
            self.USE_RUNWAYML = False; self.runway_ml_client_instance = None
            logger.info("RunwayML Service Disabled (no API key provided).")

    # --- 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

    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)
        if not text_content: return 0, default_char_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
            else: return int(len(text_content)*default_char_height*0.6),int(default_char_height*1.2)
        except Exception as e: logger.warning(f"Error in _get_text_dimensions: {e}"); return int(len(text_content)*self.current_font_size_pil*0.6),int(self.current_font_size_pil*1.2)

    def _create_placeholder_image_content(self, text_description, filename, size=None):
        # (Corrected version from previous response)
        if size is None: size = self.video_frame_size
        img = Image.new('RGB', size, color=(20, 20, 40)); d = ImageDraw.Draw(img); padding = 25
        max_w = size[0] - (2 * padding); lines = []
        if not text_description: text_description = "(Placeholder Image)"
        words = text_description.split(); current_line_text = ""
        for word_idx, word in enumerate(words):
            prospective_addition = word + (" " if word_idx < len(words) - 1 else "")
            test_line_text = current_line_text + prospective_addition
            current_w, _ = self._get_text_dimensions(test_line_text, self.font_pil)
            if current_w == 0 and test_line_text.strip(): current_w = len(test_line_text) * (self.current_font_size_pil * 0.6) # Estimate

            if current_w <= max_w: current_line_text = test_line_text
            else:
                if current_line_text.strip(): lines.append(current_line_text.strip())
                current_line_text = prospective_addition # Start new line
        if current_line_text.strip(): lines.append(current_line_text.strip())

        if 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 ""))
        elif not lines: lines.append("(Placeholder Error)")

        _, single_line_h = self._get_text_dimensions("Ay", self.font_pil); single_line_h = single_line_h if single_line_h > 0 else self.current_font_size_pil + 2
        max_lines = min(len(lines), (size[1] - (2 * padding)) // (single_line_h + 2)) if single_line_h > 0 else 1
        max_lines = max(1, max_lines) # Ensure at least one line
        
        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
        filepath = os.path.join(self.output_dir, filename)
        try: img.save(filepath); return filepath
        except Exception as e_save: logger.error(f"Saving placeholder image '{filepath}' error: {e_save}", exc_info=True); return None

    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"
        filepath = os.path.join(self.output_dir, pexels_filename)
        try:
            logger.info(f"Pexels: Searching for '{query}'")
            effective_query = " ".join(query.split()[:5])
            params["query"] = effective_query
            response = requests.get("https://api.pexels.com/v1/search", headers=headers, params=params, timeout=20)
            response.raise_for_status()
            data = response.json()
            if data.get("photos") and len(data["photos"]) > 0:
                photo_details = data["photos"][0]
                photo_url = photo_details.get("src", {}).get("large2x")
                if not photo_url: logger.warning(f"Pexels: 'large2x' URL missing for '{effective_query}'. Details: {photo_details}"); return None
                image_response = requests.get(photo_url, timeout=60); image_response.raise_for_status()
                img_data_pil = Image.open(io.BytesIO(image_response.content))
                if img_data_pil.mode != 'RGB': img_data_pil = img_data_pil.convert('RGB')
                img_data_pil.save(filepath); logger.info(f"Pexels: Image saved to {filepath}"); return filepath
            else: logger.info(f"Pexels: No photos for '{effective_query}'."); return None
        except requests.exceptions.RequestException as e_req: logger.error(f"Pexels: RequestException for '{query}': {e_req}", exc_info=False); return None # Less verbose for network
        except Exception as e: logger.error(f"Pexels: General error for '{query}': {e}", exc_info=True); return None

    # --- RunwayML Video Generation (Gen-4 Aligned with SDK) ---
    def _generate_video_clip_with_runwayml(self, text_prompt_for_motion, input_image_path, scene_identifier_filename_base, target_duration_seconds=5):
        if not self.USE_RUNWAYML or not self.runway_ml_client_instance:
            logger.warning("RunwayML not enabled or client not initialized. Cannot generate video clip.")
            return None
        if not input_image_path or not os.path.exists(input_image_path):
            logger.error(f"Runway Gen-4 requires an input image. Path not provided or invalid: {input_image_path}")
            return None

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

        runway_duration = 10 if target_duration_seconds >= 8 else 5 # Map to 5s or 10s for Gen-4
        runway_ratio_str = self._map_resolution_to_runway_ratio(self.video_frame_size[0], self.video_frame_size[1])
        
        # Use a more descriptive output filename for Runway videos
        base_name_for_runway, _ = os.path.splitext(scene_identifier_filename_base)
        output_video_filename = base_name_for_runway + f"_runway_gen4_d{runway_duration}s.mp4"
        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:
            # 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
                    
                    if not output_url:
                        logger.error(f"Runway task {task_id} SUCCEEDED, but no output URL found. Details: {vars(task_details) if hasattr(task_details,'__dict__') else str(task_details)}")
                        return None

                    logger.info(f"Runway task {task_id} SUCCEEDED. Downloading video from: {output_url}")
                    video_response = requests.get(output_url, stream=True, timeout=300)
                    video_response.raise_for_status()
                    with open(output_video_filepath, 'wb') as f:
                        for chunk in video_response.iter_content(chunk_size=8192): f.write(chunk)
                    logger.info(f"Runway Gen-4 video successfully downloaded to: {output_video_filepath}")
                    return output_video_filepath
                
                elif task_details.status in ['FAILED', 'ABORTED', 'ERROR']: # Added ERROR
                    error_msg = getattr(task_details,'error_message',None) or getattr(getattr(task_details,'output',None),'error', "Unknown error from Runway task.")
                    logger.error(f"Runway task {task_id} final status: {task_details.status}. Error: {error_msg}")
                    return None
            
            logger.warning(f"Runway task {task_id} timed out polling after {max_polling_duration_seconds} seconds.")
            return None

        except AttributeError as ae: # If SDK methods are not as expected
            logger.error(f"AttributeError with RunwayML SDK: {ae}. Ensure SDK is up to date and methods/attributes match documentation.", exc_info=True)
            return None
        except Exception as e_runway_call:
            logger.error(f"General error during Runway Gen-4 API call or processing: {e_runway_call}", exc_info=True)
            return None

    def _create_placeholder_video_content(self, text_description, filename, duration=4, size=None):
        # (Keeping as before)
        if size is None: size = self.video_frame_size; fp = os.path.join(self.output_dir, filename); tc = None
        try: tc = TextClip(text_description, fontsize=50, color='white', font=self.video_overlay_font, bg_color='black', size=size, method='caption').set_duration(duration); tc.write_videofile(fp, fps=24, codec='libx264', preset='ultrafast', logger=None, threads=2); logger.info(f"Generic placeholder video: {fp}"); return fp
        except Exception as e: logger.error(f"Generic placeholder video error {fp}: {e}", exc_info=True); return None
        finally:
            if tc and hasattr(tc, 'close'): tc.close()

    # --- generate_scene_asset (Main asset generation logic using Runway Gen-4 workflow) ---
    def generate_scene_asset(self, image_generation_prompt_text, motion_prompt_text_for_video,
                             scene_data, scene_identifier_filename_base,
                             generate_as_video_clip=False, runway_target_duration=5):
        # (Logic updated for Runway Gen-4 workflow)
        base_name, _ = os.path.splitext(scene_identifier_filename_base)
        asset_info = {'path': None, 'type': 'none', 'error': True, 'prompt_used': image_generation_prompt_text, 'error_message': 'Asset generation init failed'}
        input_image_for_runway_path = None
        # Use a distinct name for the base image if it's only an intermediate step for video
        base_image_filename = base_name + ("_base_for_video.png" if generate_as_video_clip else ".png")
        base_image_filepath = os.path.join(self.output_dir, base_image_filename)
        
        # STEP 1: Generate/acquire the base image
        # Try DALL-E
        if self.USE_AI_IMAGE_GENERATION and self.openai_api_key:
            # ... (DALL-E logic as in previous full rewrite - condensed for brevity here)
            max_r, att_n = 2,0;
            for att_n in range(max_r):
                try:logger.info(f"Att {att_n+1} DALL-E (base img): {image_generation_prompt_text[:70]}...");cl=openai.OpenAI(api_key=self.openai_api_key,timeout=90.0);r=cl.images.generate(model=self.dalle_model,prompt=image_generation_prompt_text,n=1,size=self.image_size_dalle3,quality="hd",response_format="url",style="vivid");iu=r.data[0].url;rp=getattr(r.data[0],'revised_prompt',None);
                if rp:logger.info(f"DALL-E revised: {rp[:70]}...");ir=requests.get(iu,timeout=120);ir.raise_for_status();id_img=Image.open(io.BytesIO(ir.content));
                if id_img.mode!='RGB':id_img=id_img.convert('RGB');id_img.save(base_image_filepath);logger.info(f"DALL-E base img saved: {base_image_filepath}");input_image_for_runway_path=base_image_filepath;asset_info={'path':base_image_filepath,'type':'image','error':False,'prompt_used':image_generation_prompt_text,'revised_prompt':rp};break
                except openai.RateLimitError as e:logger.warning(f"OpenAI RateLimit {att_n+1}:{e}.Retry...");time.sleep(5*(att_n+1));asset_info['error_message']=str(e)
                except Exception as e:logger.error(f"DALL-E base img error:{e}",exc_info=True);asset_info['error_message']=str(e);break
            if asset_info['error']:logger.warning(f"DALL-E failed after {att_n+1} attempts for base img.")
        
        if asset_info['error'] and self.USE_PEXELS: # Pexels Fallback
            logger.info("Trying Pexels for base img.");pqt=scene_data.get('pexels_search_query_감독',f"{scene_data.get('emotional_beat','')} {scene_data.get('setting_description','')}");pp=self._search_pexels_image(pqt,base_image_filename); # Use base_image_filename
            if pp:input_image_for_runway_path=pp;asset_info={'path':pp,'type':'image','error':False,'prompt_used':f"Pexels:{pqt}"}
            else:current_em=asset_info.get('error_message',"");asset_info['error_message']=(current_em+" Pexels failed for base.").strip()

        if asset_info['error']: # Placeholder Fallback
            logger.warning("Base img (DALL-E/Pexels) failed. Using placeholder.");ppt=asset_info.get('prompt_used',image_generation_prompt_text);php=self._create_placeholder_image_content(f"[Base Placeholder]{ppt[:70]}...",base_image_filename); # Use base_image_filename
            if php:input_image_for_runway_path=php;asset_info={'path':php,'type':'image','error':False,'prompt_used':ppt}
            else:current_em=asset_info.get('error_message',"");asset_info['error_message']=(current_em+" Base placeholder failed.").strip()

        # STEP 2: If video clip is requested, use the generated base image with RunwayML
        if generate_as_video_clip:
            if not input_image_for_runway_path:
                logger.error("RunwayML video: base image generation failed entirely. Cannot proceed.");asset_info['error']=True;asset_info['error_message']=(asset_info.get('error_message',"")+" Base img completely failed, Runway abort.").strip();asset_info['type']='none';return asset_info
            if self.USE_RUNWAYML: # This check is now more about if the service is generally enabled
                video_path=self._generate_video_clip_with_runwayml(motion_prompt_text_for_video,input_image_for_runway_path,base_name,runway_target_duration) # Pass base_name for runway output filename
                if video_path and os.path.exists(video_path):asset_info={'path':video_path,'type':'video','error':False,'prompt_used':motion_prompt_text_for_video,'base_image_path':input_image_for_runway_path}
                else:logger.warning(f"RunwayML video failed for {base_name}. Fallback to base img.");asset_info['error']=True;asset_info['error_message']=(asset_info.get('error_message',"Base img ok.")+" RunwayML video step failed; use base img.").strip();asset_info['path']=input_image_for_runway_path;asset_info['type']='image';asset_info['prompt_used']=image_generation_prompt_text
            else:logger.warning("RunwayML selected but not enabled/client not ready. Use base img.");asset_info['error']=True;asset_info['error_message']=(asset_info.get('error_message',"Base img ok.")+" RunwayML disabled; use base img.").strip();asset_info['path']=input_image_for_runway_path;asset_info['type']='image';asset_info['prompt_used']=image_generation_prompt_text
        return asset_info

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

    # --- assemble_animatic_from_assets (Still contains crucial debug saves for blank video issue) ---
    def assemble_animatic_from_assets(self, asset_data_list, overall_narration_path=None, output_filename="final_video.mp4", fps=24):
        # (Keep the version with robust image processing, C-contiguous arrays, debug saves, and pix_fmt)
        if not asset_data_list: logger.warning("No assets for animatic."); return None
        processed_clips = []; narration_clip = None; final_clip = None
        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)
                        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:
                            txt_c=TextClip(f"Scene {scene_num}\n{key_action}",fontsize=self.video_overlay_font_size,color=self.video_overlay_font_color,font=self.video_overlay_font,bg_color='rgba(10,10,20,0.7)',method='caption',align='West',size=(self.video_frame_size[0]*0.9,None),kerning=-1,stroke_color='black',stroke_width=1.5).set_duration(to_dur).set_start(to_start).set_position(('center',0.92),relative=True)
                            current_scene_mvpy_clip=CompositeVideoClip([current_scene_mvpy_clip,txt_c],size=self.video_frame_size,use_bgclip=True)
                        else: logger.warning(f"S{scene_num}: Text overlay duration is zero. Skip text.")
                    except Exception as e:logger.error(f"S{scene_num} TextClip error:{e}. No text.",exc_info=True)
                if current_scene_mvpy_clip:processed_clips.append(current_scene_mvpy_clip);logger.info(f"S{scene_num} Processed. Dur:{current_scene_mvpy_clip.duration:.2f}s.")
            except Exception as e:logger.error(f"MAJOR Error S{scene_num} ({asset_path}):{e}",exc_info=True)
            finally:
                if current_scene_mvpy_clip and hasattr(current_scene_mvpy_clip,'close'):
                    try: current_scene_mvpy_clip.close()
                    except: pass

        if not processed_clips:logger.warning("No clips processed. Abort.");return None
        td=0.75
        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_to_close in all_clips_to_close:
                if clip_obj_to_close and hasattr(clip_obj_to_close, 'close'):
                    try: clip_obj_to_close.close()
                    except Exception as e_close: logger.warning(f"Ignoring error while closing a clip: {type(clip_obj_to_close).__name__} - {e_close}")