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 # Ensure this is OpenAI v1.x.x+
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'):
             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__)
# logger.setLevel(logging.DEBUG) # Uncomment for verbose 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 (SDK v1.x.x pattern) imported successfully.")
except ImportError:
    logger.warning("ElevenLabs SDK not found (expected 'pip install elevenlabs>=1.0.0'). Audio generation will be disabled.")
except Exception as e_eleven_import_general:
    logger.warning(f"General error importing ElevenLabs client components: {e_eleven_import_general}. Audio generation disabled.")

RUNWAYML_SDK_IMPORTED = False
RunwayMLAPIClientClass = None 
try:
    from runwayml import RunwayML as ImportedRunwayMLAPIClientClass
    RunwayMLAPIClientClass = ImportedRunwayMLAPIClientClass
    RUNWAYML_SDK_IMPORTED = True
    logger.info("RunwayML SDK (runwayml) 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_general:
    logger.warning(f"General error importing RunwayML SDK: {e_runway_sdk_import_general}. RunwayML features disabled.")


class VisualEngine:
    DEFAULT_FONT_SIZE_PIL = 10
    PREFERRED_FONT_SIZE_PIL = 20
    VIDEO_OVERLAY_FONT_SIZE = 30
    VIDEO_OVERLAY_FONT_COLOR = 'white'
    DEFAULT_MOVIEPY_FONT = 'DejaVu-Sans-Bold'
    PREFERRED_MOVIEPY_FONT = 'Liberation-Sans-Bold'

    def __init__(self, output_dir="temp_cinegen_media", default_elevenlabs_voice_id="Rachel"):
        self.output_dir = output_dir
        os.makedirs(self.output_dir, exist_ok=True)
        self.font_filename_pil_preference = "DejaVuSans-Bold.ttf"
        font_paths_to_try = [ self.font_filename_pil_preference, f"/usr/share/fonts/truetype/dejavu/{self.font_filename_pil_preference}", f"/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf", f"/System/Library/Fonts/Supplemental/Arial.ttf", f"C:/Windows/Fonts/arial.ttf", f"/usr/local/share/fonts/truetype/mycustomfonts/arial.ttf"]
        self.resolved_font_path_pil = next((p for p in font_paths_to_try if os.path.exists(p)), None)
        self.active_font_pil = ImageFont.load_default(); self.active_font_size_pil = self.DEFAULT_FONT_SIZE_PIL
        self.active_moviepy_font_name = self.DEFAULT_MOVIEPY_FONT
        if self.resolved_font_path_pil:
            try: self.active_font_pil = ImageFont.truetype(self.resolved_font_path_pil, self.PREFERRED_FONT_SIZE_PIL); self.active_font_size_pil = self.PREFERRED_FONT_SIZE_PIL; logger.info(f"Pillow font: {self.resolved_font_path_pil} sz {self.active_font_size_pil}."); self.active_moviepy_font_name = 'DejaVu-Sans-Bold' if "dejavu" in self.resolved_font_path_pil.lower() else ('Liberation-Sans-Bold' if "liberation" in self.resolved_font_path_pil.lower() else self.DEFAULT_MOVIEPY_FONT)
            except IOError as e_font_load_io: logger.error(f"Pillow font IOError '{self.resolved_font_path_pil}': {e_font_load_io}. Default.")
        else: logger.warning("Preferred Pillow font not found. Default.")
        self.openai_api_key = None; self.USE_AI_IMAGE_GENERATION = False; self.dalle_model = "dall-e-3"; self.image_size_dalle3 = "1792x1024"
        self.video_frame_size = (1280, 720)
        self.elevenlabs_api_key = None; self.USE_ELEVENLABS = False; self.elevenlabs_client_instance = None
        self.elevenlabs_voice_id = default_elevenlabs_voice_id
        if VoiceSettings and ELEVENLABS_CLIENT_IMPORTED: self.elevenlabs_voice_settings_obj = VoiceSettings(stability=0.60, similarity_boost=0.80, style=0.15, use_speaker_boost=True)
        else: self.elevenlabs_voice_settings_obj = None
        self.pexels_api_key = None; self.USE_PEXELS = False
        self.runway_api_key = None; self.USE_RUNWAYML = False; self.runway_ml_sdk_client_instance = None
        if RUNWAYML_SDK_IMPORTED and RunwayMLAPIClientClass and os.getenv("RUNWAYML_API_SECRET"):
            try: self.runway_ml_sdk_client_instance = RunwayMLAPIClientClass(); self.USE_RUNWAYML = True; logger.info("RunwayML Client init from env var at startup.")
            except Exception as e_runway_init_startup: logger.error(f"Initial RunwayML client init failed: {e_runway_init_startup}"); self.USE_RUNWAYML = False
        logger.info("VisualEngine initialized.")

    def set_openai_api_key(self, api_key_value): self.openai_api_key = api_key_value; self.USE_AI_IMAGE_GENERATION = bool(api_key_value); logger.info(f"DALL-E status: {'Ready' if self.USE_AI_IMAGE_GENERATION else 'Disabled'}")
    def set_elevenlabs_api_key(self, api_key_value, voice_id_from_secret=None):
        self.elevenlabs_api_key = api_key_value
        if voice_id_from_secret: self.elevenlabs_voice_id = voice_id_from_secret
        if api_key_value and ELEVENLABS_CLIENT_IMPORTED and ElevenLabsAPIClient:
            try: self.elevenlabs_client_instance = ElevenLabsAPIClient(api_key=api_key_value); self.USE_ELEVENLABS = bool(self.elevenlabs_client_instance); logger.info(f"11L Client: {'Ready' if self.USE_ELEVENLABS else 'Failed'} (Voice: {self.elevenlabs_voice_id})")
            except Exception as e_11l_setkey_init: logger.error(f"11L client init error: {e_11l_setkey_init}. Disabled.", exc_info=True); self.USE_ELEVENLABS=False; self.elevenlabs_client_instance=None
        else: self.USE_ELEVENLABS = False; logger.info(f"11L Disabled (key/SDK).")
    def set_pexels_api_key(self, api_key_value): self.pexels_api_key = api_key_value; self.USE_PEXELS = bool(api_key_value); logger.info(f"Pexels status: {'Ready' if self.USE_PEXELS else 'Disabled'}")
    def set_runway_api_key(self, api_key_value):
        self.runway_api_key = api_key_value
        if api_key_value:
            if RUNWAYML_SDK_IMPORTED and RunwayMLAPIClientClass:
                if not self.runway_ml_sdk_client_instance:
                    try:
                        original_env_secret = os.getenv("RUNWAYML_API_SECRET")
                        if not original_env_secret: os.environ["RUNWAYML_API_SECRET"] = api_key_value; logger.info("Temp set RUNWAYML_API_SECRET for SDK.")
                        self.runway_ml_sdk_client_instance = RunwayMLAPIClientClass(); self.USE_RUNWAYML = True; logger.info("RunwayML Client init via set_runway_api_key.")
                        if not original_env_secret: del os.environ["RUNWAYML_API_SECRET"]; logger.info("Cleared temp RUNWAYML_API_SECRET.")
                    except Exception as e_runway_setkey_init: logger.error(f"RunwayML Client init in set_runway_api_key fail: {e_runway_setkey_init}", exc_info=True); self.USE_RUNWAYML=False;self.runway_ml_sdk_client_instance=None
                else: self.USE_RUNWAYML = True; logger.info("RunwayML Client already init.")
            else: logger.warning("RunwayML SDK not imported. Service disabled."); self.USE_RUNWAYML = False
        else: self.USE_RUNWAYML = False; self.runway_ml_sdk_client_instance = None; logger.info("RunwayML Disabled (no API key).")

    def _image_to_data_uri(self, image_path):
        # (Implementation from before)
        try: mime_type,_=mimetypes.guess_type(image_path)
        if not mime_type:ext=os.path.splitext(image_path)[1].lower();mime_map={".png":"image/png",".jpg":"image/jpeg",".jpeg":"image/jpeg",".webp":"image/webp"};mime_type=mime_map.get(ext,"application/octet-stream");
        if mime_type=="application/octet-stream":logger.warning(f"Unknown MIME for {image_path}, using {mime_type}.")
        with open(image_path,"rb")as image_file:encoded_string=base64.b64encode(image_file.read()).decode('utf-8')
        data_uri=f"data:{mime_type};base64,{encoded_string}";logger.debug(f"Data URI for {os.path.basename(image_path)} (start): {data_uri[:100]}...");return data_uri
        except FileNotFoundError:logger.error(f"Img not found {image_path} for data URI.");return None
        except Exception as e:logger.error(f"Error converting {image_path} to data URI:{e}",exc_info=True);return None

    def _map_resolution_to_runway_ratio(self, width, height):
        # (Implementation from before)
        ratio_str=f"{width}:{height}";supported_ratios_gen4=["1280:720","720:1280","1104:832","832:1104","960:960","1584:672"];
        if ratio_str in supported_ratios_gen4:return ratio_str
        logger.warning(f"Res {ratio_str} not in Gen-4 list. Default 1280:720.");return "1280:720"

    def _get_text_dimensions(self, text_content, font_object_pil):
        # (Implementation from before)
        dch=getattr(font_object_pil,'size',self.active_font_size_pil);
        if not text_content:return 0,dch
        try:
            if hasattr(font_object_pil,'getbbox'):bb=font_object_pil.getbbox(text_content);w=bb[2]-bb[0];h=bb[3]-bb[1];return w,h if h>0 else dch
            elif hasattr(font_object_pil,'getsize'):w,h=font_object_pil.getsize(text_content);return w,h if h>0 else dch
            else:return int(len(text_content)*dch*0.6),int(dch*1.2)
        except Exception as e_getdim_inner:logger.warning(f"Error in _get_text_dimensions:{e_getdim_inner}");return int(len(text_content)*self.active_font_size_pil*0.6),int(self.active_font_size_pil*1.2) # Renamed e

    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_for_placeholder = []
        if not text_description: text_description = "(Placeholder Image)"
        words_list = text_description.split(); current_line_buffer = ""
        for word_idx, word_item in enumerate(words_list):
            prospective_addition = word_item + (" " if word_idx < len(words_list) - 1 else "")
            test_line_candidate = current_line_buffer + prospective_addition
            current_w_text, _ = self._get_text_dimensions(test_line_candidate, self.active_font_pil)
            if current_w_text == 0 and test_line_candidate.strip(): current_w_text = len(test_line_candidate) * (self.active_font_size_pil * 0.6)
            if current_w_text <= max_w: current_line_buffer = test_line_candidate
            else:
                if current_line_buffer.strip(): lines_for_placeholder.append(current_line_buffer.strip())
                current_line_buffer = prospective_addition
        if current_line_buffer.strip(): lines_for_placeholder.append(current_line_buffer.strip())
        if not lines_for_placeholder and text_description:
            avg_char_w_est, _ = self._get_text_dimensions("W", self.active_font_pil); avg_char_w_est = avg_char_w_est or (self.active_font_size_pil * 0.6)
            chars_per_line_est = int(max_w / avg_char_w_est) if avg_char_w_est > 0 else 20
            lines_for_placeholder.append(text_description[:chars_per_line_est] + ("..." if len(text_description) > chars_per_line_est else ""))
        elif not lines_for_placeholder: lines_for_placeholder.append("(Placeholder Error)")
        _, single_h = self._get_text_dimensions("Ay", self.active_font_pil); single_h = single_h if single_h > 0 else self.active_font_size_pil + 2
        max_l = min(len(lines_for_placeholder), (size[1] - (2 * padding)) // (single_h + 2)) if single_h > 0 else 1; max_l = max(1, max_l)
        y_p = padding + (size[1] - (2 * padding) - max_l * (single_h + 2)) / 2.0
        for i_line in range(max_l):
            line_txt_content = lines_for_placeholder[i_line]; line_w_val, _ = self._get_text_dimensions(line_txt_content, self.active_font_pil)
            if line_w_val == 0 and line_txt_content.strip(): line_w_val = len(line_txt_content) * (self.active_font_size_pil * 0.6)
            x_p = (size[0] - line_w_val) / 2.0
            try: d.text((x_p, y_p), line_txt_content, font=self.active_font_pil, fill=(200, 200, 180))
            except Exception as e_draw: logger.error(f"Pillow d.text error: {e_draw} for '{line_txt_content}'")
            y_p += single_h + 2
            if i_line == 6 and max_l > 7:
                try: d.text((x_p, y_p), "...", font=self.active_font_pil, fill=(200, 200, 180))
                except Exception as e_elip: logger.error(f"Pillow d.text ellipsis error: {e_elip}"); break
        filepath_placeholder = os.path.join(self.output_dir, filename)
        try: img.save(filepath_placeholder); return filepath_placeholder
        except Exception as e_save: logger.error(f"Saving placeholder image '{filepath_placeholder}' error: {e_save}", exc_info=True); return None

    def _search_pexels_image(self, query_str, output_fn_base):
        # (Corrected version from previous response)
        if not self.USE_PEXELS or not self.pexels_api_key: return None
        http_headers = {"Authorization": self.pexels_api_key}
        http_params = {"query": query_str, "per_page": 1, "orientation": "landscape", "size": "large2x"}
        base_name_px, _ = os.path.splitext(output_fn_base)
        pexels_fn_str = base_name_px + f"_pexels_{random.randint(1000,9999)}.jpg"
        file_path_px = os.path.join(self.output_dir, pexels_fn_str)
        try:
            logger.info(f"Pexels: Searching for '{query_str}'")
            eff_query_px = " ".join(query_str.split()[:5])
            http_params["query"] = eff_query_px
            response_px = requests.get("https://api.pexels.com/v1/search", headers=http_headers, params=http_params, timeout=20)
            response_px.raise_for_status()
            data_px = response_px.json()
            if data_px.get("photos") and len(data_px["photos"]) > 0:
                photo_details_px = data_px["photos"][0]
                photo_url_px = photo_details_px.get("src", {}).get("large2x")
                if not photo_url_px: logger.warning(f"Pexels: 'large2x' URL missing for '{eff_query_px}'. Details: {photo_details_px}"); return None
                image_response_px = requests.get(photo_url_px, timeout=60); image_response_px.raise_for_status()
                img_pil_data_px = Image.open(io.BytesIO(image_response_px.content))
                if img_pil_data_px.mode != 'RGB': img_pil_data_px = img_pil_data_px.convert('RGB')
                img_pil_data_px.save(file_path_px); logger.info(f"Pexels: Image saved to {file_path_px}"); return file_path_px
            else: logger.info(f"Pexels: No photos for '{eff_query_px}'."); return None
        except requests.exceptions.RequestException as e_req_px: logger.error(f"Pexels: RequestException for '{query_str}': {e_req_px}", exc_info=False); return None
        except Exception as e_px_gen: logger.error(f"Pexels: General error for '{query_str}': {e_px_gen}", exc_info=True); return None

    def _generate_video_clip_with_runwayml(self, text_prompt_for_motion, input_image_path, scene_identifier_filename_base, target_duration_seconds=5):
        # (Updated RunwayML integration from before)
        if not self.USE_RUNWAYML or not self.runway_ml_sdk_client_instance: logger.warning("RunwayML not enabled/client not init. Skip video."); return None
        if not input_image_path or not os.path.exists(input_image_path): logger.error(f"Runway Gen-4 needs input image. Path invalid: {input_image_path}"); return None
        image_data_uri_str = self._image_to_data_uri(input_image_path)
        if not image_data_uri_str: return None
        runway_dur = 10 if target_duration_seconds >= 8 else 5
        runway_ratio = self._map_resolution_to_runway_ratio(self.video_frame_size[0], self.video_frame_size[1])
        base_name_for_runway_vid, _ = os.path.splitext(scene_identifier_filename_base); output_vid_fn = base_name_for_runway_vid + f"_runway_gen4_d{runway_dur}s.mp4"
        output_vid_fp = os.path.join(self.output_dir, output_vid_fn)
        logger.info(f"Runway Gen-4 task: motion='{text_prompt_for_motion[:100]}...', img='{os.path.basename(input_image_path)}', dur={runway_dur}s, ratio='{runway_ratio}'")
        try:
            task_submitted_runway = self.runway_ml_sdk_client_instance.image_to_video.create(model='gen4_turbo', prompt_image=image_data_uri_str, prompt_text=text_prompt_for_motion, duration=runway_dur, ratio=runway_ratio)
            task_id_runway = task_submitted_runway.id; logger.info(f"Runway Gen-4 task ID: {task_id_runway}. Polling...")
            poll_sec=10; max_poll_count=36; poll_start_time = time.time()
            while time.time() - poll_start_time < max_poll_count * poll_sec:
                time.sleep(poll_sec); task_details_runway = self.runway_ml_sdk_client_instance.tasks.retrieve(id=task_id_runway)
                logger.info(f"Runway task {task_id_runway} status: {task_details_runway.status}")
                if task_details_runway.status == 'SUCCEEDED':
                    output_url_runway = getattr(getattr(task_details_runway,'output',None),'url',None) or \
                                        (getattr(task_details_runway,'artifacts',None) and task_details_runway.artifacts[0].url if task_details_runway.artifacts and hasattr(task_details_runway.artifacts[0],'url') else None) or \
                                        (getattr(task_details_runway,'artifacts',None) and task_details_runway.artifacts[0].download_url if task_details_runway.artifacts and hasattr(task_details_runway.artifacts[0],'download_url') else None)
                    if not output_url_runway: logger.error(f"Runway task {task_id_runway} SUCCEEDED, but no output URL. Details: {vars(task_details_runway) if hasattr(task_details_runway,'__dict__') else task_details_runway}"); return None
                    logger.info(f"Runway task {task_id_runway} SUCCEEDED. Downloading: {output_url_runway}")
                    video_resp_get = requests.get(output_url_runway, stream=True, timeout=300); video_resp_get.raise_for_status()
                    with open(output_vid_fp,'wb') as f_vid:
                        for chunk_data in video_resp_get.iter_content(chunk_size=8192): f_vid.write(chunk_data)
                    logger.info(f"Runway Gen-4 video saved: {output_vid_fp}"); return output_vid_fp
                elif task_details_runway.status in ['FAILED','ABORTED','ERROR']:
                    err_msg_runway = getattr(task_details_runway,'error_message',None) or getattr(getattr(task_details_runway,'output',None),'error',"Unknown Runway error.")
                    logger.error(f"Runway task {task_id_runway} status: {task_details_runway.status}. Error: {err_msg_runway}"); return None
            logger.warning(f"Runway task {task_id_runway} timed out."); return None
        except AttributeError as ae_sdk: logger.error(f"RunwayML SDK AttrError: {ae_sdk}. SDK/methods changed?", exc_info=True); return None
        except Exception as e_runway_gen: logger.error(f"Runway Gen-4 API error: {e_runway_gen}", exc_info=True); return None

    def _create_placeholder_video_content(self, text_desc_ph, filename_ph, duration_ph=4, size_ph=None):
        # <<< THIS IS THE CORRECTED METHOD >>>
        if size_ph is None: size_ph = self.video_frame_size
        filepath_ph = os.path.join(self.output_dir, filename_ph)
        text_clip_ph = None 
        try: # Ensure try block is here
            text_clip_ph = TextClip(text_desc_ph, fontsize=50, color='white', font=self.video_overlay_font,
                                bg_color='black', size=size_ph, method='caption').set_duration(duration_ph)
            text_clip_ph.write_videofile(filepath_ph, fps=24, codec='libx264', preset='ultrafast', logger=None, threads=2)
            logger.info(f"Generic placeholder video created: {filepath_ph}")
            return filepath_ph
        except Exception as e_ph_vid: 
            logger.error(f"Failed to create generic placeholder video '{filepath_ph}': {e_ph_vid}", exc_info=True)
            return None
        finally: # Ensure finally block is here
            if text_clip_ph and hasattr(text_clip_ph, 'close'):
                text_clip_ph.close()
    
    def generate_scene_asset(self, image_generation_prompt_text, motion_prompt_text_for_video,
                             scene_data_dict, scene_identifier_fn_base,
                             generate_as_video_clip_flag=False, runway_target_dur_val=5):
        # (Corrected DALL-E loop from previous response)
        base_name_asset, _ = os.path.splitext(scene_identifier_fn_base)
        asset_info_result = {'path': None, 'type': 'none', 'error': True, 'prompt_used': image_generation_prompt_text, 'error_message': 'Asset generation init failed'}
        path_for_input_image_runway = None
        fn_for_base_image = base_name_asset + ("_base_for_video.png" if generate_as_video_clip_flag else ".png")
        fp_for_base_image = os.path.join(self.output_dir, fn_for_base_image)
        if self.USE_AI_IMAGE_GENERATION and self.openai_api_key:
            max_r_dalle, attempt_count_dalle = 2,0;
            for att_n_dalle in range(max_r_dalle):
                attempt_count_dalle = att_n_dalle + 1
                try: 
                    logger.info(f"Att {attempt_count_dalle} DALL-E (base img): {image_generation_prompt_text[:70]}..."); oai_cl = openai.OpenAI(api_key=self.openai_api_key,timeout=90.0); oai_r = oai_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"); oai_iu = oai_r.data[0].url; oai_rp = getattr(oai_r.data[0],'revised_prompt',None);
                    if oai_rp: logger.info(f"DALL-E revised: {oai_rp[:70]}...")
                    oai_ir = requests.get(oai_iu,timeout=120); oai_ir.raise_for_status(); oai_id = Image.open(io.BytesIO(oai_ir.content));
                    if oai_id.mode!='RGB': oai_id=oai_id.convert('RGB')
                    oai_id.save(fp_for_base_image); logger.info(f"DALL-E base img saved: {fp_for_base_image}"); path_for_input_image_runway=fp_for_base_image; asset_info_result={'path':fp_for_base_image,'type':'image','error':False,'prompt_used':image_generation_prompt_text,'revised_prompt':oai_rp}; break 
                except openai.RateLimitError as e_oai_rl: logger.warning(f"OpenAI RateLimit Att {attempt_count_dalle}:{e_oai_rl}.Retry...");time.sleep(5*attempt_count_dalle);asset_info_result['error_message']=str(e_oai_rl)
                except openai.APIError as e_oai_api: logger.error(f"OpenAI APIError Att {attempt_count_dalle}:{e_oai_api}");asset_info_result['error_message']=str(e_oai_api);break
                except requests.exceptions.RequestException as e_oai_req: logger.error(f"Requests Err DALL-E Att {attempt_count_dalle}:{e_oai_req}");asset_info_result['error_message']=str(e_oai_req);break
                except Exception as e_oai_gen: logger.error(f"General DALL-E Err Att {attempt_count_dalle}:{e_oai_gen}",exc_info=True);asset_info_result['error_message']=str(e_oai_gen);break
            if asset_info_result['error']: logger.warning(f"DALL-E failed after {attempt_count_dalle} attempts for base img.")
        if asset_info_result['error'] and self.USE_PEXELS:
            logger.info("Trying Pexels for base img.");px_qt=scene_data_dict.get('pexels_search_query_감독',f"{scene_data_dict.get('emotional_beat','')} {scene_data_dict.get('setting_description','')}");px_pp=self._search_pexels_image(px_qt,fn_for_base_image);
            if px_pp:path_for_input_image_runway=px_pp;asset_info_result={'path':px_pp,'type':'image','error':False,'prompt_used':f"Pexels:{px_qt}"}
            else:current_em_px=asset_info_result.get('error_message',"");asset_info_result['error_message']=(current_em_px+" Pexels failed for base.").strip()
        if asset_info_result['error']:
            logger.warning("Base img (DALL-E/Pexels) failed. Using placeholder.");ph_ppt=asset_info_result.get('prompt_used',image_generation_prompt_text);php=self._create_placeholder_image_content(f"[Base Placeholder]{ph_ppt[:70]}...",fn_for_base_image);
            if php:path_for_input_image_runway=php;asset_info_result={'path':php,'type':'image','error':False,'prompt_used':ph_ppt}
            else:current_em_ph=asset_info_result.get('error_message',"");asset_info_result['error_message']=(current_em_ph+" Base placeholder failed.").strip()
        if generate_as_video_clip_flag:
            if not path_for_input_image_runway:logger.error("RunwayML video: base img failed.");asset_info_result['error']=True;asset_info_result['error_message']=(asset_info_result.get('error_message',"")+" Base img miss, Runway abort.").strip();asset_info_result['type']='none';return asset_info_result
            if self.USE_RUNWAYML:
                runway_video_p=self._generate_video_clip_with_runwayml(motion_prompt_text_for_video,path_for_input_image_runway,base_name_asset,runway_target_dur_val)
                if runway_video_p and os.path.exists(runway_video_p):asset_info_result={'path':runway_video_p,'type':'video','error':False,'prompt_used':motion_prompt_text_for_video,'base_image_path':path_for_input_image_runway}
                else:logger.warning(f"RunwayML video failed for {base_name_asset}. Fallback to base img.");asset_info_result['error']=True;asset_info_result['error_message']=(asset_info_result.get('error_message',"Base img ok.")+" RunwayML video fail; use base img.").strip();asset_info_result['path']=path_for_input_image_runway;asset_info_result['type']='image';asset_info_result['prompt_used']=image_generation_prompt_text
            else:logger.warning("RunwayML selected but disabled. Use base img.");asset_info_result['error']=True;asset_info_result['error_message']=(asset_info_result.get('error_message',"Base img ok.")+" RunwayML disabled; use base img.").strip();asset_info_result['path']=path_for_input_image_runway;asset_info_result['type']='image';asset_info_result['prompt_used']=image_generation_prompt_text
        return asset_info_result

    def generate_narration_audio(self, text_to_narrate, output_filename="narration_overall.mp3"):
        # <<< CORRECTED VERSION OF THIS METHOD >>>
        if not self.USE_ELEVENLABS or not self.elevenlabs_client_instance or not text_to_narrate:
            logger.info("ElevenLabs conditions not met (service disabled, client not init, or no text). Skipping audio generation.")
            return None
        audio_filepath_narration = os.path.join(self.output_dir, output_filename)
        try:
            logger.info(f"Generating ElevenLabs audio (Voice ID: {self.elevenlabs_voice_id}) for text: \"{text_to_narrate[:70]}...\"")
            audio_stream_method_11l = None
            if hasattr(self.elevenlabs_client_instance, 'text_to_speech') and hasattr(self.elevenlabs_client_instance.text_to_speech, 'stream'):
                audio_stream_method_11l = self.elevenlabs_client_instance.text_to_speech.stream; logger.info("Using ElevenLabs SDK method: client.text_to_speech.stream()")
            elif hasattr(self.elevenlabs_client_instance, 'generate_stream'):
                audio_stream_method_11l = self.elevenlabs_client_instance.generate_stream; logger.info("Using ElevenLabs SDK method: client.generate_stream()")
            elif hasattr(self.elevenlabs_client_instance, 'generate'):
                logger.info("Using ElevenLabs SDK method: client.generate() (non-streaming).")
                voice_param_11l = str(self.elevenlabs_voice_id)
                if Voice and self.elevenlabs_voice_settings_obj: voice_param_11l = Voice(voice_id=str(self.elevenlabs_voice_id), settings=self.elevenlabs_voice_settings_obj)
                audio_bytes_data = self.elevenlabs_client_instance.generate(text=text_to_narrate, voice=voice_param_11l, model="eleven_multilingual_v2")
                with open(audio_filepath_narration, "wb") as audio_file_out: audio_file_out.write(audio_bytes_data)
                logger.info(f"ElevenLabs audio (non-streamed) saved successfully to: {audio_filepath_narration}"); return audio_filepath_narration
            else: logger.error("No recognized audio generation method found on the ElevenLabs client instance."); return None

            if audio_stream_method_11l: # If a streaming method was identified
                params_for_voice_stream = {"voice_id": str(self.elevenlabs_voice_id)}
                if self.elevenlabs_voice_settings_obj:
                    if hasattr(self.elevenlabs_voice_settings_obj, 'model_dump'): params_for_voice_stream["voice_settings"] = self.elevenlabs_voice_settings_obj.model_dump()
                    elif hasattr(self.elevenlabs_voice_settings_obj, 'dict'): params_for_voice_stream["voice_settings"] = self.elevenlabs_voice_settings_obj.dict()
                    else: params_for_voice_stream["voice_settings"] = self.elevenlabs_voice_settings_obj
                audio_data_iterator_11l = audio_stream_method_11l(text=text_to_narrate, model_id="eleven_multilingual_v2", **params_for_voice_stream)
                with open(audio_filepath_narration, "wb") as audio_file_out_stream:
                    for audio_chunk_data in audio_data_iterator_11l:
                        if audio_chunk_data: audio_file_out_stream.write(audio_chunk_data)
                logger.info(f"ElevenLabs audio (streamed) saved successfully to: {audio_filepath_narration}"); return audio_filepath_narration
        except AttributeError as ae_11l_sdk: logger.error(f"AttributeError with ElevenLabs SDK client: {ae_11l_sdk}. SDK version/methods might differ.", exc_info=True); return None
        except Exception as e_11l_general_audio: logger.error(f"General error during ElevenLabs audio generation: {e_11l_general_audio}", 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):
        # (Keep as in the version with robust image processing, C-contiguous array, debug saves, and pix_fmt)
        # ... (This extensive method is assumed to be largely correct from the previous iteration focusing on blank video issues)
        if not asset_data_list: logger.warning("No assets for animatic."); return None
        processed_moviepy_clips_list = []; narration_audio_clip_mvpy = None; final_video_output_clip = None
        logger.info(f"Assembling from {len(asset_data_list)} assets. Target Frame: {self.video_frame_size}.")
        for i_asset, asset_info_item_loop in enumerate(asset_data_list):
            path_of_asset, type_of_asset, duration_for_scene = asset_info_item_loop.get('path'), asset_info_item_loop.get('type'), asset_info_item_loop.get('duration', 4.5)
            num_of_scene, action_in_key = asset_info_item_loop.get('scene_num', i_asset + 1), asset_info_item_loop.get('key_action', '')
            logger.info(f"S{num_of_scene}: Path='{path_of_asset}', Type='{type_of_asset}', Dur='{duration_for_scene}'s")
            if not (path_of_asset and os.path.exists(path_of_asset)): logger.warning(f"S{num_of_scene}: Not found '{path_of_asset}'. Skip."); continue
            if duration_for_scene <= 0: logger.warning(f"S{num_of_scene}: Invalid duration ({duration_for_scene}s). Skip."); continue
            active_scene_clip = None
            try:
                if type_of_asset == 'image':
                    opened_pil_img = Image.open(path_of_asset); logger.debug(f"S{num_of_scene}: Loaded img. Mode:{opened_pil_img.mode}, Size:{opened_pil_img.size}")
                    converted_img_rgba = opened_pil_img.convert('RGBA') if opened_pil_img.mode != 'RGBA' else opened_pil_img.copy()
                    thumbnailed_img = converted_img_rgba.copy(); resample_f = Image.Resampling.LANCZOS if hasattr(Image.Resampling,'LANCZOS') else Image.BILINEAR; thumbnailed_img.thumbnail(self.video_frame_size,resample_f)
                    rgba_canvas = Image.new('RGBA',self.video_frame_size,(0,0,0,0)); pos_x,pos_y=(self.video_frame_size[0]-thumbnailed_img.width)//2,(self.video_frame_size[1]-thumbnailed_img.height)//2
                    rgba_canvas.paste(thumbnailed_img,(pos_x,pos_y),thumbnailed_img)
                    final_rgb_img_pil = Image.new("RGB",self.video_frame_size,(0,0,0)); final_rgb_img_pil.paste(rgba_canvas,mask=rgba_canvas.split()[3])
                    debug_path_img_pre_numpy = os.path.join(self.output_dir,f"debug_PRE_NUMPY_S{num_of_scene}.png"); final_rgb_img_pil.save(debug_path_img_pre_numpy); logger.info(f"DEBUG: Saved PRE_NUMPY_S{num_of_scene} to {debug_path_img_pre_numpy}")
                    numpy_frame_arr = np.array(final_rgb_img_pil,dtype=np.uint8);
                    if not numpy_frame_arr.flags['C_CONTIGUOUS']: numpy_frame_arr=np.ascontiguousarray(numpy_frame_arr,dtype=np.uint8)
                    logger.debug(f"S{num_of_scene}: NumPy for MoviePy. Shape:{numpy_frame_arr.shape}, DType:{numpy_frame_arr.dtype}, C-Contig:{numpy_frame_arr.flags['C_CONTIGUOUS']}")
                    if numpy_frame_arr.size==0 or numpy_frame_arr.ndim!=3 or numpy_frame_arr.shape[2]!=3: logger.error(f"S{num_of_scene}: Invalid NumPy array for MoviePy. Skip."); continue
                    base_image_clip = ImageClip(numpy_frame_arr,transparent=False).set_duration(duration_for_scene)
                    debug_path_moviepy_frame=os.path.join(self.output_dir,f"debug_MOVIEPY_FRAME_S{num_of_scene}.png"); base_image_clip.save_frame(debug_path_moviepy_frame,t=0.1); logger.info(f"DEBUG: Saved MOVIEPY_FRAME_S{num_of_scene} to {debug_path_moviepy_frame}")
                    fx_image_clip = base_image_clip
                    try: scale_end_kb=random.uniform(1.03,1.08); fx_image_clip=base_image_clip.fx(vfx.resize,lambda t_val:1+(scale_end_kb-1)*(t_val/duration_for_scene) if duration_for_scene>0 else 1).set_position('center')
                    except Exception as e_kb_fx: logger.error(f"S{num_of_scene} Ken Burns error: {e_kb_fx}",exc_info=False)
                    active_scene_clip = fx_image_clip
                elif type_of_asset == 'video':
                    source_video_clip_obj=None
                    try:
                        source_video_clip_obj=VideoFileClip(path_of_asset,target_resolution=(self.video_frame_size[1],self.video_frame_size[0])if self.video_frame_size else None, audio=False)
                        temp_video_clip_obj_loop=source_video_clip_obj
                        if source_video_clip_obj.duration!=duration_for_scene:
                            if source_video_clip_obj.duration>duration_for_scene:temp_video_clip_obj_loop=source_video_clip_obj.subclip(0,duration_for_scene)
                            else:
                                if duration_for_scene/source_video_clip_obj.duration > 1.5 and source_video_clip_obj.duration>0.1:temp_video_clip_obj_loop=source_video_clip_obj.loop(duration=duration_for_scene)
                                else:temp_video_clip_obj_loop=source_video_clip_obj.set_duration(source_video_clip_obj.duration);logger.info(f"S{num_of_scene} Video clip ({source_video_clip_obj.duration:.2f}s) shorter than target ({duration_for_scene:.2f}s).")
                        active_scene_clip=temp_video_clip_obj_loop.set_duration(duration_for_scene)
                        if active_scene_clip.size!=list(self.video_frame_size):active_scene_clip=active_scene_clip.resize(self.video_frame_size)
                    except Exception as e_vid_load_loop:logger.error(f"S{num_of_scene} Video load error '{path_of_asset}':{e_vid_load_loop}",exc_info=True);continue
                    finally:
                        if source_video_clip_obj and source_video_clip_obj is not active_scene_clip and hasattr(source_video_clip_obj,'close'):source_video_clip_obj.close()
                else: logger.warning(f"S{num_of_scene} Unknown asset type '{type_of_asset}'. Skip."); continue
                if active_scene_clip and action_in_key:
                    try:
                        dur_text_overlay=min(active_scene_clip.duration-0.5,active_scene_clip.duration*0.8)if active_scene_clip.duration>0.5 else active_scene_clip.duration; start_text_overlay=0.25
                        if dur_text_overlay > 0:
                            text_clip_for_overlay=TextClip(f"Scene {num_of_scene}\n{action_in_key}",fontsize=self.VIDEO_OVERLAY_FONT_SIZE,color=self.VIDEO_OVERLAY_FONT_COLOR,font=self.active_moviepy_font_name,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(dur_text_overlay).set_start(start_text_overlay).set_position(('center',0.92),relative=True)
                            active_scene_clip=CompositeVideoClip([active_scene_clip,text_clip_for_overlay],size=self.video_frame_size,use_bgclip=True)
                        else: logger.warning(f"S{num_of_scene}: Text overlay duration zero. Skip text.")
                    except Exception as e_txt_comp:logger.error(f"S{num_of_scene} TextClip error:{e_txt_comp}. No text.",exc_info=True)
                if active_scene_clip:processed_moviepy_clips_list.append(active_scene_clip);logger.info(f"S{num_of_scene} Processed. Dur:{active_scene_clip.duration:.2f}s.")
            except Exception as e_asset_loop_main:logger.error(f"MAJOR Error processing asset for S{num_of_scene} ({path_of_asset}):{e_asset_loop_main}",exc_info=True)
            finally:
                if active_scene_clip and hasattr(active_scene_clip,'close'):
                    try: active_scene_clip.close()
                    except: pass
        if not processed_moviepy_clips_list:logger.warning("No clips processed for animatic. Aborting.");return None
        transition_duration_val=0.75
        try:
            logger.info(f"Concatenating {len(processed_moviepy_clips_list)} clips for final animatic.");
            if len(processed_moviepy_clips_list)>1:final_video_output_clip=concatenate_videoclips(processed_moviepy_clips_list,padding=-transition_duration_val if transition_duration_val>0 else 0,method="compose")
            elif processed_moviepy_clips_list:final_video_output_clip=processed_moviepy_clips_list[0]
            if not final_video_output_clip:logger.error("Concatenation resulted in a None clip. Aborting.");return None
            logger.info(f"Concatenated animatic duration:{final_video_output_clip.duration:.2f}s")
            if transition_duration_val>0 and final_video_output_clip.duration>0:
                if final_video_output_clip.duration>transition_duration_val*2:final_video_output_clip=final_video_output_clip.fx(vfx.fadein,transition_duration_val).fx(vfx.fadeout,transition_duration_val)
                else:final_video_output_clip=final_video_output_clip.fx(vfx.fadein,min(transition_duration_val,final_video_output_clip.duration/2.0))
            if overall_narration_path and os.path.exists(overall_narration_path) and final_video_output_clip.duration>0:
                try:narration_audio_clip_mvpy=AudioFileClip(overall_narration_path);final_video_output_clip=final_video_output_clip.set_audio(narration_audio_clip_mvpy);logger.info("Overall narration added to animatic.")
                except Exception as e_narr_add:logger.error(f"Error adding narration to animatic:{e_narr_add}",exc_info=True)
            elif final_video_output_clip.duration<=0:logger.warning("Animatic has no duration. Audio not added.")
            if final_video_output_clip and final_video_output_clip.duration>0:
                final_output_path_str=os.path.join(self.output_dir,output_filename);logger.info(f"Writing final animatic video to:{final_output_path_str} (Duration:{final_video_output_clip.duration:.2f}s)")
                final_video_output_clip.write_videofile(final_output_path_str,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"Animatic video created successfully:{final_output_path_str}");return final_output_path_str
            else:logger.error("Final animatic clip is invalid or has zero duration. Cannot write video file.");return None
        except Exception as e_vid_write_final:logger.error(f"Error during final animatic video file writing or composition:{e_vid_write_final}",exc_info=True);return None
        finally:
            logger.debug("Closing all MoviePy clips in `assemble_animatic_from_assets` main finally block.")
            all_clips_to_close_list = processed_moviepy_clips_list + ([narration_audio_clip_mvpy] if narration_audio_clip_mvpy else []) + ([final_video_output_clip] if final_video_output_clip else [])
            for clip_item_to_close in all_clips_to_close_list:
                if clip_item_to_close and hasattr(clip_item_to_close, 'close'):
                    try: clip_item_to_close.close()
                    except Exception as e_final_close: logger.warning(f"Ignoring error while closing a MoviePy clip: {type(clip_item_to_close).__name__} - {e_final_close}")