# 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}")