AI Image Studio, AI podcast Maker, AI product Marketing
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ajaysi
379
backend/services/image_studio/transform_service.py
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379
backend/services/image_studio/transform_service.py
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"""Transform Studio service for image-to-video and talking avatar generation."""
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import os
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import uuid
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from pathlib import Path
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from typing import Any, Dict, Optional
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from dataclasses import dataclass
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from fastapi import HTTPException
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from loguru import logger
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from .wan25_service import WAN25Service
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from .infinitetalk_adapter import InfiniteTalkService
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from services.llm_providers.main_video_generation import track_video_usage
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from utils.logger_utils import get_service_logger
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from utils.file_storage import save_file_safely, sanitize_filename
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logger = get_service_logger("image_studio.transform")
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@dataclass
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class TransformImageToVideoRequest:
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"""Request for WAN 2.5 image-to-video."""
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image_base64: str
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prompt: str
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audio_base64: Optional[str] = None
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resolution: str = "720p" # 480p, 720p, 1080p
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duration: int = 5 # 5 or 10 seconds
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negative_prompt: Optional[str] = None
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seed: Optional[int] = None
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enable_prompt_expansion: bool = True
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@dataclass
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class TalkingAvatarRequest:
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"""Request for InfiniteTalk talking avatar."""
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image_base64: str
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audio_base64: str
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resolution: str = "720p" # 480p or 720p
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prompt: Optional[str] = None
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mask_image_base64: Optional[str] = None
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seed: Optional[int] = None
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class TransformStudioService:
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"""Service for Transform Studio operations."""
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def __init__(self):
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"""Initialize Transform Studio service."""
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self.wan25_service = WAN25Service()
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self.infinitetalk_service = InfiniteTalkService()
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# Video output directory
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# __file__ is: backend/services/image_studio/transform_service.py
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# We need: backend/transform_videos
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base_dir = Path(__file__).parent.parent.parent.parent
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self.output_dir = base_dir / "transform_videos"
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self.output_dir.mkdir(parents=True, exist_ok=True)
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# Verify directory was created
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if not self.output_dir.exists():
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raise RuntimeError(f"Failed to create transform_videos directory: {self.output_dir}")
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logger.info(f"[Transform Studio] Initialized with output directory: {self.output_dir}")
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def _save_video_file(
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self,
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video_bytes: bytes,
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operation_type: str,
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user_id: str,
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) -> Dict[str, Any]:
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"""Save video file to disk.
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Args:
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video_bytes: Video content as bytes
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operation_type: Type of operation (e.g., "image-to-video", "talking-avatar")
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user_id: User ID for directory organization
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Returns:
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Dictionary with filename, file_path, and file_url
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"""
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# Create user-specific directory
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user_dir = self.output_dir / user_id
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user_dir.mkdir(parents=True, exist_ok=True)
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# Generate filename
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filename = f"{operation_type}_{uuid.uuid4().hex[:8]}.mp4"
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filename = sanitize_filename(filename)
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# Save file
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file_path, error = save_file_safely(
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content=video_bytes,
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directory=user_dir,
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filename=filename,
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max_file_size=500 * 1024 * 1024 # 500MB max for videos
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)
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if error:
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raise HTTPException(
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status_code=500,
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detail=f"Failed to save video file: {error}"
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)
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file_url = f"/api/image-studio/videos/{user_id}/{filename}"
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return {
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"filename": filename,
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"file_path": str(file_path),
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"file_url": file_url,
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"file_size": len(video_bytes),
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}
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async def transform_image_to_video(
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self,
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request: TransformImageToVideoRequest,
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user_id: str,
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) -> Dict[str, Any]:
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"""Transform image to video using WAN 2.5.
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Args:
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request: Transform request
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user_id: User ID for tracking and file organization
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Returns:
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Dictionary with video URL, metadata, and cost
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"""
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logger.info(
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f"[Transform Studio] Image-to-video request from user {user_id}: "
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f"resolution={request.resolution}, duration={request.duration}s"
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)
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# Generate video using WAN 2.5
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result = await self.wan25_service.generate_video(
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image_base64=request.image_base64,
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prompt=request.prompt,
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audio_base64=request.audio_base64,
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resolution=request.resolution,
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duration=request.duration,
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negative_prompt=request.negative_prompt,
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seed=request.seed,
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enable_prompt_expansion=request.enable_prompt_expansion,
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)
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# Save video to disk
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save_result = self._save_video_file(
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video_bytes=result["video_bytes"],
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operation_type="image-to-video",
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user_id=user_id,
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)
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# Track usage
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try:
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usage_info = track_video_usage(
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user_id=user_id,
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provider=result["provider"],
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model_name=result["model_name"],
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prompt=result["prompt"],
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video_bytes=result["video_bytes"],
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cost_override=result["cost"],
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)
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logger.info(
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f"[Transform Studio] Usage tracked: {usage_info.get('current_calls', 0)} / "
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f"{usage_info.get('video_limit_display', '∞')} videos, "
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f"cost=${result['cost']:.2f}"
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)
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except Exception as e:
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logger.warning(f"[Transform Studio] Failed to track usage: {e}")
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# Save to asset library
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try:
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from services.database import get_db
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from utils.asset_tracker import save_asset_to_library
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db = next(get_db())
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try:
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save_asset_to_library(
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db=db,
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user_id=user_id,
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asset_type="video",
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source_module="image_studio",
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filename=save_result["filename"],
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file_url=save_result["file_url"],
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file_path=save_result["file_path"],
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file_size=save_result["file_size"],
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mime_type="video/mp4",
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title=f"Transform: Image-to-Video ({request.resolution})",
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description=f"Generated video using WAN 2.5: {request.prompt[:100]}",
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prompt=result["prompt"],
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tags=["image_studio", "transform", "video", "image-to-video", request.resolution],
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provider=result["provider"],
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model=result["model_name"],
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cost=result["cost"],
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asset_metadata={
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"resolution": request.resolution,
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"duration": result["duration"],
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"operation": "image-to-video",
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"width": result["width"],
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"height": result["height"],
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}
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)
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logger.info(f"[Transform Studio] Video saved to asset library")
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finally:
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db.close()
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except Exception as e:
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logger.warning(f"[Transform Studio] Failed to save to asset library: {e}")
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return {
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"success": True,
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"video_url": save_result["file_url"],
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"video_base64": None, # Don't include base64 for large videos
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"duration": result["duration"],
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"resolution": result["resolution"],
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"width": result["width"],
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"height": result["height"],
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"file_size": save_result["file_size"],
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"cost": result["cost"],
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"provider": result["provider"],
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"model": result["model_name"],
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"metadata": result.get("metadata", {}),
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}
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async def create_talking_avatar(
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self,
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request: TalkingAvatarRequest,
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user_id: str,
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) -> Dict[str, Any]:
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"""Create talking avatar using InfiniteTalk.
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Args:
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request: Talking avatar request
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user_id: User ID for tracking and file organization
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Returns:
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Dictionary with video URL, metadata, and cost
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"""
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logger.info(
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f"[Transform Studio] Talking avatar request from user {user_id}: "
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f"resolution={request.resolution}"
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)
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# Generate video using InfiniteTalk
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result = await self.infinitetalk_service.create_talking_avatar(
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image_base64=request.image_base64,
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audio_base64=request.audio_base64,
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resolution=request.resolution,
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prompt=request.prompt,
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mask_image_base64=request.mask_image_base64,
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seed=request.seed,
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user_id=user_id,
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)
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# Save video to disk
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save_result = self._save_video_file(
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video_bytes=result["video_bytes"],
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operation_type="talking-avatar",
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user_id=user_id,
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)
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# Track usage
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try:
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usage_info = track_video_usage(
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user_id=user_id,
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provider=result["provider"],
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model_name=result["model_name"],
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prompt=result.get("prompt", ""),
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video_bytes=result["video_bytes"],
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cost_override=result["cost"],
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)
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logger.info(
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f"[Transform Studio] Usage tracked: {usage_info.get('current_calls', 0)} / "
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f"{usage_info.get('video_limit_display', '∞')} videos, "
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f"cost=${result['cost']:.2f}"
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)
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except Exception as e:
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logger.warning(f"[Transform Studio] Failed to track usage: {e}")
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# Save to asset library
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try:
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from services.database import get_db
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from utils.asset_tracker import save_asset_to_library
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db = next(get_db())
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try:
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save_asset_to_library(
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db=db,
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user_id=user_id,
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asset_type="video",
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source_module="image_studio",
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filename=save_result["filename"],
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file_url=save_result["file_url"],
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file_path=save_result["file_path"],
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file_size=save_result["file_size"],
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mime_type="video/mp4",
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title=f"Transform: Talking Avatar ({request.resolution})",
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description="Generated talking avatar video using InfiniteTalk",
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prompt=result.get("prompt", ""),
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tags=["image_studio", "transform", "video", "talking-avatar", request.resolution],
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provider=result["provider"],
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model=result["model_name"],
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cost=result["cost"],
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asset_metadata={
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"resolution": request.resolution,
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"duration": result.get("duration", 5.0),
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"operation": "talking-avatar",
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"width": result.get("width", 1280),
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"height": result.get("height", 720),
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}
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)
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logger.info(f"[Transform Studio] Video saved to asset library")
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finally:
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db.close()
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except Exception as e:
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logger.warning(f"[Transform Studio] Failed to save to asset library: {e}")
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return {
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"success": True,
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"video_url": save_result["file_url"],
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"video_base64": None, # Don't include base64 for large videos
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"duration": result.get("duration", 5.0),
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"resolution": result.get("resolution", request.resolution),
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"width": result.get("width", 1280),
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"height": result.get("height", 720),
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"file_size": save_result["file_size"],
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"cost": result["cost"],
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"provider": result["provider"],
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"model": result["model_name"],
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"metadata": result.get("metadata", {}),
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}
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def estimate_cost(
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self,
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operation: str,
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resolution: str,
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duration: Optional[int] = None,
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) -> Dict[str, Any]:
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"""Estimate cost for transform operation.
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Args:
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operation: Operation type ("image-to-video" or "talking-avatar")
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resolution: Output resolution
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duration: Video duration in seconds (for image-to-video)
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Returns:
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Cost estimation details
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"""
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if operation == "image-to-video":
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if duration is None:
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duration = 5
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cost = self.wan25_service.calculate_cost(resolution, duration)
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return {
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"estimated_cost": cost,
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"breakdown": {
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"base_cost": 0.0,
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"per_second": self.wan25_service.calculate_cost(resolution, 1),
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"duration": duration,
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"total": cost,
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},
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"currency": "USD",
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"provider": "wavespeed",
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"model": "alibaba/wan-2.5/image-to-video",
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}
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elif operation == "talking-avatar":
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# InfiniteTalk minimum is 5 seconds
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estimated_duration = duration or 5.0
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cost = self.infinitetalk_service.calculate_cost(resolution, estimated_duration)
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return {
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"estimated_cost": cost,
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"breakdown": {
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"base_cost": 0.0,
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"per_second": self.infinitetalk_service.calculate_cost(resolution, 1.0),
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"duration": estimated_duration,
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"total": cost,
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},
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"currency": "USD",
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"provider": "wavespeed",
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"model": "wavespeed-ai/infinitetalk",
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}
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else:
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raise ValueError(f"Unknown operation: {operation}")
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