33a165a615
- app.py: Streamlit UI for video generation workflow - main_flow.py: CLI tool with argparse support - modules/: Business logic modules (script_gen, image_gen, video_gen, composer, etc.) - config.py: Configuration with API keys and paths - requirements.txt: Python dependencies - docs/: System prompt documentation
152 lines
3.9 KiB
Python
152 lines
3.9 KiB
Python
"""
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MatchMe Studio - Project State Management (R2 Persistence)
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"""
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import json
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import logging
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import uuid
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from datetime import datetime
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from typing import Dict, Any, Optional, List
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from dataclasses import dataclass, asdict, field
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import config
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from modules import storage
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logger = logging.getLogger(__name__)
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@dataclass
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class Scene:
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id: int
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duration: int = 5
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timeline: str = ""
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keyframe: Dict[str, str] = field(default_factory=dict)
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camera_movement: str = ""
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story_beat: str = ""
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voiceover: str = ""
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rhythm: Dict[str, Any] = field(default_factory=dict)
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image_url: str = ""
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video_url: str = ""
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@dataclass
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class Project:
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id: str = field(default_factory=lambda: str(uuid.uuid4())[:8])
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created_at: str = field(default_factory=lambda: datetime.now().isoformat())
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status: str = "draft" # draft | analyzing | scripting | imaging | video | rendering | done
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# Step 0: Input
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input_mode: str = "" # text | images | video
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prompt: str = ""
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image_urls: List[str] = field(default_factory=list)
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video_url: str = ""
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asr_text: str = ""
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# Step 1: Analysis
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analysis: str = ""
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questions: List[Dict[str, Any]] = field(default_factory=list)
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answers: Dict[str, str] = field(default_factory=dict)
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# Step 2: Script
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hook: str = ""
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scenes: List[Dict[str, Any]] = field(default_factory=list)
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cta: str = ""
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# Step 6: Final
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final_video_url: str = ""
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bgm_url: str = ""
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def save_project(project: Project) -> str:
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"""Save project state to R2 as JSON."""
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data = asdict(project)
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json_str = json.dumps(data, ensure_ascii=False, indent=2)
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# Write to temp file
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temp_path = config.TEMP_DIR / f"project_{project.id}.json"
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with open(temp_path, "w", encoding="utf-8") as f:
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f.write(json_str)
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# Upload to R2
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object_name = f"projects/{project.id}.json"
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s3 = storage.get_s3_client()
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try:
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s3.upload_file(
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str(temp_path),
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config.R2_BUCKET_NAME,
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object_name,
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ExtraArgs={'ContentType': 'application/json'}
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)
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logger.info(f"Project {project.id} saved to R2")
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return project.id
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except Exception as e:
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logger.error(f"Failed to save project: {e}")
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raise
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def load_project(project_id: str) -> Optional[Project]:
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"""Load project state from R2."""
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object_name = f"projects/{project_id}.json"
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temp_path = config.TEMP_DIR / f"project_{project_id}.json"
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s3 = storage.get_s3_client()
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try:
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s3.download_file(config.R2_BUCKET_NAME, object_name, str(temp_path))
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with open(temp_path, "r", encoding="utf-8") as f:
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data = json.load(f)
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# Reconstruct Project
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project = Project(
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id=data.get("id", project_id),
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created_at=data.get("created_at", ""),
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status=data.get("status", "draft"),
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input_mode=data.get("input_mode", ""),
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prompt=data.get("prompt", ""),
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image_urls=data.get("image_urls", []),
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video_url=data.get("video_url", ""),
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asr_text=data.get("asr_text", ""),
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analysis=data.get("analysis", ""),
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questions=data.get("questions", []),
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answers=data.get("answers", {}),
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hook=data.get("hook", ""),
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scenes=data.get("scenes", []),
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cta=data.get("cta", ""),
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final_video_url=data.get("final_video_url", ""),
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bgm_url=data.get("bgm_url", "")
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)
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logger.info(f"Project {project_id} loaded from R2")
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return project
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except Exception as e:
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logger.warning(f"Failed to load project {project_id}: {e}")
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return None
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def create_project() -> Project:
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"""Create a new project with unique ID."""
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project = Project()
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logger.info(f"Created new project: {project.id}")
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return project
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