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Recovered state: integrated TrendSurferAgent, restored frontend/backend files, and cleaned up recovery scripts

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This commit is contained in:
ajaysi
2026-02-08 13:56:57 +05:30
parent 1db10ccd0f
commit e404a86502
333 changed files with 42223 additions and 10875 deletions
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111
backend/services/linkedin/image_generation/linkedin_image_storage.py
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@@ -41,8 +41,10 @@ class LinkedInImageStorage:
if storage_path:
self.base_storage_path = Path(storage_path)
else:
# Default to project-relative path
self.base_storage_path = Path(__file__).parent.parent.parent.parent / "linkedin_images"
# Default to project-relative path: root/data/media/linkedin_images
# services/linkedin/image_generation/linkedin_image_storage.py -> image_generation -> linkedin -> services -> backend -> root
root_dir = Path(__file__).parent.parent.parent.parent.parent
self.base_storage_path = root_dir / "data" / "media" / "linkedin_images"
# Create storage directories
self.images_path = self.base_storage_path / "images"
@@ -82,15 +84,17 @@ class LinkedInImageStorage:
self,
image_data: bytes,
metadata: Dict[str, Any],
content_type: str = "post"
content_type: str = "post",
user_id: Optional[str] = None
) -> Dict[str, Any]:
"""
Store generated image with metadata.
Args:
image_data: Image data in bytes
image_metadata: Image metadata and context
metadata: Image metadata and context
content_type: Type of LinkedIn content (post, article, carousel, video_script)
user_id: Optional user ID for workspace storage
Returns:
Dict containing storage result and image ID
@@ -110,7 +114,7 @@ class LinkedInImageStorage:
}
# Determine storage path based on content type
storage_path = self._get_storage_path(content_type, image_id)
storage_path = self._get_storage_path(content_type, image_id, user_id)
# Store image file
image_stored = await self._store_image_file(image_data, storage_path)
@@ -121,7 +125,7 @@ class LinkedInImageStorage:
}
# Store metadata
metadata_stored = await self._store_metadata(image_id, metadata, storage_path)
metadata_stored = await self._store_metadata(image_id, metadata, storage_path, user_id)
if not metadata_stored:
# Clean up image file if metadata storage fails
await self._cleanup_failed_storage(storage_path)
@@ -154,19 +158,20 @@ class LinkedInImageStorage:
'error': f"Image storage failed: {str(e)}"
}
async def retrieve_image(self, image_id: str) -> Dict[str, Any]:
async def retrieve_image(self, image_id: str, user_id: Optional[str] = None) -> Dict[str, Any]:
"""
Retrieve stored image by ID.
Args:
image_id: Unique image identifier
user_id: Optional user ID to locate the image
Returns:
Dict containing image data and metadata
"""
try:
# Find image file
image_path = await self._find_image_by_id(image_id)
image_path = await self._find_image_by_id(image_id, user_id)
if not image_path:
return {
'success': False,
@@ -174,7 +179,7 @@ class LinkedInImageStorage:
}
# Load metadata
metadata = await self._load_metadata(image_id)
metadata = await self._load_metadata(image_id, user_id)
if not metadata:
return {
'success': False,
@@ -199,19 +204,20 @@ class LinkedInImageStorage:
'error': f"Image retrieval failed: {str(e)}"
}
async def delete_image(self, image_id: str) -> Dict[str, Any]:
async def delete_image(self, image_id: str, user_id: Optional[str] = None) -> Dict[str, Any]:
"""
Delete stored image and metadata.
Args:
image_id: Unique image identifier
user_id: Optional user ID to locate the image
Returns:
Dict containing deletion result
"""
try:
# Find image file
image_path = await self._find_image_by_id(image_id)
image_path = await self._find_image_by_id(image_id, user_id)
if not image_path:
return {
'success': False,
@@ -224,7 +230,8 @@ class LinkedInImageStorage:
logger.info(f"Deleted image file: {image_path}")
# Delete metadata
metadata_path = self.metadata_path / f"{image_id}.json"
_, metadata_base = self._get_workspace_paths(user_id)
metadata_path = metadata_base / f"{image_id}.json"
if metadata_path.exists():
metadata_path.unlink()
logger.info(f"Deleted metadata file: {metadata_path}")
@@ -449,7 +456,35 @@ class LinkedInImageStorage:
'error': f'Validation error: {str(e)}'
}
def _get_storage_path(self, content_type: str, image_id: str) -> Path:
def _get_workspace_paths(self, user_id: Optional[str]) -> Tuple[Path, Path]:
"""
Get images and metadata paths for a user or default global paths.
Returns (images_path, metadata_path).
"""
if user_id:
try:
# Use local import to avoid circular dependency
from services.database import get_db
from services.user_workspace_manager import UserWorkspaceManager
db_gen = get_db()
db = next(db_gen)
try:
workspace_manager = UserWorkspaceManager(db)
workspace = workspace_manager.get_user_workspace(user_id)
if workspace:
# Align with global structure: linkedin_images/images and linkedin_images/metadata
base = Path(workspace['workspace_path']) / "media" / "linkedin_images"
return (base / "images", base / "metadata")
finally:
if 'db' in locals():
db.close()
except Exception as e:
logger.warning(f"Failed to resolve user workspace path: {e}")
return (self.images_path, self.metadata_path)
def _get_storage_path(self, content_type: str, image_id: str, user_id: Optional[str] = None) -> Path:
"""Get storage path for image based on content type."""
# Map content types to directory names
content_type_map = {
@@ -460,7 +495,9 @@ class LinkedInImageStorage:
}
directory = content_type_map.get(content_type, 'posts')
return self.images_path / directory / f"{image_id}.png"
images_path, _ = self._get_workspace_paths(user_id)
return images_path / directory / f"{image_id}.png"
async def _store_image_file(self, image_data: bytes, storage_path: Path) -> bool:
"""Store image file to disk."""
@@ -479,7 +516,7 @@ class LinkedInImageStorage:
logger.error(f"Error storing image file: {str(e)}")
return False
async def _store_metadata(self, image_id: str, metadata: Dict[str, Any], storage_path: Path) -> bool:
async def _store_metadata(self, image_id: str, metadata: Dict[str, Any], storage_path: Path, user_id: Optional[str] = None) -> bool:
"""Store image metadata to JSON file."""
try:
# Add storage metadata
@@ -487,8 +524,12 @@ class LinkedInImageStorage:
metadata['storage_path'] = str(storage_path)
metadata['stored_at'] = datetime.now().isoformat()
# Determine metadata path
_, metadata_base = self._get_workspace_paths(user_id)
metadata_base.mkdir(parents=True, exist_ok=True)
# Write metadata file
metadata_path = self.metadata_path / f"{image_id}.json"
metadata_path = metadata_base / f"{image_id}.json"
with open(metadata_path, 'w') as f:
json.dump(metadata, f, indent=2, default=str)
@@ -499,20 +540,42 @@ class LinkedInImageStorage:
logger.error(f"Error storing metadata: {str(e)}")
return False
async def _find_image_by_id(self, image_id: str) -> Optional[Path]:
async def _find_image_by_id(self, image_id: str, user_id: Optional[str] = None) -> Optional[Path]:
"""Find image file by ID across all content type directories."""
for content_dir in self.images_path.iterdir():
if content_dir.is_dir():
image_path = content_dir / f"{image_id}.png"
if image_path.exists():
return image_path
images_path, _ = self._get_workspace_paths(user_id)
# If user_id is NOT provided, we might want to check global path only,
# OR we might want to check if it's a global image.
# Current implementation assumes if user_id is provided, look there.
# If not provided, look in global.
if images_path.exists():
for content_dir in images_path.iterdir():
if content_dir.is_dir():
image_path = content_dir / f"{image_id}.png"
if image_path.exists():
return image_path
return None
async def _load_metadata(self, image_id: str) -> Optional[Dict[str, Any]]:
async def get_image_metadata(self, image_id: str, user_id: Optional[str] = None) -> Optional[Dict[str, Any]]:
"""
Get metadata for an image.
Args:
image_id: Unique image identifier
user_id: Optional user ID
Returns:
Dict containing image metadata if found
"""
return await self._load_metadata(image_id, user_id)
async def _load_metadata(self, image_id: str, user_id: Optional[str] = None) -> Optional[Dict[str, Any]]:
"""Load metadata for image ID."""
try:
metadata_path = self.metadata_path / f"{image_id}.json"
_, metadata_base = self._get_workspace_paths(user_id)
metadata_path = metadata_base / f"{image_id}.json"
if metadata_path.exists():
with open(metadata_path, 'r') as f:
return json.load(f)