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| from tinydb import TinyDB, Query, where | |
| from tinydb.storages import MemoryStorage | |
| import chromadb | |
| from chromadb.api.types import EmbeddingFunction, Embeddings, Image, Images | |
| from keras_facenet import FaceNet | |
| from typing import Any | |
| from datetime import datetime, timedelta | |
| CHROMADB_LOC = "/home/user/data/chromadb" | |
| class TinyDBHelper: | |
| def __init__(self): | |
| self.db = TinyDB(storage=MemoryStorage) | |
| self.tokens_table = self.db.table('tokens') | |
| def insert_token(self, user_id: str, token: str, expires_at: str): | |
| self.tokens_table.insert({'user_id': user_id, 'token': token, 'expires_at': expires_at}) | |
| def query_token(self, user_id: str, token: str) -> bool: | |
| """Query to check if the token exists and is valid.""" | |
| User = Query() | |
| # Assuming your tokens table contains 'user_id', 'token', and 'expires_at' | |
| result = self.tokens_table.search((User.user_id == user_id) & (User.token == token)) | |
| # Optionally, check if the token is expired | |
| expires_at = datetime.fromisoformat(result[0]['expires_at']) | |
| if datetime.utcnow() > expires_at: | |
| return False | |
| return bool(result) | |
| def remove_token_by_value(self, token: str): | |
| """Remove a token based on its value.""" | |
| self.tokens_table.remove((where('token') == token)) | |
| ###### Class implementing Custom Embedding function for chroma db | |
| # | |
| class UserFaceEmbeddingFunction(EmbeddingFunction[Images]): | |
| def __init__(self): | |
| # Intitialize the FaceNet model | |
| self.facenet = FaceNet() | |
| def __call__(self, input: Images) -> Embeddings: | |
| # Since the input images are assumed to be `numpy.ndarray` objects already, | |
| # we can directly use them for embeddings extraction without additional processing. | |
| # Ensure the input images are pre-cropped face images ready for embedding extraction. | |
| # Extract embeddings using FaceNet for the pre-cropped face images. | |
| embeddings_array = self.facenet.embeddings(input) | |
| # Convert numpy array of embeddings to list of lists, as expected by Chroma. | |
| return embeddings_array.tolist() | |
| # Usage example: | |
| # user_face_embedding_function = UserFaceEmbeddingFunction() | |
| # Assuming `images` is a list of `numpy.ndarray` objects where each represents a pre-cropped face image ready for embedding extraction. | |
| # embeddings = user_face_embedding_function(images) | |
| class ChromaDBFaceHelper: | |
| def __init__(self, db_path: str): | |
| self.client = chromadb.PersistentClient(db_path) | |
| self.user_faces_db = self.client.get_or_create_collection(name="user_faces_db", embedding_function=UserFaceEmbeddingFunction()) | |
| def query_user_face(self, presented_face: Any, n_results: int = 1): | |
| return self.user_faces_db.query(query_images=[presented_face], n_results=n_results) | |
| def print_query_results(self, query_results: dict) -> None: | |
| for id, distance, metadata in zip(query_results["ids"][0], query_results['distances'][0], query_results['metadatas'][0]): | |
| print(f'id: {id}, distance: {distance}, metadata: {metadata}') | |
| # Initialize these helpers globally if they are to be used across multiple modules | |
| tinydb_helper = TinyDBHelper() | |
| chromadb_face_helper = ChromaDBFaceHelper(CHROMADB_LOC) # Initialization requires db_path | |