Spaces:
Sleeping
Sleeping
File size: 14,971 Bytes
f7e620e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 |
"""
Database Manager for EmotionMirror Application
This module provides functionality for database operations, including:
- Creating and initializing the SQLite database
- Storing analysis results
- Retrieving historical data
- Managing database connections
All database operations are encapsulated in this module to ensure clean separation
of concerns and modularity.
"""
import os
import json
import sqlite3
import logging
from datetime import datetime
from typing import Dict, List, Any, Optional, Tuple
# Configure logging
logger = logging.getLogger(__name__)
class DatabaseManager:
"""
Manages database operations for the EmotionMirror application.
This class handles all interactions with the SQLite database, providing
a clean interface for the rest of the application to store and retrieve data.
"""
def __init__(self, db_path: str):
"""
Initialize the database manager.
Args:
db_path: Path to the SQLite database file
"""
self.db_path = db_path
self._initialize_db()
def _initialize_db(self) -> None:
"""
Initialize the database by creating necessary tables if they don't exist.
"""
os.makedirs(os.path.dirname(self.db_path), exist_ok=True)
conn = None
try:
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
# Create analyses table
cursor.execute('''
CREATE TABLE IF NOT EXISTS analyses (
id INTEGER PRIMARY KEY AUTOINCREMENT,
session_id TEXT NOT NULL,
timestamp TEXT NOT NULL,
image_path TEXT NOT NULL,
face_count INTEGER NOT NULL,
results TEXT NOT NULL,
tags TEXT
)
''')
# Create faces table
cursor.execute('''
CREATE TABLE IF NOT EXISTS faces (
id INTEGER PRIMARY KEY AUTOINCREMENT,
analysis_id INTEGER NOT NULL,
face_index INTEGER NOT NULL,
emotion TEXT NOT NULL,
confidence REAL NOT NULL,
features TEXT NOT NULL,
emotions TEXT NOT NULL,
FOREIGN KEY (analysis_id) REFERENCES analyses (id)
)
''')
conn.commit()
logger.info("Database initialized successfully")
except sqlite3.Error as e:
logger.error(f"Database initialization error: {e}")
finally:
if conn:
conn.close()
def save_analysis(self,
session_id: str,
image_path: str,
results: Dict[str, Any],
tags: Optional[List[str]] = None) -> int:
"""
Save analysis results to the database.
Args:
session_id: Current session identifier
image_path: Path to the analyzed image
results: Analysis results dictionary
tags: Optional list of tags for the analysis
Returns:
The ID of the newly inserted analysis record
"""
conn = None
try:
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
# Convert tags list to JSON string if provided
tags_json = json.dumps(tags) if tags else None
# Insert analysis record
cursor.execute(
'''
INSERT INTO analyses
(session_id, timestamp, image_path, face_count, results, tags)
VALUES (?, ?, ?, ?, ?, ?)
''',
(
session_id,
datetime.now().isoformat(),
image_path,
results.get('face_count', 0),
json.dumps(results),
tags_json
)
)
analysis_id = cursor.lastrowid
# Insert face records if present
faces = results.get('faces', [])
for i, face in enumerate(faces):
cursor.execute(
'''
INSERT INTO faces
(analysis_id, face_index, emotion, confidence, features, emotions)
VALUES (?, ?, ?, ?, ?, ?)
''',
(
analysis_id,
i,
face.get('emotion', 'unknown'),
face.get('confidence', 0.0),
json.dumps(face.get('features', {})),
json.dumps(face.get('emotions', {}))
)
)
conn.commit()
logger.info(f"Saved analysis with ID {analysis_id} and {len(faces)} faces")
return analysis_id
except sqlite3.Error as e:
logger.error(f"Error saving analysis: {e}")
if conn:
conn.rollback()
return -1
finally:
if conn:
conn.close()
def get_analysis_history(self,
session_id: Optional[str] = None,
limit: int = 10) -> List[Dict[str, Any]]:
"""
Retrieve analysis history from the database.
Args:
session_id: Optional session ID to filter by
limit: Maximum number of records to return
Returns:
List of analysis records as dictionaries
"""
conn = None
try:
conn = sqlite3.connect(self.db_path)
conn.row_factory = sqlite3.Row # This enables name-based access to columns
cursor = conn.cursor()
query = '''
SELECT
id,
session_id,
timestamp,
image_path,
face_count,
results,
tags
FROM analyses
'''
params = []
if session_id:
query += ' WHERE session_id = ?'
params.append(session_id)
query += '''
ORDER BY timestamp DESC
LIMIT ?
'''
params.append(limit)
cursor.execute(query, params)
# Convert row objects to dictionaries
analyses = []
for row in cursor.fetchall():
analysis = dict(row)
# Parse JSON fields
analysis['results'] = json.loads(analysis['results'])
if analysis['tags']:
analysis['tags'] = json.loads(analysis['tags'])
# Get faces for this analysis
face_cursor = conn.cursor()
face_cursor.execute(
'''
SELECT
id,
face_index,
emotion,
confidence,
features,
emotions
FROM faces
WHERE analysis_id = ?
ORDER BY face_index
''',
(analysis['id'],)
)
faces = []
for face_row in face_cursor.fetchall():
face = dict(face_row)
# Parse JSON fields
face['features'] = json.loads(face['features'])
face['emotions'] = json.loads(face['emotions'])
faces.append(face)
analysis['faces'] = faces
analyses.append(analysis)
return analyses
except sqlite3.Error as e:
logger.error(f"Error retrieving analysis history: {e}")
return []
finally:
if conn:
conn.close()
def get_analysis_by_id(self, analysis_id: int) -> Optional[Dict[str, Any]]:
"""
Retrieve a specific analysis by ID.
Args:
analysis_id: ID of the analysis to retrieve
Returns:
Analysis record as a dictionary, or None if not found
"""
conn = None
try:
conn = sqlite3.connect(self.db_path)
conn.row_factory = sqlite3.Row
cursor = conn.cursor()
cursor.execute(
'''
SELECT
id,
session_id,
timestamp,
image_path,
face_count,
results,
tags
FROM analyses
WHERE id = ?
''',
(analysis_id,)
)
row = cursor.fetchone()
if not row:
return None
analysis = dict(row)
# Parse JSON fields
analysis['results'] = json.loads(analysis['results'])
if analysis['tags']:
analysis['tags'] = json.loads(analysis['tags'])
# Get faces for this analysis
face_cursor = conn.cursor()
face_cursor.execute(
'''
SELECT
id,
face_index,
emotion,
confidence,
features,
emotions
FROM faces
WHERE analysis_id = ?
ORDER BY face_index
''',
(analysis_id,)
)
faces = []
for face_row in face_cursor.fetchall():
face = dict(face_row)
# Parse JSON fields
face['features'] = json.loads(face['features'])
face['emotions'] = json.loads(face['emotions'])
faces.append(face)
analysis['faces'] = faces
return analysis
except sqlite3.Error as e:
logger.error(f"Error retrieving analysis by ID: {e}")
return None
finally:
if conn:
conn.close()
def get_emotion_statistics(self,
session_id: Optional[str] = None,
limit: int = 50) -> Dict[str, float]:
"""
Compute emotion statistics across multiple analyses.
Args:
session_id: Optional session ID to filter by
limit: Maximum number of records to analyze
Returns:
Dictionary of emotion frequencies
"""
conn = None
try:
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
query = '''
SELECT emotion, COUNT(*) as count
FROM faces
'''
params = []
if session_id:
query += '''
JOIN analyses ON faces.analysis_id = analyses.id
WHERE analyses.session_id = ?
'''
params.append(session_id)
query += '''
GROUP BY emotion
ORDER BY count DESC
'''
cursor.execute(query, params)
rows = cursor.fetchall()
# Calculate emotion frequencies
total = sum(count for _, count in rows)
stats = {emotion: count / total for emotion, count in rows} if total > 0 else {}
return stats
except sqlite3.Error as e:
logger.error(f"Error computing emotion statistics: {e}")
return {}
finally:
if conn:
conn.close()
def delete_analysis(self, analysis_id: int) -> bool:
"""
Delete an analysis and its associated faces.
Args:
analysis_id: ID of the analysis to delete
Returns:
True if deletion was successful, False otherwise
"""
conn = None
try:
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
# Delete associated faces first (due to foreign key constraint)
cursor.execute('DELETE FROM faces WHERE analysis_id = ?', (analysis_id,))
# Delete the analysis
cursor.execute('DELETE FROM analyses WHERE id = ?', (analysis_id,))
conn.commit()
return cursor.rowcount > 0
except sqlite3.Error as e:
logger.error(f"Error deleting analysis: {e}")
if conn:
conn.rollback()
return False
finally:
if conn:
conn.close()
def export_analysis_data(self,
analysis_id: Optional[int] = None,
session_id: Optional[str] = None,
limit: int = 100) -> Dict[str, Any]:
"""
Export analysis data in a structured format suitable for JSON/CSV export.
Args:
analysis_id: Optional specific analysis ID to export
session_id: Optional session ID to filter by
limit: Maximum number of records to export
Returns:
Dictionary containing the exported data
"""
if analysis_id:
# Export a single analysis
analysis = self.get_analysis_by_id(analysis_id)
if not analysis:
return {'error': f'Analysis with ID {analysis_id} not found'}
return {
'metadata': {
'exported_at': datetime.now().isoformat(),
'record_count': 1
},
'analyses': [analysis]
}
else:
# Export multiple analyses
analyses = self.get_analysis_history(session_id, limit)
return {
'metadata': {
'exported_at': datetime.now().isoformat(),
'record_count': len(analyses),
'session_id': session_id
},
'analyses': analyses
}
|