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import time
from typing import Any
from typing import List
from typing import Dict
from typing import Tuple
from pathlib import Path
from typing import Callable
from collections import Counter
from utils.logger import get_logger
from config.settings import settings
from config.schemas import AnalysisResult
from config.constants import FinalDecision
from concurrent.futures import TimeoutError
from concurrent.futures import as_completed
from config.constants import DetectionStatus
from config.schemas import BatchAnalysisResult
from concurrent.futures import ThreadPoolExecutor
from metrics.signal_aggregator import SignalAggregator
from features.threshold_manager import ThresholdManager
from decision_builders.decision_policy import DecisionPolicy
from evidence_analyzers.evidence_aggregator import EvidenceAggregator
# Setup Logging
logger = get_logger(__name__)
class BatchProcessor:
"""
Process multiple images in parallel or sequential mode
Features:
---------
- Parallel processing using ThreadPoolExecutor
- Sequential fallback for single images or disabled parallel mode
- Automatic error handling and recovery
- Progress tracking and logging
"""
def __init__(self, threshold_manager: ThresholdManager):
"""
Initialize Batch Processor
"""
# Instantiate threshold manager
self.threshold_manager = threshold_manager
# Initialize signal aggregators
self.aggregator = SignalAggregator(threshold_manager = threshold_manager)
# Initialize evidence-based aggregator
self.evidence_aggregator = EvidenceAggregator()
# Initialize decision-policy engine
self.decision_policy = DecisionPolicy()
# Fix number of workers
self.max_workers = settings.MAX_WORKERS if settings.PARALLEL_PROCESSING else 1
logger.info(f"BatchProcessor initialized with max_workers={self.max_workers}, parallel={settings.PARALLEL_PROCESSING}")
def process_batch(self, image_files: List[Dict[str, Any]], on_progress: Callable[[int, int, str], None] | None = None) -> BatchAnalysisResult:
"""
Process multiple images with automatic parallel/sequential switching
Arguments:
----------
image_files { list } : List of dicts with keys:
- 'path' : Path object
- 'filename' : str
- 'size' : tuple (width, height)
on_progress { Callablel } : Optional callback invoked after each image is processed
Returns:
--------
{ BatchAnalysisResult } : Complete batch analysis result
"""
start_time = time.time()
total_images = len(image_files)
logger.info(f"Starting batch processing of {total_images} images")
# Validate input
if (total_images == 0):
logger.warning("Empty batch provided")
return self._create_empty_batch_result()
if (total_images > settings.MAX_BATCH_SIZE):
logger.error(f"Batch size {total_images} exceeds maximum {settings.MAX_BATCH_SIZE}")
raise ValueError(f"Batch size {total_images} exceeds maximum allowed {settings.MAX_BATCH_SIZE}")
# Choose processing strategy
if (settings.PARALLEL_PROCESSING and (total_images > 1)):
results, failed = self._process_parallel(image_files = image_files,
on_progress = on_progress,
)
else:
results, failed = self._process_sequential(image_files = image_files,
on_progress = on_progress,
)
total_time = time.time() - start_time
# Create batch result
batch_result = BatchAnalysisResult(total_images = total_images,
processed = len(results),
failed = failed,
results = results,
total_processing_time = total_time,
)
# Calculate summary statistics
batch_result.summary = self._calculate_summary(results = results,
total = total_images,
)
logger.info(f"Batch processing complete: {len(results)}/{total_images} successful, {failed} failed in {total_time:.2f}s")
return batch_result
def _process_parallel(self, image_files: List[Dict], on_progress: Callable[[int, int, str], None] | None = None) -> Tuple[List[AnalysisResult], int]:
"""
Process images in parallel using ThreadPoolExecutor
Arguments:
----------
image_files { list } : List of image file dictionaries
on_progress { Callablel } : Optional callback invoked after each image is processed
Returns:
--------
{ tuple } : (results_list, failed_count)
"""
results = list()
failed = 0
logger.debug(f"Using parallel processing with {self.max_workers} workers")
with ThreadPoolExecutor(max_workers = self.max_workers) as executor:
# Submit all tasks
future_to_file = {executor.submit(self.process_single,
image['path'],
image['filename'],
image['size'],
): image for image in image_files
}
# Collect results as they complete
completed = 0
for future in as_completed(future_to_file):
completed += 1
image = future_to_file[future]
if on_progress:
on_progress(completed, len(image_files), image["filename"])
try:
result = future.result(timeout = settings.PROCESSING_TIMEOUT)
if result:
results.append(result)
logger.debug(f"✓ Completed: {image['filename']}")
else:
failed += 1
logger.warning(f"✗ Failed: {image['filename']} (returned None)")
except TimeoutError:
failed += 1
logger.error(f"✗ Timeout: {image['filename']} (exceeded {settings.PROCESSING_TIMEOUT}s)")
except Exception as e:
failed += 1
logger.error(f"✗ Error: {image['filename']} - {e}")
return results, failed
def _process_sequential(self, image_files: List[Dict], on_progress: Callable[[int, int, str], None] | None = None) -> Tuple[List[AnalysisResult], int]:
"""
Process images sequentially (fallback or single image)
Arguments:
----------
image_files { list } : List of image file dictionaries
on_progress { Callabel } : Optional callback invoked after each image is processed
Returns:
--------
{ tuple } : (results_list, failed_count)
"""
results = list()
failed = 0
logger.debug("Using sequential processing")
for idx, image in enumerate(image_files, 1):
try:
if on_progress:
on_progress(idx, len(image_files), image["filename"])
result = self.process_single(image_path = image['path'],
filename = image['filename'],
image_size = image['size'],
)
if result:
results.append(result)
logger.debug(f"✓ Completed: {image['filename']}")
else:
failed += 1
logger.warning(f"✗ Failed: {image['filename']} (returned None)")
except Exception as e:
failed += 1
logger.error(f"✗ Error: {image['filename']} - {e}")
return results, failed
def process_single(self, image_path: Path, filename: str, image_size: Tuple[int, int]) -> AnalysisResult:
"""
Process single image (called by both parallel and sequential)
Arguments:
----------
image_path { Path } : Path to image file
filename { str } : Original filename
image_size { tuple } : (width, height)
Returns:
--------
{ AnalysisResult } : Analysis result or None on error
"""
try:
# Tier-1 Signal
analysis = self.aggregator.analyze_image(image_path = image_path,
filename = filename,
image_size = image_size,
)
# Tier-2 evidence
analysis.evidence = self.evidence_aggregator.analyze(image_path = image_path)
# Final decision
final_analysis_result = self.decision_policy.apply(analysis = analysis)
return final_analysis_result
except Exception as e:
logger.error(f"Failed to process {filename}: {e}", exc_info = True)
return None
def _calculate_summary(self, results: List[AnalysisResult], total: int) -> Dict[str, Any]:
"""
Calculate summary statistics from results
Arguments:
----------
results { list } : List of analysis results
total { int } : Total number of images
Returns:
--------
{ dict } : Summary statistics
"""
# Calculate processing stats
processed = len(results)
failed = total - processed
success_rate = int((processed / total * 100) if total > 0 else 0)
# Count final decisions safely
decision_counts = Counter(result.final_decision.value for result in results)
# Calculate average scores
avg_score = sum(r.overall_score for r in results) / processed if results else 0.0
avg_confidence = sum(r.confidence for r in results) / processed if results else 0
avg_proc_time = sum(r.processing_time for r in results) / processed if results else 0.0
# Final decision distribution
decision_distribution = {FinalDecision.CONFIRMED_AI_GENERATED.value : decision_counts.get(FinalDecision.CONFIRMED_AI_GENERATED.value, 0),
FinalDecision.SUSPICIOUS_AI_LIKELY.value : decision_counts.get(FinalDecision.SUSPICIOUS_AI_LIKELY.value, 0),
FinalDecision.AUTHENTIC_BUT_REVIEW.value : decision_counts.get(FinalDecision.AUTHENTIC_BUT_REVIEW.value, 0),
FinalDecision.MOSTLY_AUTHENTIC.value : decision_counts.get(FinalDecision.MOSTLY_AUTHENTIC.value, 0),
}
summary = {"processed" : processed,
"failed" : failed,
"success_rate" : success_rate,
"avg_score" : round(avg_score, 3),
"avg_confidence" : int(avg_confidence),
"avg_proc_time" : round(avg_proc_time, 2),
}
# Update summary dictb with decision_distriubution dict
summary.update(decision_distribution)
return summary
def _create_empty_batch_result(self) -> BatchAnalysisResult:
"""
Create empty batch result for edge cases
Returns:
--------
{ BatchAnalysisResult } : Empty batch result
"""
return BatchAnalysisResult(total_images = 0,
processed = 0,
failed = 0,
results = [],
summary = {FinalDecision.CONFIRMED_AI_GENERATED.value : 0,
FinalDecision.SUSPICIOUS_AI_LIKELY.value : 0,
FinalDecision.AUTHENTIC_BUT_REVIEW.value : 0,
FinalDecision.MOSTLY_AUTHENTIC.value : 0,
"success_rate" : 0,
},
total_processing_time = 0.0,
)
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