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| """
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| K1RL QUANT - Accuracy Tracking API
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| HuggingFace Spaces Edition
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| Standalone module to track training accuracy
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| """
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| import re
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| import json
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| import time
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| import os
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| from datetime import datetime
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| from collections import deque
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| from pathlib import Path
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| import threading
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| BASE_DIR = Path('/home/user/app')
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| DATA_DIR = BASE_DIR / 'data'
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| LOG_DIR = BASE_DIR / 'logs'
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| DATA_DIR.mkdir(parents=True, exist_ok=True)
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| LOG_DIR.mkdir(parents=True, exist_ok=True)
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| accuracy_history = deque(maxlen=1000)
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| accuracy_file = DATA_DIR / 'accuracy_data.json'
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| if accuracy_file.exists():
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| try:
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| with open(accuracy_file, 'r') as f:
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| data = json.load(f)
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| accuracy_history.extend(data)
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| print(f"✅ Loaded {len(accuracy_history)} accuracy readings")
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| except Exception as e:
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| print(f"⚠️ Could not load accuracy data: {e}")
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| def parse_accuracy_from_logs():
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| """Parse accuracy from quasar logs (HF Spaces compatible)"""
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| log_file = LOG_DIR / 'quasar_engine.log'
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| last_position = 0
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| print(f"🔍 Starting accuracy parser on {log_file}")
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|
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| while True:
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| try:
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|
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| if not log_file.exists():
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| print(f"⚠️ Log file not found: {log_file}")
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| time.sleep(10)
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| continue
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|
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| with open(log_file, 'r') as f:
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| f.seek(last_position)
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| new_lines = f.readlines()
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| last_position = f.tell()
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|
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| for line in new_lines:
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| patterns = [
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| r'🎯 Avg Accuracy: ([\d.]+)%',
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| r'AVN Accuracy: ([\d.]+)%?',
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| r'Avg Accuracy: ([\d.]+)%?',
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| r'accuracy[:\s]+([\d.]+)%?',
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| r'acc[:\s]+([\d.]+)%?'
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| ]
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| for pattern in patterns:
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| match = re.search(pattern, line, re.IGNORECASE)
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| if match:
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| accuracy = float(match.group(1))
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| if 0 <= accuracy <= 100:
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| timestamp = datetime.now().isoformat()
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|
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| data_point = {
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| 'timestamp': timestamp,
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| 'accuracy': accuracy,
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| 'iteration': len(accuracy_history) + 1
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| }
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| accuracy_history.append(data_point)
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| print(f"📈 Accuracy: {accuracy:.1f}% (iteration {data_point['iteration']})")
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| if len(accuracy_history) % 10 == 0:
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| save_accuracy_data()
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| break
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|
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| time.sleep(5)
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| except Exception as e:
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| print(f"⚠️ Error parsing logs: {e}")
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| time.sleep(10)
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|
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| def save_accuracy_data():
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| """Save accuracy data to file"""
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| try:
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| with open(accuracy_file, 'w') as f:
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| json.dump(list(accuracy_history), f, indent=2)
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| except Exception as e:
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| print(f"⚠️ Could not save accuracy data: {e}")
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|
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| def get_current_accuracy():
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| """Get current accuracy"""
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| if accuracy_history:
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| return accuracy_history[-1]
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| return {'accuracy': 0, 'timestamp': None, 'iteration': 0}
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|
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| def get_accuracy_history():
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| """Get all history"""
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| return list(accuracy_history)
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|
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| def get_accuracy_stats():
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| """Get comprehensive statistics"""
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| if not accuracy_history:
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| return {
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| 'current': 0,
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| 'average': 0,
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| 'min': 0,
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| 'max': 0,
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| 'count': 0,
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| 'last_10_avg': 0,
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| 'trend': 'unknown'
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| }
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| accuracies = [d['accuracy'] for d in accuracy_history]
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| trend = 'stable'
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| if len(accuracies) >= 20:
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| recent_avg = sum(accuracies[-10:]) / 10
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| older_avg = sum(accuracies[-20:-10]) / 10
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|
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| if recent_avg > older_avg * 1.02:
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| trend = 'up'
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| elif recent_avg < older_avg * 0.98:
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| trend = 'down'
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|
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| return {
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| 'current': accuracies[-1],
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| 'average': sum(accuracies) / len(accuracies),
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| 'min': min(accuracies),
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| 'max': max(accuracies),
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| 'count': len(accuracies),
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| 'last_10_avg': sum(accuracies[-10:]) / min(10, len(accuracies)),
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| 'trend': trend,
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| 'std_dev': (sum((x - sum(accuracies)/len(accuracies))**2 for x in accuracies) / len(accuracies))**0.5,
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| 'latest_timestamp': accuracy_history[-1]['timestamp'] if accuracy_history else None
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| }
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|
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| def get_accuracy_by_timerange(hours=24):
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| """Get accuracy data for specific time range"""
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| if not accuracy_history:
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| return []
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| cutoff_time = datetime.now().timestamp() - (hours * 3600)
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|
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| filtered = []
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| for entry in accuracy_history:
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| try:
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| entry_time = datetime.fromisoformat(entry['timestamp']).timestamp()
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| if entry_time >= cutoff_time:
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| filtered.append(entry)
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| except:
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| filtered.append(entry)
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| return filtered
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|
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| def get_parser_status():
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| """Get parser health status"""
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| log_file = LOG_DIR / 'quasar_engine.log'
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|
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| status = {
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| 'running': parser_thread.is_alive() if 'parser_thread' in globals() else False,
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| 'log_file_exists': log_file.exists(),
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| 'log_file_path': str(log_file),
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| 'total_readings': len(accuracy_history),
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| 'last_reading': accuracy_history[-1] if accuracy_history else None,
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| 'data_file_exists': accuracy_file.exists(),
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| 'data_file_path': str(accuracy_file)
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| }
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|
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| return status
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|
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|
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| print("🚀 Starting accuracy parser thread...")
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| parser_thread = threading.Thread(target=parse_accuracy_from_logs, daemon=True)
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| parser_thread.start()
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|
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| import atexit
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|
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| def cleanup():
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| """Save data on shutdown"""
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| print("💾 Saving accuracy data on shutdown...")
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| save_accuracy_data()
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|
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| atexit.register(cleanup)
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|
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| if __name__ == "__main__":
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| print("=" * 60)
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| print("K1RL QUANT - Accuracy Tracker")
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| print("HuggingFace Spaces Edition 🤗")
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| print("=" * 60)
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| print(f"📁 Data directory: {DATA_DIR}")
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| print(f"📁 Log directory: {LOG_DIR}")
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| print(f"📊 Loaded {len(accuracy_history)} existing readings")
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| print("🔄 Parser running in background...")
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| print("=" * 60)
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| try:
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| while True:
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| time.sleep(60)
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| if len(accuracy_history) > 0:
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| latest = accuracy_history[-1]
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| print(f"📈 Latest accuracy: {latest['accuracy']:.1f}% ({latest['timestamp']})")
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| except KeyboardInterrupt:
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| print("🛑 Shutting down accuracy tracker...")
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| cleanup()
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