Initial commit — Serveur Lucas SmartEye
API réception alertes chute (SmartEye/YOLO), analyse IA (Gemini 2.5 Flash), gestion alertes avec escalade (watchdog), notifications Firebase, dashboard web, documentation MkDocs. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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266
analyze_errors_and_learn.py
Executable file
266
analyze_errors_and_learn.py
Executable file
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#!/usr/bin/env python3
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"""
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Système d'apprentissage des erreurs
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Analyse les logs d'erreurs pour identifier les patterns et proposer des améliorations
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"""
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import os
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import sys
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import json
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import re
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from collections import Counter, defaultdict
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from datetime import datetime
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def analyze_error_logs():
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"""Analyse les logs d'erreurs Gemini"""
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error_log = "/tmp/smarteye_gemini_errors.log"
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if not os.path.exists(error_log):
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return {
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"status": "no_errors",
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"message": "Aucun log d'erreur trouvé - Système sain !"
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}
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with open(error_log, 'r') as f:
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content = f.read()
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# Patterns à détecter
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patterns = {
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"JSONDecodeError": r"JSONDecodeError",
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"ValueError": r"ValueError",
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"TimeoutError": r"TimeoutError",
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"ConnectionError": r"ConnectionError",
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"API quota": r"quota|rate.?limit",
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"Empty response": r"Réponse vide|empty response",
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"Malformed JSON": r"JSON invalide|malformed",
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}
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# Comptage des erreurs
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error_counts = {}
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for name, pattern in patterns.items():
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matches = re.findall(pattern, content, re.IGNORECASE)
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if matches:
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error_counts[name] = len(matches)
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# Extraction des tentatives
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tentative_pattern = r"TENTATIVE (\d)/(\d)"
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tentatives = re.findall(tentative_pattern, content)
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# Statistiques
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total_errors = sum(error_counts.values())
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retry_success = len([t for t in tentatives if t[0] != t[1]])
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return {
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"status": "analyzed",
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"total_errors": total_errors,
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"error_counts": error_counts,
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"retry_attempts": len(tentatives),
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"retry_success_rate": (retry_success / len(tentatives) * 100) if tentatives else 0,
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"most_common": max(error_counts.items(), key=lambda x: x[1])[0] if error_counts else None
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}
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def analyze_image_results():
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"""Analyse les résultats d'analyse des images"""
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client_folder = "/var/www/lucas/clients/Demo_01"
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if not os.path.exists(client_folder):
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return None
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# Statistiques des analyses
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total_images = 0
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with_json = 0
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urgence_count = 0
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false_positive_count = 0
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confidence_scores = []
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messages = defaultdict(int)
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for filename in os.listdir(client_folder):
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if filename.endswith('.jpg'):
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total_images += 1
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json_file = os.path.join(client_folder, filename + '.json')
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if os.path.exists(json_file):
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with_json += 1
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try:
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with open(json_file, 'r') as f:
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data = json.load(f)
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if data.get('urgence') is True:
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urgence_count += 1
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elif data.get('urgence') is False:
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false_positive_count += 1
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if 'confiance' in data:
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confidence_scores.append(data['confiance'])
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msg = data.get('message', 'Unknown')[:50]
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messages[msg] += 1
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except:
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pass
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avg_confidence = sum(confidence_scores) / len(confidence_scores) if confidence_scores else 0
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return {
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"total_images": total_images,
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"analyzed": with_json,
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"coverage": (with_json / total_images * 100) if total_images else 0,
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"urgence_rate": (urgence_count / with_json * 100) if with_json else 0,
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"false_positive_rate": (false_positive_count / with_json * 100) if with_json else 0,
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"avg_confidence": avg_confidence,
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"top_messages": dict(sorted(messages.items(), key=lambda x: x[1], reverse=True)[:5])
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}
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def generate_recommendations(error_analysis, image_analysis):
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"""Génère des recommandations basées sur l'analyse"""
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recommendations = []
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if not error_analysis or error_analysis["status"] == "no_errors":
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recommendations.append({
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"priority": "✅ SUCCESS",
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"title": "Système stable",
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"description": "Aucune erreur détectée - Le correctif fonctionne parfaitement !",
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"action": None
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})
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else:
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# Analyse des erreurs
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if error_analysis["total_errors"] > 10:
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recommendations.append({
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"priority": "⚠️ MOYEN",
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"title": f"{error_analysis['total_errors']} erreurs détectées",
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"description": "Taux d'erreur encore élevé",
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"action": "Vérifier la clé API Gemini et les quotas"
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})
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if error_analysis.get("most_common") == "TimeoutError":
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recommendations.append({
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"priority": "⚠️ MOYEN",
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"title": "Timeouts fréquents",
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"description": "L'API Gemini répond lentement",
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"action": "Augmenter le timeout à 60s dans analyze.py"
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})
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if error_analysis.get("retry_success_rate", 0) < 50:
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recommendations.append({
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"priority": "🔴 URGENT",
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"title": "Faible taux de succès des retries",
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"description": f"Seulement {error_analysis['retry_success_rate']:.1f}% de succès",
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"action": "Augmenter max_retries à 5 ou vérifier la connexion"
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})
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if image_analysis:
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# Analyse des images
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if image_analysis["coverage"] < 95:
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recommendations.append({
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"priority": "ℹ️ INFO",
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"title": f"Couverture: {image_analysis['coverage']:.1f}%",
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"description": f"{image_analysis['total_images'] - image_analysis['analyzed']} images sans analyse",
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"action": "Exécuter repair_missing_analyses.py si besoin"
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})
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if image_analysis["urgence_rate"] > 50:
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recommendations.append({
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"priority": "⚠️ MOYEN",
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"title": f"Taux d'urgence élevé: {image_analysis['urgence_rate']:.1f}%",
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"description": "Beaucoup de vraies alertes détectées",
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"action": "Vérifier le positionnement des caméras et le prompt IA"
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})
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if image_analysis["avg_confidence"] < 70:
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recommendations.append({
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"priority": "ℹ️ INFO",
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"title": f"Confiance moyenne: {image_analysis['avg_confidence']:.0f}%",
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"description": "L'IA hésite sur certaines analyses",
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"action": "Améliorer le prompt ou utiliser un modèle plus puissant"
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})
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if not recommendations:
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recommendations.append({
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"priority": "✅ SUCCESS",
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"title": "Système optimal",
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"description": "Aucune amélioration nécessaire",
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"action": None
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})
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return recommendations
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def main():
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print("="*70)
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print("ANALYSE DES ERREURS ET APPRENTISSAGE")
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print("="*70)
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print()
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# Analyse des erreurs
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print("📊 ANALYSE DES LOGS D'ERREURS")
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print("-"*70)
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error_analysis = analyze_error_logs()
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if error_analysis["status"] == "no_errors":
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print("✅ Aucune erreur trouvée - Système parfaitement stable !")
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else:
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print(f"Total erreurs: {error_analysis['total_errors']}")
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print(f"Tentatives de retry: {error_analysis['retry_attempts']}")
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print(f"Taux de succès retry: {error_analysis.get('retry_success_rate', 0):.1f}%")
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print()
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print("Types d'erreurs:")
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for error_type, count in sorted(error_analysis['error_counts'].items(), key=lambda x: x[1], reverse=True):
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print(f" • {error_type}: {count}")
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print()
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print("📸 ANALYSE DES IMAGES")
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print("-"*70)
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image_analysis = analyze_image_results()
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if image_analysis:
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print(f"Total images: {image_analysis['total_images']}")
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print(f"Analysées: {image_analysis['analyzed']} ({image_analysis['coverage']:.1f}%)")
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print(f"Taux d'urgence: {image_analysis['urgence_rate']:.1f}%")
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print(f"Fausses alertes: {image_analysis['false_positive_rate']:.1f}%")
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print(f"Confiance moyenne: {image_analysis['avg_confidence']:.0f}%")
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print()
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print("Messages les plus fréquents:")
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for msg, count in list(image_analysis['top_messages'].items())[:3]:
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print(f" • {msg}: {count}x")
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else:
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print("⚠️ Dossier client introuvable")
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print()
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print("💡 RECOMMANDATIONS")
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print("="*70)
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recommendations = generate_recommendations(error_analysis, image_analysis)
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for i, rec in enumerate(recommendations, 1):
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print(f"\n{rec['priority']} {rec['title']}")
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print(f" {rec['description']}")
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if rec['action']:
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print(f" → Action: {rec['action']}")
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print()
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print("="*70)
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# Sauvegarde du rapport
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report = {
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"timestamp": datetime.now().isoformat(),
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"error_analysis": error_analysis,
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"image_analysis": image_analysis,
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"recommendations": recommendations
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}
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report_file = "/var/www/lucas/last_analysis_report.json"
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with open(report_file, 'w') as f:
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json.dump(report, f, indent=2)
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print(f"📄 Rapport sauvegardé: {report_file}")
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print()
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if __name__ == "__main__":
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try:
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main()
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except KeyboardInterrupt:
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print("\n\n⚠️ Interruption")
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sys.exit(1)
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except Exception as e:
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print(f"\n❌ Erreur: {type(e).__name__}: {e}")
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import traceback
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traceback.print_exc()
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sys.exit(1)
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