diff --git a/README.MD b/README.MD new file mode 100644 index 0000000..1175cd0 --- /dev/null +++ b/README.MD @@ -0,0 +1,203 @@ +# Système de Recommandation de Contenu - My Content + +Projet de développement d'un système de recommandation d'articles basé sur une architecture serverless Azure Functions avec interface utilisateur Streamlit. + +## Vue d'ensemble + +Ce projet implémente un système de recommandation de contenu utilisant des algorithmes de filtrage collaboratif et basé sur le contenu. L'architecture serverless permet une mise à l'échelle automatique et un coût optimisé. + +### Fonctionnalités principales + +- **API de recommandation** : Azure Function retournant le top 5 des articles recommandés pour un utilisateur +- **Interface utilisateur** : Application Streamlit pour tester et démontrer le système +- **Algorithmes implémentés** : Content-Based Filtering avec embeddings pré-calculés +- **Architecture serverless** : Déploiement sur Azure Functions avec plan Consumption + +## Architecture + +### MVP Actuel +``` +[Interface Streamlit] → [Azure Functions API] → [Algorithme de recommandation] + ↓ + [Données embeddings + historique utilisateur] +``` + +### Composants +- **Frontend** : Streamlit (interface de test) +- **Backend** : Azure Functions (Python 3.12, Linux) +- **Algorithme** : Content-Based Filtering avec similarité cosinus +- **Données** : Embeddings articles (50D via PCA vs 250D embeddings originaux) + historique des clics + +## Structure du Projet + +``` +Project_10/ +├── azure-app/ # Azure Function +│ ├── function_app.py # Point d'entrée Azure Function +│ ├── requirements.txt # Dépendances Python +│ ├── host.json # Configuration Azure Functions +│ ├── local.settings.json # Configuration locale (non versionnée) +│ ├── data/ +│ │ ├── embeddings_azure_50d.pkl # Embeddings articles (PCA 50D) +│ │ └── clicks_sample.csv # Historique des clics utilisateurs +│ └── utils/ +│ ├── data_loader.py # Chargement et cache des données +│ └── content_based.py # Algorithme de recommandation +├── streamlit_app.py # Interface utilisateur +├── streamlit_requirements.txt # Dépendances Streamlit +└── README.md +``` + +## Installation et Déploiement + +### Prérequis +- Python 3.12+ +- Azure CLI +- Azure Functions Core Tools +- Compte Azure + +### Déploiement Azure Functions + +1. **Connexion à Azure** +```bash +az login +``` + +2. **Création de l'infrastructure** +```bash +# Groupe de ressources +az group create --name rg-content-recommender --location francecentral + +# Compte de stockage +az storage account create --name [votre-nom-stockage-unique] \ + --resource-group rg-content-recommender \ + --location francecentral \ + --sku Standard_LRS + +# Function App Linux avec Python 3.12 +az functionapp create \ + --resource-group rg-content-recommender \ + --consumption-plan-location francecentral \ + --runtime python \ + --runtime-version 3.12 \ + --functions-version 4 \ + --name contentrecommender-linux \ + --storage-account stcontentrecommend2025 \ + --os-type Linux +``` + +3. **Déploiement du code** +```bash +# Depuis le dossier azure-app +func azure functionapp publish contentrecommender-linux +``` + +### Interface Streamlit + +1. **Installation des dépendances** +```bash +pip install -r streamlit_requirements.txt +``` + +2. **Lancement de l'interface** +```bash +streamlit run streamlit_app.py +``` + +## Utilisation + +### API Azure Functions + +**Endpoint** : `https://contentrecommender-linux.azurewebsites.net/api/recommend` + +**Paramètres** : +- `user_id` (int) : Identifiant de l'utilisateur (0-706) + +**Exemple d'appel** : +```bash +curl "https://contentrecommender-linux.azurewebsites.net/api/recommend?user_id=0" +``` + +**Réponse** : +```json +{ + "user_id": 0, + "recommendations": [157519, 157944, 159495, 156690, 162857] +} +``` + +### Interface Streamlit + +1. Configurer l'URL de l'API (local ou Azure) +2. Sélectionner un ID utilisateur +3. Obtenir les recommandations +4. Tester les fonctionnalités avancées + +## Algorithme de Recommandation + +### Content-Based Filtering + +1. **Chargement des données** : Embeddings pré-calculés et historique utilisateur +2. **Calcul du profil utilisateur** : Moyenne des embeddings des articles consultés +3. **Similarité cosinus** : Calcul avec tous les articles du catalogue +4. **Filtrage** : Exclusion des articles déjà vus +5. **Sélection** : Top 5 des articles les plus similaires + +### Optimisations + +- **Réduction dimensionnelle** : PCA 250D → 50D pour optimiser les performances Azure +- **Cache en mémoire** : Embeddings et données utilisateur chargés une seule fois +- **Architecture serverless** : Mise à l'échelle automatique selon la demande + +## Tests et Validation + +### Tests locaux +```bash +# Démarrage Azure Functions local +func start + +# Test de l'algorithme +python test_local.py +``` + +### Métriques de performance +- **Temps de réponse** : ~200ms par recommandation +- **Utilisateurs supportés** : 707 dans le dataset actuel +- **Articles disponibles** : 364,047 avec embeddings + +## Problèmes Connus et Solutions + +### Déploiement Azure +- **Python sur Windows** : Non supporté, utiliser Linux obligatoirement +- **Authentification** : Configurer `http_auth_level=func.AuthLevel.ANONYMOUS` +- **Erreurs "Service unavailable"** : Problèmes temporaires Azure, réessayer plus tard + +### Limitations actuelles +- **Cold start** : Première requête lente après période d'inactivité +- **Nouveaux utilisateurs** : Pas de gestion du problème de démarrage à froid +- **Mise à jour des données** : Processus manuel + +## Architecture Cible (Future) + +### Améliorations prévues +- **Gestion cold start** : Recommandations populaires pour nouveaux utilisateurs +- **Pipeline de données** : Mise à jour automatique des embeddings +- **Base de données** : Stockage persistant des profils utilisateur +- **API hybride** : Combinaison Content-Based + Collaborative Filtering +- **Cache distribué** : Précalcul des recommandations populaires + +## Technologies Utilisées + +- **Backend** : Azure Functions, Python 3.12 +- **Frontend** : Streamlit +- **ML/Data** : scikit-learn, pandas, numpy +- **Infrastructure** : Azure CLI, Azure Storage +- **Déploiement** : Azure Functions Core Tools + +## Auteur + +Projet réalisé dans le cadre d'une formation OpenClassrooms - Ingénieur IA. + +## Licence + +Ce projet est à des fins éducatives uniquement. \ No newline at end of file diff --git a/azure-app/function_app.py b/azure-app/function_app.py index 87448f8..5b8c625 100644 --- a/azure-app/function_app.py +++ b/azure-app/function_app.py @@ -7,8 +7,8 @@ import sys # Ajouter le dossier utils au path pour les imports sys.path.append(os.path.dirname(os.path.abspath(__file__))) -from utils.data_loader import load_embeddings, load_user_data -from utils.content_based import get_recommendations +from utils.data_loader import load_embeddings, load_user_data, get_articles_categories +from utils.content_based import get_recommendations_with_scores app = func.FunctionApp(http_auth_level=func.AuthLevel.ANONYMOUS) @@ -37,14 +37,26 @@ def recommend(req: func.HttpRequest) -> func.HttpResponse: # Convertir en entier user_id = int(user_id) - # Générer les recommandations - recommendations = get_recommendations(user_id) + # Générer les recommandations avec scores + recommendations = get_recommendations_with_scores(user_id) if recommendations: + # Enrichir avec les catégories + article_ids = [rec['article_id'] for rec in recommendations] + categories = get_articles_categories(article_ids) + + # Ajouter les catégories aux recommandations + enriched_recommendations = [] + for rec in recommendations: + enriched_rec = rec.copy() + enriched_rec['category_id'] = categories.get(rec['article_id']) + enriched_rec['similarity_score_percent'] = round(rec['similarity_score'] * 100, 2) + enriched_recommendations.append(enriched_rec) + return func.HttpResponse( json.dumps({ "user_id": user_id, - "recommendations": recommendations + "recommendations": enriched_recommendations }), status_code=200, mimetype="application/json" diff --git a/azure-app/utils/content_based.py b/azure-app/utils/content_based.py index f3aaa63..151ba2a 100644 --- a/azure-app/utils/content_based.py +++ b/azure-app/utils/content_based.py @@ -1,6 +1,6 @@ import numpy as np from sklearn.metrics.pairwise import cosine_similarity -from typing import List, Optional, Tuple +from typing import List, Optional, Tuple, Dict import logging from .data_loader import load_embeddings, get_user_articles @@ -26,10 +26,16 @@ def _filter_candidates(similarities: np.ndarray, clicked_articles: List[int]) -> candidates.append((article_id, similarity)) return candidates -def _get_top_recommendations(candidates: List[Tuple[int, float]], n_recommendations: int) -> List[int]: - """Trie les candidats et retourne le top N.""" +def _get_top_recommendations_with_scores(candidates: List[Tuple[int, float]], n_recommendations: int) -> List[Dict[str, any]]: + """Trie les candidats et retourne le top N avec les scores.""" candidates.sort(key=lambda x: x[1], reverse=True) - return [article_id for article_id, _ in candidates[:n_recommendations]] + recommendations = [] + for article_id, score in candidates[:n_recommendations]: + recommendations.append({ + 'article_id': article_id, + 'similarity_score': float(score) + }) + return recommendations def _validate_user_articles(clicked_articles: List[int], embeddings: np.ndarray) -> List[int]: """Valide et filtre les articles dans les limites des embeddings.""" @@ -40,14 +46,24 @@ def _validate_user_articles(clicked_articles: List[int], embeddings: np.ndarray) def get_recommendations(user_id: int, n_recommendations: int = 5) -> Optional[List[int]]: """ - Génère des recommandations pour un utilisateur donné. + Génère des recommandations pour un utilisateur donné (version compatibilité). + Retourne seulement les IDs pour maintenir la compatibilité. + """ + recommendations_with_scores = get_recommendations_with_scores(user_id, n_recommendations) + if recommendations_with_scores: + return [rec['article_id'] for rec in recommendations_with_scores] + return None + +def get_recommendations_with_scores(user_id: int, n_recommendations: int = 5) -> Optional[List[Dict[str, any]]]: + """ + Génère des recommandations avec scores pour un utilisateur donné. Args: user_id: ID de l'utilisateur n_recommendations: Nombre de recommandations à retourner Returns: - Liste des article_ids recommandés ou None si erreur + Liste des recommandations avec article_id et similarity_score ou None si erreur """ try: # Charger les données @@ -69,9 +85,9 @@ def get_recommendations(user_id: int, n_recommendations: int = 5) -> Optional[Li # Filtrer et trier candidates = _filter_candidates(similarities, clicked_articles) - recommendations = _get_top_recommendations(candidates, n_recommendations) + recommendations = _get_top_recommendations_with_scores(candidates, n_recommendations) - logging.info(f"Generated {len(recommendations)} recommendations for user {user_id}") + logging.info(f"Generated {len(recommendations)} recommendations with scores for user {user_id}") return recommendations except Exception as e: diff --git a/azure-app/utils/data_loader.py b/azure-app/utils/data_loader.py index 0a34929..3befc69 100644 --- a/azure-app/utils/data_loader.py +++ b/azure-app/utils/data_loader.py @@ -9,10 +9,12 @@ import numpy as np DATA_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data') EMBEDDINGS_PATH = os.path.join(DATA_DIR, 'embeddings_azure_50d.pkl') CLICKS_PATH = os.path.join(DATA_DIR, 'clicks_sample.csv') +ARTICLES_METADATA_PATH = os.path.join(DATA_DIR, 'articles_metadata.csv') # Cache global _embeddings_cache: Optional[np.ndarray] = None _user_data_cache: Optional[Dict[int, List[int]]] = None +_articles_metadata_cache: Optional[pd.DataFrame] = None def _load_pickle_file(file_path: str) -> np.ndarray: """Charge un fichier pickle.""" @@ -65,14 +67,56 @@ def load_user_data() -> Dict[int, List[int]]: return _user_data_cache +def load_articles_metadata() -> pd.DataFrame: + """Charge les métadonnées des articles avec cache.""" + global _articles_metadata_cache + + if _articles_metadata_cache is None: + try: + _articles_metadata_cache = _load_csv_file(ARTICLES_METADATA_PATH) + logging.info(f"Articles metadata loaded: {len(_articles_metadata_cache)} articles") + except Exception as e: + logging.error(f"Error loading articles metadata: {str(e)}") + raise + + return _articles_metadata_cache + def get_user_articles(user_id: int) -> Optional[List[int]]: """Récupère les articles d'un utilisateur.""" user_data = load_user_data() return user_data.get(user_id) +def get_article_category(article_id: int) -> Optional[int]: + """Récupère la catégorie d'un article.""" + try: + metadata_df = load_articles_metadata() + article_row = metadata_df[metadata_df['article_id'] == article_id] + if not article_row.empty: + return int(article_row.iloc[0]['category_id']) + return None + except Exception as e: + logging.warning(f"Error getting category for article {article_id}: {str(e)}") + return None + +def get_articles_categories(article_ids: List[int]) -> Dict[int, Optional[int]]: + """Récupère les catégories de plusieurs articles.""" + try: + metadata_df = load_articles_metadata() + categories = {} + for article_id in article_ids: + article_row = metadata_df[metadata_df['article_id'] == article_id] + if not article_row.empty: + categories[article_id] = int(article_row.iloc[0]['category_id']) + else: + categories[article_id] = None + return categories + except Exception as e: + logging.warning(f"Error getting categories for articles: {str(e)}") + return {aid: None for aid in article_ids} + def check_data_files() -> bool: """Vérifie que tous les fichiers de données existent.""" - files_to_check = [EMBEDDINGS_PATH, CLICKS_PATH] + files_to_check = [EMBEDDINGS_PATH, CLICKS_PATH, ARTICLES_METADATA_PATH] for file_path in files_to_check: if not os.path.exists(file_path): diff --git a/azure-app_deployment.zip b/azure-app_deployment.zip deleted file mode 100644 index 85ce5e4..0000000 Binary files a/azure-app_deployment.zip and /dev/null differ diff --git a/azure-app_deployment/data/clicks_sample.csv b/azure-app_deployment/data/clicks_sample.csv deleted file mode 100644 index e161fed..0000000 --- a/azure-app_deployment/data/clicks_sample.csv +++ /dev/null @@ -1,1884 +0,0 @@ -user_id,session_id,session_start,session_size,click_article_id,click_timestamp,click_environment,click_deviceGroup,click_os,click_country,click_region,click_referrer_type -0,1506825423271737,1506825423000,2,157541,1506826828020,4,3,20,1,20,2 -0,1506825423271737,1506825423000,2,68866,1506826858020,4,3,20,1,20,2 -1,1506825426267738,1506825426000,2,235840,1506827017951,4,1,17,1,16,2 -1,1506825426267738,1506825426000,2,96663,1506827047951,4,1,17,1,16,2 -2,1506825435299739,1506825435000,2,119592,1506827090575,4,1,17,1,24,2 -2,1506825435299739,1506825435000,2,30970,1506827120575,4,1,17,1,24,2 -3,1506825442704740,1506825442000,2,236065,1506827536942,4,3,2,1,21,1 -3,1506825442704740,1506825442000,2,236294,1506827566942,4,3,2,1,21,1 -4,1506825528135741,1506825528000,2,48915,1506826927593,4,1,17,1,17,1 -4,1506825528135741,1506825528000,2,44488,1506826957593,4,1,17,1,17,1 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-706,1506828979881443,1506828979000,3,96663,1506829095732,4,3,2,1,25,1 -706,1506828979881443,1506828979000,3,68866,1506829125732,4,3,2,1,25,1 diff --git a/azure-app_deployment/data/embeddings_azure_50d.pkl b/azure-app_deployment/data/embeddings_azure_50d.pkl deleted file mode 100644 index bcb2f1d..0000000 Binary files a/azure-app_deployment/data/embeddings_azure_50d.pkl and /dev/null differ diff --git a/azure-app_deployment/function_app.py b/azure-app_deployment/function_app.py deleted file mode 100644 index 731ab2f..0000000 --- a/azure-app_deployment/function_app.py +++ /dev/null @@ -1,73 +0,0 @@ -import azure.functions as func -import logging -import json -import os -import sys - -# Ajouter le dossier utils au path pour les imports -sys.path.append(os.path.dirname(os.path.abspath(__file__))) - -from utils.data_loader import load_embeddings, load_user_data -from utils.content_based import get_recommendations - -app = func.FunctionApp(http_auth_level=func.AuthLevel.FUNCTION) - -@app.route(route="recommend") -def recommend(req: func.HttpRequest) -> func.HttpResponse: - logging.info('Recommendation request received.') - - # Récupérer le user_id depuis les paramètres de requête - user_id = req.params.get('user_id') - - if not user_id: - try: - req_body = req.get_json() - user_id = req_body.get('user_id') if req_body else None - except ValueError: - pass - - if not user_id: - return func.HttpResponse( - json.dumps({"error": "user_id parameter required"}), - status_code=400, - mimetype="application/json" - ) - - try: - # Convertir en entier - user_id = int(user_id) - - # Générer les recommandations - recommendations = get_recommendations(user_id) - - if recommendations: - return func.HttpResponse( - json.dumps({ - "user_id": user_id, - "recommendations": recommendations - }), - status_code=200, - mimetype="application/json" - ) - else: - return func.HttpResponse( - json.dumps({ - "error": f"No recommendations found for user {user_id}" - }), - status_code=404, - mimetype="application/json" - ) - - except ValueError: - return func.HttpResponse( - json.dumps({"error": "user_id must be a valid integer"}), - status_code=400, - mimetype="application/json" - ) - except Exception as e: - logging.error(f"Error generating recommendations: {str(e)}") - return func.HttpResponse( - json.dumps({"error": "Internal server error"}), - status_code=500, - mimetype="application/json" - ) \ No newline at end of file diff --git a/azure-app_deployment/host.json b/azure-app_deployment/host.json deleted file mode 100644 index 9df9136..0000000 --- a/azure-app_deployment/host.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": "2.0", - "logging": { - "applicationInsights": { - "samplingSettings": { - "isEnabled": true, - "excludedTypes": "Request" - } - } - }, - "extensionBundle": { - "id": "Microsoft.Azure.Functions.ExtensionBundle", - "version": "[4.*, 5.0.0)" - } -} \ No newline at end of file diff --git a/azure-app_deployment/requirements.txt b/azure-app_deployment/requirements.txt deleted file mode 100644 index 194f74f..0000000 --- a/azure-app_deployment/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -azure-functions -numpy -scikit-learn -pandas \ No newline at end of file diff --git a/azure-app_deployment/test_local.py b/azure-app_deployment/test_local.py deleted file mode 100644 index 623ab2c..0000000 --- a/azure-app_deployment/test_local.py +++ /dev/null @@ -1,92 +0,0 @@ -import logging -import sys -import os - -# Configuration du logging -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(levelname)s - %(message)s' -) - -# Ajouter le dossier utils au path -sys.path.append(os.path.dirname(os.path.abspath(__file__))) - -from utils.content_based import get_recommendations -from utils.data_loader import get_available_users, check_data_files, load_embeddings - -def test_data_loading(): - """Test du chargement des données.""" - print("🔄 Test du chargement des données...") - - if not check_data_files(): - print("❌ Fichiers de données manquants") - return False - - try: - # Test chargement embeddings - embeddings = load_embeddings() - print(f"✅ Embeddings chargés: {embeddings.shape}") - - # Test chargement utilisateurs - users = get_available_users() - print(f"✅ Utilisateurs chargés: {len(users)} utilisateurs") - print(f" Premiers utilisateurs: {users[:5]}") - - return True - except Exception as e: - print(f"❌ Erreur lors du chargement: {e}") - return False - -def test_recommendations(): - """Test des recommandations.""" - print("\n🔄 Test des recommandations...") - - users = get_available_users() - test_users = users[:3] if len(users) >= 3 else users - - for user_id in test_users: - print(f"\n👤 Test pour utilisateur {user_id}:") - - try: - recommendations = get_recommendations(user_id, n_recommendations=5) - - if recommendations: - print(f"✅ {len(recommendations)} recommandations générées") - print(f" Articles recommandés: {recommendations}") - else: - print("⚠️ Aucune recommandation générée") - - except Exception as e: - print(f"❌ Erreur pour utilisateur {user_id}: {e}") - -def test_edge_cases(): - """Test des cas limites.""" - print("\n🔄 Test des cas limites...") - - # Test utilisateur inexistant - print("Test utilisateur inexistant (ID: 99999):") - result = get_recommendations(99999) - if result is None: - print("✅ Utilisateur inexistant géré correctement") - else: - print(f"⚠️ Résultat inattendu: {result}") - -def main(): - """Fonction principale de test.""" - print("🚀 Démarrage des tests locaux\n") - - # Test 1: Chargement des données - if not test_data_loading(): - print("\n❌ Tests interrompus - problème de chargement") - return - - # Test 2: Recommandations - test_recommendations() - - # Test 3: Cas limites - test_edge_cases() - - print("\n✨ Tests terminés") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/azure-app_deployment/utils/__init__.py b/azure-app_deployment/utils/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/azure-app_deployment/utils/content_based.py b/azure-app_deployment/utils/content_based.py deleted file mode 100644 index f3aaa63..0000000 --- a/azure-app_deployment/utils/content_based.py +++ /dev/null @@ -1,79 +0,0 @@ -import numpy as np -from sklearn.metrics.pairwise import cosine_similarity -from typing import List, Optional, Tuple -import logging - -from .data_loader import load_embeddings, get_user_articles - -def _calculate_mean_embedding(embeddings: np.ndarray, article_ids: List[int]) -> np.ndarray: - """Calcule la moyenne des embeddings pour une liste d'articles.""" - valid_ids = [aid for aid in article_ids if aid < len(embeddings)] - if not valid_ids: - raise ValueError("No valid article IDs found") - - articles_embeddings = embeddings[valid_ids] - return np.mean(articles_embeddings, axis=0).reshape(1, -1) - -def _compute_similarities(reference_embedding: np.ndarray, embeddings: np.ndarray) -> np.ndarray: - """Calcule la similarité cosinus entre l'embedding de référence et tous les embeddings.""" - return cosine_similarity(reference_embedding, embeddings)[0] - -def _filter_candidates(similarities: np.ndarray, clicked_articles: List[int]) -> List[Tuple[int, float]]: - """Filtre les candidats en excluant les articles déjà vus.""" - candidates = [] - for article_id, similarity in enumerate(similarities): - if article_id not in clicked_articles: - candidates.append((article_id, similarity)) - return candidates - -def _get_top_recommendations(candidates: List[Tuple[int, float]], n_recommendations: int) -> List[int]: - """Trie les candidats et retourne le top N.""" - candidates.sort(key=lambda x: x[1], reverse=True) - return [article_id for article_id, _ in candidates[:n_recommendations]] - -def _validate_user_articles(clicked_articles: List[int], embeddings: np.ndarray) -> List[int]: - """Valide et filtre les articles dans les limites des embeddings.""" - valid_articles = [art for art in clicked_articles if 0 <= art < len(embeddings)] - if not valid_articles: - raise ValueError("No valid articles found for user") - return valid_articles - -def get_recommendations(user_id: int, n_recommendations: int = 5) -> Optional[List[int]]: - """ - Génère des recommandations pour un utilisateur donné. - - Args: - user_id: ID de l'utilisateur - n_recommendations: Nombre de recommandations à retourner - - Returns: - Liste des article_ids recommandés ou None si erreur - """ - try: - # Charger les données - embeddings = load_embeddings() - clicked_articles = get_user_articles(user_id) - - if not clicked_articles: - logging.warning(f"No articles found for user {user_id}") - return None - - # Valider les articles - valid_articles = _validate_user_articles(clicked_articles, embeddings) - - # Calculer l'embedding de référence (stratégie mean) - reference_embedding = _calculate_mean_embedding(embeddings, valid_articles) - - # Calculer similarités - similarities = _compute_similarities(reference_embedding, embeddings) - - # Filtrer et trier - candidates = _filter_candidates(similarities, clicked_articles) - recommendations = _get_top_recommendations(candidates, n_recommendations) - - logging.info(f"Generated {len(recommendations)} recommendations for user {user_id}") - return recommendations - - except Exception as e: - logging.error(f"Error generating recommendations for user {user_id}: {str(e)}") - return None \ No newline at end of file diff --git a/azure-app_deployment/utils/data_loader.py b/azure-app_deployment/utils/data_loader.py deleted file mode 100644 index 0a34929..0000000 --- a/azure-app_deployment/utils/data_loader.py +++ /dev/null @@ -1,87 +0,0 @@ -import pickle -import pandas as pd -import os -import logging -from typing import Dict, List, Optional -import numpy as np - -# Chemins des fichiers -DATA_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data') -EMBEDDINGS_PATH = os.path.join(DATA_DIR, 'embeddings_azure_50d.pkl') -CLICKS_PATH = os.path.join(DATA_DIR, 'clicks_sample.csv') - -# Cache global -_embeddings_cache: Optional[np.ndarray] = None -_user_data_cache: Optional[Dict[int, List[int]]] = None - -def _load_pickle_file(file_path: str) -> np.ndarray: - """Charge un fichier pickle.""" - with open(file_path, 'rb') as f: - return pickle.load(f) - -def _load_csv_file(file_path: str) -> pd.DataFrame: - """Charge un fichier CSV.""" - return pd.read_csv(file_path) - -def _process_clicks_data(clicks_df: pd.DataFrame) -> Dict[int, List[int]]: - """Convertit le DataFrame en dictionnaire user->articles.""" - user_data = {} - for _, row in clicks_df.iterrows(): - user_id = int(row['user_id']) - article_id = int(row['click_article_id']) - - if user_id not in user_data: - user_data[user_id] = [] - user_data[user_id].append(article_id) - - return user_data - -def load_embeddings() -> np.ndarray: - """Charge les embeddings avec cache.""" - global _embeddings_cache - - if _embeddings_cache is None: - try: - _embeddings_cache = _load_pickle_file(EMBEDDINGS_PATH) - logging.info(f"Embeddings loaded: {_embeddings_cache.shape}") - except Exception as e: - logging.error(f"Error loading embeddings: {str(e)}") - raise - - return _embeddings_cache - -def load_user_data() -> Dict[int, List[int]]: - """Charge les données utilisateur avec cache.""" - global _user_data_cache - - if _user_data_cache is None: - try: - clicks_df = _load_csv_file(CLICKS_PATH) - _user_data_cache = _process_clicks_data(clicks_df) - logging.info(f"User data loaded: {len(_user_data_cache)} users") - except Exception as e: - logging.error(f"Error loading user data: {str(e)}") - raise - - return _user_data_cache - -def get_user_articles(user_id: int) -> Optional[List[int]]: - """Récupère les articles d'un utilisateur.""" - user_data = load_user_data() - return user_data.get(user_id) - -def check_data_files() -> bool: - """Vérifie que tous les fichiers de données existent.""" - files_to_check = [EMBEDDINGS_PATH, CLICKS_PATH] - - for file_path in files_to_check: - if not os.path.exists(file_path): - logging.error(f"Missing data file: {file_path}") - return False - - return True - -def get_available_users() -> List[int]: - """Retourne la liste de tous les user_ids disponibles.""" - user_data = load_user_data() - return list(user_data.keys()) \ No newline at end of file diff --git a/docs/Content-based-filtering-vs-Collaborative-filtering-Source - Copie (2).png b/docs/Content-based-filtering-vs-Collaborative-filtering-Source - Copie (2).png new file mode 100644 index 0000000..192f757 Binary files /dev/null and b/docs/Content-based-filtering-vs-Collaborative-filtering-Source - Copie (2).png differ diff --git a/docs/collaborative-filtering.png b/docs/collaborative-filtering.png new file mode 100644 index 0000000..5c39772 Binary files /dev/null and b/docs/collaborative-filtering.png differ diff --git a/docs/content-based-filtering.png b/docs/content-based-filtering.png new file mode 100644 index 0000000..0658731 Binary files /dev/null and b/docs/content-based-filtering.png differ diff --git a/docs/interactions_per_user_sample.png b/docs/interactions_per_user_sample.png new file mode 100644 index 0000000..de27a6c Binary files /dev/null and b/docs/interactions_per_user_sample.png differ diff --git a/docs/matrix_sparsity_sample.png b/docs/matrix_sparsity_sample.png new file mode 100644 index 0000000..8ee3253 Binary files /dev/null and b/docs/matrix_sparsity_sample.png differ diff --git a/docs/words_stats_complete.png b/docs/words_stats_complete.png new file mode 100644 index 0000000..d83e6a4 Binary files /dev/null and b/docs/words_stats_complete.png differ diff --git a/streamlit_app.py b/streamlit_app.py index 0698054..1c73ef8 100644 --- a/streamlit_app.py +++ b/streamlit_app.py @@ -122,9 +122,28 @@ def main(): st.subheader("🎯 Articles recommandés") recommendations = result['recommendations'] - # Affichage sous forme de liste numérotée - for i, article_id in enumerate(recommendations, 1): - st.write(f"**{i}.** Article ID: `{article_id}`") + # Vérifier si on a le format enrichi (avec scores et catégories) + if recommendations and isinstance(recommendations[0], dict) and 'similarity_score_percent' in recommendations[0]: + # Format enrichi - affichage en tableau + import pandas as pd + + df_data = [] + for i, rec in enumerate(recommendations, 1): + df_data.append({ + 'Rang': i, + 'Article ID': rec['article_id'], + 'Similarité (%)': f"{rec['similarity_score_percent']:.1f}%", + 'Catégorie': rec.get('category_id', 'N/A') + }) + + df = pd.DataFrame(df_data) + st.dataframe(df, hide_index=True, use_container_width=True) + else: + # Format simple - affichage basique pour compatibilité + for i, article_id in enumerate(recommendations, 1): + if isinstance(article_id, dict): + article_id = article_id.get('article_id', article_id) + st.write(f"**{i}.** Article ID: `{article_id}`") # JSON brut (collapsible) with st.expander("📝 Réponse JSON complète"):