docs: README bilingue FR/EN

Co-authored-by: Cursor <cursoragent@cursor.com>

README.fr.md

@@ -9,6 +9,8 @@ Dataset : voir `fetch-data.sh` (hors git si volumineux).
 
 # Système de Recommandation de Contenu - My Content
 
+📘 Ce projet est également disponible en [anglais 🇬🇧](./README.md)
+
 Projet de développement d'un système de recommandation d'articles basé sur une architecture serverless Azure Functions avec interface utilisateur Streamlit.
 
 

README.md

@@ -0,0 +1,236 @@
+**Source of truth** : https://git.gregoiremureau.com/openclassroom-ai/oc-p10-content-recommendation
+
+```bash
+git clone https://git.gregoiremureau.com/openclassroom-ai/oc-p10-content-recommendation.git
+git clone https://git.gregoiremureau.com/openclassroom-ai/oc-p10-content-recommendation.git
+```
+
+Dataset: see `fetch-data.sh` (excluded from git if large).
+
+# Content Recommendation System - My Content
+
+📘 This project is also available in [French 🇫🇷](./README.fr.md)
+
+Project developing an article recommendation system based on a serverless Azure Functions architecture with a Streamlit user interface.
+
+
+## Project Deliverables
+1. **Working application**: Streamlit + Azure Functions deployed
+2. **Versioned source code**: Complete GitHub repository
+3. **Technical presentation**: PDF deck (docs/presentation.pdf)
+
+## Overview
+
+This project implements a content recommendation system using collaborative filtering and content-based algorithms. The serverless architecture enables automatic scaling and optimized cost.
+
+### Main Features
+
+- **Recommendation API**: Azure Function returning the top 5 recommended articles for a user
+- **User interface**: Streamlit application to test and demonstrate the system
+- **Implemented algorithms**: Content-Based Filtering with precomputed embeddings
+- **Serverless architecture**: Deployment on Azure Functions with Consumption plan
+
+
+## Analysis and Development
+
+### Jupyter Notebook
+- **Data exploration**: Analysis of the Kaggle Globo.com dataset
+- **Algorithm implementation**: Content-Based and Collaborative Filtering
+- **Approach comparison**: Evaluation and technical choice
+- **PCA optimization**: Dimensionality reduction 250D → 50D
+
+Location: `notebook/p10_notebook.ipynb`
+
+## Architecture
+
+### Current MVP
+```
+[Streamlit Interface] → [Azure Functions API] → [Recommendation Algorithm]
+                                ↓
+                        [Embeddings data + user history]
+```
+
+### Components
+- **Frontend**: Streamlit (test interface)
+- **Backend**: Azure Functions (Python 3.12, Linux)
+- **Algorithm**: Content-Based Filtering with cosine similarity
+- **Data**: Article embeddings (50D via PCA vs 250D original embeddings) + click history
+
+## Project Structure Overview
+
+```
+Project_10/
+├── azure-app/                  # Azure Function
+│   ├── function_app.py         # Azure Function entry point
+│   ├── requirements.txt        # Python dependencies
+│   ├── host.json              # Azure Functions configuration
+│   ├── local.settings.json    # Local configuration (not versioned)
+│   ├── data/
+│   │   ├── embeddings_azure_50d.pkl    # Article embeddings (PCA 50D)
+│   │   └── clicks_sample.csv           # User click history
+│   └── utils/
+│       ├── data_loader.py      # Data loading and caching
+│       └── content_based.py    # Recommendation algorithm
+├── notebook/
+│   └── p10_notebook.ipynb     # Data analysis + model development
+├── docs/
+│   ├── presentation.pdf       # Presentation deck (15-25 slides)
+│   └── illustrations/         # Architecture diagrams and screenshots
+├── streamlit_app.py           # User interface
+├── streamlit_requirements.txt # Streamlit dependencies
+└── README.md
+```
+
+## Installation and Deployment
+
+### Prerequisites
+- Python 3.12+
+- Azure CLI
+- Azure Functions Core Tools
+- Azure account
+
+### Azure Functions Deployment
+
+1. **Sign in to Azure**
+```bash
+az login
+```
+
+2. **Infrastructure creation**
+```bash
+# Resource group
+az group create --name rg-content-recommender --location francecentral
+
+# Storage account
+az storage account create --name [your-unique-storage-name] \
+  --resource-group rg-content-recommender \
+  --location francecentral \
+  --sku Standard_LRS
+
+# Linux Function App with 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. **Code deployment**
+```bash
+# From the azure-app folder
+func azure functionapp publish contentrecommender-linux
+```
+
+### Streamlit Interface
+
+1. **Install dependencies**
+```bash
+pip install -r streamlit_requirements.txt
+```
+
+2. **Launch the interface**
+```bash
+streamlit run streamlit_app.py
+```
+
+## Usage
+
+### Azure Functions API
+
+**Endpoint**: `https://contentrecommender-linux.azurewebsites.net/api/recommend`
+
+**Parameters**:
+- `user_id` (int): User identifier (0-706)
+
+**Example call**:
+```bash
+curl "https://contentrecommender-linux.azurewebsites.net/api/recommend?user_id=0"
+```
+
+**Response**:
+```json
+{
+  "user_id": 0,
+  "recommendations": [157519, 157944, 159495, 156690, 162857]
+}
+```
+
+### Streamlit Interface
+
+1. Configure the API URL (local or Azure)
+2. Select a user ID
+3. Get the recommendations
+4. Test the advanced features
+
+## Recommendation Algorithm
+
+### Content-Based Filtering
+
+1. **Data loading**: Precomputed embeddings and user history
+2. **User profile computation**: Average of the embeddings of the viewed articles
+3. **Cosine similarity**: Computed against all articles in the catalog
+4. **Filtering**: Exclusion of already-seen articles
+5. **Selection**: Top 5 most similar articles
+
+### Optimizations
+
+- **Dimensionality reduction**: PCA 250D → 50D to optimize Azure performance
+- **In-memory cache**: Embeddings and user data loaded only once
+- **Serverless architecture**: Automatic scaling based on demand
+
+## Tests and Validation
+
+### Local tests
+```bash
+# Start Azure Functions locally
+func start
+
+# Test the algorithm
+python test_local.py
+```
+
+### Performance metrics
+- **Response time**: ~200ms per recommendation
+- **Supported users**: 707 in the current dataset
+- **Available articles**: 364,047 with embeddings
+
+## Known Issues and Solutions
+
+### Azure Deployment
+- **Python on Windows**: Not supported, must use Linux
+- **Authentication**: Set `http_auth_level=func.AuthLevel.ANONYMOUS`
+- **"Service unavailable" errors**: Temporary Azure issues, retry later
+
+### Current Limitations
+- **Cold start**: First request is slow after a period of inactivity
+- **New users**: No handling of the cold-start problem
+- **Data updates**: Manual process
+
+## Target Architecture (Future)
+
+### Planned Improvements
+- **Cold start handling**: Popular recommendations for new users
+- **Data pipeline**: Automatic embeddings update
+- **Database**: Persistent storage of user profiles
+- **Hybrid API**: Combination of Content-Based + Collaborative Filtering
+- **Distributed cache**: Precomputation of popular recommendations
+
+## Technologies Used
+
+- **Backend**: Azure Functions, Python 3.12
+- **Frontend**: Streamlit
+- **ML/Data**: scikit-learn, pandas, numpy
+- **Infrastructure**: Azure CLI, Azure Storage
+- **Deployment**: Azure Functions Core Tools
+
+## Author
+
+Project carried out as part of an OpenClassrooms training program - AI Engineer.
+
+## License
+
+This project is for educational purposes only.