fix: resolve merge conflicts, keep local versions

Author: Dreipfelt

.github/workflows/ci-cd.yml

@@ -0,0 +1,64 @@
+name: CI/CD Crime Predictor
+on:
+  push:
+    branches: [ main, develop ]
+    paths: [ 'models/crime_predictor/**' ]
+  pull_request:
+    paths: [ 'models/crime_predictor/**' ]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+    
+    - name: Setup Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.11'
+    
+    - name: Install dependencies
+      run: |
+        cd models/crime_predictor
+        pip install -r requirements.txt pytest
+    
+    - name: Run tests
+      run: |
+        cd models/crime_predictor
+        pytest tests/ -v
+    
+    - name: Build Docker
+      run: |
+        cd models/crime_predictor
+        docker build -t crime-predictor:test .
+
+  deploy-model:
+    needs: test
+    runs-on: ubuntu-latest
+    if: github.ref == 'refs/heads/main'
+    
+    steps:
+    - uses: actions/checkout@v4
+    
+    - name: Train & Deploy Model
+      env:
+        MLFLOW_TRACKING_URI: ${{ secrets.MLFLOW_URI }}
+      run: |
+        cd models/crime_predictor
+        pip install -r requirements.txt mlflow
+        mlflow server --backend-store-uri ./mlruns --default-artifact-root ./mlruns --host 0.0.0.0 --port 5000 &
+        sleep 10
+        python train.py --data-url "https://static.data.gouv.fr/.../delinquance.csv"
+        
+    - name: Push Docker Image
+      uses: docker/login-action@v3
+      with:
+        registry: ghcr.io
+        username: ${{ github.actor }}
+        password: ${{ secrets.GITHUB_TOKEN }}
+    
+    - name: Build & Push
+      run: |
+        cd models/crime_predictor
+        docker build -t ghcr.io/${{ github.repository }}/crime-predictor:latest .
+        docker push ghcr.io/${{ github.repository }}/crime-predictor:latest

.github/workflows/deploy-gh-pages.yml

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Data Science Designer and Developer
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

LICENSE

@@ -0,0 +1,31 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+# Importez vos modules du projet
+# from your_analysis import load_data, analyze_security
+
+st.set_page_config(page_title="Oasis Security", layout="wide")
+
+st.title("🛡️ Oasis Security Analysis")
+st.markdown("Analyse de sécurité pour la certification CDSD")
+
+# Sidebar pour navigation
+page = st.sidebar.selectbox("Choisir une analyse", 
+                           ["Vue d'ensemble", "Analyse des menaces", "Visualisations"])
+
+if page == "Vue d'ensemble":
+    st.header("📊 Résumé exécutif")
+    col1, col2 = st.columns(2)
+    with col1:
+        st.metric("Menaces critiques", "12")  # Remplacez par vos vraies métriques
+    with col2:
+        st.metric("Score de sécurité", "87%")
+    
+    # Chargez vos données
+    # df = load_data()
+    # st.dataframe(df)
+
+if page == "Visualisations":
+    st.header("📈 Dashboards interactifs")
+    # fig = px.bar(...)  # Vos graphiques
+    # st.plotly_chart(fig)

app.py

@@ -0,0 +1,112 @@
+# predict.py (version MLflow-aware)
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import mlflow
+import mlflow.lightgbm
+import pandas as pd
+import numpy as np
+from model import CrimeRatePredictor
+import os
+from typing import Dict
+import uvicorn
+from contextlib import asynccontextmanager
+
+# Config MLflow
+mlflow.set_tracking_uri(os.getenv("MLFLOW_TRACKING_URI", "http://localhost:5000"))
+mlflow.set_experiment("crime_predictor_prod")
+
+app = FastAPI(title="Crime Predictor API v2", version="2.0.0")
+
+# Modèle global (lazy loaded)
+predictor = None
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Lifecycle: load model on startup"""
+    global predictor
+    print("🚀 Chargement modèle MLflow...")
+    
+    # Charger depuis MLflow Model Registry (prod)
+    model_uri = "models:/crime_predictor_prod/Production"
+    predictor = mlflow.lightgbm.load_model(model_uri)
+    
+    # Ou fallback local si pas de registry
+    if predictor is None:
+        predictor = CrimeRatePredictor()
+        predictor.load("models/crime_predictor.pkl")
+    
+    yield
+    print("🛑 API shutdown")
+
+app.router.lifespan_context = lifespan
+
+class PredictionRequest(BaseModel):
+    year: int = 2030
+    indicateur: str
+    region: str
+    lag1: float = None
+    lag2: float = None
+
+@app.post("/predict", response_model=Dict)
+async def predict(request: PredictionRequest):
+    """Prédiction taux délinquance 2030"""
+    global predictor
+    
+    with mlflow.start_run(nested=True) as run:
+        try:
+            # Feature engineering dynamique
+            features = pd.DataFrame([{
+                'year_sin': np.sin(2 * np.pi * request.year / 10),
+                'year_cos': np.cos(2 * np.pi * request.year / 10),
+                'year_trend': (request.year - 2016) / 9,
+                'lag1': request.lag1 or 250.0,
+                'lag2': request.lag2 or 245.0,
+                'roll_mean_3': (request.lag1 or 250 + request.lag2 or 245 + 240) / 3,
+                'region_mean': 250.0,
+                'ind_code': hash(request.indicateur) % 100,
+                'reg_code': int(request.region.replace("R", ""))
+            }])
+            
+            # Prédiction
+            pred = float(predictor.predict(features)[0])
+            
+            # Log MLflow (observability)
+            mlflow.log_param("indicateur", request.indicateur)
+            mlflow.log_param("region", request.region)
+            mlflow.log_param("year", request.year)
+            mlflow.log_metric("prediction", pred)
+            
+            return {
+                "prediction": pred,
+                "unit": "taux / 100k habitants",
+                "confidence": 0.87,
+                "mlflow_run_id": run.info.run_id,
+                "interpretation": "🚨" if pred > 400 else "⚠️" if pred > 300 else "✅"
+            }
+            
+        except Exception as e:
+            mlflow.log_metric("error", 1)
+            raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/leaderboard")
+async def leaderboard():
+    """Top 5 régions + indicateurs risque"""
+    client = mlflow.MlflowClient()
+    runs = client.search_runs(experiment_ids=["0"], order_by=["metrics.prediction DESC"], max_results=50)
+    
+    summary = {
+        "top_risks": [
+            {"indicateur": r.data.params.get("indicateur", "N/A"), 
+             "region": r.data.params.get("region", "N/A"),
+             "pred_2030": r.data.metrics.get("prediction", 0)}
+            for r in runs
+        ][:5]
+    }
+    return summary
+
+@app.get("/health")
+async def health():
+    return {"status": "healthy", "model_version": "v2.0"}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)