Upload app.py

app.py

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+
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel, Field
+import joblib
+import json
+import numpy as np
+from datetime import datetime
+from typing import Optional
+import os
+
+# Initialisation
+app = FastAPI(
+    title="API Détection de Fraude",
+    description="API de détection de fraude dans les transactions bancaires",
+    version="1.0.0"
+)
+
+# Chargement du modèle et des encodeurs
+model = joblib.load('fraud_model.pkl')
+le_category = joblib.load('le_category.pkl')
+le_gender = joblib.load('le_gender.pkl')
+le_state = joblib.load('le_state.pkl')
+
+with open('mappings.json', 'r') as f:
+    mappings = json.load(f)
+
+# Modèle de données
+class Transaction(BaseModel):
+    amt: float = Field(..., description="Montant de la transaction", example=125.50)
+    category: str = Field(..., description="Catégorie du marchand", example="personal_care")
+    merchant: str = Field(..., description="Nom du marchand", example="fraud_Kirlin and Sons")
+    trans_date_trans_time: str = Field(..., description="Date et heure", example="2020-06-21 12:14:25")
+    gender: str = Field(..., description="Genre (M/F)", example="M")
+    state: str = Field(..., description="État (code à 2 lettres)", example="SC")
+    lat: float = Field(..., description="Latitude du client", example=33.9659)
+    long: float = Field(..., description="Longitude du client", example=-80.9355)
+    city_pop: int = Field(..., description="Population de la ville", example=333497)
+    dob: str = Field(..., description="Date de naissance", example="1968-03-19")
+    merch_lat: float = Field(..., description="Latitude du marchand", example=33.986391)
+    merch_long: float = Field(..., description="Longitude du marchand", example=-81.200714)
+    cc_num: Optional[str] = Field(None, description="Numéro de carte (optionnel)")
+    avg_amt: Optional[float] = Field(50.0, description="Montant moyen historique")
+    std_amt: Optional[float] = Field(30.0, description="Écart-type historique")
+    nb_trans: Optional[int] = Field(10, description="Nombre de transactions historiques")
+
+class PredictionResponse(BaseModel):
+    is_fraud: bool
+    fraud_probability: float
+    risk_level: str
+    details: dict
+
+# Routes
+@app.get("/")
+def read_root():
+    return {
+        "message": "API de Détection de Fraude",
+        "version": "1.0.0",
+        "endpoints": {
+            "/predict": "POST - Prédire une transaction",
+            "/health": "GET - Statut de l'API",
+            "/categories": "GET - Liste des catégories",
+            "/docs": "GET - Documentation interactive"
+        }
+    }
+
+@app.get("/health")
+def health_check():
+    return {
+        "status": "healthy",
+        "model_loaded": model is not None,
+        "timestamp": datetime.now().isoformat()
+    }
+
+@app.get("/categories")
+def get_categories():
+    return {
+        "categories": list(mappings['categories'].keys()),
+        "states": list(mappings['states'].keys()),
+        "genders": list(mappings['genders'].keys())
+    }
+
+@app.post("/predict", response_model=PredictionResponse)
+def predict_fraud(transaction: Transaction):
+    try:
+        # Parsing des dates
+        trans_dt = datetime.strptime(transaction.trans_date_trans_time, "%Y-%m-%d %H:%M:%S")
+        dob_dt = datetime.strptime(transaction.dob, "%Y-%m-%d")
+        
+        # Features temporelles
+        hour = trans_dt.hour
+        day_of_week = trans_dt.weekday()
+        day = trans_dt.day
+        month = trans_dt.month
+        age = (trans_dt - dob_dt).days // 365
+        
+        # Distance
+        distance = np.sqrt(
+            (transaction.lat - transaction.merch_lat)**2 + 
+            (transaction.long - transaction.merch_long)**2
+        ) * 111
+        
+        # Encodage
+        if transaction.category not in mappings['categories']:
+            raise HTTPException(400, f"Catégorie inconnue: {transaction.category}")
+        if transaction.gender not in mappings['genders']:
+            raise HTTPException(400, f"Genre inconnu: {transaction.gender}")
+        if transaction.state not in mappings['states']:
+            raise HTTPException(400, f"État inconnu: {transaction.state}")
+        
+        category_encoded = mappings['categories'][transaction.category]
+        gender_encoded = mappings['genders'][transaction.gender]
+        state_encoded = mappings['states'][transaction.state]
+        
+        # Construction du vecteur de features
+        features = np.array([[
+            transaction.amt,
+            hour,
+            day_of_week,
+            day,
+            month,
+            age,
+            category_encoded,
+            gender_encoded,
+            state_encoded,
+            transaction.lat,
+            transaction.long,
+            transaction.city_pop,
+            distance,
+            transaction.avg_amt,
+            transaction.std_amt,
+            transaction.nb_trans
+        ]])
+        
+        # Prédiction
+        fraud_proba = model.predict_proba(features)[0][1]
+        is_fraud = fraud_proba > 0.5
+        
+        # Niveau de risque
+        if fraud_proba < 0.3:
+            risk_level = "Faible"
+        elif fraud_proba < 0.7:
+            risk_level = "Moyen"
+        else:
+            risk_level = "Élevé"
+        
+        return PredictionResponse(
+            is_fraud=bool(is_fraud),
+            fraud_probability=float(fraud_proba),
+            risk_level=risk_level,
+            details={
+                "montant": transaction.amt,
+                "categorie": transaction.category,
+                "heure": hour,
+                "age_client": age,
+                "distance_km": round(distance, 2)
+            }
+        )
+        
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=f"Erreur de format: {str(e)}")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur interne: {str(e)}")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)