feat(prediction): add single prediction

app.py

@@ -1,12 +1,11 @@
 from fastapi import FastAPI
 from src.routes import user_routes,organization_routes, prediction_routes
-from src.routes import user_routes
 
 app = FastAPI(title="NetSentinel Backend")
 
 # Register routers
-app.include_router(user_routes.router)
-app.include_router(organization_routes.router)
+# app.include_router(user_routes.router)
+# app.include_router(organization_routes.router)
 app.include_router(prediction_routes.router)
 
 

src/controllers/prediction_controller.py

@@ -7,6 +7,15 @@ from src.agents.l1_screener import Screener
 from src.agents.l2_supervisor import Supervisor
 from src.agents.l3_classifier import Classifier
 
+required_columns = [
+    "Header_Length", "Protocol Type", "Time_To_Live", "Rate",
+    "fin_flag_number", "syn_flag_number", "rst_flag_number",
+    "psh_flag_number", "ack_flag_number", "ece_flag_number",
+    "cwr_flag_number", "ack_count", "syn_count", "fin_count",
+    "rst_count", "TCP", "UDP", "Tot sum", "Min", "Max", "AVG",
+    "Std", "Tot size", "IAT", "Number", "Variance"
+]
+
 def global_prediction_on_csv(file: UploadFile):
     try:
         # Vérifier l'extension et les colonnes du fichier
@@ -24,17 +33,40 @@ def global_prediction_on_csv(file: UploadFile):
         supervisor = Supervisor()
         l2_summary = summarize_predictions(supervisor.predict, data)
         
-        classifier = Classifier()
-        l3_summary = summarize_predictions(classifier.predict, data)
+        # classifier = Classifier()
+        # l3_summary = summarize_predictions(classifier.predict, data)
         
 
         return {
                 "l1": l1_summary,
                 "l2": l2_summary,
-                "l3": l3_summary
+                # "l3": l3_summary
                 }
 
     except HTTPException:
         raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
+
+def single_prediction(data: dict):
+    try:
+    # log dans la console
+        if not isinstance(data, dict):
+            raise HTTPException(status_code=400, detail="Invalid JSON payload")
+
+        missing_columns = [col for col in required_columns if col not in data]
+        if missing_columns:
+            raise HTTPException(
+                status_code=422,
+                detail=f"Missing required columns: {missing_columns}"
+            )
+
+        # Ici tu peux faire la prédiction avec data
+        print("Received data for single prediction:", data)
+
+        return {"message": "All required columns present", "to_do": "Not yet implemented"}
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+

src/routes/prediction_routes.py

@@ -9,3 +9,8 @@ def predict_from_csv(file: UploadFile = File(...)):
     Route pour uploader un CSV, le prétraiter et obtenir les prédictions.
     """
     return prediction_controller.global_prediction_on_csv(file)
+
+@router.post("/single-prediction")
+def list_users():
+    return prediction_controller.single_prediction()
+