Update model version tracking for training history
Dynamically set the model version to "2.0.0" for hybrid detectors and "1.0.0" for legacy detectors, and update the database insertion logic in `main.py` to use this dynamic version. Replit-Commit-Author: Agent Replit-Commit-Session-Id: 7a657272-55ba-4a79-9a2e-f1ed9bc7a528 Replit-Commit-Checkpoint-Type: full_checkpoint Replit-Commit-Event-Id: 25db5356-3182-4db3-be10-c524c0561b39 Replit-Commit-Screenshot-Url: https://storage.googleapis.com/screenshot-production-us-central1/449cf7c4-c97a-45ae-8234-e5c5b8d6a84f/7a657272-55ba-4a79-9a2e-f1ed9bc7a528/RJGlbTt
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@ -62,6 +62,9 @@ app.add_middleware(
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# Global instances - Try hybrid first, fallback to legacy
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USE_HYBRID_DETECTOR = os.getenv("USE_HYBRID_DETECTOR", "true").lower() == "true"
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# Model version based on detector type
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MODEL_VERSION = "2.0.0" if USE_HYBRID_DETECTOR else "1.0.0"
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if USE_HYBRID_DETECTOR:
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print("[ML] Using Hybrid ML Detector (Extended Isolation Forest + Feature Selection)")
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ml_detector = MLHybridDetector(model_dir="models")
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@ -212,7 +215,7 @@ async def train_model(request: TrainRequest, background_tasks: BackgroundTasks):
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(model_version, records_processed, features_count, training_duration, status, notes)
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VALUES (%s, %s, %s, %s, %s, %s)
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""", (
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"1.0.0",
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MODEL_VERSION,
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len(df),
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0,
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0,
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@ -232,7 +235,7 @@ async def train_model(request: TrainRequest, background_tasks: BackgroundTasks):
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(model_version, records_processed, features_count, training_duration, status, notes)
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VALUES (%s, %s, %s, %s, %s, %s)
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""", (
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"1.0.0",
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MODEL_VERSION,
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result['records_processed'],
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result['features_count'],
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0, # duration non ancora implementato
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