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README.md

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 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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+#### Server Health Sentinel AI
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+Predicting Thermal Runaway Before It Happens
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+This project is a Proof-of-Concept (PoC) for AIOps (Artificial Intelligence for IT Operations). It demonstrates how machine learning can move beyond simple "threshold-based" monitoring to predictive failure analysis.
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+### Try the Demo
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+Adjust the sliders in the Live Telemetry Simulation panel to see how the model reacts to different stress scenarios.
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+Scenario A (Idle): Low CPU, Low Temp -> System Normal
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+Scenario B (Gaming/Load): High Sustained CPU, High Temp -> CRITICAL FAILURE IMMINENT
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+Scenario C (Cool Down): Low Current CPU but High Sustained Load + High Temp -> CRITICAL (Predicting residual heat)
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+### The Model: Random Forest Classifier
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+Unlike simple if/else logic, this system uses a Random Forest Classifier (an ensemble of 100 decision trees) to weigh multiple factors simultaneously.
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+It was trained on a custom dataset of 10,000+ telemetry points collected from a high-performance Linux gaming laptop (HP Victus 15 / Ryzen 5 5600H) under various real-world conditions:
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+Idle/Web Browsing (Baseline)
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+Compilation/Workloads (CPU Spikes)
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+Gaming (Sekiro: Shadows Die Twice) (Sustained CPU+GPU Thermal Stress)
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+### Feature Engineering
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+The model doesn't just look at current stats. It relies on engineered trend features to understand context:
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+Rolling Averages: A 1-minute sustained load is more dangerous than a 1-second spike.
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+Thermal Inertia: Combining current temp with recent load history to predict "heat soak."
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+Rate of Change: How fast is the temperature climbing?
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+### Performance
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+AUC Score: ~0.99 (Highly accurate on test set)
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+False Positive Rate: <0.5%
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+False Negative Rate: <1.0%
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+### Tech Stack
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+Training: Scikit-Learn, Pandas, Psutil
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+Deployment: Gradio, Hugging Face Spaces
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+Hardware Target: x86_64 Linux Systems