Create risk_model.py

risk_model.py

@@ -0,0 +1,35 @@
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from joblib import dump, load
+
+MODEL_PATH = "heating_model.pkl"
+DATA_PATH = "mantle_training.csv"
+HISTORY = []
+
+def load_model():
+    return load(MODEL_PATH)
+
+model = load_model()
+
+def predict_risk(temp, duration):
+    global model
+    pred = model.predict([[temp, duration]])[0]
+    score = max(model.predict_proba([[temp, duration]])[0]) * 100
+    HISTORY.append({"Temperature": temp, "Duration": duration, "Risk": pred, "Confidence": round(score, 2)})
+    return pred, round(score, 2)
+
+def retrain_model():
+    try:
+        data = pd.read_csv(DATA_PATH)
+        X = data[["temperature", "duration"]]
+        y = data["risk_level"]
+        clf = RandomForestClassifier().fit(X, y)
+        dump(clf, MODEL_PATH)
+        global model
+        model = clf
+        return "✅ Model retrained successfully"
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+def get_history_df():
+    return pd.DataFrame(HISTORY)