Update risk_model.py

risk_model.py

@@ -1,3 +1,4 @@
+import os
 import pandas as pd
 from sklearn.ensemble import RandomForestClassifier
 from joblib import dump, load
@@ -6,30 +7,20 @@ MODEL_PATH = "heating_model.pkl"
 DATA_PATH = "mantle_training.csv"
 HISTORY = []
 
+# Function to train the model from scratch
+def train_and_save_model():
+    data = pd.read_csv(DATA_PATH)
+    X = data[["temperature", "duration"]]
+    y = data["risk_level"]
+    model = RandomForestClassifier()
+    model.fit(X, y)
+    dump(model, MODEL_PATH)
+    return model
+
+# Safe model loader
 def load_model():
+    if not os.path.exists(MODEL_PATH):
+        return train_and_save_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)