Upload app.py

app.py

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+import pickle
+import numpy as np
+
+# Load model and feature columns
+with open("logistic_regression_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+with open("feature_columns.pkl", "rb") as f:
+    feature_columns = pickle.load(f)
+
+def predict_capacity_risk(features: dict):
+    X = np.array([[features[col] for col in feature_columns]])
+    prob = model.predict_proba(X)[0][1]
+    return prob
+
+if __name__ == "__main__":
+    sample = {
+        "daily_capacity": 720,
+        "demand": 5000,
+        "material_available": 6000
+    }
+    print("Risk score:", predict_capacity_risk(sample))