Update predictive_model and requirements

predictive_model.py

@@ -66,4 +66,86 @@ def predict_readmission_risk(model, patient_data: dict) -> float:
         # If it's a classifier with predict_proba
         prob = model.predict_proba(X)[0,1]
         return float(prob)
-    
+
+# Add this main function
+if __name__ == "__main__":
+    # Set up logging
+    logging.basicConfig(level=logging.INFO, 
+                        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    
+    print("==== Discharge Guard Readmission Risk Prediction Demo ====")
+    
+    # Create test patient data
+    test_patients = [
+        {
+            "id": "P001",
+            "name": "John Doe",
+            "age": 45,
+            "num_conditions": 1,
+            "num_medications": 2
+        },
+        {
+            "id": "P002",
+            "name": "Jane Smith",
+            "age": 72,
+            "num_conditions": 4,
+            "num_medications": 7
+        },
+        {
+            "id": "P003",
+            "name": "Bob Johnson",
+            "age": 65,
+            "num_conditions": 3,
+            "num_medications": 5
+        }
+    ]
+    
+    # Create an instance of the SimpleReadmissionModel
+    print("\n1. Testing SimpleReadmissionModel:")
+    simple_model = SimpleReadmissionModel()
+    
+    # Test with each patient
+    for patient in test_patients:
+        risk_score = simple_model.predict(patient)
+        risk_percent = risk_score * 100
+        print(f"  Patient {patient['id']} ({patient['name']}): Risk Score = {risk_percent:.1f}%")
+    
+    # Try to create and save a sample model for demonstration
+    try:
+        from sklearn.ensemble import RandomForestClassifier
+        from sklearn.datasets import make_classification
+        
+        print("\n2. Creating sample RandomForest model for demonstration:")
+        # Generate synthetic data
+        X, y = make_classification(n_samples=1000, n_features=3, 
+                                  n_informative=3, n_redundant=0, 
+                                  random_state=42)
+        
+        # Create and fit a simple model
+        rf_model = RandomForestClassifier(n_estimators=10, random_state=42)
+        rf_model.fit(X, y)
+        
+        # Save the model
+        model_path = "model.joblib"
+        joblib.dump(rf_model, model_path)
+        print(f"  Sample model created and saved to {model_path}")
+        
+        # Now load and use the model
+        loaded_model = load_model(model_path)
+        
+        print("\n3. Testing loaded model predictions:")
+        for patient in test_patients:
+            risk_score = predict_readmission_risk(loaded_model, patient)
+            risk_percent = risk_score * 100
+            print(f"  Patient {patient['id']} ({patient['name']}): Risk Score = {risk_percent:.1f}%")
+            
+    except ImportError:
+        print("\nSkipping sklearn model creation (sklearn not available).")
+        print("Using dummy prediction function instead:")
+        
+        for patient in test_patients:
+            risk_score = predict_readmission_risk(None, patient)
+            risk_percent = risk_score * 100
+            print(f"  Patient {patient['id']} ({patient['name']}): Risk Score = {risk_percent:.1f}%")
+
+    print("\nDemo complete. Implement this model in your discharge workflow to identify high-risk patients.")

requirements.txt

@@ -2,4 +2,5 @@ openai
 reportlab
 numpy
 pandas
-joblib
+joblib
+scikit-learn