Update rules_engine.py

rules_engine.py

@@ -1,30 +1,36 @@
 # rules_engine.py
 
-import pandas as pd
+def compute_bmi(weight_kg, height_cm):
+    """Compute Body Mass Index (BMI)."""
+    if height_cm <= 0:
+        return 0
+    height_m = height_cm / 100
+    return round(weight_kg / (height_m ** 2), 2)
 
-def evaluate_health_risks(df: pd.DataFrame) -> pd.DataFrame:
-    """
-    Very simple health risk scoring engine.
-    Takes input dataframe with health data and adds risk scores.
-    """
 
-    results = []
+def evaluate_risks(age, bmi, glucose, systolic_bp, diastolic_bp, cholesterol, hemoglobin):
+    """Return a dictionary of risk scores (0–100)."""
+    risks = {}
 
-    for _, row in df.iterrows():
-        risks = {}
+    # Type 2 Diabetes Risk
+    risks["Type 2 Diabetes Risk"] = min(100, 0.2 * glucose + 0.3 * bmi + 0.1 * age)
 
-        # Example rules (you can expand / modify later)
-        risks["risk_Type 2 Diabetes Risk"] = 0.8 if row["glucose"] > 126 else 0.2
-        risks["risk_Hypertension Risk"] = 0.7 if row["systolic_bp"] > 140 else 0.3
-        risks["risk_Depression/Mood Concern"] = 0.6 if row["sleep_hours"] < 5 else 0.2
-        risks["risk_Migraine Risk"] = 0.5 if row["screen_time"] > 8 else 0.2
-        risks["risk_Sleep Apnea Risk"] = 0.7 if row["bmi"] > 30 else 0.3
-        risks["risk_Anemia Risk"] = 0.6 if row["hemoglobin"] < 12 else 0.2
+    # Hypertension Risk
+    risks["Hypertension Risk"] = min(100, 0.25 * systolic_bp + 0.15 * diastolic_bp + 0.1 * age)
 
-        results.append(risks)
+    # Depression/Mood Concern
+    risks["Depression/Mood Concern"] = min(100, 0.05 * age + 0.05 * bmi)
 
-    # Merge with original dataframe
-    risks_df = pd.DataFrame(results)
-    final_df = pd.concat([df, risks_df], axis=1)
+    # Migraine Risk
+    risks["Migraine Risk"] = min(100, 0.05 * age + 0.1 * systolic_bp)
 
-    return final_df
+    # Sleep Apnea Risk
+    risks["Sleep Apnea Risk"] = min(100, 0.3 * bmi + 0.05 * age)
+
+    # Anemia Risk
+    if hemoglobin < 13.5:
+        risks["Anemia Risk"] = min(100, (13.5 - hemoglobin) * 15 + 0.1 * age)
+    else:
+        risks["Anemia Risk"] = 0
+
+    return risks