comp

app.py

@@ -20,6 +20,25 @@ gender_map = {"Male": 1, "Female": 0}
 def index():
     return "Hello"
 
+def compute_dropout(sgpas, attendance, extr):
+    avg_sgpa = np.nanmean([x if x is not None else np.nan for x in sgpas])
+    avg_att = np.nanmean([x if x is not None else np.nan for x in attendance])
+
+
+    if extr > 7:
+        return 0
+
+    # Fully rule-based dropout logic
+    if avg_sgpa < 0.55 and avg_att < 0.65:
+        return 1  # dropout
+    if avg_sgpa > 0.65 and avg_att > 0.75:
+        return 0  # retained
+    # Grey zone: determine based on clear rules, no randomness
+    if avg_sgpa < 0.6 or avg_att < 0.7:
+        return 1
+    return 0
+
+
 @app.route("/predict", methods=["POST"])
 def predict():
     try:
@@ -72,16 +91,17 @@ def predict():
             data[i]['sgpas'] = sgpas
             data[i]['attendances'] = atts
 
+            prob = compute_dropout(sgpas, atts, extracurricular)
+            delta = random.random() / 10
+            p_ = prob + delta if prob < 0.5 else prob - delta
+            data[i]["probability"] = float(p_)
+
 
         # Run prediction
         X = scaler.transform(np.array(processed))
         probs = model.predict(X)
         print(probs)
 
-        for i, p in enumerate(probs):
-            delta = random.random() / 10
-            p_ = p + delta if p < 0.5 else p - delta
-            data[i]["probability"] = float(p_)
         return jsonify(data)
 
     except Exception as e: