Update app.py

app.py

@@ -9,21 +9,41 @@ def load_model():
         raise FileNotFoundError("student_model.pkl not found. Upload or run train.py first.")
     with open(MODEL_PATH, "rb") as f:
         bundle = pickle.load(f)
-    return bundle["model"], bundle["features"], bundle["targets"]
 
-model, FEATURE_COLS, TARGET_COLS = load_model()
+    # Backward-compatible defaults if old pickle didn't include bounds
+    feature_mins = bundle.get("feature_mins", {
+        "Attendance": 70, "StudyHours": 1, "ParentalSupport": 1, "SleepHours": 5,
+        "ReadingHours": 0, "BehaviorScore": 1, "PretestScore": 0,
+        "HomeworkCompletion": 0, "Participation": 1
+    })
+    feature_maxs = bundle.get("feature_maxs", {
+        "Attendance": 100, "StudyHours": 20, "ParentalSupport": 5, "SleepHours": 12,
+        "ReadingHours": 20, "BehaviorScore": 10, "PretestScore": 100,
+        "HomeworkCompletion": 100, "Participation": 10
+    })
+
+    return bundle["model"], bundle["features"], bundle["targets"], feature_mins, feature_maxs
+
+model, FEATURE_COLS, TARGET_COLS, FEATURE_MINS, FEATURE_MAXS = load_model()
+
+def _clip(name, val):
+    lo = FEATURE_MINS.get(name, None)
+    hi = FEATURE_MAXS.get(name, None)
+    if lo is not None and val < lo: val = lo
+    if hi is not None and val > hi: val = hi
+    return val
 
 def predict_fn(attendance, study_hours, parent_support, sleep_hours, reading_hours, behavior_score, pretest_score, homework_completion, participation):
     row = pd.DataFrame([{
-        "Attendance": attendance,
-        "StudyHours": study_hours,
-        "ParentalSupport": parent_support,
-        "SleepHours": sleep_hours,
-        "ReadingHours": reading_hours,
-        "BehaviorScore": behavior_score,
-        "PretestScore": pretest_score,
-        "HomeworkCompletion": homework_completion,
-        "Participation": participation
+        "Attendance": _clip("Attendance", attendance),
+        "StudyHours": _clip("StudyHours", study_hours),
+        "ParentalSupport": _clip("ParentalSupport", parent_support),
+        "SleepHours": _clip("SleepHours", sleep_hours),
+        "ReadingHours": _clip("ReadingHours", reading_hours),
+        "BehaviorScore": _clip("BehaviorScore", behavior_score),
+        "PretestScore": _clip("PretestScore", pretest_score),
+        "HomeworkCompletion": _clip("HomeworkCompletion", homework_completion),
+        "Participation": _clip("Participation", participation)
     }])
     y_pred = model.predict(row)[0]
     return {TARGET_COLS[0]: float(y_pred[0]), TARGET_COLS[1]: float(y_pred[1])}
@@ -43,7 +63,6 @@ with gr.Blocks() as iface:
         homework_completion = gr.Slider(0, 100, value=85, step=1, label="Homework Completion (%)")
         participation = gr.Slider(1, 10, value=6, step=1, label="Participation (1-10)")
 
-
     out = gr.JSON(label="Predicted Scores")
     gr.Button("Predict").click(
         predict_fn,