Upload app.py

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+import pandas as pd
+import pickle, os
+
+MODEL_PATH = "student_model.pkl"
+
+def load_model():
+    if not os.path.exists(MODEL_PATH):
+        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()
+
+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
+    }])
+    y_pred = model.predict(row)[0]
+    return {TARGET_COLS[0]: float(y_pred[0]), TARGET_COLS[1]: float(y_pred[1])}
+
+with gr.Blocks() as iface:
+    gr.Markdown("# Student Score Predictor (Pickle Model)")
+    with gr.Row():
+        attendance = gr.Slider(0, 100, value=90, step=1, label="Attendance (%)")
+        study_hours = gr.Slider(0, 20, value=5, step=1, label="Study Hours / week")
+        parent_support = gr.Slider(1, 5, value=3, step=1, label="Parental Support (1-5)")
+    with gr.Row():
+        sleep_hours = gr.Slider(0, 12, value=8, step=1, label="Sleep Hours / night")
+        reading_hours = gr.Slider(0, 20, value=2, step=1, label="Reading Hours / week")
+        behavior_score = gr.Slider(1, 10, value=7, step=1, label="Behavior Score (1-10)")
+    with gr.Row():
+        pretest_score = gr.Slider(0, 100, value=70, step=1, label="Pretest Score")
+        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,
+        [attendance, study_hours, parent_support, sleep_hours, reading_hours, behavior_score, pretest_score, homework_completion, participation],
+        out
+    )
+
+if __name__ == "__main__":
+    iface.launch()