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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)
# Backward-compatible defaults if old pickle didn't include bounds
feature_mins = bundle.get("feature_mins", {
"Attendance": 0, "StudyHours": 0, "ParentalSupport": 0, "SleepHours": 0,
"ReadingHours": 0, "BehaviorScore": 0, "PretestScore": 0,
"HomeworkCompletion": 0, "Participation": 0
})
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": _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])}
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(0, 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(0, 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(0, 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()
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