# app.py
import gradio as gr
import pandas as pd
import joblib
from huggingface_hub import hf_hub_download

MODEL_REPO = "DetectiveShadow/Grade_predictor"  # where assignment_predictor.pkl lives

def load_model():
    pipe = joblib.load(hf_hub_download(MODEL_REPO, "assignment_predictor.pkl"))
    return pipe

PIPE = load_model()

SUBJECTS = ["Math","Science","English","History","Tech","Drama","Elective"]
DIFFICULTY = ["Regular","Honors","AP"]
ASSIGN_TYPES = ["Assignment","Test","Project"]

def predict(attendance, hours, grade_level, subject, difficulty, assignment_type, confidence):
    row = pd.DataFrame([{
        "attendance": float(attendance),
        "hours_studied": int(hours),
        "grade_level": int(grade_level),
        "subject": subject,
        "course_difficulty": difficulty,
        "assignment_type": assignment_type,
        "confidence_before_assessment": int(confidence),
    }])
    score = float(PIPE.predict(row)[0])

    def to_letter(x):
        if x >= 90: return "A"
        if x >= 80: return "B"
        if x >= 70: return "C"
        if x >= 60: return "D"
        return "F"

    return {"Predicted Assignment Score": round(score, 1), "Letter": to_letter(score)}

with gr.Blocks(title="Assignment Score Predictor") as demo:
    gr.Markdown("# 📝 Assignment Score Predictor")
    gr.Markdown("Predict a single assignment score using your study & course details (no uploads).")

    with gr.Row():
        attendance = gr.Slider(0.5, 1.0, value=0.95, step=0.01, label="Attendance (0–1)")
        hours      = gr.Slider(0, 30, value=8, step=1, label="Hours studied")
    with gr.Row():
        grade_lvl  = gr.Slider(5, 12, value=11, step=1, label="Grade level")
        subject    = gr.Dropdown(SUBJECTS, value="Math", label="Subject", allow_custom_value=True)
    with gr.Row():
        difficulty = gr.Dropdown(DIFFICULTY, value="Regular", label="Course difficulty")
        a_type     = gr.Dropdown(ASSIGN_TYPES, value="Assignment", label="Assignment type")
    confidence = gr.Slider(0, 10, value=6, step=1, label="Confidence before assessment")

    go = gr.Button("Predict")
    out = gr.JSON(label="Prediction")

    go.click(predict, [attendance, hours, grade_lvl, subject, difficulty, a_type, confidence], out)

if __name__ == "__main__":
    demo.launch()