Pearson_Grader.py

import gradio as gr
import pandas as pd

# Global list to store student data
student_data = []

# Function to calculate and store the grade
def calculate_grade(name, id, pass_criteria, merit_criteria, distinct_criteria):
    total_criteria = pass_criteria + merit_criteria + distinct_criteria
    grade = "Not Achieved"

    if pass_criteria == 11 or (pass_criteria >= 8 and total_criteria >= 12):
        grade = "P"
    if pass_criteria >= 8 and merit_criteria >= 6 and total_criteria >= 17:
        grade = "M"
    if pass_criteria >= 8 and merit_criteria >= 6 and distinct_criteria >= 3 and total_criteria >= 20:
        grade = "D"

    student_data.append([name, id, grade])
    return f"Added: {name}, {id}, {grade}"

# Function to export data to Excel
def export_to_excel():
    if not student_data:
        return "No data to export."

    df = pd.DataFrame(student_data, columns=["Name", "ID", "Grade"])
    df.to_excel("grades.xlsx", index=False)
    return "Data exported to grades.xlsx"

# Gradio interface
with gr.Blocks() as app:
    gr.Markdown("Student Grading App")
    with gr.Row():
        name = gr.Textbox(label="Student Name")
        id = gr.Number(label="Student ID", precision=0)
    with gr.Row():
        pass_criteria = gr.Slider(minimum=0, maximum=11, label="Pass Criteria Met")
        merit_criteria = gr.Slider(minimum=0, maximum=8, label="Merit Criteria Met")
        distinct_criteria = gr.Slider(minimum=0, maximum=4, label="Distinct Criteria Met")
    with gr.Row():
        grade_button = gr.Button("Add Student")
        export_button = gr.Button("Export to Excel")
    output = gr.Textbox(label="Output", lines=2)
    export_status = gr.Text(label="Export Status")
    grade_button.click(calculate_grade, inputs=[name, id, pass_criteria, merit_criteria, distinct_criteria], outputs=output)
    export_button.click(export_to_excel, inputs=[], outputs=export_status)

    app.launch(share=True)