Upload Pearson_Grader.py

Pearson_Grader.py

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+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)
+
+