import gradio as gr
import pandas as pd

# Grading scheme mapping function
def percentage_to_gpa_letter(percent):
    if percent >= 90:
        return 4.0, "A+"
    elif percent >= 85:
        return 4.0, "A"
    elif percent >= 80:
        return 3.66, "A-"
    elif percent >= 75:
        return 3.33, "B+"
    elif percent >= 71:
        return 3.0, "B"
    elif percent >= 68:
        return 2.66, "B-"
    elif percent >= 61:
        return 2.0, "C"
    elif percent >= 50:
        return 1.0, "D"
    else:
        return 0.0, "F"

# Calculate projected GPA based on weightage and achieved percentages
def calculate_projected_gpa(df):
    total_score = 0
    total_weight = 0
    for _, row in df.iterrows():
        # Calculate total score based on weightage and achieved %
        total_weight_row = row['Assignment Weight'] + row['Quiz Weight'] + row['GDB Weight'] + row['Midterm Weight'] + row['Final Weight']
        achieved = (row['Assignment Weight']*row['Assignment Achieved'] +
                    row['Quiz Weight']*row['Quiz Achieved'] +
                    row['GDB Weight']*row['GDB Achieved'] +
                    row['Midterm Weight']*row['Midterm Achieved'] +
                    row['Final Weight']*row['Final Achieved']) / total_weight_row
        gpa, grade = percentage_to_gpa_letter(achieved)
        df.at[_, 'GPA'] = gpa
        df.at[_, 'Grade'] = grade
        total_score += gpa
        total_weight += 1
    projected_gpa = total_score / total_weight if total_weight != 0 else 0
    return df, f"{projected_gpa:.2f}"

# Default course table
data = {
    "Include": [True]*5,
    "Course Code": ["CS401","CS403","CS502","CS604","MTH501"],
    "Assignment Weight": [10,10,10,10,10],
    "Quiz Weight": [10,10,10,10,10],
    "GDB Weight": [10,10,10,10,10],
    "Midterm Weight": [30,30,30,30,30],
    "Final Weight": [40,40,40,40,40],
    "Assignment Achieved": [80,70,90,85,75],
    "Quiz Achieved": [85,75,95,80,70],
    "GDB Achieved": [90,80,85,90,75],
    "Midterm Achieved": [75,70,80,85,70],
    "Final Achieved": [50,50,50,50,50], # Default slider value
    "GPA": [0]*5,
    "Grade": [""]*5
}
df = pd.DataFrame(data)

# Gradio app
with gr.Blocks() as app:
    gr.Markdown("## 📊 Projected CGPA Calculator (Weighted Scores)")
    with gr.Row():
        with gr.Column(scale=3):
            table = gr.Dataframe(
                value=df,
                headers=list(df.columns),
                datatype=["bool","str"] + ["number"]*10 + ["number","str"],
                row_count=(5,"dynamic"),
                interactive=True
            )
        with gr.Column(scale=1):
            gr.Markdown("### 🎓 Projected CGPA")
            cgpa_display = gr.Textbox(value="0.00", interactive=False, elem_id="gpa_big", lines=1)

    # Slider style for Final Achieved
    app.css = """
    #gpa_big textarea { 
        font-size: 120px !important; 
        font-weight: bold; 
        text-align: center;
        color: #1a73e8;
    }
    """

    # Update GPA function
    def update_table(df):
        df, projected_gpa = calculate_projected_gpa(df)
        return df, projected_gpa

    table.change(update_table, inputs=table, outputs=[table,cgpa_display])

app.launch()