# app.py
import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame()

def load_csv(file):
    global df
    try:
        df = pd.read_csv(file.name)

        required_cols = {"Name", "Age", "Salary", "Performance Score"}
        if not required_cols.issubset(df.columns):
            return None, f"❌ CSV must contain {required_cols}"

        # Add Age Group
        df["Age Group"] = pd.cut(
            df["Age"],
            bins=[20, 25, 30, 35, 40],
            labels=["21-25", "26-30", "31-35", "36-40"]
        )
        return df, "✅ File loaded successfully!"
    except Exception as e:
        return None, f"❌ Error: {e}"


def show_analysis():
    if df.empty:
        return "⚠️ Please load a CSV first!"
    avg_salary = df["Salary"].mean()
    top_salary = df.loc[df["Salary"].idxmax(), "Name"]
    top_perf = df.loc[df["Performance Score"].idxmax(), "Name"]

    return f"""
    📊 **Average Salary:** {avg_salary:.2f}  
    💰 **Highest Salary Holder:** {top_salary}  
    🏆 **Top Performer:** {top_perf}
    """


def salary_chart():
    if df.empty:
        return None
    plt.figure(figsize=(8, 5))
    plt.bar(df["Name"], df["Salary"], color="skyblue", edgecolor="black")
    plt.title("Employee Salaries", fontsize=14)
    plt.ylabel("Salary")
    plt.xticks(rotation=45)
    plt.grid(axis="y", linestyle="--", alpha=0.7)
    plt.tight_layout()

    filepath = "salary_chart.png"
    plt.savefig(filepath)
    plt.close()
    return filepath


def performance_chart():
    if df.empty:
        return None
    plt.figure(figsize=(8, 5))
    plt.plot(df["Name"], df["Performance Score"], marker="o",
             color="green", linewidth=2, markersize=8)
    plt.title("Employee Performance Scores", fontsize=14)
    plt.ylabel("Performance Score")
    plt.xticks(rotation=45)
    plt.grid(True, linestyle="--", alpha=0.6)
    plt.tight_layout()

    filepath = "performance_chart.png"
    plt.savefig(filepath)
    plt.close()
    return filepath


def search_employee(query):
    if df.empty:
        return None
    if not query.strip():
        return df
    return df[df["Name"].str.lower().str.contains(query.lower())]


def show_missing():
    if df.empty:
        return "⚠️ Please load a CSV first!"
    missing = df.isnull().sum()
    percent = (df.isnull().mean() * 100).round(2)
    report = "### 🔎 Missing Values Report\n\n"
    for col in df.columns:
        report += f"- **{col}** → {missing[col]} missing ({percent[col]}%)\n"
    return report


def fill_missing():
    global df
    if df.empty:
        return None, "⚠️ Please load a CSV first!"

    # Fill numeric cols with mean, categorical with mode
    for col in df.columns:
        if df[col].dtype in ["float64", "int64"]:
            df[col] = df[col].fillna(df[col].mean())
        else:
            df[col] = df[col].fillna(df[col].mode()[0] if not df[col].mode().empty else "Unknown")

    return df, "✅ Missing values filled (mean for numeric, mode for categorical)"


# Build Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("## ✨ Employee Data Analysis Dashboard ✨")

    with gr.Row():
        file_input = gr.File(label="📂 Upload CSV", file_types=[".csv"])
        status = gr.Textbox(label="Status", interactive=False)

    with gr.Row():
        search_box = gr.Textbox(label="🔍 Search Employee")
        analysis_btn = gr.Button("📊 Show Analysis")

    data_table = gr.Dataframe(
        headers=["Name", "Age", "Salary", "Performance Score", "Age Group"], 
        label="Employee Data"
    )
    analysis_output = gr.Markdown()

    with gr.Row():
        salary_btn = gr.Button("💰 Salary Chart")
        performance_btn = gr.Button("🏆 Performance Chart")

    salary_plot = gr.Image(type="filepath", label="Salary Chart")
    performance_plot = gr.Image(type="filepath", label="Performance Chart")

    # 🔎 Missing value section
    with gr.Row():
        missing_btn = gr.Button("🔎 Show Missing Values")
        fill_btn = gr.Button("🛠 Fill Missing Values")

    missing_output = gr.Markdown()

    # Events
    file_input.change(load_csv, inputs=file_input, outputs=[data_table, status])
    search_box.submit(search_employee, inputs=search_box, outputs=data_table)
    analysis_btn.click(show_analysis, inputs=None, outputs=analysis_output)
    salary_btn.click(salary_chart, inputs=None, outputs=salary_plot)
    performance_btn.click(performance_chart, inputs=None, outputs=performance_plot)
    missing_btn.click(show_missing, inputs=None, outputs=missing_output)
    fill_btn.click(fill_missing, inputs=None, outputs=[data_table, status])

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