import argparse
import pandas as pd
import matplotlib.pyplot as plt

def main():
    parser = argparse.ArgumentParser(description="Generate latency graph from CSV.")
    parser.add_argument("--input", required=True, help="Input CSV file")
    parser.add_argument("--output", required=True, help="Output PNG path")
    parser.add_argument("--user", required=True, help="Username for graph title")
    args = parser.parse_args()

    # Load CSV
    df = pd.read_csv(args.input)

    # Convert to float
    df["Latency(s)"] = pd.to_numeric(df["Latency(s)"], errors="coerce")
    df = df.dropna(subset=["Latency(s)"])

    latencies = df["Latency(s)"].values
    titles = df["Title"].values
    short_titles = [t.split('_')[0] for t in titles]

    # Compute average
    avg_latency = latencies.mean()

    plt.figure(figsize=(14, 6))

    # --- Bar chart ---
    bars = plt.bar(short_titles, latencies)

    # --- Average line ---
    plt.axhline(y=avg_latency, linestyle="--")
    plt.text(
        -0.4, avg_latency,
        f"Avg = {avg_latency:.3f}s",
        va="bottom",
        fontsize=10,
        fontweight="bold"
    )

    # --- Label each bar with exact value ---
    for i, value in enumerate(latencies):
        plt.text(i, value, f"{value:.3f}", ha="center", va="bottom", fontsize=8)

    plt.xlabel("Image Cases")
    plt.ylabel("Latency (seconds)")
    plt.title(f"Latency Performance of {args.user}")
    plt.ylim(1, 300)

    plt.tight_layout()
    plt.savefig(args.output)

    print(f"Graph saved to: {args.output}")

if __name__ == "__main__":
    main()