import fev
import gradio as gr
import pandas as pd

# Load the CSV data into a pandas DataFrame
df = pd.read_csv(
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/seasonal_naive.csv"
)

markdown_text = """
Hello world
"""

summary_urls = [
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/auto_arima.csv",
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/auto_ets.csv",
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/auto_theta.csv",
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_base.csv",
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_large.csv",
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_mini.csv",
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_small.csv",
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_tiny.csv",
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_bolt_base.csv",
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_bolt_mini.csv",
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_bolt_small.csv",
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_bolt_tiny.csv",
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/moirai_base.csv",
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/moirai_large.csv",
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/moirai_small.csv",
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/seasonal_naive.csv",
    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/timesfm.csv",
]

selected_cols = ["gmean_relative_error", "avg_rank", "median_inference_time_s"]
rename_cols = {
    "gmean_relative_error": "Average relative error",
    "avg_rank": "Average rank",
    "median_inference_time_s": "Median inference time (s)",
}

lb_mase = fev.leaderboard(summary_urls, metric_column="MASE")[selected_cols].rename(columns=rename_cols)
lb_wql = fev.leaderboard(summary_urls, metric_column="WQL")[selected_cols].rename(columns=rename_cols)


with gr.Blocks() as demo:
    with gr.Tab("Leaderboard"):
        gr.Markdown("## Point forecast accuracy (measured by MASE)")
        gr.Dataframe(
            value=lb_mase,
            interactive=False,
        )

        gr.Markdown("## Probabilistic forecast accuracy (measured by WQL)")
        gr.Dataframe(
            value=lb_wql,
            interactive=False,
        )

    with gr.Tab("About"):
        gr.Markdown(markdown_text)

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