import gradio as gr  # type: ignore
import plotly.express as px  # type: ignore

from backend.data import load_cot_data
from backend.envs import API, REPO_ID, TOKEN

logo1_url = "https://raw.githubusercontent.com/logikon-ai/cot-eval/main/assets/AI2_Logo_Square.png"
logo2_url = "https://raw.githubusercontent.com/logikon-ai/cot-eval/main/assets/logo_logikon_notext_withborder.png"
LOGOS = f'<div style="display: flex; justify-content: center;"><a href="https://allenai.org/"><img src="{logo1_url}" alt="AI2" style="width: 30vw; min-width: 20px; max-width: 60px;"></a> <a href="https://logikon.ai"><img src="{logo2_url}" alt="Logikon AI" style="width: 30vw; min-width: 20px; max-width: 60px; margin-left: 10px;"></a></div>'

TITLE = f'<h1 align="center" id="space-title"> Open CoT Dashboard</h1> {LOGOS}'

INTRODUCTION_TEXT = """
Baseline accuracies and marginal accuracy gains for specific models and CoT regimes from the [Open CoT Leaderboard](https://huggingface.co/spaces/logikon/open_cot_leaderboard).
"""

def restart_space():
    API.restart_space(repo_id=REPO_ID, token=TOKEN)

try:
    df_cot_err, df_cot_regimes = load_cot_data()
except Exception as err:
    print(err)
    # sleep for 10 seconds before restarting the space
    import time
    time.sleep(10)
    restart_space()


def plot_evals_init(model_id, regex_model_filter, plotly_mode, request: gr.Request):
    if request and "model" in request.query_params:
        model_param = request.query_params["model"]
        if model_param in df_cot_err.model.to_list():
            model_id = model_param
    return plot_evals(model_id, regex_model_filter, plotly_mode)


def plot_evals(model_id, regex_model_filter, plotly_mode):
    df = df_cot_err.copy()
    df["selected"] = df_cot_err.model.apply(lambda x: "selected" if x==model_id else "-")
    
    try:
        df_filter = df.model.str.contains(regex_model_filter)
    except Exception as err:
        gr.Warning("Failed to apply regex filter", duration=4)
        print("Failed to apply regex filter" + err)
        df_filter = df.model.str.contains(".*")    
    df = df[df_filter | df.selected.eq("selected")]

    #df.sort_values(["selected", "model"], inplace=True, ascending=True)  # has currently no effect with px.scatter
    template = "plotly_dark" if plotly_mode=="dark" else "plotly" 
    fig = px.scatter(df, x="base accuracy", y="marginal acc. gain", color="selected", symbol="model",
                    facet_col="task", facet_col_wrap=3,
                    category_orders={"selected": ["selected", "-"]},
                    color_discrete_sequence=["Orange", "Gray"],
                    template=template,
                    error_y="acc_gain-err", hover_data=['model', "cot accuracy"],
                    custom_data=['visibility'],
                    width=1200, height=700)
    
    fig.update_layout(
        title={"automargin": True},
    )
    return fig, model_id


def styled_model_table_init(model_id, request: gr.Request):
    if request and "model" in request.query_params:
        model_param = request.query_params["model"]
        if model_param in df_cot_regimes.model.to_list():
            model_id = model_param
    return styled_model_table(model_id)


def styled_model_table(model_id):

    def make_pretty(styler):
        styler.hide(axis="index")
        styler.format(precision=1),
        styler.background_gradient(
            axis=None,
            subset=["acc_base", "acc_cot"],
            vmin=20, vmax=100, cmap="YlGnBu"
        )
        styler.background_gradient(
            axis=None,
            subset=["acc_gain"],
            vmin=-20, vmax=20, cmap="coolwarm"
        )
        styler.set_table_styles({
            'task': [{'selector': '',
                  'props': [('font-weight', 'bold')]}],
            'B': [{'selector': 'td',
                  'props': 'color: blue;'}]
        }, overwrite=False)
        return styler

    df_cot_model = df_cot_regimes[df_cot_regimes.model.eq(model_id)][['task', 'cot_chain', 'best_of',
          'temperature', 'top_k', 'top_p', 'acc_base', 'acc_cot', 'acc_gain']]
    
    df_cot_model = df_cot_model \
      .rename(columns={"temperature": "temp"}) \
      .replace({'cot_chain': 'ReflectBeforeRun'}, "Reflect") \
      .sort_values(["task", "cot_chain"]) \
      .reset_index(drop=True)
    
    return df_cot_model.style.pipe(make_pretty)


demo = gr.Blocks()

with demo:

    gr.HTML(TITLE)
    gr.Markdown(INTRODUCTION_TEXT)
    with gr.Row():
        selected_model = gr.Dropdown(list(df_cot_err.model.unique()), value="allenai/tulu-2-70b", label="Model", scale=2)
        regex_model_filter = gr.Textbox(".*", label="Regex", info="to filter models shown in plots", scale=2)
        plotly_mode = gr.Radio(["dark","light"], value="light", label="Plot theme", scale=1)
        submit = gr.Button("Update", scale=1)
    table = gr.DataFrame()
    plot = gr.Plot(label="evals")


    submit.click(plot_evals, [selected_model, regex_model_filter, plotly_mode], [plot, selected_model])
    submit.click(styled_model_table, selected_model, table)

    demo.load(plot_evals_init, [selected_model, regex_model_filter, plotly_mode], [plot, selected_model])
    demo.load(styled_model_table_init, selected_model, table)

demo.launch()