from datetime import datetime, timedelta
import gradio as gr
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import duckdb
import logging


def get_logger():
    logger = logging.getLogger(__name__)
    logger.setLevel(logging.DEBUG)
    # stream handler and formatter
    stream_handler = logging.StreamHandler()
    stream_handler.setLevel(logging.DEBUG)
    formatter = logging.Formatter(
        "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
    )
    stream_handler.setFormatter(formatter)
    logger.addHandler(stream_handler)
    return logger


logger = get_logger()


def prepare_data():
    """
    Get all data from the parquet files
    """
    logger.info("Getting all data")
    con = duckdb.connect(":memory:")

    # Query to fetch invalid trades data
    query = f"""
    SELECT *
    FROM read_parquet('./live_data/markets_live_data.parquet')
    """
    df = con.execute(query).fetchdf()
    return df


demo = gr.Blocks()
markets_data = prepare_data()

with demo:
    gr.HTML("<h1>Olas Predict Live Markets </h1>")
    gr.Markdown("This app shows the distributions of predictions on the live markets.")

    with gr.Tabs():
        with gr.TabItem("💹Probability distributions"):
            with gr.Row():
                gr.Markdown("# Daily probability distribution of live markets")
            with gr.Row():
                # TODO
                print("WIP")
                gr.Markdown("Under construction (WIP)")
                # daily_distributions = plot_daily_market_distributions(markets_data)

    demo.queue(default_concurrency_limit=40).launch()