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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()
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