| 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() | |