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import gradio as gr
import pandas as pd
from tabs.market_plots import (
plot_top_10_ranking_by_nr_trades,
plot_trades_and_traders_ranking,
plot_wordcloud_topics,
)
import logging
from huggingface_hub import hf_hub_download
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
def load_data():
# closed_markets metrics
closed_markets_df = hf_hub_download(
repo_id="valory/Olas-predict-dataset",
filename="closed_market_metrics.parquet",
repo_type="dataset",
)
df = pd.read_parquet(closed_markets_df)
return df
logger = get_logger()
logger.info("Loading data from Olas predict dataset")
market_metrics = load_data()
demo = gr.Blocks(theme=gr.themes.Origin())
with demo:
gr.HTML("<h1>Prediction markets popularity dashboard (WIP)</h1>")
gr.Markdown(
"""This app shows the popularity ranking of prediction markets in Olas Predict. Popularity based on two main metrics:
* number of generated trades on the market
* number of traders active on the market.
These are computed only for closed markets."""
)
with gr.Tabs():
with gr.TabItem("🔥 Market Popularity metrics"):
with gr.Row():
gr.Markdown("# 🔝 Top 10 markets based on number of trades")
with gr.Row():
top_10_plot = plot_top_10_ranking_by_nr_trades(
market_metrics=market_metrics
)
with gr.Row(equal_height=True):
gr.Markdown(
"# 🏁 Classification based on nr of trades and nr of traders"
)
with gr.Row():
scatterplot = plot_trades_and_traders_ranking(
market_metrics=market_metrics
)
with gr.Row():
gr.Markdown(
"# ☁️ Wordcloud composed with words from most popular markets"
)
with gr.Row():
wordcloud = plot_wordcloud_topics(market_metrics=market_metrics)
demo.queue(default_concurrency_limit=40).launch()
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