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import streamlit as st
import numpy as np
import pandas as pd
from datasets import load_dataset
st.set_page_config(layout="wide")
col1, col2 = st.columns([2, 3]) # Adjust the width ratio as needed
sources = [
"https://huggingface.co/datasets/cfahlgren1/hub-stats",
"https://huggingface.co/datasets/maxiw/hf-posts",
]
with col1:
st.header("HuggingFace 🤗 Posts leaderboard")
with col2:
selected_source = st.selectbox(
"Data Source:",
options=sources,
index=0,
)
if selected_source == sources[0]:
try:
df = pd.read_parquet("hf://datasets/cfahlgren1/hub-stats/posts.parquet")
# ds = load_dataset("cfahlgren1/hub-stats", "posts")
# df = pd.DataFrame(ds['train']).info()
df["Name"] = df.fullname
df["username"] = df.name
except Exception as exp:
st.error(f'ERROR >> pd.read_parquet("hf://datasets/cfahlgren1/hub-stats/posts.parquet")\n{exp}', icon="🚨")
selected_source == sources[1]
st.info(f'This can be solved by "Space Restart"\nSwitching Sources for now...\nNew Source: {selected_source}', icon="ℹ️")
if selected_source == sources[1]:
df = pd.read_json("hf://datasets/maxiw/hf-posts/posts.jsonl", lines=True)
df["publishedAt"] = pd.to_datetime(df.publishedAt)
print(">>> ", df.columns)
df["Name"] = df.author.apply(lambda x: x["fullname"])
df["username"] = df.author.apply(lambda x: x["name"])
# Define the metrics
metrics = ["totalUniqueImpressions", "totalReactions", "numComments", "Num of posts"]
# Get min and max dates from the DataFrame
min_date = df["publishedAt"].min().to_pydatetime()
max_date = df["publishedAt"].max().to_pydatetime()
# Create columns for the slider and the selectbox
col1, col2 = st.columns([3, 1]) # Adjust the width ratio as needed
with col1:
date_range = st.slider(
"Select Date Range",
min_value=min_date,
max_value=max_date,
value=(min_date, max_date),
format="DD/MMM/YYYY",
)
with col2:
selected_metric = st.selectbox(
"Sort by:",
options=metrics,
index=0,
)
# Filter the DataFrame based on selected date range
mask = df["publishedAt"].between(*date_range)
df = df[mask]
df["totalReactions"] = df.reactions.apply(lambda x: sum([_["count"] for _ in x]))
df["Num of posts"] = 1
# Ensure metrics columns are integers, handling NaN values
df[metrics] = df[metrics].fillna(0).astype(int)
data = (
df.groupby(["username", "Name"])[metrics]
.sum()
.sort_values(selected_metric, ascending=False)
.reset_index()
)
data.index = np.arange(1, len(data) + 1)
data.index.name = "Rank"
# Format metrics columns with commas
data[metrics] = data[metrics].applymap(lambda x: f"{x:,}")
def make_clickable(val):
return f'<a target="_blank" href="https://huggingface.co/{val}">{val}</a>'
df_styled = data.style.format({"username": make_clickable})
st.write(
f"""<center>{df_styled.to_html(escape=False, index=False)}""",
unsafe_allow_html=True,
)
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