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from datetime import datetime
import streamlit as st
import pandas as pd
# from load_dataframe import get_data
# Main Streamlit app
def main():
st.title("Hugging Face Papers KPI Dashboard")
# TODO use this instead
# df = get_data()
df = pd.read_csv('/Users/nielsrogge/Downloads/daily_papers_enriched (1).csv')
df = df.drop(['Unnamed: 0'], axis=1)
# Use date as index
# Note that it's a string, not a datetime
df = df.set_index('date')
df.index = pd.to_datetime(df.index).strftime('%d-%m-%Y')
df = df.sort_index()
# Button to select day, month or week
# Add streamlit selectbox.
view_level = st.selectbox(label="View data per day, week or month", options=["day", "week", "month"])
if view_level == "day":
# make a button to select the day, defaulting to today
day = st.date_input("Select day", value="today", format="DD/MM/YYYY")
# convert to the day of a Pandas Timestamp
day = pd.Timestamp(day)
print("Day:", day)
df = df.loc[day.strftime('%d-%m-%Y'):day.strftime('%d-%m-%Y')]
st.write(f"Showing data for {day.strftime('%d/%m/%Y')}")
st.markdown(f"""
## Number of papers: {df.shape[0]}
#### Number of papers with a Github link: {df['github'].notnull().sum()}
#### Number of papers with at least one HF artifact: {df['num_models'].sum()}
""")
st.dataframe(df,
hide_index=True,
column_order=("paper_page", "title", "github", "num_models", "num_datasets", "num_spaces"),
column_config={"github": st.column_config.LinkColumn(),
"paper_page": st.column_config.LinkColumn()},
width=2000)
elif view_level == "week":
# make a button to select the week
week = st.sidebar.date_input("Select week", value=pd.Timestamp.today().isocalendar())
df = df.loc[df['date'].dt.isocalendar().week == week.isocalendar().week]
st.write(f"Showing data for {day}")
st.dataframe(df)
elif view_level == "month":
# make a button to select the month, defaulting to current month
month = st.sidebar.date_input("Select month", value=pd.Timestamp.today().month_name())
df = df.loc[df['date'].dt.month_name() == month]
st.write(f"Showing data for {day}")
st.dataframe(df)
# Display data based on aggregation level
if __name__ == "__main__":
main() |