first commit

app.py

@@ -0,0 +1,119 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from html import escape
+
+
+st.set_page_config(layout="wide")
+
+column_config = {
+    "Downloads": st.column_config.NumberColumn(
+        "Downloads", format="%d 📥"
+    ),
+    "Likes": st.column_config.NumberColumn(
+        "Likes", format="%d ❤️"
+    ),
+    "Hugging Face URL": st.column_config.LinkColumn("Hugging Face URL", display_text="Open"),
+    "Arxiv URL": st.column_config.LinkColumn("Arxiv URL", display_text="Open"),
+    "PapersWithCode URL": st.column_config.LinkColumn("PapersWithCode URL", display_text="Open")
+}
+
+
+@st.cache_data
+def load_data():
+    file_path = 'HuggingFaceBenchmarkDatasetsWithTags - Copy of HuggingFaceBenchmarkDatasetsWithTags (1).csv'
+    data = pd.read_csv(file_path, na_values=['NA', ''])
+
+    data['Created At'] = pd.to_datetime(data['Created At'], errors='coerce')
+    data['Last Modified'] = pd.to_datetime(data['Last Modified'], errors='coerce')
+
+    numeric_cols = ['Downloads', 'Likes', 'Total Examples', 'Dataset Size (bytes)']
+    for col in numeric_cols:
+        data[col] = pd.to_numeric(data[col], errors='coerce')
+
+    data.replace("", np.nan, inplace=True)
+
+    data = data.drop(columns=['Card Data', 'Model Card README'])
+    return data
+
+def escape_html(val):
+    return escape(val) if isinstance(val, str) else val
+    
+df = load_data()
+
+st.title('Bench1k: LLM Benchmarks & Evals Database')
+st.subheader('Explore 1,327+ benchmarks. By default, sorted by # of downloads.')
+st.write("Use the sidebar to apply filters.")
+
+search_query = st.text_input("Search benchmarks by keyword")
+
+
+task_ids = df['Task IDs'].dropna().unique().tolist()
+selected_task_id = st.sidebar.multiselect('Filter by Task IDs', task_ids)
+
+task_categories = df['Task Categories'].dropna().unique().tolist()
+selected_task_category = st.sidebar.multiselect('Filter by Task Categories', task_categories)
+
+licenses = df['Licenses'].dropna().unique().tolist()
+selected_license = st.sidebar.multiselect('Filter by License', licenses)
+
+min_likes, max_likes = int(df['Likes'].min(skipna=True)), int(df['Likes'].max(skipna=True))
+selected_likes = st.sidebar.slider('Filter by Likes', min_likes, max_likes, (min_likes, max_likes))
+
+min_size, max_size = df['Dataset Size (bytes)'].min(skipna=True), df['Dataset Size (bytes)'].max(skipna=True)
+selected_size = st.sidebar.slider('Filter by Dataset Size (bytes)', min_size, max_size, (min_size, max_size))
+
+min_examples, max_examples = df['Total Examples'].min(skipna=True), df['Total Examples'].max(skipna=True)
+selected_examples = st.sidebar.slider('Filter by Total Examples', min_examples, max_examples, (min_examples, max_examples))
+
+filtered_df = df
+
+if search_query:
+    search_cols = df.select_dtypes(include=[object]).columns
+    filtered_df = filtered_df[filtered_df[search_cols].apply(lambda x: x.str.contains(search_query, case=False, na=False)).any(axis=1)]
+    
+if selected_task_id:
+    mask_task_id = filtered_df['Task IDs'].apply(lambda x: any(task_id.strip() in str(x).split(',') for task_id in selected_task_id))
+    filtered_df = filtered_df[mask_task_id]
+
+if selected_task_category:
+    mask_task_category = filtered_df['Task Categories'].apply(lambda x: any(category.strip() in str(x).split(',') for category in selected_task_category))
+    filtered_df = filtered_df[mask_task_category]
+
+if selected_license:
+    filtered_df = filtered_df[filtered_df['Licenses'].isin(selected_license)]
+
+if selected_likes:
+    filtered_df = filtered_df[filtered_df['Likes'].between(selected_likes[0], selected_likes[1])]
+
+if selected_size:
+    filtered_df = filtered_df[filtered_df['Dataset Size (bytes)'].between(selected_size[0], selected_size[1])]
+
+if selected_examples:
+    filtered_df = filtered_df[filtered_df['Total Examples'].between(selected_examples[0], selected_examples[1])]
+
+def clean_html_sensitive_content(val):
+    if isinstance(val, str):
+        val = ''.join(e for e in val if e.isalnum() or e in [' ', '-', '_'])
+    return val
+
+filtered_df['Task IDs'] = filtered_df['Task IDs'].apply(clean_html_sensitive_content)
+filtered_df['Task Categories'] = filtered_df['Task Categories'].apply(clean_html_sensitive_content)
+
+st.dataframe(filtered_df, column_config=column_config, hide_index=True)
+
+st.sidebar.info("Use the filters above to explore different aspects of the benchmark datasets.")
+
+
+@st.cache_data
+def convert_df(df):
+    return df.to_csv().encode('utf-8')
+
+csv = convert_df(df)
+
+st.download_button(
+    label="Download database as CSV",
+    data=csv,
+    file_name='bench1k_database_full.csv',
+    mime='text/csv',
+)