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import warnings
import streamlit as st
warnings.filterwarnings("ignore", category=UserWarning, module="streamlit")
import pandas as pd
import json
import os
from huggingface_hub import HfApi, login
from streamlit_cookies_manager import EncryptedCookieManager
st.set_page_config(
page_title="Holistic AI - ML Verticals papers",
page_icon="👋",
layout='wide'
)
def program():
st.title("Papers")
dataset_name = "holistic-ai/mitigation_ml_bias_strategies"
token = os.getenv("HF_TOKEN")
api = HfApi()
login(token)
repo_path = api.snapshot_download(repo_id=dataset_name, repo_type="dataset")
dirnames = [dirname for dirname in os.listdir(repo_path) if not dirname.startswith(".")]
dirnames = sorted(dirnames, key=lambda x:len(x))
st.sidebar.title("Namespaces")
selected_namespace = st.sidebar.selectbox("Select Namespace", dirnames)
selected_paper_type = st.sidebar.selectbox("Select Paper Type", ['Metrics',"Mitigators"])
if selected_namespace:
if selected_paper_type=='Metrics':
with open(f'{repo_path}/{selected_namespace}/grouped_metrics.json') as file:
data = json.load(file)
elif selected_paper_type=='Mitigators':
with open(f'{repo_path}/{selected_namespace}/grouped_mitigators.json') as file:
data = json.load(file)
task_names = list(data.keys())
st.sidebar.title("Tasks")
selected_task = st.sidebar.selectbox("Select a Task", task_names)
if selected_task:
st.header(selected_task)
results = data[selected_task]
rec = {str(r['id']): r for r in results['recommendations']}
for group in results['groups']:
ids = [i.strip() for i in group['ids'].split(",")]
selected_rec = [rec[i] for i in ids]
selected_rec = pd.DataFrame(selected_rec)
selected_rec['date'] = pd.to_datetime(selected_rec.apply(lambda x:x['metadata']['date'], axis=1))
selected_rec = selected_rec.sort_values(by='date', ascending=False).to_dict('records')
rec2html = ''.join([f"""<tr><td style="border: 1px solid #ddd; padding: 8px;">{i+1}</td><td style="border: 1px solid #ddd; padding: 8px;"><a href="{rec['metadata']['id']}" target="_blank">{rec['title']}</a></td><td style="border: 1px solid #ddd; padding: 8px;">{rec['metadata']['date']}</td></tr>""" for i, rec in enumerate(selected_rec)])
title = group['title'].split(':', 1)[1].strip()
st.markdown(f"""
<div style="border: 1px solid #ccc; padding: 10px; margin: 10px 0; border-radius: 5px; width: 100%;">
<p><b>{title}</b></p>
<p>{group['recommendation']}</p>
<table style="width: 100%; border-collapse: collapse;">
<thead>
<tr>
<th style="border: 1px solid #ddd; padding: 8px;">Index</th>
<th style="border: 1px solid #ddd; padding: 8px;">Paper</th>
<th style="border: 1px solid #ddd; padding: 8px;">Year</th>
</tr>
</thead>
<tbody>
{rec2html}
</tbody>
</table>
</div>
""", unsafe_allow_html=True)
SECRET_KEY = os.getenv('SECRET_KEY')
cookies = EncryptedCookieManager(
prefix="login",
password=os.getenv('COOKIES_PASSWORD')
)
if not cookies.ready():
st.stop()
def main():
# Título de la aplicación
st.title("Holistic AI - ML Papers")
if not cookies.get("authenticated"):
# Entrada de la clave secreta
user_key = st.text_input("Password:", type="password")
if st.button("Login"):
# Verificar si la clave ingresada coincide con la clave secreta
if user_key == SECRET_KEY:
cookies.__setitem__("authenticated", "True")
st.experimental_rerun()
else:
st.error("Access not granted. Incorrect Password.")
else:
program()
if __name__ == "__main__":
main() |