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import streamlit as st
import datetime
# from .utils import PACKAGE_ROOT
from lrt.utils.functions import template
APP_VERSION = 'v1.4.1'
def render_sidebar():
icons = f'''
<center>
<a href="https://github.com/haoqi7"><img src = "https://cdn-icons-png.flaticon.com/512/733/733609.png" width="23"></img></a> <a href="mailto:w00989988@gmail.com"><img src="https://cdn-icons-png.flaticon.com/512/646/646094.png" alt="email" width = "27" ></a>
</center>
'''
sidebar_markdown = f'''
<center>
<img src="https://raw.githubusercontent.com/leoxiang66/streamlit-tutorial/IDP/widgets/static/tum.png" alt="TUM" width="150"/>
<h1>
Literature Research Tool
</h1>
<code>
{APP_VERSION}
</code>
</center>
{icons}
---
## Choose the Paper Search Platforms'''
st.sidebar.markdown(sidebar_markdown,unsafe_allow_html=True)
# elvsier = st.sidebar.checkbox('Elvsier',value=True)
# IEEE = st.sidebar.checkbox('IEEE',value=False)
# google = st.sidebar.checkbox('Google Scholar')
platforms = st.sidebar.multiselect('Platforms',options=
[
# 'Elvsier',
'IEEE',
# 'Google Scholar',
'Arxiv',
'Paper with Code'
], default=[
# 'Elvsier',
'IEEE',
# 'Google Scholar',
'Arxiv',
'Paper with Code'
])
st.sidebar.markdown('## Choose the max number of papers to search')
number_papers=st.sidebar.slider('number', 10, 100, 20, 5)
st.sidebar.markdown('## Choose the start year of publication')
this_year = datetime.date.today().year
start_year = st.sidebar.slider('year start:', 2000, this_year, 2010, 1)
st.sidebar.markdown('## Choose the end year of publication')
end_year = st.sidebar.slider('year end:', 2000, this_year, this_year, 1)
with st.sidebar:
st.markdown('## Adjust hyperparameters')
with st.expander('Clustering Options'):
standardization = st.selectbox('1) Standardization before clustering', options=['no', 'yes'], index=0 )
dr = st.selectbox('2) Dimension reduction', options=['none', 'pca'], index=0)
tmp = min(number_papers,15)
max_k = st.slider('3) Max number of clusters', 2,tmp , tmp//2)
cluster_model = st.selectbox('4) Clustering model', options=['Gaussian Mixture Model', 'K-means'], index=0)
with st.expander('Keyphrases Generation Options'):
model_cpt = st.selectbox(label='Model checkpoint', options=template.keywords_extraction.keys(),index=0)
st.markdown('---')
st.markdown(icons,unsafe_allow_html=True)
st.markdown('''<center>Copyright © 2022 by HAO Qi</center>''',unsafe_allow_html=True)
# st.sidebar.markdown('## Choose the number of clusters')
# k = st.sidebar.slider('number',1,10,3)
return platforms, number_papers, start_year, end_year, dict(
dimension_reduction= dr,
max_k = max_k,
model_cpt = model_cpt,
standardization = True if standardization == 'yes' else False,
cluster_model = 'gmm' if cluster_model == 'Gaussian Mixture Model' else 'kmeans-euclidean'
) |