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import streamlit as st
from api_ import ArxivQuery, IEEEQuery, PaperWithCodeQuery
from lrt.clustering.clusters import SingleCluster
from lrt import ArticleList
from lrt_instance import *
# from pyecharts.charts import Bar
# from pyecharts import options as opts
# import streamlit.components.v1 as st_render
# from .utils import generate_html_pyecharts
from .charts import build_bar_charts
def __preview__(platforms, num_papers, num_papers_preview, query_input,start_year,end_year):
with st.spinner('Searching...'):
paperInGeneral = st.empty() # paper的大概
paperInGeneral_md = '''# 0 Query Results Preview
We have found following papers for you! (displaying 5 papers for each literature platforms)
'''
if 'IEEE' in platforms:
paperInGeneral_md += '''## IEEE
| ID| Paper Title | Publication Year |
| -------- | -------- | -------- |
'''
IEEEQuery.__setup_api_key__('vpd9yy325enruv27zj2d353e')
ieee = IEEEQuery.query(query_input,start_year,end_year,num_papers)
num_papers_preview = min(len(ieee), num_papers_preview)
for i in range(num_papers_preview):
title = str(ieee[i]['title']).replace('\n', ' ')
publication_year = str(ieee[i]['publication_year']).replace('\n', ' ')
paperInGeneral_md += f'''|{i + 1}|{title}|{publication_year}|\n'''
if 'Arxiv' in platforms:
paperInGeneral_md += '''
## Arxiv
| ID| Paper Title | Publication Year |
| -------- | -------- | -------- |
'''
arxiv = ArxivQuery.query(query_input, max_results=num_papers)
num_papers_preview = min(len(arxiv), num_papers_preview)
for i in range(num_papers_preview):
title = str(arxiv[i]['title']).replace('\n', ' ')
publication_year = str(arxiv[i]['published']).replace('\n', ' ')
paperInGeneral_md += f'''|{i + 1}|{title}|{publication_year}|\n'''
if 'Paper with Code' in platforms:
paperInGeneral_md += '''
## Paper with Code
| ID| Paper Title | Publication Year |
| -------- | -------- | -------- |
'''
pwc = PaperWithCodeQuery.query(query_input, items_per_page=num_papers)
num_papers_preview = min(len(pwc), num_papers_preview)
for i in range(num_papers_preview):
title = str(pwc[i]['title']).replace('\n', ' ')
publication_year = str(pwc[i]['published']).replace('\n', ' ')
paperInGeneral_md += f'''|{i + 1}|{title}|{publication_year}|\n'''
paperInGeneral.markdown(paperInGeneral_md)
def render_body(platforms, num_papers, num_papers_preview, query_input, show_preview:bool,start_year,end_year,k):
tmp = st.empty()
if query_input != '':
tmp.markdown(f'You entered query: `{query_input}`')
# preview
if show_preview:
__preview__(platforms,num_papers,num_papers_preview,query_input,start_year,end_year)
# lrt results
generator = baseline_lrt(query_input,num_papers,start_year,end_year,platforms, best_k=k)
for i,plat in enumerate(platforms):
clusters, articles = next(generator)
st.markdown(f'''# {i+1} {plat} Results''')
clusters.sort()
st.markdown(f'''## {i+1}.1 Clusters Overview''')
st.markdown(f'''In this section we show the overview of the clusters, more specifically,''')
st.markdown(f'''\n- the number of papers in each cluster\n- the number of keyphrases of each cluster''')
st.bokeh_chart(build_bar_charts(
x_range=[f'Cluster {i + 1}' for i in range(len(clusters))],
y_names= ['Number of Papers', 'Number of Keyphrases'],
y_data=[[len(c) for c in clusters],[len(c.get_keyphrases()) for c in clusters]]
))
st.markdown(f'''## {i+1}.2 Cluster Details''')
st.markdown(f'''In this section we show the details of each cluster, including''')
st.markdown(f'''\n- the article information in the cluster\n- the keyphrases of the cluster''')
for j,cluster in enumerate(clusters):
assert isinstance(cluster,SingleCluster) #TODO: remove this line
ids = cluster.elements()
articles_in_cluster = ArticleList([articles[id] for id in ids])
st.markdown(f'''**Cluster {j + 1}**''')
st.dataframe(articles_in_cluster.getDataFrame())
st.markdown(f'''The top 5 keyphrases of this cluster are:''')
md = ''
for keyphrase in cluster.top_5_keyphrases:
md += f'''- `{keyphrase}`\n'''
st.markdown(md)
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