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| import streamlit as st | |
| from .sidebar import render_sidebar | |
| from requests_toolkit import ArxivQuery,IEEEQuery,PaperWithCodeQuery | |
| from trendflow.lrt.clustering.clusters import SingleCluster | |
| from trendflow.lrt.clustering.config import Configuration | |
| from trendflow.lrt import ArticleList, LiteratureResearchTool | |
| from trendflow.lrt_instance import * | |
| from .charts import build_bar_charts | |
| def home(): | |
| # sidebar content | |
| platforms, number_papers, start_year, end_year, hyperparams = render_sidebar() | |
| # body head | |
| with st.form("my_form", clear_on_submit=False): | |
| st.markdown('''# 👋 Hi, enter your query here :)''') | |
| query_input = st.text_input( | |
| 'Enter your query:', | |
| placeholder='''e.g. "Machine learning"''', | |
| # label_visibility='collapsed', | |
| value='' | |
| ) | |
| show_preview = st.checkbox('show paper preview') | |
| # Every form must have a submit button. | |
| submitted = st.form_submit_button("Search") | |
| if submitted: | |
| # body | |
| render_body(platforms, number_papers, 5, query_input, | |
| show_preview, start_year, end_year, | |
| hyperparams, | |
| hyperparams['standardization']) | |
| 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, | |
| hyperparams: dict, standardization=False): | |
| 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) | |
| with st.spinner("Clustering and generating..."): | |
| # lrt results | |
| ## baseline | |
| if hyperparams['dimension_reduction'] == 'none' \ | |
| and hyperparams['model_cpt'] == 'keyphrase-transformer' \ | |
| and hyperparams['cluster_model'] == 'kmeans-euclidean': | |
| model = baseline_lrt | |
| else: | |
| config = Configuration( | |
| plm='''all-mpnet-base-v2''', | |
| dimension_reduction=hyperparams['dimension_reduction'], | |
| clustering=hyperparams['cluster_model'], | |
| keywords_extraction=hyperparams['model_cpt'] | |
| ) | |
| model = LiteratureResearchTool(config) | |
| generator = model.yield_(query_input, num_papers, start_year, end_year, max_k=hyperparams['max_k'], | |
| platforms=platforms, standardization=standardization) | |
| 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.get_elements() | |
| articles_in_cluster = ArticleList([articles[id] for id in ids]) | |
| st.markdown(f'''**Cluster {j + 1}**''') | |
| st.dataframe(articles_in_cluster.to_dataframe()) | |
| 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) | |