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| import streamlit as st | |
| import arxiv | |
| import pandas as pd | |
| from model import get_recs | |
| # Function to extract the details of the paper | |
| def arxiv_search(input_id): | |
| paper = next(arxiv.Search(id_list=[input_id]).results()) | |
| return paper | |
| if __name__ == "__main__": | |
| st.set_page_config(layout="wide") | |
| # Title for the dashboard | |
| st.title("ArXiv recommender") | |
| # Input article; currently only one input | |
| input_arxiv_id = st.text_input("Insert arXiv id here: ") | |
| if input_arxiv_id: | |
| # Details of the extracted paper are stored | |
| input_data = arxiv_search(input_arxiv_id) | |
| # Dropdown for the input article | |
| with st.expander("%s" % input_data.title): | |
| st.write("Abstract: ", input_data.summary) | |
| if st.button("Show Abstract"): | |
| st.write("Abstract: ", input_data.summary) | |
| # Loading the stored corpus and embeddings and topics | |
| embeddings = pd.read_feather( | |
| "./data/libraries/APSP_50_allenai-specter/embeddings.feather" | |
| ).values | |
| # # Initializing the model | |
| # model = sentence_transformers.SentenceTransformer("allenai-specter") | |
| # # Encoding the title and summary of the input article | |
| # input_embedding = model.encode(input_data.summary) | |
| # # Top 5 recommendations from the corpus | |
| # reco = sentence_transformers.util.semantic_search( | |
| # query_embeddings=input_embedding, corpus_embeddings=embeddings, top_k=5 | |
| # ) | |
| # reco_id = [recs["corpus_id"] for recs in reco[0]] | |
| # # Loading the metadata | |
| # corpus = pd.read_feather( | |
| # "./data/libraries/APSP_50_allenai-specter/metadata.feather" | |
| # ) | |
| recs = get_recs(id_list=[input_arxiv_id]) | |
| st.write("Top 5 similar articles") | |
| for i in range(5): | |
| with st.expander("%s" % recs.title.tolist()[i]): | |
| st.write("Abstract: ", recs.abstract.tolist()[i]) | |
| else: | |
| pass | |