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import streamlit as st |
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import streamlit.components.v1 as components |
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import pandas as pd |
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import numpy as np |
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import torch |
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from sentence_transformers import SentenceTransformer, util |
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st.set_page_config(page_title="App", layout="wide") |
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st.title("Semantic Search on HAL UNIV-COTEDAZUR Collection (articles)") |
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with st.spinner('Loading dataset...'): |
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df = pd.read_csv("data/hal_articles.csv", sep=",", encoding="utf-8") |
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df = df.replace(np.nan, '') |
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df = df.astype(str) |
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def llm_response(query, model_option): |
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embedder = SentenceTransformer(model_option) |
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question_embedding = embedder.encode(query, convert_to_tensor=True) |
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corpus_embeddings = torch.load(f"embeddings/embeddings_{model_option}.pt", map_location=torch.device('cpu')) |
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hits = util.semantic_search(question_embedding, corpus_embeddings, top_k=5) |
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article_data_list = [] |
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data_list = [] |
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for hit in hits[0]: |
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hit_id = hit['corpus_id'] |
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article_data = df.iloc[hit_id] |
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article_data_list.append({"title": article_data["title_s"] + ". " + article_data["subTitle_s"], |
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"date": article_data["producedDate_s"], |
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"journal" : article_data["journalTitle_s"], |
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"pub": article_data["journalPublisher_s"], |
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"abstract": article_data["abstract_s"] |
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}) |
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return article_data_list |
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models = ['all-MiniLM-L6-v2', 'all-mpnet-base-v2'] |
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model_option = st.sidebar.selectbox("Choose the open embeddings model to use ?", models) |
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with st.container(): |
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if query := st.text_input( |
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"Enter your question :"): |
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st.markdown(f"### :green[{model_option} results]") |
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with st.expander(":blue[click here to see the HAL search engine results]"): |
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components.iframe(f"https://hal.univ-cotedazur.fr/search/index/?q={query}&rows=30&docType_s=ART", height=800, scrolling=True) |
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with st.spinner('Calculating...'): |
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response = llm_response(query, model_option) |
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for x in response: |
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st.success("**Title** : " + x["title"] + " \n " + "**Date** : " + x["date"] + " \n " + "**Journal** : " + x["journal"] + "(" + x["pub"] + ")" + " \n " + "**Abstract** : " + x["abstract"]) |
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