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import streamlit as st
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import requests
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from datetime import datetime, timedelta
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import pandas as pd
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from dotenv import load_dotenv
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import os
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load_dotenv()
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SCOPUS_API_KEY = "8e94f85eb6044ef1cde06e8b5a426a09"
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def search_scopus(query, start_year, end_year, max_results=50):
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base_url = "https://api.elsevier.com/content/search/scopus"
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params = {
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"query": query,
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"date": f"{start_year}-{end_year}",
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"count": max_results,
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"sort": "citedby-count desc",
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"field": "title,author,year,publicationName,description,citedby-count,doi,eid"
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}
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headers = {
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"X-ELS-APIKey": SCOPUS_API_KEY,
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"Accept": "application/json"
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}
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try:
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response = requests.get(base_url, params=params, headers=headers)
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response.raise_for_status()
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return response.json()["search-results"]["entry"]
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except requests.exceptions.RequestException as e:
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st.error(f"An error occurred while searching Scopus: {e}")
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return []
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def format_authors(author_info):
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if isinstance(author_info, list):
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return ", ".join([author.get("authname", "") for author in author_info])
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elif isinstance(author_info, dict):
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return author_info.get("authname", "")
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else:
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return "N/A"
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def safe_get(dictionary, keys, default="N/A"):
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for key in keys:
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if isinstance(dictionary, dict) and key in dictionary:
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dictionary = dictionary[key]
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else:
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return default
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return dictionary
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def get_paper_link(paper):
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doi = safe_get(paper, ["prism:doi"])
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if doi != "N/A":
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return f"https://doi.org/{doi}"
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eid = safe_get(paper, ["eid"])
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if eid != "N/A":
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return f"https://www.scopus.com/record/display.uri?eid={eid}&origin=resultslist"
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return "#"
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def main():
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st.set_page_config(page_title="S.H.E.R.L.O.C.K. Research Assistant", page_icon="🔬", layout="wide")
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st.sidebar.title("S.H.E.R.L.O.C.K.")
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st.sidebar.markdown("""
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**S**ystematic **H**olistic **E**ducational **R**esource for **L**iterature and **O**ptimizing **C**ognitive **K**nowledge
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Enhance your research capabilities with AI-powered literature search and analysis.
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""")
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query = st.sidebar.text_input("What topic would you like to research?", "")
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current_year = datetime.now().year
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start_year, end_year = st.sidebar.slider(
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"Publication Year Range",
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min_value=1900,
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max_value=current_year,
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value=(current_year-5, current_year)
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)
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max_results = st.sidebar.slider("Maximum number of results", 10, 100, 50)
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search_button = st.sidebar.button("Search for Research Papers")
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st.title("Research Papers and Articles")
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if search_button and query:
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with st.spinner("Searching for the most relevant research papers..."):
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results = search_scopus(query, start_year, end_year, max_results)
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if results:
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papers = []
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for paper in results:
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papers.append({
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"Title": safe_get(paper, ["dc:title"]),
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"Authors": format_authors(safe_get(paper, ["author"])),
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"Year": safe_get(paper, ["prism:coverDate"])[:4],
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"Journal": safe_get(paper, ["prism:publicationName"]),
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"Abstract": safe_get(paper, ["dc:description"]),
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"Citations": safe_get(paper, ["citedby-count"], "0"),
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"Link": get_paper_link(paper)
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})
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df = pd.DataFrame(papers)
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st.markdown(f"### Found {len(results)} papers on '{query}'")
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for _, paper in df.iterrows():
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with st.container():
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col1, col2 = st.columns([3, 1])
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with col1:
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st.markdown(f"#### [{paper['Title']}]({paper['Link']})")
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st.markdown(f"**Authors:** {paper['Authors']}")
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st.markdown(f"**Published in:** {paper['Journal']} ({paper['Year']})")
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st.markdown(f"**Abstract:** {paper['Abstract']}")
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with col2:
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st.metric("Citations", paper["Citations"])
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st.markdown("---")
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csv = df.to_csv(index=False).encode('utf-8')
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st.download_button(
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label="Download results as CSV",
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data=csv,
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file_name=f"{query.replace(' ', '_')}_research_papers.csv",
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mime="text/csv",
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)
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else:
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st.warning("No results found. Please try a different search query or adjust the year range.")
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if __name__ == "__main__":
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main() |
