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Update app.py
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app.py
CHANGED
@@ -2,98 +2,151 @@ import os
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import streamlit as st
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import arxiv
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import random
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import networkx as nx
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import matplotlib.pyplot as plt
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import datetime
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def
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def
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def retrieve_papers(query, max_results=5):
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search = arxiv.Search(query=query, max_results=max_results)
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papers = []
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for result in search.results():
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paper = {
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"title": result.title,
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"summary": result.summary,
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"url": result.pdf_url,
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"authors": [author.name for author in result.authors],
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"published": result.published,
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"
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"
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"litmaps": f"https://app.litmaps.com/preview/{paper_id}",
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"trust_score": random.randint(60, 100),
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"relevance_score": random.randint(50, 100)
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}
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papers.append(paper)
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return papers
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def summarize_text(text):
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return groq_summarize(text)
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def get_cached_summary(paper_id, text):
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if 'summaries' not in st.session_state:
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st.session_state.summaries = {}
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if paper_id not in st.session_state.summaries:
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st.session_state.summaries[paper_id] = summarize_text(text)
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return st.session_state.summaries[paper_id]
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def random_paper_search():
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st.title("π PaperPilot β Intelligent Academic Navigator")
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st.sidebar
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st.
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with st.spinner("Searching arXiv..."):
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papers = retrieve_papers(query
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if papers:
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st.session_state.papers = papers
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st.success(f"Found {len(papers)} papers!")
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st.session_state.active_section = "articles"
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else:
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st.error("No papers found. Try different keywords.")
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st.
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st.session_state.active_section = "none"
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if 'papers' in st.session_state
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st.write(groq_generate(f"Explain this in simple terms: {summary}"))
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import streamlit as st
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import arxiv
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import random
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import datetime
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import requests
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from scholarly import scholarly
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# -------------------------------
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# Helper Functions
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# -------------------------------
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def get_paper_metadata(arxiv_id):
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"""Fetch metadata like citations and connected papers for scoring."""
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metadata = {
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"citations": 0,
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"institution": "Unknown",
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"authors": [],
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"connected_papers": 0
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}
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# Fetch citation count from scite.ai
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scite_url = f"https://api.scite.ai/v1/papers/arxiv:{arxiv_id}"
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response = requests.get(scite_url)
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if response.status_code == 200:
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data = response.json()
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metadata["citations"] = data.get("citation_count", 0)
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# Fetch connected paper count from Connected Papers
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connected_papers_url = f"https://www.connectedpapers.com/api/graph/{arxiv_id}"
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response = requests.get(connected_papers_url)
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if response.status_code == 200:
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data = response.json()
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metadata["connected_papers"] = len(data.get("nodes", []))
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return metadata
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def calculate_trust_score(metadata):
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"""Compute trust score based on citations and author credibility."""
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trust_score = 50 # Base score
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# Citations factor (max boost 30 points)
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if metadata["citations"] > 100:
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trust_score += 30
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elif metadata["citations"] > 50:
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trust_score += 20
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elif metadata["citations"] > 10:
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trust_score += 10
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# Connected Papers factor (max boost 20 points)
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if metadata["connected_papers"] > 20:
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trust_score += 20
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elif metadata["connected_papers"] > 10:
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trust_score += 10
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return min(trust_score, 100)
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def calculate_relevance_score(paper, query):
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"""Compute relevance score based on keyword match and recency."""
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relevance_score = 50 # Base score
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# Keyword match factor
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query_terms = query.lower().split()
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title_terms = paper['title'].lower().split()
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match_count = len(set(query_terms) & set(title_terms))
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relevance_score += match_count * 5
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# Publication date factor
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if isinstance(paper['published'], datetime.datetime):
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years_old = datetime.datetime.now().year - paper['published'].year
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if years_old < 1:
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relevance_score += 15
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elif years_old < 3:
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relevance_score += 10
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elif years_old < 5:
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relevance_score += 5
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return min(relevance_score, 100)
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def retrieve_papers(query, max_results=5):
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"""Retrieve academic papers from arXiv."""
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search = arxiv.Search(query=query, max_results=max_results)
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papers = []
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for result in search.results():
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arxiv_id = result.entry_id.split("/")[-1]
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metadata = get_paper_metadata(arxiv_id)
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trust_score = calculate_trust_score(metadata)
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relevance_score = calculate_relevance_score({"title": result.title, "published": result.published}, query)
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paper = {
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"title": result.title,
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"summary": result.summary,
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"url": result.pdf_url,
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"doi": f"https://doi.org/10.48550/arXiv.{arxiv_id}",
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"bib_explorer": f"https://arxiv.org/abs/{arxiv_id}",
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"connected_papers": f"https://www.connectedpapers.com/api/graph/{arxiv_id}",
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"litmaps": f"https://app.litmaps.com/preview/{arxiv_id}",
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"scite": f"https://scite.ai/reports/arxiv:{arxiv_id}",
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"authors": [author.name for author in result.authors],
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"published": result.published,
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"trust_score": trust_score,
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"relevance_score": relevance_score
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}
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papers.append(paper)
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return papers
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def random_paper_search():
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"""Retrieve random papers without user input."""
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random_queries = ["artificial intelligence", "quantum computing", "neuroscience", "climate change", "robotics"]
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query = random.choice(random_queries)
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return retrieve_papers(query, max_results=random.randint(5, 15))
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# -------------------------------
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# Streamlit UI
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# -------------------------------
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st.title("π PaperPilot β Intelligent Academic Navigator")
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with st.sidebar:
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st.header("π Search Parameters")
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query = st.text_input("Research topic or question:")
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col1, col2 = st.columns([3, 1])
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with col1:
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search_button = st.button("π Find Articles")
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with col2:
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random_button = st.button("π² Random Papers")
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if search_button and query.strip():
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with st.spinner("Searching arXiv..."):
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papers = retrieve_papers(query)
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if papers:
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st.session_state.papers = papers
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else:
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st.error("No papers found. Try different keywords.")
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elif random_button:
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with st.spinner("Fetching random papers..."):
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papers = random_paper_search()
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st.session_state.papers = papers
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if 'papers' in st.session_state:
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for idx, paper in enumerate(st.session_state.papers, 1):
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with st.expander(f"{idx}. {paper['title']}"):
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st.markdown(f"**Authors:** {', '.join(paper['authors'])}")
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st.markdown(f"**Published:** {paper['published'].strftime('%Y-%m-%d') if isinstance(paper['published'], datetime.datetime) else 'Unknown'}")
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st.markdown(f"**Trust Score:** {paper['trust_score']} / 100")
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st.markdown(f"**Relevance Score:** {paper['relevance_score']} / 100")
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st.markdown(f"**DOI:** [Link]({paper['doi']})")
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st.markdown(f"**Bibliographic Explorer:** [Link]({paper['bib_explorer']})")
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st.markdown(f"**Connected Papers:** [Link]({paper['connected_papers']})")
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st.markdown(f"**Litmaps:** [Link]({paper['litmaps']})")
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st.markdown(f"**Scite:** [Link]({paper['scite']})")
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st.markdown("**Abstract:**")
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st.write(paper['summary'])
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