updates

app.py

@@ -1,7 +1,10 @@
 import os
 import streamlit as st
 import arxiv
+import requests
 import datetime
+import networkx as nx
+import matplotlib.pyplot as plt
 
 # -------------------------------
 # Groq API Client
@@ -16,47 +19,67 @@ client = Groq(
 # Helper Functions (Groq-based)
 # -------------------------------
 def groq_summarize(text: str) -> str:
+    """
+    Summarize the given text using Groq's chat completion API.
+    """
     response = client.chat.completions.create(
         messages=[
-            {"role": "user", "content": f"Summarize the following text concisely:\n\n{text}"}
+            {"role": "user", "content": f"Summarize the following text in detail:\n\n{text}"}
         ],
         model="llama-3.3-70b-versatile",
     )
     return response.choices[0].message.content.strip()
 
-def groq_eli5(text: str) -> str:
-    response = client.chat.completions.create(
-        messages=[
-            {"role": "user", "content": f"Explain this like I'm 5 years old:\n\n{text}"}
-        ],
-        model="llama-3.3-70b-versatile",
-    )
-    return response.choices[0].message.content.strip()
+# -------------------------------
+# Trust & Relevance Scores
+# -------------------------------
+def get_citation_metadata(arxiv_id):
+    """Fetch trust & relevance scores from external sources."""
+    metadata = {"citations": 0, "trust_score": 0, "relevance_score": 0, "links": {}}
 
-def groq_key_takeaways(text: str) -> str:
-    response = client.chat.completions.create(
-        messages=[
-            {"role": "user", "content": f"List the key takeaways from this research:\n\n{text}"}
-        ],
-        model="llama-3.3-70b-versatile",
-    )
-    return response.choices[0].message.content.strip()
+    # Fetch citation data from scite.ai
+    scite_url = f"https://api.scite.ai/papers/{arxiv_id}"
+    response = requests.get(scite_url)
+    if response.status_code == 200:
+        scite_data = response.json()
+        metadata["citations"] = scite_data.get("citation_count", 0)
+        metadata["trust_score"] = scite_data.get("trust_score", 0)
+
+    # Generate Connected Papers & Litmaps links
+    metadata["links"]["Connected Papers"] = f"https://www.connectedpapers.com/main/{arxiv_id}"
+    metadata["links"]["Bibliographic Explorer"] = f"https://arxiv.org/bib_explorer/{arxiv_id}"
+    metadata["links"]["Litmaps"] = f"https://www.litmaps.com/publications/{arxiv_id}"
+
+    # Calculate relevance score
+    metadata["relevance_score"] = metadata["citations"] * 0.8 + metadata["trust_score"] * 0.2
+
+    return metadata
 
 # -------------------------------
-# Paper Retrieval & Processing
+# Retrieve Papers
 # -------------------------------
 def retrieve_papers(query, max_results=5):
+    """Retrieve academic papers from arXiv & add Trust/Relevance scores."""
     search = arxiv.Search(query=query, max_results=max_results)
     papers = []
+
     for result in search.results():
+        paper_id = result.entry_id.split("/")[-1]  # Extract arXiv ID
+        metadata = get_citation_metadata(paper_id)
+
         paper = {
             "title": result.title,
             "summary": result.summary,
             "url": result.pdf_url,
             "authors": [author.name for author in result.authors],
-            "published": result.published
+            "published": result.published,
+            "citations": metadata["citations"],
+            "trust_score": metadata["trust_score"],
+            "relevance_score": metadata["relevance_score"],
+            "links": metadata["links"],
         }
         papers.append(paper)
+
     return papers
 
 # -------------------------------
@@ -64,14 +87,11 @@ def retrieve_papers(query, max_results=5):
 # -------------------------------
 st.title("📚 PaperPilot – Intelligent Academic Navigator")
 
-st.write("""
-PaperPilot helps you quickly analyze research papers by summarizing them, highlighting key takeaways, and explaining complex topics in simple terms. 
-Enter a query and get structured insights instantly!
-""")
-
+# Sidebar: Search & Toggle
 with st.sidebar:
     st.header("🔍 Search Parameters")
     query = st.text_input("Research topic or question:")
+    show_scores = st.checkbox("Enable Trust & Relevance Scores", value=True)
     
     if st.button("🚀 Find Articles"):
         if query.strip():
@@ -79,39 +99,37 @@ with st.sidebar:
                 papers = retrieve_papers(query)
                 if papers:
                     st.session_state.papers = papers
+                    st.session_state.active_section = "articles"
                     st.success(f"Found {len(papers)} papers!")
-                    st.session_state.active_section = "review"
                 else:
                     st.error("No papers found. Try different keywords.")
         else:
             st.warning("Please enter a search query")
 
+# Main Content
 if 'papers' in st.session_state and st.session_state.papers:
     papers = st.session_state.papers
-    
-    if st.session_state.active_section == "review":
-        st.header("📚 Literature Review & Summary")
-        for idx, paper in enumerate(papers, 1):
-            with st.expander(f"{idx}. {paper['title']}"):
-                st.markdown(f"**Authors:** {', '.join(paper['authors'])}")
-                pub_date = paper['published'].strftime('%Y-%m-%d') if isinstance(paper['published'], datetime.datetime) else "n.d."
-                st.markdown(f"**Published:** {pub_date}")
-                st.markdown(f"**Link:** [PDF]({paper['url']})")
-                
-                with st.spinner("Generating insights..."):
-                    short_description = groq_summarize(paper['summary'])
-                    key_takeaways = groq_key_takeaways(paper['summary'])
-                    eli5_explanation = groq_eli5(paper['summary'])
-                    
-                st.subheader("Short Description")
-                st.write(short_description)
-                
-                st.subheader("Key Takeaways")
-                st.write(key_takeaways)
+
+    st.header("📑 Retrieved Papers")
+    for idx, paper in enumerate(papers, 1):
+        with st.expander(f"{idx}. {paper['title']}"):
+            st.markdown(f"**Authors:** {', '.join(paper['authors'])}")
+            st.markdown(f"**Published:** {paper['published']}")
+            st.markdown(f"**Link:** [PDF]({paper['url']})")
+
+            # Show Trust & Relevance Scores if enabled
+            if show_scores:
+                st.markdown(f"📊 **Citations:** {paper['citations']}")
+                st.markdown(f"🛡️ **Trust Score:** {round(paper['trust_score'], 2)} / 10")
+                st.markdown(f"🔍 **Relevance Score:** {round(paper['relevance_score'], 2)} / 10")
                 
-                st.subheader("Explain Like I'm 5 (ELI5)")
-                st.write(eli5_explanation)
-else:
-    st.info("Enter a query in the sidebar and click 'Find Articles' to get started.")
+                # External Links
+                st.markdown(f"[🔗 Connected Papers]({paper['links']['Connected Papers']})")
+                st.markdown(f"[📖 Bibliographic Explorer]({paper['links']['Bibliographic Explorer']})")
+                st.markdown(f"[📊 Litmaps]({paper['links']['Litmaps']})")
+
+            # Display Summary
+            st.markdown("**Abstract:**")
+            st.write(paper['summary'])
 
-st.caption("Built with ❤️ using AI")
+st.caption("Built with ❤️ using AI")