Update app.py

app.py

@@ -2,18 +2,35 @@ import os
 import streamlit as st
 import arxiv
 import random
+import datetime
+import networkx as nx
+import matplotlib.pyplot as plt
+from groq import Groq
+
+# -------------------------------
+# Initialize Groq API Client
+# -------------------------------
+client = Groq(
+    api_key=os.environ.get("GROQ_API_KEY"),
+)
 
 # -------------------------------
 # Helper Functions
 # -------------------------------
-def retrieve_papers(query=None, max_results=10, random_search=False):
+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:\n\n{text}"}],
+        model="llama-3.3-70b-versatile",
+    )
+    return response.choices[0].message.content.strip()
+
+def retrieve_papers(query=None, max_results=5):
     """Retrieve academic papers from arXiv."""
-    if random_search:
-        search_query = "all:"
+    if query:
+        search = arxiv.Search(query=query, max_results=max_results)
     else:
-        search_query = query if query else ""
-    
-    search = arxiv.Search(query=search_query, max_results=max_results)
+        search = arxiv.Search(sort_by=arxiv.SortCriterion.SubmittedDate, max_results=max_results)
     papers = []
     for result in search.results():
         paper_id = result.entry_id.split("/")[-1]
@@ -23,62 +40,90 @@ def retrieve_papers(query=None, max_results=10, random_search=False):
             "url": result.pdf_url,
             "authors": [author.name for author in result.authors],
             "published": result.published,
+            "arxiv_id": paper_id,
             "doi": f"https://doi.org/10.48550/arXiv.{paper_id}",
-            "arxiv_link": f"https://arxiv.org/abs/{paper_id}",
-            "litmaps": f"https://app.litmaps.com/preview/{paper_id}",
+            "abs_url": f"https://arxiv.org/abs/{paper_id}",
             "bib_explorer": f"https://arxiv.org/abs/{paper_id}",
             "connected_papers": f"https://www.connectedpapers.com/{paper_id}",
-            "scite": f"https://scite.ai/reports/{paper_id}",
-            "trust_score": round(random.uniform(70, 99), 2),
-            "relevance_score": round(random.uniform(60, 95), 2),
+            "litmaps": f"https://app.litmaps.com/preview/{paper_id}",
+            "scite_ai": f"https://scite.ai/reports/{paper_id}"
         }
         papers.append(paper)
     return papers
 
+def calculate_scores(paper):
+    """Generate Trust & Relevance Scores."""
+    return {
+        "trust_score": round(random.uniform(60, 95), 1),
+        "relevance_score": round(random.uniform(50, 100), 1)
+    }
+
+def get_cached_summary(paper_id, text):
+    """Retrieve or create a cached summary for a given paper."""
+    if 'summaries' not in st.session_state:
+        st.session_state.summaries = {}
+    if paper_id not in st.session_state.summaries:
+        full_summary = groq_summarize(text)
+        eli5_summary = groq_summarize(f"Explain the following like I'm 5:\n\n{text}")
+        key_takeaways = groq_summarize(f"Provide key takeaways for:\n\n{text}")
+        st.session_state.summaries[paper_id] = {
+            "full": full_summary,
+            "eli5": eli5_summary,
+            "takeaways": key_takeaways
+        }
+    return st.session_state.summaries[paper_id]
+
 # -------------------------------
-# Streamlit Interface
+# Streamlit UI
 # -------------------------------
 st.title("📚 PaperPilot – Intelligent Academic Navigator")
 
 with st.sidebar:
     st.header("🔍 Search Parameters")
     query = st.text_input("Research topic or question:")
+    random_search = st.button("🎲 Random Papers")
     
-    col1, col2 = st.columns([3, 1])
-    with col1:
-        search_button = st.button("🚀 Find Articles")
-    with col2:
-        random_search_button = st.button("🎲 Random Papers")
-    
-    if search_button or random_search_button:
-        with st.spinner("Fetching papers..."):
-            if random_search_button:
-                papers = retrieve_papers(max_results=random.randint(5, 15), random_search=True)
+    if st.button("🚀 Find Articles") or random_search:
+        with st.spinner("Searching arXiv..."):
+            if random_search:
+                papers = retrieve_papers(max_results=random.randint(5, 15))
             else:
-                papers = retrieve_papers(query=query, max_results=10)
+                papers = retrieve_papers(query)
             
             if papers:
                 st.session_state.papers = papers
                 st.success(f"Found {len(papers)} papers!")
+                st.session_state.active_section = "articles"
             else:
-                st.error("No papers found. Try a different keyword.")
+                st.error("No papers found. Try different keywords.")
 
-if "papers" in st.session_state and st.session_state.papers:
+if 'papers' in st.session_state and st.session_state.papers:
+    papers = st.session_state.papers
     st.header("📑 Retrieved Papers")
-    for idx, paper in enumerate(st.session_state.papers, 1):
+    for idx, paper in enumerate(papers, 1):
         with st.expander(f"{idx}. {paper['title']}"):
+            scores = calculate_scores(paper)
             st.markdown(f"**Authors:** {', '.join(paper['authors'])}")
-            pub_date = paper["published"].strftime('%Y-%m-%d') if paper["published"] else "Unknown"
+            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"**[PDF Link]({paper['url']}) | [arXiv]({paper['arxiv_link']}) | [DOI]({paper['doi']})**")
-            st.markdown(f"**Bibliographic Explorer:** [Link]({paper['bib_explorer']})")
-            st.markdown(f"**Connected Papers:** [Link]({paper['connected_papers']})")
-            st.markdown(f"**Litmaps:** [Link]({paper['litmaps']})")
-            st.markdown(f"**scite.ai Citations:** [Link]({paper['scite']})")
-            st.markdown(f"**Trust Score:** {paper['trust_score']}% | **Relevance Score:** {paper['relevance_score']}%")
-            st.markdown("**Abstract:**")
-            st.write(paper['summary'])
+            st.markdown(f"**DOI:** [Link]({paper['doi']})")
+            st.markdown(f"**Original Paper:** [arXiv]({paper['abs_url']})")
+            st.markdown(f"**Bibliographic Explorer:** [Explore]({paper['bib_explorer']})")
+            st.markdown(f"**Connected Papers:** [View]({paper['connected_papers']})")
+            st.markdown(f"**Litmaps:** [Preview]({paper['litmaps']})")
+            st.markdown(f"**scite.ai:** [Smart Citations]({paper['scite_ai']})")
+            st.markdown(f"**Trust Score:** {scores['trust_score']}% | **Relevance Score:** {scores['relevance_score']}%")
+            
+            summary_data = get_cached_summary(paper['arxiv_id'], paper['summary'])
+            st.subheader("📌 Summary")
+            st.write(summary_data['full'])
+            
+            st.subheader("🧒 ELI5 Explanation")
+            st.write(summary_data['eli5'])
+            
+            st.subheader("🔑 Key Takeaways")
+            st.write(summary_data['takeaways'])
 else:
-    st.info("Enter a query in the sidebar or click the 🎲 button to find papers.")
+    st.info("Enter a query or click the dice icon to fetch random papers!")
 
 st.caption("Built with ❤️ using AI")