Update app.py

app.py

@@ -3,131 +3,107 @@ import streamlit as st
 import arxiv
 import random
 import datetime
+from groq import Groq
 
 # -------------------------------
-# Groq API Client
+# API Clients
 # -------------------------------
-from groq import Groq
-
 client = Groq(
     api_key=os.environ.get("GROQ_API_KEY"),
 )
 
 # -------------------------------
-# Helper Functions (Groq-based)
+# Helper Functions
 # -------------------------------
 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 this paper in one sentence and provide 3 key takeaways:\n\n{text}"
-            }
-        ],
+        messages=[{"role": "user", "content": f"Summarize in 250 characters:
+
+{text}"}],
         model="llama-3.3-70b-versatile",
     )
     return response.choices[0].message.content.strip()
 
-
 def groq_eli5(text: str) -> str:
-    """
-    Explain the paper like I'm 5 years old.
-    """
     response = client.chat.completions.create(
-        messages=[
-            {
-                "role": "user",
-                "content": f"Explain this paper as if I were 5 years old in one sentence:\n\n{text}"
-            }
-        ],
+        messages=[{"role": "user", "content": f"Explain like I'm 5:
+
+{text}"}],
         model="llama-3.3-70b-versatile",
     )
     return response.choices[0].message.content.strip()
 
+def calculate_trust_relevance(paper_title):
+    random.seed(hash(paper_title))
+    return random.randint(60, 95), random.randint(50, 90)
 
-def calculate_scores(paper):
-    """
-    Generate trust and relevance scores for a paper.
-    """
-    trust_score = random.randint(5, 10)  # Placeholder, can be improved with citations data
-    relevance_score = random.randint(5, 10)  # Placeholder, can use NLP topic matching
-    return trust_score, relevance_score
-
-
-def retrieve_papers(query=None, max_results=10, random_mode=False):
-    """
-    Retrieve academic papers from arXiv, either based on search or randomly.
-    """
-    if random_mode:
-        query = ""  # Empty query fetches random results
-    
+def retrieve_papers(query, max_results=5):
     search = arxiv.Search(query=query, max_results=max_results)
     papers = []
-    
     for result in search.results():
-        trust_score, relevance_score = calculate_scores(result)
+        trust, relevance = calculate_trust_relevance(result.title)
+        paper_id = result.entry_id.split('/')[-1]
         paper = {
             "title": result.title,
             "summary": result.summary,
             "url": result.pdf_url,
             "authors": [author.name for author in result.authors],
             "published": result.published.strftime('%Y-%m-%d') if isinstance(result.published, datetime.datetime) else "n.d.",
-            "doi": f"https://doi.org/10.48550/arXiv.{result.entry_id.split('/')[-1]}",
-            "bib_explorer": f"https://arxiv.org/abs/{result.entry_id.split('/')[-1]}",
-            "litmaps": f"https://app.litmaps.com/preview/{result.entry_id.split('/')[-1]}",
-            "connected_papers": f"https://www.connectedpapers.com/{result.entry_id.split('/')[-1]}",
-            "trust_score": trust_score,
-            "relevance_score": relevance_score
+            "doi": f"https://doi.org/10.48550/arXiv.{paper_id}",
+            "bib_explorer": f"https://arxiv.org/abs/{paper_id}",
+            "litmaps": f"https://app.litmaps.com/preview/{paper_id}",
+            "trust_score": trust,
+            "relevance_score": relevance
         }
         papers.append(paper)
     return papers
 
+def get_random_papers():
+    sample_topics = ["AI ethics", "Quantum computing", "Neuroscience", "Robotics", "Renewable energy", "Space exploration"]
+    query = random.choice(sample_topics)
+    return retrieve_papers(query, random.randint(5, 15))
+
 # -------------------------------
-# Streamlit Interface
+# Streamlit UI
 # -------------------------------
-st.title("📚 PaperPilot – Intelligent Academic Navigator")
+st.title("📚 PaperPilot – Intelligent Research Navigator")
 
+# Sidebar Controls
 with st.sidebar:
-    st.header("🔍 Search Parameters")
-    query = st.text_input("Research topic or question:")
-    
-    col1, col2 = st.columns([4, 1])
-    with col1:
-        search_button = st.button("🚀 Find Articles")
-    with col2:
-        random_button = st.button("🎲 Random Papers")
-    
-    if search_button:
+    st.header("🔍 Search or Discover")
+    query = st.text_input("Search topic:")
+    if st.button("🚀 Find Articles"):
         if query.strip():
             with st.spinner("Searching arXiv..."):
-                st.session_state.papers = retrieve_papers(query=query, max_results=10)
+                st.session_state.papers = retrieve_papers(query)
                 st.success(f"Found {len(st.session_state.papers)} papers!")
         else:
             st.warning("Please enter a search query")
-    
-    if random_button:
+    if st.button("🎲 Random Papers"):
         with st.spinner("Fetching random papers..."):
-            st.session_state.papers = retrieve_papers(max_results=random.randint(5, 15), random_mode=True)
-            st.success(f"Fetched {len(st.session_state.papers)} random papers!")
+            st.session_state.papers = get_random_papers()
+            st.success(f"Found {len(st.session_state.papers)} random papers!")
 
-if 'papers' in st.session_state and st.session_state.papers:
-    st.header("📑 Retrieved Papers")
-    for idx, paper in enumerate(st.session_state.papers, 1):
-        with st.expander(f"{idx}. {paper['title']}"):
+if "papers" in st.session_state and st.session_state.papers:
+    st.header("📑 Research Feed")
+    for paper in st.session_state.papers:
+        with st.expander(f"🔍 {paper['title']}"):
             st.markdown(f"**Authors:** {', '.join(paper['authors'])}")
             st.markdown(f"**Published:** {paper['published']}")
-            st.markdown(f"**[PDF Link]({paper['url']})** | **[DOI]({paper['doi']})** | **[Bib Explorer]({paper['bib_explorer']})** | **[Litmaps]({paper['litmaps']})** | **[Connected Papers]({paper['connected_papers']})**")
+            st.markdown(f"**[PDF Link]({paper['url']})** | **[DOI]({paper['doi']})** | **[Bibliographic Explorer]({paper['bib_explorer']})** | **[Litmaps]({paper['litmaps']})**")
             
-            with st.spinner("Generating summaries..."):
+            with st.spinner("Summarizing..."):
                 summary = groq_summarize(paper['summary'])
-                eli5_summary = groq_eli5(paper['summary'])
-            
             st.markdown(f"**Summary:** {summary}")
-            st.markdown(f"**ELI5:** {eli5_summary}")
             
-            st.markdown(f"**Trust Score:** {paper['trust_score']} / 10  ⭐ | **Relevance Score:** {paper['relevance_score']} / 10 🔥")
+            if st.button(f"Explain like I'm 5 🧸 - {paper['title']}"):
+                with st.spinner("Simplifying..."):
+                    st.write(groq_eli5(paper['summary']))
+            
+            st.markdown("**Trust & Relevance Scores:**")
+            st.progress(paper['trust_score'] / 100)
+            st.caption(f"🔹 Trust Score: {paper['trust_score']}%")
+            st.progress(paper['relevance_score'] / 100)
+            st.caption(f"🔹 Relevance Score: {paper['relevance_score']}%")
 else:
-    st.info("Enter a query or click 🎲 Random Papers to get started.")
+    st.info("Enter a query or use the 🎲 Random Papers button to get started!")