Update app.py

app.py

@@ -33,14 +33,14 @@ def call_llm(messages, model="deepseek/deepseek-chat-v3-0324:free", max_tokens=2
         raise RuntimeError(f"Invalid response: {result}")
     return result["choices"][0]["message"]["content"]
 
-# --- Search Utilities ---
+# --- Source Utilities ---
 def get_arxiv_papers(query, max_results=3):
     from urllib.parse import quote_plus
     url = f"http://export.arxiv.org/api/query?search_query=all:{quote_plus(query)}&start=0&max_results={max_results}"
     feed = feedparser.parse(url)
     return [{
-        "title": e.title,
-        "summary": e.summary.strip().replace("\n", " "),
+        "title": e.title or "Untitled",
+        "summary": (e.summary or "No summary available").replace("\n", " ").strip(),
         "url": next((l.href for l in e.links if l.type == "application/pdf"), "")
     } for e in feed.entries]
 
@@ -48,25 +48,26 @@ def get_semantic_scholar_papers(query, max_results=3):
     url = "https://api.semanticscholar.org/graph/v1/paper/search"
     params = {"query": query, "limit": max_results, "fields": "title,abstract,url"}
     response = requests.get(url, params=params)
+    papers = response.json().get("data", [])
     return [{
-        "title": p.get("title", ""),
-        "summary": p.get("abstract", "No abstract").strip(),
+        "title": p.get("title") or "Untitled",
+        "summary": (p.get("abstract") or "No abstract available").strip(),
         "url": p.get("url", "")
-    } for p in response.json().get("data", [])]
+    } for p in papers]
 
 def search_duckduckgo(query, max_results=3):
     with DDGS() as ddgs:
         return [{
-            "title": r["title"],
-            "snippet": r["body"],
-            "url": r["href"]
+            "title": r["title"] or "Untitled",
+            "snippet": r["body"] or "",
+            "url": r["href"] or ""
         } for r in ddgs.text(query, max_results=max_results)]
 
 def get_image_urls(query, max_images=3):
     with DDGS() as ddgs:
         return [img["image"] for img in ddgs.images(query, max_results=max_images)]
 
-# --- Autonomous Research Agent ---
+# --- Research Agent ---
 def autonomous_research_agent(topic):
     arxiv = get_arxiv_papers(topic)
     scholar = get_semantic_scholar_papers(topic)
@@ -102,6 +103,7 @@ Now synthesize this information into:
 2. A novel research direction
 3. A full markdown-formatted research article (continuous, no section labels, academic tone)
 """
+
     response = call_llm([{"role": "user", "content": prompt}], max_tokens=3000)
 
     # Append Sources
@@ -140,7 +142,7 @@ if st.button("Run Research Agent"):
         except Exception as e:
             st.error(f"Failed: {e}")
 
-# --- Follow-up Q&A ---
+# --- Follow-up Chat ---
 st.divider()
 st.subheader("💬 Follow-up Q&A")
 followup = st.text_input("Ask a follow-up question:")