Update app.py

app.py

@@ -41,33 +41,29 @@ def summarize_paragraph(all_texts, section_name, mode="summary"):
             unique.append(l)
     return " ".join(unique[:4])[:600]
 
-def get_novel_topic(question, all_texts):
-    """Generate a unique, new, high-impact topic for research/industry/market based on the user question and current context."""
-    # Try OpenAI to generate one, fallback to a built-in one
+def get_novel_topic_paragraph(question, all_texts):
+    """Return a single concise paragraph about a unique, high-potential topic for research, industry, and market."""
     try:
         openai_key = os.getenv("OPENAI_API_KEY")
         if openai_key:
             client = openai.OpenAI(api_key=openai_key)
             prompt = (
-                f"Given this question: {question}\n\n"
+                f"Given this user question: {question}\n\n"
                 f"And these partial answers:\n{chr(10).join(all_texts)}\n\n"
-                "Propose one specific, novel, valuable, future-oriented research or technology topic that has high potential to advance science, industry, and the market. "
-                "This idea should be unique, have few existing competitors, and could provide societal and economic benefit if pursued."
+                "Suggest one new, original, valuable research and technology topic, with a very brief explanation. "
+                "This should have strong potential for future science, industry, and the global market. The paragraph should be concise and readable for all audiences."
             )
             resp = client.chat.completions.create(
                 model="gpt-4o",
                 messages=[{"role": "user", "content": prompt}],
                 max_tokens=100
             )
-            idea = resp.choices[0].message.content.strip()
-            # Shorten if too long
-            return idea[:350]
+            return resp.choices[0].message.content.strip()
     except Exception:
         pass
-    # Fallback idea—customize as needed!
+    # Fallback topic if no LLM is available
     return (
-        "Self-healing, AI-optimized materials and manufacturing processes for climate-adaptive infrastructure—"
-        "enabling bridges, buildings, and devices to sense damage, adapt, and self-repair using intelligent materials and real-time data."
+        "Adaptive, AI-driven self-repairing materials: Combining smart sensors, machine learning, and advanced manufacturing to create infrastructure and products that can monitor their own condition, detect damage, and trigger self-healing processes—opening vast new possibilities for smart cities, robust devices, and long-term sustainability."
     )
 
 def osint_agent_answer(question):
@@ -162,11 +158,9 @@ def osint_agent_answer(question):
     output_lines.append(summarize_paragraph(all_answers, "Best Answer", mode="best"))
 
     # === RESEARCH AND INDUSTRY GAPS ===
-    novel_topic = get_novel_topic(question, all_answers)
+    novel_topic = get_novel_topic_paragraph(question, all_answers)
     output_lines.append("=== Research and Industry Gaps ===")
-    output_lines.append(f"- Science: {novel_topic}")
-    output_lines.append(f"- Industry: {novel_topic}")
-    output_lines.append(f"- Market: {novel_topic}")
+    output_lines.append(novel_topic)
 
     if errors:
         output_lines.append("\n=== API Errors ===")