Update app.py

app.py

@@ -401,9 +401,19 @@ Researchers at the University of California, Davis have used artificial intellig
 By altering key amino acids, the team reactivated weakened receptors, restoring the plants’ ability to detect pathogens. This innovation could provide broad-spectrum disease resistance in major crops, including protection against Ralstonia solanacearum, the bacterium that causes bacterial wilt. The researchers now plan to extend this strategy to other plants using machine learning.
 
 #CCA_RnD #TuesdayTrivia #ArtificialIntelligence #ProteinEngineering #PlantImmunity #AgriTech #BacterialWilt #GreenTech"""
+    },
+    {
+        "title": "Researchers Have Developed a Flexible, Low-Cost Robotic Skin That Allows Machines to Sense Touch Like Humans",
+        "content": """Researchers Have Developed a Flexible, Low-Cost Robotic Skin That Allows Machines to Sense Touch Like Humans
+
+Scientists at the University of Cambridge have created a revolutionary skin-like material that brings us a step closer to giving robots a true sense of touch. This soft, flexible “robotic skin” is made from a smart gel that covers the entire surface of a robot, acting as a single, large sensor. Unlike older technologies that required hundreds of tiny sensors, this material can detect pressure, heat, and even pain—all at the same time and across multiple areas.
+This breakthrough means robots can now respond more naturally and sensitively to their environment. They can handle delicate objects with care or react safely to human contact. This is especially important in settings like hospitals, elderly care, or even homes, where robots may assist people directly. Just like human skin helps us feel and respond to the world around us, this innovation allows machines to become more aware, making them more helpful, empathetic, and human-friendly than ever before.
+
+#TuesdayTrivia #Robotic_Skin #Cambridge_University #Smart_skin_Revolution #RnD_Cell #CCA"""
     }
 ]
 
+
     return json.dumps(examples, indent=2)
 
 
@@ -548,16 +558,16 @@ Output Format:
         return state
 
 def run_curator(state: EnhancedAgentState, progress_callback=None) -> EnhancedAgentState:
-    """Curator Agent - Uses mistral-large-latest"""
+    """Curator Agent - Uses mistral-small-latest"""
     try:
         print("\n" + "="*70)
         print("🤖 CURATOR AGENT")
-        print(f"📊 Model: mistral-large-latest")
+        print(f"📊 Model: mistral-small-latest")
         print(f"🎯 Purpose: Rank candidates and select best story")
         print("="*70)
         
         if progress_callback:
-            progress_callback("🎯 Curator (mistral-large) selecting story...")
+            progress_callback("🎯 Curator (mistral-small) selecting story...")
         
         candidates = state.get("candidates", [])
         candidates_text = candidates[0].get("raw", "") if candidates else ""
@@ -578,7 +588,7 @@ Output: RANKED CANDIDATES, then SELECTED STORY with title uniqueness check.""")
         
         user_msg = HumanMessage(content=f"Rank and select the best story. CHECK TITLE SIMILARITY FIRST:\n{candidates_text}")
         
-        curator_llm = llm_large.bind_tools([check_topic_similarity, get_all_previous_posts])
+        curator_llm = llm_small.bind_tools([check_topic_similarity, get_all_previous_posts])
         response = curator_llm.invoke([system_msg, user_msg])
         conversation = [system_msg, user_msg, response]
         
@@ -635,9 +645,7 @@ Paragraph 2
 #TuesdayTrivia #RnDCell #CCA #Topic1 #Topic2
 
 TITLE REQUIREMENTS:
-- Must be unique and compelling
-- Use check_topic_similarity to verify uniqueness
-- If title is too similar (>80% overlap), create a completely different title approach
+- Use get_example_posts_for_writer to make similar titles
 
 CONTENT: 
 - 140-180 words, technical but accessible
@@ -696,10 +704,8 @@ def run_critic(state: EnhancedAgentState, progress_callback=None) -> EnhancedAge
 
 EVALUATION CRITERIA:
 
-1. TITLE UNIQUENESS (2 points):
-   - Use check_topic_similarity to verify title uniqueness
-   - Title must have <90% word overlap with existing posts
-   - But title also must have some similarity to example posts , use get_example_posts_for_critic for this
+1. TITLE  (2 points):
+   - Title should have some similarity to example posts , use get_example_posts_for_critic for this
 
 2. FORMAT (2 points):
    - Title on first line
@@ -1203,23 +1209,6 @@ with gr.Blocks(css=css, title="Tuesday Trivia Agent", theme=gr.themes.Soft()) as
                 
                 manual_sync_btn = gr.Button("🔄 Manual Sync to HF", size="sm")
             
-            with gr.Accordion("📊 Model Information", open=False):
-                gr.Markdown("""
-                **Discovery Agent:** mistral-small-latest  
-                → Fast, efficient for search and filtering
-                
-                **Curator Agent:** mistral-large-latest  
-                → Best reasoning for ranking and selection
-                
-                **Writer Agent:** mistral-medium-latest  
-                → Balanced creativity and quality
-                
-                **Critic Agent:** mistral-large-latest  
-                → Detailed analysis and evaluation
-                
-                All agents check **title similarity** to avoid duplicates.
-                """)
-            
             gr.Markdown("---")
             gr.Markdown("""
             ### ℹ️ Instructions