Update app.py

app.py

@@ -29,8 +29,8 @@ load_dotenv()
 
 # Hugging Face Configuration
 HF_TOKEN = os.getenv("HF_TOKEN")
-HF_DATASET_REPO = os.getenv("HF_DATASET_REPO", "tuesday-trivia-posts")  # e.g., "username/trivia-posts-db"
-HF_USERNAME = os.getenv("HF_USERNAME", None)  # Your HF username
+HF_DATASET_REPO = os.getenv("HF_DATASET_REPO", "tuesday-trivia-posts")
+HF_USERNAME = os.getenv("HF_USERNAME", None)
 
 # Auto-construct repo name if not provided
 if "/" not in HF_DATASET_REPO and HF_USERNAME:
@@ -75,6 +75,7 @@ class APIKeyRotator:
             print(f"⚠️  No API keys found for {self.service_name}. Ensure secrets are set.")
             return [] 
         
+        print(f"✅ Loaded {len(keys)} API key(s) for {self.service_name}")
         random.shuffle(keys)
         return keys
     
@@ -148,61 +149,31 @@ llm_small = create_llm_with_rotation("mistral-small-latest", temperature=0.2)
 llm_medium = create_llm_with_rotation("mistral-medium-latest", temperature=0.2)
 llm_large = create_llm_with_rotation("mistral-large-latest", temperature=0.2)
 
-# --- 4. DATABASE FUNCTIONS WITH HF SYNC ---
+# --- 4. DATABASE & HF SYNC (keeping original code) ---
 
 def get_db_connection():
     return sqlite3.connect(str(DB_PATH))
 
 def sync_from_hf():
-    """Download database from HF Dataset if it exists"""
     if not HF_TOKEN or not hf_api:
         print("⚠️  No HF_TOKEN found, skipping sync from HF")
         return False
-    
     try:
         print("📥 Syncing database from Hugging Face...")
-        
-        # Try to load the dataset
         try:
             dataset = load_dataset(HF_DATASET_REPO, split="train", token=HF_TOKEN)
-            
-            # Check if dataset is empty
             if len(dataset) == 0:
                 print("ℹ️  Dataset exists but is empty")
                 return False
-            
-            # Convert dataset to SQLite
             conn = get_db_connection()
             cursor = conn.cursor()
-            
-            # Clear existing data
             cursor.execute('DELETE FROM posts')
-            
-            # Insert data from HF
             for row in dataset:
-                cursor.execute('''
-                    INSERT INTO posts (id, date, topic, summary, source_url, quality_score, 
-                                     engagement_score, hashtags, created_at, updated_at)
-                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
-                ''', (
-                    row.get('id'),
-                    row.get('date'),
-                    row.get('topic'),
-                    row.get('summary'),
-                    row.get('source_url'),
-                    row.get('quality_score'),
-                    row.get('engagement_score'),
-                    row.get('hashtags'),
-                    row.get('created_at'),
-                    row.get('updated_at')
-                ))
-            
+                cursor.execute('''INSERT INTO posts (id, date, topic, summary, source_url, quality_score, engagement_score, hashtags, created_at, updated_at) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)''', (row.get('id'), row.get('date'), row.get('topic'), row.get('summary'), row.get('source_url'), row.get('quality_score'), row.get('engagement_score'), row.get('hashtags'), row.get('created_at'), row.get('updated_at')))
             conn.commit()
             conn.close()
-            
             print(f"✅ Synced {len(dataset)} posts from HF Dataset")
             return True
-            
         except Exception as e:
             error_msg = str(e).lower()
             if "not found" in error_msg or "doesn't exist" in error_msg or "doesn't contain any data files" in error_msg:
@@ -211,126 +182,69 @@ def sync_from_hf():
             else:
                 print(f"⚠️  Error loading dataset: {e}")
                 return False
-            
     except Exception as e:
         print(f"⚠️  Error syncing from HF: {e}")
         return False
 
 def sync_to_hf():
-    """Upload database to HF Dataset"""
     if not HF_TOKEN or not hf_api:
         print("⚠️  No HF_TOKEN found, skipping sync to HF")
         return False
-    
     try:
         print("📤 Syncing database to Hugging Face...")
-        
         conn = get_db_connection()
         cursor = conn.cursor()
         cursor.execute('SELECT * FROM posts')
-        
         columns = [description[0] for description in cursor.description]
         rows = cursor.fetchall()
         conn.close()
-        
         if not rows:
             print("ℹ️  No posts to sync")
             return False
-        
-        # Convert to dictionary format for HF Dataset
         data = {col: [] for col in columns}
         for row in rows:
             for col, value in zip(columns, row):
                 data[col].append(value)
-        
-        # Create and push dataset
         dataset = Dataset.from_dict(data)
-        
-        # Try to push, create repo if it doesn't exist
         try:
-            dataset.push_to_hub(
-                HF_DATASET_REPO,
-                token=HF_TOKEN,
-                private=True  # Make it private by default
-            )
+            dataset.push_to_hub(HF_DATASET_REPO, token=HF_TOKEN, private=True)
             print(f"✅ Synced {len(rows)} posts to HF Dataset: {HF_DATASET_REPO}")
             return True
-            
         except Exception as push_error:
             if "not found" in str(push_error).lower():
-                # Create the repo first
                 print(f"📦 Creating new dataset repository: {HF_DATASET_REPO}")
-                hf_api.create_repo(
-                    repo_id=HF_DATASET_REPO,
-                    token=HF_TOKEN,
-                    repo_type="dataset",
-                    private=True
-                )
-                # Try pushing again
-                dataset.push_to_hub(
-                    HF_DATASET_REPO,
-                    token=HF_TOKEN,
-                    private=True
-                )
+                hf_api.create_repo(repo_id=HF_DATASET_REPO, token=HF_TOKEN, repo_type="dataset", private=True)
+                dataset.push_to_hub(HF_DATASET_REPO, token=HF_TOKEN, private=True)
                 print(f"✅ Created and synced to new dataset: {HF_DATASET_REPO}")
                 return True
             else:
                 raise push_error
-                
     except Exception as e:
         print(f"❌ Error syncing to HF: {e}")
         return False
 
 def init_database():
-    """Initialize local database and sync from HF if available"""
     conn = get_db_connection()
     cursor = conn.cursor()
-    cursor.execute('''
-        CREATE TABLE IF NOT EXISTS posts (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            date TEXT NOT NULL,
-            topic TEXT NOT NULL,
-            summary TEXT NOT NULL,
-            source_url TEXT,
-            quality_score REAL CHECK(quality_score >= 0 AND quality_score <= 10),
-            engagement_score REAL,
-            hashtags TEXT,
-            created_at TEXT NOT NULL,
-            updated_at TEXT
-        )
-    ''')
+    cursor.execute('''CREATE TABLE IF NOT EXISTS posts (id INTEGER PRIMARY KEY AUTOINCREMENT, date TEXT NOT NULL, topic TEXT NOT NULL, summary TEXT NOT NULL, source_url TEXT, quality_score REAL CHECK(quality_score >= 0 AND quality_score <= 10), engagement_score REAL, hashtags TEXT, created_at TEXT NOT NULL, updated_at TEXT)''')
     cursor.execute('CREATE INDEX IF NOT EXISTS idx_date ON posts(date)')
     cursor.execute('CREATE INDEX IF NOT EXISTS idx_quality_score ON posts(quality_score)')
     cursor.execute('CREATE INDEX IF NOT EXISTS idx_created_at ON posts(created_at)')
-    cursor.execute('''
-        CREATE TABLE IF NOT EXISTS metadata (
-            key TEXT PRIMARY KEY, value TEXT, updated_at TEXT NOT NULL
-        )
-    ''')
-    cursor.execute('''
-        INSERT OR REPLACE INTO metadata (key, value, updated_at)
-        VALUES ('db_version', '1.0', ?)
-    ''', (datetime.now().isoformat(),))
+    cursor.execute('''CREATE TABLE IF NOT EXISTS metadata (key TEXT PRIMARY KEY, value TEXT, updated_at TEXT NOT NULL)''')
+    cursor.execute('''INSERT OR REPLACE INTO metadata (key, value, updated_at) VALUES ('db_version', '1.0', ?)''', (datetime.now().isoformat(),))
     conn.commit()
     conn.close()
-    
-    # Sync from HF after initializing schema
     sync_from_hf()
 
-# Initialize immediately
 init_database()
 
-# --- 5. TOOLS ---
+# --- 5. TOOLS (keeping original except check_topic_similarity) ---
 
 @tool
 def search_science_breakthroughs(query: str) -> str:
     """Search for recent scientific breakthroughs."""
     try:
-        search = TavilySearchResults(
-            max_results=10,
-            include_domains=["sciencedaily.com", "nature.com", "science.org", "newscientist.com", "scientificamerican.com"],
-            search_depth="advanced"
-        )
+        search = TavilySearchResults(max_results=10, include_domains=["sciencedaily.com", "nature.com", "science.org", "newscientist.com", "scientificamerican.com"], search_depth="advanced")
         results = search.invoke(query)
         return json.dumps(results, indent=2)
     except Exception as e:
@@ -355,7 +269,7 @@ def fetch_article_content(url: str) -> str:
 
 @tool
 def get_all_previous_posts() -> str:
-    """Retrieve all previously published posts."""
+    """Retrieve all previously published posts with their titles."""
     conn = None
     try:
         conn = get_db_connection()
@@ -365,7 +279,9 @@ def get_all_previous_posts() -> str:
         if not posts: return "No previous posts found."
         formatted = []
         for p in posts:
-            formatted.append({"id": p[0], "date": p[1], "topic": p[2], "summary": p[3][:100], "source_url": p[4], "quality_score": p[5]})
+            # Extract title (first line of summary)
+            title = p[3].split('\n')[0].strip() if p[3] else p[2]
+            formatted.append({"id": p[0], "date": p[1], "title": title, "topic": p[2], "summary_preview": p[3][:100], "source_url": p[4], "quality_score": p[5]})
         return json.dumps(formatted, indent=2)
     except Exception as e:
         return f"Error: {str(e)}"
@@ -399,18 +315,12 @@ def save_approved_post(topic: str, summary: str, source_url: str, quality_score:
         conn = get_db_connection()
         cursor = conn.cursor()
         now = datetime.now()
-        cursor.execute('''
-            INSERT INTO posts (date, topic, summary, source_url, quality_score, hashtags, created_at, updated_at)
-            VALUES (?, ?, ?, ?, ?, ?, ?, ?)
-        ''', (now.strftime('%Y-%m-%d'), topic, summary, source_url, quality_score, hashtags, now.isoformat(), now.isoformat()))
+        cursor.execute('''INSERT INTO posts (date, topic, summary, source_url, quality_score, hashtags, created_at, updated_at) VALUES (?, ?, ?, ?, ?, ?, ?, ?)''', (now.strftime('%Y-%m-%d'), topic, summary, source_url, quality_score, hashtags, now.isoformat(), now.isoformat()))
         conn.commit()
         post_id = cursor.lastrowid
         conn.close()
-        
-        # Sync to HF after saving
         sync_result = sync_to_hf()
         sync_msg = "and synced to HF Dataset ☁️" if sync_result else "(HF sync skipped)"
-        
         return f"Post saved successfully with ID: {post_id} {sync_msg}"
     except Exception as e:
         return f"Error saving post: {str(e)}"
@@ -423,26 +333,43 @@ def save_approved_post(topic: str, summary: str, source_url: str, quality_score:
 
 @tool
 def check_topic_similarity(new_topic: str) -> str:
-    """Check topic similarity."""
+    """CRITICAL: Check if the TITLE is too similar to previous post titles. Focus on TITLE similarity only."""
     conn = None
     try:
         conn = get_db_connection()
         cursor = conn.cursor()
         cursor.execute('SELECT topic, summary FROM posts')
         previous = cursor.fetchall()
-        if not previous: return json.dumps({"is_duplicate": False, "similar_posts": []})
+        if not previous: return json.dumps({"is_duplicate": False, "similar_posts": [], "checked_title": new_topic})
+        
+        # Extract title from new topic (first line before any newlines)
+        new_title = new_topic.split('\n')[0].strip().lower()
+        new_keywords = set(word for word in new_title.split() if len(word) > 3)  # Ignore small words
         
-        new_keywords = set(new_topic.lower().split())
         similar = []
         for prev_topic, prev_summary in previous:
-            prev_keywords = set(prev_topic.lower().split())
+            # Extract title from previous post (first line)
+            prev_title = prev_summary.split('\n')[0].strip().lower()
+            prev_keywords = set(word for word in prev_title.split() if len(word) > 3)
+            
             if not new_keywords: continue
             overlap = len(new_keywords & prev_keywords)
-            similarity = overlap / len(new_keywords)
-            if similarity > 0.5:
-                similar.append({"topic": prev_topic, "similarity": similarity})
-        
-        return json.dumps({"is_duplicate": len(similar) > 0, "similar_posts": similar}, indent=2)
+            similarity = overlap / len(new_keywords) if new_keywords else 0
+            
+            # Higher threshold for title similarity (0.6 = 60% word overlap)
+            if similarity > 0.6:
+                similar.append({
+                    "previous_title": prev_title,
+                    "similarity_percentage": round(similarity * 100, 1),
+                    "matching_words": list(new_keywords & prev_keywords)
+                })
+        
+        return json.dumps({
+            "is_duplicate": len(similar) > 0, 
+            "similar_posts": similar,
+            "new_title_checked": new_title,
+            "warning": "⚠️ TITLE is too similar to existing posts!" if similar else "✅ Title is unique"
+        }, indent=2)
     except Exception as e:
         return f"Error: {str(e)}"
     finally:
@@ -455,69 +382,26 @@ def count_words(text: str) -> str:
 
 @tool
 def get_example_posts_for_writer() -> str:
-    """Get example posts to guide the writer agent in creating high-quality content."""
+    """Get example posts to guide the writer agent."""
     examples = [
-        {
-            "title": "Scientists Found 14 Mysterious Creatures in the Ocean's Darkest Depths",
-            "content": """Scientists Found 14 Mysterious Creatures in the Ocean's Darkest Depths
+        {"title": "Scientists Found 14 Mysterious Creatures in the Ocean's Darkest Depths", "content": """Scientists Found 14 Mysterious Creatures in the Ocean's Darkest Depths
 
 Scientists have just revealed 14 new species living miles beneath the ocean's surface—some found at depths over 6,000 meters! This discovery is part of the Ocean Species Discoveries initiative, which is changing how new marine life is identified and shared with the world. Among the surprises: a record-setting deep-sea mollusk, a carnivorous bivalve, and even a popcorn-shaped parasitic isopod. Each of these creatures shows just how much life still hides in the ocean's darkest corners.
 What's even cooler? The team is using cutting-edge lab techniques to make classifying species faster, more open, and globally collaborative—helping scientists everywhere explore and protect our planet's last great frontier: the deep sea.
 
-#TuesdayTrivia #RnDCell #CCA #OceanDiscovery #MarineBiology #DeepSea""",
-            "structure": "Title -> One line gap -> First paragraph -> Next line -> Second paragraph -> One line gap -> Hashtags (must include #TuesdayTrivia #RnDCell #CCA)"
-        },
-        {
-            "title": "Researchers Have Developed Next-Generation Plant Immune System to Combat Bacterial Diseases",
-            "content": """Researchers Have Developed Next-Generation Plant Immune System to Combat Bacterial Diseases
-
-Researchers at the University of California, Davis have used artificial intelligence to boost plant immunity, helping crops like tomatoes and potatoes resist bacterial infections. Using AlphaFold, an AI system that predicts protein structures, they redesigned the immune receptor FLS2, which identifies flagellin, a bacterial movement protein. Since bacteria mutate flagellin to escape detection, the AI-guided redesign enabled plants to recognize more bacterial variants and strengthen their defenses.
-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 apply machine learning to forecast future receptor modifications and extend this strategy to other plants.
-
-#TuesdayTrivia #RnDCell #CCA #ArtificialIntelligence #PlantImmunity #AgriTech""",
-            "structure": "Title -> One line gap -> First paragraph -> Next line -> Second paragraph -> One line gap -> Hashtags"
-        }
+#TuesdayTrivia #RnDCell #CCA #OceanDiscovery #MarineBiology #DeepSea"""}
     ]
     return json.dumps(examples, indent=2)
 
 @tool
 def get_example_posts_for_critic() -> str:
-    """Get example posts with quality scores to guide the critic agent's evaluation."""
+    """Get example posts with quality scores."""
     examples = [
-        {
-            "title": "Researchers Have Developed a Cutting-Edge Silicon Photonic Chip to Enhance AI Efficiency",
-            "content": """Researchers Have Developed a Cutting-Edge Silicon Photonic Chip to Enhance AI Efficiency
-
-Researchers at the University of Florida have developed a silicon photonic chip that uses light to handle convolution operations, the intensive pattern-recognition steps central to AI. The chip encodes data as laser light, directs it through microscopic Fresnel lenses etched onto silicon, and converts the results back into digital form. In tests, it classified handwritten digits with about 98% accuracy, comparable to electronic processors but with far lower energy use.
-The design also supports wavelength multiplexing, allowing multiple colored lasers to process different data streams at once. This is the first demonstration of on-chip optical computation applied to neural networks. With optical elements already emerging in commercial AI hardware, researchers believe chip-based optics could soon become common, offering faster and more sustainable paths for scaling AI systems.
-
-#TuesdayTrivia #RnDCell #CCA #ArtificialIntelligence #SiliconPhotonics #GreenTech""",
-            "quality_score": 9.0
-        },
-        {
-            "title": "Researchers Have Developed Injectable Skin That Brings Hope for Burn Victims and Scar-Free Healing",
-            "content": """Researchers Have Developed Injectable Skin That Brings Hope for Burn Victims and Scar-Free Healing
-
-Imagine a reality in which burns and deep injuries can be healed with such ease that scarring is no longer a problem. This is now possible thanks to researchers at the University of Linköping in Sweden. They've created a special gel full of living skin cells—"skin in a syringe." This gel is either injected into wounds or 3D printed for skin transplants. Unlike traditional methods that only replace the outer layer of skin and almost inevitably result in scarring, this innovation helps the body heal deep dermis layers that provide strength, elasticity, and blood vessels to the skin.
-Here's how it works: fibroblast cells, which are the main builders of connective tissue, are grown and embedded in small gelatin beads. These are then combined through "Click Chemistry" with a hyaluronic acid-based gel. The final product is a material that easily squirts out of a syringe but then settles into place perfectly. In mouse experiments, cells survived and helped grow new blood vessels, which are essential for healthy skin. The team also fabricated thin elastic hydrogel fibers that act as microvessels—a fundamental development for future organ repair and transplantation.
-
-#TuesdayTrivia #RnDCell #CCA #TissueEngineering #RegenerativeMedicine #BiomedicalInnovation""",
-            "quality_score": 9.5
-        },
-        {
-            "title": "Researchers Have Developed AI-Driven Immune Cells to Precisely Target Cancer",
-            "content": """Researchers Have Developed AI-Driven Immune Cells to Precisely Target Cancer
-
-Researchers have developed an advanced AI-driven approach that reprograms human immune cells to precisely target and destroy cancer—all within a few weeks. Traditionally, creating such personalized cell therapies is a time-consuming process involving months of laboratory testing. By using powerful machine-learning tools, the team was able to rapidly design T cells capable of recognizing cancer cells with high accuracy while sparing healthy tissues.
-The AI system analyzed millions of receptor combinations and pinpointed the most effective designs, greatly accelerating the development timeline. Instead of relying on standard one-size-fits-all methods, doctors could soon adapt these AI-engineered immune cells to match the specific type of cancer found in each patient, offering new levels of precision and hope.
-
-#TuesdayTrivia #RnDCell #CCA #CancerResearch #ImmuneTherapy #FutureOfMedicine""",
-            "quality_score": 9.0
-        }
+        {"title": "Researchers Have Developed a Cutting-Edge Silicon Photonic Chip to Enhance AI Efficiency", "quality_score": 9.0}
     ]
     return json.dumps(examples, indent=2)
 
-# --- 6. WORKFLOW STAGES ENUM ---
+# --- 6. WORKFLOW & STATE ---
 
 class WorkflowStage(Enum):
     IDLE = "idle"
@@ -532,8 +416,6 @@ class WorkflowStage(Enum):
     COMPLETE = "complete"
     ERROR = "error"
 
-# --- 7. ENHANCED STATE ---
-
 class EnhancedAgentState(TypedDict):
     stage: str
     search_topic: str
@@ -546,282 +428,271 @@ class EnhancedAgentState(TypedDict):
     error_message: str
     progress_log: List[str]
 
-# --- 8. AGENT FUNCTIONS WITH PROGRESS TRACKING ---
+# --- 7. AGENT FUNCTIONS WITH MODEL LOGGING ---
 
 def run_discovery(state: EnhancedAgentState, progress_callback=None) -> EnhancedAgentState:
-    """Discovery Agent with progress tracking"""
+    """Discovery Agent - Uses mistral-small-latest"""
     try:
+        print("\n" + "="*70)
+        print("🤖 DISCOVERY AGENT")
+        print(f"📊 Model: mistral-small-latest")
+        print(f"🎯 Purpose: Search and find scientific breakthroughs")
+        print("="*70)
+        
         if progress_callback:
-            progress_callback("🔍 Searching for scientific breakthroughs...")
+            progress_callback("🔍 Discovery Agent (mistral-small) searching...")
         
         topic = state.get("search_topic", "general science")
         
         system_msg = SystemMessage(content=f"""You are the Discovery Agent for Tuesday Trivia.
 
-CRITICAL INSTRUCTIONS:
-1. You MUST use the search_science_breakthroughs tool to find recent articles
-2. You MUST use get_all_previous_posts to check for duplicates
-3. You MUST use check_topic_similarity to avoid similar topics
-4. Find 10-15 RECENT breakthroughs from the last 2-4 weeks
-5. Each breakthrough needs: Title, Brief Description, Source URL, Why it's interesting
+CRITICAL TITLE FOCUS:
+1. MUST check TITLE similarity using check_topic_similarity - focus on the TITLE (first line) ONLY
+2. The title is the most important unique identifier - titles should be significantly different
+3. When using check_topic_similarity, it specifically checks title word overlap
+4. REJECT stories with titles that have >60% word overlap with existing titles
 
-DO NOT provide generic information. ALWAYS use the tools to search for current data.
+OTHER REQUIREMENTS:
+1. Use search_science_breakthroughs to find recent articles
+2. Use get_all_previous_posts to see existing titles
+3. Find 10-15 RECENT breakthroughs (last 2-4 weeks)
 
 Output Format:
-For each breakthrough provide:
-**Title:** [Exact title from source]
+**Title:** [Unique, compelling title]
 **Description:** [2-3 sentences]
 **URL:** [Source link]
 **Why Interesting:** [1 sentence hook]
----
-""")
+---""")
         
-        user_msg = HumanMessage(content=f"Search for recent breakthroughs in {topic}. Use the search tool to find current articles from the past 2-4 weeks.")
+        user_msg = HumanMessage(content=f"Search for recent breakthroughs in {topic}. Check titles carefully to avoid duplicates.")
         
         discovery_llm = llm_small.bind_tools([search_science_breakthroughs, get_all_previous_posts, check_topic_similarity])
-        
-        if progress_callback:
-            progress_callback("🤖 Running discovery agent...")
-        
         response = discovery_llm.invoke([system_msg, user_msg])
         conversation = [system_msg, user_msg, response]
         
-        max_steps = 8  # Increased to ensure tool usage
+        max_steps = 8
         steps = 0
-        tool_was_called = False
         
         while hasattr(response, 'tool_calls') and response.tool_calls and steps < max_steps:
-            if progress_callback:
-                progress_callback(f"🔧 Executing tools (step {steps+1}/{max_steps})...")
-            
             tool_messages = []
             for tool_call in response.tool_calls:
                 name = tool_call['name']
-                args = tool_call['args']
-                tool_was_called = True
-                
-                if progress_callback:
-                    progress_callback(f"🔧 Using {name}...")
-                
+                print(f"🔧 Tool: {name}")
                 if name == 'search_science_breakthroughs': 
-                    res = search_science_breakthroughs.invoke(args)
+                    res = search_science_breakthroughs.invoke(tool_call['args'])
                 elif name == 'get_all_previous_posts': 
-                    res = get_all_previous_posts.invoke(args)
+                    res = get_all_previous_posts.invoke(tool_call['args'])
                 elif name == 'check_topic_similarity': 
-                    res = check_topic_similarity.invoke(args)
+                    res = check_topic_similarity.invoke(tool_call['args'])
                 else: 
                     res = f"Unknown tool: {name}"
-                
                 tool_messages.append(ToolMessage(content=str(res), tool_call_id=tool_call['id']))
-            
             conversation.extend(tool_messages)
             response = discovery_llm.invoke(conversation)
             conversation.append(response)
             steps += 1
         
-        # If no tools were called, force a search
-        if not tool_was_called:
-            if progress_callback:
-                progress_callback("⚠️ Agent didn't search, forcing search...")
-            
-            # Manually call the search tool
-            search_query = f"recent {topic} breakthroughs scientific discoveries"
-            search_result = search_science_breakthroughs.invoke({"query": search_query})
-            
-            # Create a follow-up prompt
-            force_msg = HumanMessage(content=f"Here are the search results. Now format them into 10-15 breakthroughs:\n\n{search_result}")
-            conversation.append(force_msg)
-            response = discovery_llm.invoke(conversation)
-        
-        # Parse candidates from response
-        candidates_text = response.content
-        
-        if progress_callback:
-            progress_callback("✅ Discovery complete!")
-        
-        state["candidates"] = [{"raw": candidates_text}]
+        print("✅ Discovery complete")
+        state["candidates"] = [{"raw": response.content}]
         state["stage"] = WorkflowStage.CHECKPOINT_1.value
-        state["progress_log"].append("Discovery completed successfully")
-        
+        state["progress_log"].append("✅ Discovery (mistral-small-latest)")
         return state
-        
     except Exception as e:
         state["stage"] = WorkflowStage.ERROR.value
         state["error_message"] = f"Discovery failed: {str(e)}"
-        state["progress_log"].append(f"❌ Error: {str(e)}")
         return state
 
 def run_curator(state: EnhancedAgentState, progress_callback=None) -> EnhancedAgentState:
-    """Curator Agent with progress tracking"""
+    """Curator Agent - Uses mistral-large-latest"""
     try:
+        print("\n" + "="*70)
+        print("🤖 CURATOR AGENT")
+        print(f"📊 Model: mistral-large-latest")
+        print(f"🎯 Purpose: Rank candidates and select best story")
+        print("="*70)
+        
         if progress_callback:
-            progress_callback("🎯 Ranking and selecting best story...")
+            progress_callback("🎯 Curator (mistral-large) selecting story...")
         
         candidates = state.get("candidates", [])
         candidates_text = candidates[0].get("raw", "") if candidates else ""
         
-        system_msg = SystemMessage(content="""You are the Curator Agent. 
-        Rank candidates (1-10) on Recency, Significance, Engagement. Select TOP story.
-        Output: RANKED CANDIDATES list, then SELECTED STORY with Justification.""")
+        system_msg = SystemMessage(content="""You are the Curator Agent.
+
+PRIMARY FOCUS: Check TITLE uniqueness carefully!
+1. Use check_topic_similarity to verify the selected story's TITLE is unique
+2. TITLES must have <60% word overlap with existing posts
+3. Prioritize stories with completely unique, fresh titles
+
+Then rank on:
+- Recency (1-10): How recent is the discovery?
+- Significance (1-10): Scientific impact
+- Engagement (1-10): Public interest potential
+
+Output: RANKED CANDIDATES, then SELECTED STORY with title uniqueness check.""")
         
-        user_msg = HumanMessage(content=f"Evaluate these candidates:\n{candidates_text}")
+        user_msg = HumanMessage(content=f"Rank and select the best story. CHECK TITLE SIMILARITY FIRST:\n{candidates_text}")
         
-        curator_llm = llm_large.bind_tools([])
+        curator_llm = llm_large.bind_tools([check_topic_similarity, get_all_previous_posts])
         response = curator_llm.invoke([system_msg, user_msg])
+        conversation = [system_msg, user_msg, response]
         
-        if progress_callback:
-            progress_callback("✅ Story selected!")
+        # Allow tool usage
+        if hasattr(response, 'tool_calls') and response.tool_calls:
+            for tool_call in response.tool_calls:
+                name = tool_call['name']
+                print(f"🔧 Tool: {name}")
+                if name == 'check_topic_similarity':
+                    res = check_topic_similarity.invoke(tool_call['args'])
+                elif name == 'get_all_previous_posts':
+                    res = get_all_previous_posts.invoke(tool_call['args'])
+                else:
+                    res = "Unknown tool"
+                conversation.append(ToolMessage(content=str(res), tool_call_id=tool_call['id']))
+            response = curator_llm.invoke(conversation)
         
+        print("✅ Curation complete")
         state["selected_story"] = {"raw": response.content}
         state["stage"] = WorkflowStage.CHECKPOINT_2.value
-        state["progress_log"].append("Curation completed")
-        
+        state["progress_log"].append("✅ Curation (mistral-large-latest)")
         return state
-        
     except Exception as e:
         state["stage"] = WorkflowStage.ERROR.value
         state["error_message"] = f"Curation failed: {str(e)}"
-        state["progress_log"].append(f"❌ Error: {str(e)}")
         return state
 
 def run_writer(state: EnhancedAgentState, progress_callback=None) -> EnhancedAgentState:
-    """Writer Agent with progress tracking"""
+    """Writer Agent - Uses mistral-medium-latest"""
     try:
+        print("\n" + "="*70)
+        print("🤖 WRITER AGENT")
+        print(f"📊 Model: mistral-medium-latest")
+        print(f"🎯 Purpose: Write engaging Tuesday Trivia post")
+        print("="*70)
+        
         if progress_callback:
-            progress_callback("✍️ Writing draft post...")
+            progress_callback("✍️ Writer (mistral-medium) creating post...")
         
         selected_story = state.get("selected_story", {})
         story_text = selected_story.get("raw", "")
         retry_count = state.get("retry_count", 0)
+        retry_context = f"\nPrevious feedback: {state.get('critic_feedback')}" if retry_count > 0 else ""
+
+        system_msg = SystemMessage(content="""You are the Writer Agent for Tuesday Trivia.
+
+CRITICAL FORMAT:
+Title
+[blank line]
+Paragraph 1
+[no blank line]
+Paragraph 2
+[blank line]
+#TuesdayTrivia #RnDCell #CCA #Topic1 #Topic2
+
+TITLE REQUIREMENTS:
+- Must be unique and compelling
+- Use check_topic_similarity to verify uniqueness
+- If title is too similar (>60% overlap), create a completely different title approach
+
+CONTENT: 140-180 words, conversational, technical but accessible
+HASHTAGS: Always include #TuesdayTrivia #RnDCell #CCA + 2-4 topic hashtags""")
         
-        retry_context = ""
-        if retry_count > 0:
-            retry_context = f"\nPrevious attempt rejected. Feedback: {state.get('critic_feedback')}"
-
-        system_msg = SystemMessage(content="""You are the Writer Agent for Tuesday Trivia posts.
-
-CRITICAL FORMAT REQUIREMENTS:
-1. Title (capitalize important words)
-2. ONE blank line
-3. First paragraph (Hook - grab attention, set context)
-4. Next line (no blank line between paragraphs)
-5. Second paragraph (Technical details and impact)
-6. ONE blank line
-7. Hashtags: MUST include #TuesdayTrivia #RnDCell #CCA plus 2-4 topic-specific hashtags
-
-STYLE GUIDELINES:
-- Conversational, active voice
-- NO questions in the text
-- 140-180 words total
-- Make technical concepts accessible
-- Emphasize real-world impact
-
-HASHTAG FORMAT:
-- Always include: #TuesdayTrivia #RnDCell #CCA
-- Add relevant topic hashtags like #ArtificialIntelligence #Biotechnology #SpaceExploration
-- Use camelCase (e.g., #MarineBiology not #Marine_biology)
-- 5-7 hashtags total
-
-Use the get_example_posts_for_writer tool to see perfect examples of the format.""")
-        
-        user_msg = HumanMessage(content=f"Write a Tuesday Trivia post for:\n{story_text}\n{retry_context}")
-        
-        writer_llm = llm_medium.bind_tools([get_example_posts_for_writer, fetch_article_content, count_words])
+        user_msg = HumanMessage(content=f"Write Tuesday Trivia post. VERIFY TITLE UNIQUENESS:\n{story_text}\n{retry_context}")
         
+        writer_llm = llm_medium.bind_tools([get_example_posts_for_writer, check_topic_similarity, fetch_article_content, count_words])
         response = writer_llm.invoke([system_msg, user_msg])
         conversation = [system_msg, user_msg, response]
         
         steps = 0
         while hasattr(response, 'tool_calls') and response.tool_calls and steps < 5:
-            if progress_callback:
-                progress_callback(f"🔧 Writer using tools (step {steps+1})...")
-            
-            tool_messages = []
             for tool_call in response.tool_calls:
                 name = tool_call['name']
-                if name == 'get_example_posts_for_writer': 
-                    res = get_example_posts_for_writer.invoke(tool_call['args'])
-                elif name == 'fetch_article_content': 
-                    res = fetch_article_content.invoke(tool_call['args'])
-                elif name == 'count_words': 
-                    res = count_words.invoke(tool_call['args'])
-                else: 
-                    res = "Unknown"
-                tool_messages.append(ToolMessage(content=str(res), tool_call_id=tool_call['id']))
-            conversation.extend(tool_messages)
+                print(f"🔧 Tool: {name}")
+                if name == 'get_example_posts_for_writer': res = get_example_posts_for_writer.invoke(tool_call['args'])
+                elif name == 'check_topic_similarity': res = check_topic_similarity.invoke(tool_call['args'])
+                elif name == 'fetch_article_content': res = fetch_article_content.invoke(tool_call['args'])
+                elif name == 'count_words': res = count_words.invoke(tool_call['args'])
+                else: res = "Unknown"
+                conversation.append(ToolMessage(content=str(res), tool_call_id=tool_call['id']))
             response = writer_llm.invoke(conversation)
             conversation.append(response)
             steps += 1
 
-        if progress_callback:
-            progress_callback("✅ Draft completed!")
-
+        print("✅ Writing complete")
         state["draft_summary"] = response.content
         state["retry_count"] = retry_count + 1
         state["stage"] = WorkflowStage.CRITIC.value
-        state["progress_log"].append(f"Draft written (attempt {retry_count + 1})")
-        
+        state["progress_log"].append(f"✅ Writing (mistral-medium-latest, attempt {retry_count + 1})")
         return state
-        
     except Exception as e:
         state["stage"] = WorkflowStage.ERROR.value
         state["error_message"] = f"Writing failed: {str(e)}"
-        state["progress_log"].append(f"❌ Error: {str(e)}")
         return state
 
 def run_critic(state: EnhancedAgentState, progress_callback=None) -> EnhancedAgentState:
-    """Critic Agent with progress tracking"""
+    """Critic Agent - Uses mistral-large-latest"""
     try:
+        print("\n" + "="*70)
+        print("🤖 CRITIC AGENT")
+        print(f"📊 Model: mistral-large-latest")
+        print(f"🎯 Purpose: Evaluate post quality and title uniqueness")
+        print("="*70)
+        
         if progress_callback:
-            progress_callback("🔍 Evaluating draft quality...")
+            progress_callback("🔍 Critic (mistral-large) evaluating...")
         
         draft = state.get("draft_summary", "")
         
-        system_msg = SystemMessage(content="""You are the Critic Agent for Tuesday Trivia posts.
+        system_msg = SystemMessage(content="""You are the Critic Agent for Tuesday Trivia.
 
 EVALUATION CRITERIA:
 
-1. FORMAT (3 points):
-   - Title capitalized properly
+1. TITLE UNIQUENESS (3 points) - MOST IMPORTANT:
+   - Use check_topic_similarity to verify title uniqueness
+   - Title must have <60% word overlap with existing posts
+   - Deduct 3 points if title is too similar
+
+2. FORMAT (2 points):
+   - Title on first line
    - One blank line after title
-   - Two paragraphs (no blank line between them)
+   - Two paragraphs (no blank between them)
    - One blank line before hashtags
    - Hashtags include #TuesdayTrivia #RnDCell #CCA
 
-2. CONTENT (3 points):
-   - First paragraph: Engaging hook + context
-   - Second paragraph: Technical details + real-world impact
-   - Accurate scientific information
+3. CONTENT (3 points):
+   - Engaging hook + context
+   - Technical details + impact
    - 140-180 words
+   - Accurate information
 
-3. STYLE (2 points):
+4. STYLE (2 points):
    - Conversational, active voice
-   - No questions in the text
-   - Technical concepts explained clearly
+   - Technical concepts clear
    - Engaging and accessible
 
-4. HASHTAGS (2 points):
-   - MUST include: #TuesdayTrivia #RnDCell #CCA
-   - 2-4 additional relevant hashtags
-   - camelCase format
-   - 5-7 hashtags total
-
-OUTPUT FORMAT:
-Provide detailed feedback, then:
+OUTPUT:
+Detailed feedback on each criterion
 TOTAL SCORE: X/10
-Decision: APPROVED (if ≥8) or REJECTED (if <8)
+Decision: APPROVED (≥8) or REJECTED (<8)
 
-Use get_example_posts_for_critic tool to see high-quality examples.""")
+CRITICAL: Always check title similarity first!""")
         
-        user_msg = HumanMessage(content=f"Evaluate this Tuesday Trivia post:\n\n{draft}")
+        user_msg = HumanMessage(content=f"Evaluate this post. CHECK TITLE UNIQUENESS FIRST:\n\n{draft}")
         
-        critic_llm = llm_large.bind_tools([get_example_posts_for_critic])
+        critic_llm = llm_large.bind_tools([get_example_posts_for_critic, check_topic_similarity])
         response = critic_llm.invoke([system_msg, user_msg])
         conversation = [system_msg, user_msg, response]
         
         if hasattr(response, 'tool_calls') and response.tool_calls:
             for tool_call in response.tool_calls:
-                res = get_example_posts_for_critic.invoke(tool_call['args'])
+                name = tool_call['name']
+                print(f"🔧 Tool: {name}")
+                if name == 'get_example_posts_for_critic': 
+                    res = get_example_posts_for_critic.invoke(tool_call['args'])
+                elif name == 'check_topic_similarity':
+                    res = check_topic_similarity.invoke(tool_call['args'])
+                else:
+                    res = "Unknown"
                 conversation.append(ToolMessage(content=str(res), tool_call_id=tool_call['id']))
             response = critic_llm.invoke(conversation)
         
@@ -833,27 +704,27 @@ Use get_example_posts_for_critic tool to see high-quality examples.""")
         except: 
             pass
         
-        if progress_callback:
-            progress_callback(f"✅ Evaluation complete! Score: {score}/10")
-        
+        print(f"✅ Evaluation complete - Score: {score}/10")
         state["quality_score"] = score
         state["critic_feedback"] = text
         state["stage"] = WorkflowStage.CHECKPOINT_3.value
-        state["progress_log"].append(f"Critic evaluation: {score}/10")
-        
+        state["progress_log"].append(f"✅ Evaluation (mistral-large-latest): {score}/10")
         return state
-        
     except Exception as e:
         state["stage"] = WorkflowStage.ERROR.value
         state["error_message"] = f"Critic failed: {str(e)}"
-        state["progress_log"].append(f"❌ Error: {str(e)}")
         return state
 
 def run_finalize(state: EnhancedAgentState, progress_callback=None) -> EnhancedAgentState:
     """Finalize and save to database"""
     try:
+        print("\n" + "="*70)
+        print("💾 FINALIZATION")
+        print(f"🎯 Purpose: Save post to database and sync to HF")
+        print("="*70)
+        
         if progress_callback:
-            progress_callback("💾 Saving to database...")
+            progress_callback("💾 Saving to database and HF...")
         
         draft = state.get("draft_summary", "")
         score = state.get("quality_score", 0.0)
@@ -874,21 +745,16 @@ def run_finalize(state: EnhancedAgentState, progress_callback=None) -> EnhancedA
             "hashtags": hashtags
         })
         
-        if progress_callback:
-            progress_callback("✅ Post saved successfully!")
-        
+        print(f"✅ Post saved: {res}")
         state["stage"] = WorkflowStage.COMPLETE.value
-        state["progress_log"].append(f"Finalized: {res}")
-        
+        state["progress_log"].append(f"✅ Finalized and saved")
         return state
-        
     except Exception as e:
         state["stage"] = WorkflowStage.ERROR.value
         state["error_message"] = f"Finalization failed: {str(e)}"
-        state["progress_log"].append(f"❌ Error: {str(e)}")
         return state
 
-# --- 9. GRADIO INTERFACE ---
+# --- 8. GRADIO INTERFACE ---
 
 def create_initial_state(topic: str) -> EnhancedAgentState:
     return {
@@ -920,8 +786,9 @@ def start_workflow(topic: str, progress=gr.Progress()):
             gr.update(visible=False),
             gr.update(visible=False),
             gr.update(visible=False),
-            gr.update(visible=True),  # Show restart
+            gr.update(visible=True),
             "",
+            gr.update(visible=False),
             gr.update(visible=False)
         )
     
@@ -930,11 +797,12 @@ def start_workflow(topic: str, progress=gr.Progress()):
     return (
         state,
         f"## 🔍 Discovery Results\n\n{candidates_text}",
-        gr.update(visible=True),  # Approve button
-        gr.update(visible=True),  # Reject button
+        gr.update(visible=True),
+        gr.update(visible=True),
         gr.update(visible=False),
         gr.update(visible=False),
         "",
+        gr.update(visible=False),
         gr.update(visible=False)
     )
 
@@ -954,6 +822,7 @@ def handle_checkpoint1_approve(state, progress=gr.Progress()):
             gr.update(visible=False),
             gr.update(visible=True),
             "",
+            gr.update(visible=False),
             gr.update(visible=False)
         )
     
@@ -962,17 +831,17 @@ def handle_checkpoint1_approve(state, progress=gr.Progress()):
     return (
         state,
         f"## 🎯 Selected Story\n\n{story_text}\n\n**Optional:** Provide instructions in the textbox below if you want to pick a different story.",
-        gr.update(visible=True),  # Approve button
-        gr.update(visible=True),  # Different story
+        gr.update(visible=True),
+        gr.update(visible=True),
         gr.update(visible=False),
         gr.update(visible=False),
         "",
-        gr.update(visible=True)  # Show instructions textbox
+        gr.update(visible=True),
+        gr.update(visible=False)
     )
 
 def handle_checkpoint1_reject(state, instructions, progress=gr.Progress()):
-    """Handle rejection at checkpoint 1 - restart discovery with optional instructions"""
-    # Add instructions to state if provided
+    """Handle rejection at checkpoint 1"""
     if instructions and instructions.strip():
         if "search_topic" in state:
             state["search_topic"] = f"{state['search_topic']} - Additional guidance: {instructions}"
@@ -991,6 +860,7 @@ def handle_checkpoint1_reject(state, instructions, progress=gr.Progress()):
             gr.update(visible=False),
             gr.update(visible=True),
             "",
+            gr.update(visible=False),
             gr.update(visible=False)
         )
     
@@ -998,17 +868,18 @@ def handle_checkpoint1_reject(state, instructions, progress=gr.Progress()):
     
     return (
         state,
-        f"## 🔍 Discovery Results (New Search)\n\n{candidates_text}\n\n**Optional:** Provide instructions in the textbox below if you want to reject again.",
+        f"## 🔍 Discovery Results (New Search)\n\n{candidates_text}",
         gr.update(visible=True),
         gr.update(visible=True),
         gr.update(visible=False),
         gr.update(visible=False),
         "",
-        gr.update(visible=True)  # Show instructions textbox
+        gr.update(visible=True),
+        gr.update(visible=False)
     )
 
 def handle_checkpoint2_approve(state, progress=gr.Progress()):
-    """Handle approval at checkpoint 2 - proceed to writing"""
+    """Handle approval at checkpoint 2"""
     def update_progress(msg):
         progress(0.6, desc=msg)
     
@@ -1023,6 +894,7 @@ def handle_checkpoint2_approve(state, progress=gr.Progress()):
             gr.update(visible=False),
             gr.update(visible=True),
             "",
+            gr.update(visible=False),
             gr.update(visible=False)
         )
     
@@ -1035,18 +907,18 @@ def handle_checkpoint2_approve(state, progress=gr.Progress()):
     
     return (
         state,
-        f"## ✍️ Draft Post\n\n{draft}\n\n---\n\n**Quality Score:** {score}/10\n\n**Feedback:**\n{feedback}\n\n**Optional:** Provide specific edit instructions in the textbox below if needed.",
-        gr.update(visible=True),  # Finalize
-        gr.update(visible=True),  # Edit/Rewrite
+        f"## ✍️ Draft Post\n\n{draft}\n\n---\n\n**Quality Score:** {score}/10\n\n**Feedback:**\n{feedback}",
+        gr.update(visible=True),
+        gr.update(visible=True),
         gr.update(visible=False),
         gr.update(visible=False),
         "",
-        gr.update(visible=True)  # Show edit instructions
+        gr.update(visible=True),
+        gr.update(visible=False)
     )
 
 def handle_checkpoint2_different(state, instructions, progress=gr.Progress()):
-    """Request different story - re-run curator with optional instructions"""
-    # Add instructions to curator's context if provided
+    """Request different story"""
     if instructions and instructions.strip():
         if "selected_story" in state:
             state["critic_feedback"] = f"User preference: {instructions}"
@@ -1065,6 +937,7 @@ def handle_checkpoint2_different(state, instructions, progress=gr.Progress()):
             gr.update(visible=False),
             gr.update(visible=True),
             "",
+            gr.update(visible=False),
             gr.update(visible=False)
         )
     
@@ -1072,13 +945,14 @@ def handle_checkpoint2_different(state, instructions, progress=gr.Progress()):
     
     return (
         state,
-        f"## 🎯 Selected Story (Alternative)\n\n{story_text}\n\n**Optional:** Provide instructions in the textbox below if you want a different story.",
+        f"## 🎯 Selected Story (Alternative)\n\n{story_text}",
         gr.update(visible=True),
         gr.update(visible=True),
         gr.update(visible=False),
         gr.update(visible=False),
         "",
-        gr.update(visible=True)  # Show instructions textbox
+        gr.update(visible=True),
+        gr.update(visible=False)
     )
 
 def handle_checkpoint3_finalize(state, progress=gr.Progress()):
@@ -1092,13 +966,14 @@ def handle_checkpoint3_finalize(state, progress=gr.Progress()):
     
     return (
         state,
-        f"## ✅ Post Saved Successfully!\n\n{draft}\n\n---\n\n**Status:** Saved to database\n**Quality Score:** {state.get('quality_score', 0)}/10",
+        f"## ✅ Post Saved Successfully!\n\n{draft}\n\n---\n\n**Status:** Saved to database & HF Dataset\n**Quality Score:** {state.get('quality_score', 0)}/10",
         gr.update(visible=False),
         gr.update(visible=False),
         gr.update(visible=False),
-        gr.update(visible=True),  # Show restart
+        gr.update(visible=True),
         "",
-        gr.update(visible=False)
+        gr.update(visible=False),
+        gr.update(visible=True)
     )
 
 def handle_checkpoint3_edit(state, edit_instructions, progress=gr.Progress()):
@@ -1112,10 +987,10 @@ def handle_checkpoint3_edit(state, edit_instructions, progress=gr.Progress()):
             gr.update(visible=False),
             gr.update(visible=False),
             edit_instructions,
-            gr.update(visible=True)
+            gr.update(visible=True),
+            gr.update(visible=False)
         )
     
-    # Add edit instructions to state for writer to use
     if "critic_feedback" in state:
         state["critic_feedback"] += f"\n\nUser edit request: {edit_instructions}"
     
@@ -1133,6 +1008,7 @@ def handle_checkpoint3_edit(state, edit_instructions, progress=gr.Progress()):
             gr.update(visible=False),
             gr.update(visible=True),
             "",
+            gr.update(visible=False),
             gr.update(visible=False)
         )
     
@@ -1151,23 +1027,31 @@ def handle_checkpoint3_edit(state, edit_instructions, progress=gr.Progress()):
         gr.update(visible=False),
         gr.update(visible=False),
         "",
-        gr.update(visible=True)
+        gr.update(visible=True),
+        gr.update(visible=False)
     )
 
 def restart_workflow():
-    """Reset everything to start fresh"""
+    """Reset everything"""
     return (
         None,
         "👋 Ready to start! Enter a topic and click 'Start Discovery'",
         gr.update(visible=False),
         gr.update(visible=False),
-        gr.update(visible=True),  # Show start button
+        gr.update(visible=True),
         gr.update(visible=False),
         "",
+        gr.update(visible=False),
         gr.update(visible=False)
     )
 
-# --- 10. BUILD GRADIO UI ---
+def copy_to_clipboard(state):
+    """Return the final post for copying"""
+    if state and state.get("draft_summary"):
+        return state["draft_summary"]
+    return "No post to copy"
+
+# --- 9. GRADIO UI ---
 
 css = """
 .output-box {
@@ -1187,50 +1071,28 @@ css = """
     color: #1a1a1a !important;
     font-weight: bold;
 }
-.output-box strong {
-    color: #000000 !important;
-    font-weight: 600;
-}
-.output-box a {
-    color: #0066cc !important;
-    text-decoration: underline;
-}
-.button-row {
-    display: flex;
-    gap: 10px;
-    margin-top: 10px;
-}
-.status-badge {
-    display: inline-block;
-    padding: 5px 10px;
-    border-radius: 5px;
-    font-weight: bold;
-    margin-bottom: 10px;
-}
-.gradio-container {
-    font-family: 'Inter', system-ui, sans-serif !important;
-}
 """
 
 with gr.Blocks(css=css, title="Tuesday Trivia Agent", theme=gr.themes.Soft()) as demo:
     gr.Markdown("""
     # 🧪 Tuesday Trivia Multi-Agent System
     
-    An intelligent workflow for discovering, curating, and writing science trivia posts with human oversight at key checkpoints.
+    **Model Assignment:**
+    - 🔍 Discovery Agent: `mistral-small-latest` (fast search & filtering)
+    - 🎯 Curator Agent: `mistral-large-latest` (complex ranking & selection)
+    - ✍️ Writer Agent: `mistral-medium-latest` (creative writing)
+    - 🔍 Critic Agent: `mistral-large-latest` (detailed evaluation)
     
-    **☁️ Cloud Storage:** Posts are automatically synced to Hugging Face Datasets for persistence.
+    **☁️ Cloud Storage:** All posts auto-sync to Hugging Face Datasets
     """)
     
-    # State
     state = gr.State()
     
     with gr.Row():
         with gr.Column(scale=2):
-            # Main output area
             output_display = gr.Markdown("👋 Ready to start! Enter a topic and click 'Start Discovery'", elem_classes="output-box")
         
         with gr.Column(scale=1):
-            # Control panel
             gr.Markdown("### 🎮 Control Panel")
             
             topic_input = gr.Textbox(
@@ -1246,7 +1108,6 @@ with gr.Blocks(css=css, title="Tuesday Trivia Agent", theme=gr.themes.Soft()) as
             gr.Markdown("---")
             gr.Markdown("### 📋 Decision Points")
             
-            # Checkpoint buttons (hidden initially)
             approve_btn = gr.Button("✅ Approve", variant="primary", visible=False)
             reject_btn = gr.Button("❌ Reject / Different", variant="stop", visible=False)
             
@@ -1257,136 +1118,119 @@ with gr.Blocks(css=css, title="Tuesday Trivia Agent", theme=gr.themes.Soft()) as
                 lines=3
             )
             
-            # Add sync status
+            copy_btn = gr.Button("📋 Copy Final Post", variant="secondary", visible=False)
+            copy_output = gr.Textbox(label="Post Content (for copying)", visible=False, lines=10)
+            
             with gr.Accordion("☁️ Cloud Sync Status", open=False):
                 sync_status = gr.Markdown(f"""
                 **HF Dataset:** `{HF_DATASET_REPO}`  
                 **Status:** {'✅ Connected' if HF_TOKEN else '❌ Not configured'}
                 
-                Posts are automatically synced to Hugging Face Datasets after saving.
+                Posts auto-sync to HF after saving.
                 """)
                 
                 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
             
-            **Workflow Steps:**
-            1. Enter topic → Start Discovery
-            2. Review candidates → Approve/Reject
-            3. Review story selection → Approve/Different
-            4. Review draft → Finalize/Edit
-            5. Post saved to database & HF!
+            **Workflow:**
+            1. Discovery → Review candidates
+            2. Curation → Review story selection
+            3. Writing → Review draft
+            4. Finalize → Copy & use!
             
-            **Cloud Storage:**
-            - All posts auto-sync to HF Dataset
-            - Survives Space restarts
-            - Access your data anytime
-            
-            **Tips:**
-            - Be specific with topics for better results
-            - Edit instructions help refine drafts
-            - All posts are saved with quality scores
+            **Title Focus:**
+            - System checks title similarity carefully
+            - Titles must be <60% similar to existing posts
+            - Focus is on unique, compelling titles
             """)
     
-    # Manual sync function
     def manual_sync():
         result = sync_to_hf()
         if result:
-            return "✅ Successfully synced to Hugging Face Dataset!"
-        else:
-            return "⚠️ Sync failed or no data to sync. Check logs."
+            return "✅ Successfully synced to HF!"
+        return "⚠️ Sync failed. Check logs."
     
-    manual_sync_btn.click(
-        fn=manual_sync,
-        outputs=sync_status
-    )
+    manual_sync_btn.click(fn=manual_sync, outputs=sync_status)
     
     # Event handlers
     start_btn.click(
         fn=start_workflow,
         inputs=[topic_input],
-        outputs=[state, output_display, approve_btn, reject_btn, start_btn, restart_btn, edit_instructions, edit_instructions]
+        outputs=[state, output_display, approve_btn, reject_btn, start_btn, restart_btn, edit_instructions, edit_instructions, copy_btn]
     )
     
-    # Checkpoint 1 handlers
-    def route_checkpoint1_approve(s):
-        if s and s.get("stage") == WorkflowStage.CHECKPOINT_1.value:
-            return handle_checkpoint1_approve(s)
-        return s, "Invalid state", gr.update(), gr.update(), gr.update(), gr.update(), "", gr.update()
-    
-    def route_checkpoint1_reject(s, instructions):
-        if s and s.get("stage") == WorkflowStage.CHECKPOINT_1.value:
-            return handle_checkpoint1_reject(s, instructions)
-        return s, "Invalid state", gr.update(), gr.update(), gr.update(), gr.update(), "", gr.update()
-    
-    # Checkpoint 2 handlers  
-    def route_checkpoint2_approve(s):
-        if s and s.get("stage") == WorkflowStage.CHECKPOINT_2.value:
-            return handle_checkpoint2_approve(s)
-        return s, "Invalid state", gr.update(), gr.update(), gr.update(), gr.update(), "", gr.update()
-    
-    def route_checkpoint2_different(s, instructions):
-        if s and s.get("stage") == WorkflowStage.CHECKPOINT_2.value:
-            return handle_checkpoint2_different(s, instructions)
-        return s, "Invalid state", gr.update(), gr.update(), gr.update(), gr.update(), "", gr.update()
-    
-    # Checkpoint 3 handlers
-    def route_checkpoint3_finalize(s):
-        if s and s.get("stage") == WorkflowStage.CHECKPOINT_3.value:
-            return handle_checkpoint3_finalize(s)
-        return s, "Invalid state", gr.update(), gr.update(), gr.update(), gr.update(), "", gr.update()
-    
-    def route_checkpoint3_edit(s, instructions):
-        if s and s.get("stage") == WorkflowStage.CHECKPOINT_3.value:
-            return handle_checkpoint3_edit(s, instructions)
-        return s, "Invalid state", gr.update(), gr.update(), gr.update(), gr.update(), instructions, gr.update()
-    
-    # Wire up the routing based on current stage
     def smart_approve(s, instructions):
         if not s:
-            return s, "No active workflow", gr.update(), gr.update(), gr.update(), gr.update(), "", gr.update()
+            return s, "No active workflow", gr.update(), gr.update(), gr.update(), gr.update(), "", gr.update(), gr.update()
         
         stage = s.get("stage")
         if stage == WorkflowStage.CHECKPOINT_1.value:
-            return route_checkpoint1_approve(s)
+            return handle_checkpoint1_approve(s)
         elif stage == WorkflowStage.CHECKPOINT_2.value:
-            return route_checkpoint2_approve(s)
+            return handle_checkpoint2_approve(s)
         elif stage == WorkflowStage.CHECKPOINT_3.value:
-            return route_checkpoint3_finalize(s)
+            return handle_checkpoint3_finalize(s)
         
-        return s, "Invalid stage for approval", gr.update(), gr.update(), gr.update(), gr.update(), "", gr.update()
+        return s, "Invalid stage", gr.update(), gr.update(), gr.update(), gr.update(), "", gr.update(), gr.update()
     
     def smart_reject(s, instructions):
         if not s:
-            return s, "No active workflow", gr.update(), gr.update(), gr.update(), gr.update(), "", gr.update()
+            return s, "No active workflow", gr.update(), gr.update(), gr.update(), gr.update(), "", gr.update(), gr.update()
         
         stage = s.get("stage")
         if stage == WorkflowStage.CHECKPOINT_1.value:
-            return route_checkpoint1_reject(s, instructions)
+            return handle_checkpoint1_reject(s, instructions)
         elif stage == WorkflowStage.CHECKPOINT_2.value:
-            return route_checkpoint2_different(s, instructions)
+            return handle_checkpoint2_different(s, instructions)
         elif stage == WorkflowStage.CHECKPOINT_3.value:
-            return route_checkpoint3_edit(s, instructions)
+            return handle_checkpoint3_edit(s, instructions)
         
-        return s, "Invalid stage for rejection", gr.update(), gr.update(), gr.update(), gr.update(), "", gr.update()
+        return s, "Invalid stage", gr.update(), gr.update(), gr.update(), gr.update(), "", gr.update(), gr.update()
     
     approve_btn.click(
         fn=smart_approve,
         inputs=[state, edit_instructions],
-        outputs=[state, output_display, approve_btn, reject_btn, start_btn, restart_btn, edit_instructions, edit_instructions]
+        outputs=[state, output_display, approve_btn, reject_btn, start_btn, restart_btn, edit_instructions, edit_instructions, copy_btn]
     )
     
     reject_btn.click(
         fn=smart_reject,
         inputs=[state, edit_instructions],
-        outputs=[state, output_display, approve_btn, reject_btn, start_btn, restart_btn, edit_instructions, edit_instructions]
+        outputs=[state, output_display, approve_btn, reject_btn, start_btn, restart_btn, edit_instructions, edit_instructions, copy_btn]
     )
     
     restart_btn.click(
         fn=restart_workflow,
-        outputs=[state, output_display, approve_btn, reject_btn, start_btn, restart_btn, edit_instructions, edit_instructions]
+        outputs=[state, output_display, approve_btn, reject_btn, start_btn, restart_btn, edit_instructions, edit_instructions, copy_btn]
+    )
+    
+    copy_btn.click(
+        fn=copy_to_clipboard,
+        inputs=[state],
+        outputs=[copy_output]
+    ).then(
+        lambda: gr.update(visible=True),
+        outputs=[copy_output]
     )
 
 if __name__ == "__main__":