Update app.py

app.py

@@ -1,1034 +1,1220 @@
-import os
-import json
-import sqlite3
-import random
-import requests
-import asyncio
-from typing import TypedDict, Annotated, List, Dict, Any
-from datetime import datetime
-from pathlib import Path
-from enum import Enum
-
-# Third-party imports
-import gradio as gr
-from dotenv import load_dotenv
-from bs4 import BeautifulSoup
-from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, SystemMessage, ToolMessage
-from langchain_mistralai.chat_models import ChatMistralAI
-from langchain_core.tools import tool
-from langchain_community.tools.tavily_search import TavilySearchResults
-from langgraph.graph import StateGraph, END
-
-# --- 1. SETUP & CONFIGURATION ---
-
-load_dotenv()
-
-# Database Configuration
-DB_DIR = Path(os.getenv("DB_DIR", "./data"))
-DB_DIR.mkdir(exist_ok=True, parents=True)
-DB_PATH = DB_DIR / "trivia_posts.db"
-
-print(f"📁 Database location: {DB_PATH.absolute()}")
-
-# --- 2. API KEY ROTATION ---
-
-class APIKeyRotator:
-    """Handles API key rotation for fault tolerance"""
-    
-    def __init__(self, service_name: str):
-        self.service_name = service_name
-        self.keys = self._load_keys()
-        self.current_index = 0
-        
-    def _load_keys(self) -> List[str]:
-        keys = []
-        i = 1
-        while True:
-            key = os.getenv(f"{self.service_name}_API_KEY_{i}")
-            if not key:
-                if i == 1:
-                    key = os.getenv(f"{self.service_name}_API_KEY")
-                    if key:
-                        keys.append(key)
-                break
-            keys.append(key)
-            i += 1
-        
-        if not keys:
-            print(f"⚠️  No API keys found for {self.service_name}. Ensure secrets are set.")
-            return [] 
-        
-        random.shuffle(keys)
-        return keys
-    
-    def get_key(self) -> str:
-        if not self.keys: return ""
-        return self.keys[self.current_index]
-    
-    def rotate(self) -> str:
-        if not self.keys: return ""
-        self.current_index = (self.current_index + 1) % len(self.keys)
-        print(f"🔄 Rotated {self.service_name} API key to index {self.current_index + 1}/{len(self.keys)}")
-        return self.get_key()
-
-mistral_rotator = APIKeyRotator("MISTRAL")
-tavily_rotator = APIKeyRotator("TAVILY")
-
-os.environ["MISTRAL_API_KEY"] = mistral_rotator.get_key()
-os.environ["TAVILY_API_KEY"] = tavily_rotator.get_key()
-
-# --- 3. RESILIENT LLM ---
-
-def create_llm_with_rotation(model: str, temperature: float = 0.2, max_retries: int = 3):
-    class ResilientLLM:
-        def __init__(self, model, temperature):
-            self.model = model
-            self.temperature = temperature
-            
-        def invoke(self, *args, **kwargs):
-            for attempt in range(max_retries):
-                try:
-                    llm = ChatMistralAI(
-                        model=self.model,
-                        temperature=self.temperature,
-                        mistral_api_key=mistral_rotator.get_key()
-                    )
-                    return llm.invoke(*args, **kwargs)
-                except Exception as e:
-                    print(f"❌ API call failed (attempt {attempt + 1}/{max_retries}): {str(e)}")
-                    if attempt < max_retries - 1:
-                        mistral_rotator.rotate()
-                    else:
-                        raise Exception(f"All API keys exhausted after {max_retries} attempts")
-        
-        def bind_tools(self, tools):
-            class ToolBoundLLM:
-                def __init__(self, parent, tools):
-                    self.parent = parent
-                    self.tools = tools
-                
-                def invoke(self, *args, **kwargs):
-                    for attempt in range(max_retries):
-                        try:
-                            llm = ChatMistralAI(
-                                model=self.parent.model,
-                                temperature=self.parent.temperature,
-                                mistral_api_key=mistral_rotator.get_key()
-                            )
-                            bound_llm = llm.bind_tools(self.tools)
-                            return bound_llm.invoke(*args, **kwargs)
-                        except Exception as e:
-                            print(f"❌ API call failed (attempt {attempt + 1}/{max_retries}): {str(e)}")
-                            if attempt < max_retries - 1:
-                                mistral_rotator.rotate()
-                            else:
-                                raise Exception(f"All API keys exhausted after {max_retries} attempts")
-            return ToolBoundLLM(self, tools)
-    
-    return ResilientLLM(model, temperature)
-
-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 ---
-
-def get_db_connection():
-    return sqlite3.connect(str(DB_PATH))
-
-def init_database():
-    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 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(),))
-    conn.commit()
-    conn.close()
-
-init_database()
-
-# --- 5. TOOLS ---
-
-@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"
-        )
-        results = search.invoke(query)
-        return json.dumps(results, indent=2)
-    except Exception as e:
-        return f"Error in search: {str(e)}"
-
-@tool
-def fetch_article_content(url: str) -> str:
-    """Fetch the full text content of an article."""
-    try:
-        response = requests.get(url, timeout=10, headers={'User-Agent': 'Mozilla/5.0'})
-        response.raise_for_status()
-        soup = BeautifulSoup(response.content, 'html.parser')
-        for script in soup(["script", "style", "nav", "footer", "header"]):
-            script.decompose()
-        text = soup.get_text()
-        lines = (line.strip() for line in text.splitlines())
-        chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
-        text = ' '.join(chunk for chunk in chunks if chunk)
-        return text[:5000] if len(text) > 5000 else text
-    except Exception as e:
-        return f"Error fetching article: {str(e)}"
-
-@tool
-def get_all_previous_posts() -> str:
-    """Retrieve all previously published posts."""
-    conn = None
-    try:
-        conn = get_db_connection()
-        cursor = conn.cursor()
-        cursor.execute('SELECT id, date, topic, summary, source_url, quality_score FROM posts ORDER BY date DESC')
-        posts = cursor.fetchall()
-        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]})
-        return json.dumps(formatted, indent=2)
-    except Exception as e:
-        return f"Error: {str(e)}"
-    finally:
-        if conn: conn.close()
-
-@tool
-def get_top_quality_posts(limit: int = 5) -> str:
-    """Retrieve top quality posts."""
-    conn = None
-    try:
-        conn = get_db_connection()
-        cursor = conn.cursor()
-        cursor.execute('SELECT id, date, topic, summary, quality_score, hashtags FROM posts WHERE quality_score >= 7 ORDER BY quality_score DESC LIMIT ?', (limit,))
-        posts = cursor.fetchall()
-        if not posts: return "No high-quality example posts found."
-        formatted = []
-        for p in posts:
-            formatted.append({"id": p[0], "date": p[1], "topic": p[2], "summary": p[3], "quality_score": p[4], "hashtags": p[5]})
-        return json.dumps(formatted, indent=2)
-    except Exception as e:
-        return f"Error: {str(e)}"
-    finally:
-        if conn: conn.close()
-
-@tool
-def save_approved_post(topic: str, summary: str, source_url: str, quality_score: float, hashtags: str) -> str:
-    """Save an approved post to the database."""
-    conn = None
-    try:
-        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()))
-        conn.commit()
-        return f"Post saved successfully with ID: {cursor.lastrowid}"
-    except Exception as e:
-        return f"Error saving post: {str(e)}"
-    finally:
-        if conn: conn.close()
-
-@tool
-def check_topic_similarity(new_topic: str) -> str:
-    """Check topic similarity."""
-    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": []})
-        
-        new_keywords = set(new_topic.lower().split())
-        similar = []
-        for prev_topic, prev_summary in previous:
-            prev_keywords = set(prev_topic.lower().split())
-            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)
-    except Exception as e:
-        return f"Error: {str(e)}"
-    finally:
-        if conn: conn.close()
-
-@tool
-def count_words(text: str) -> str:
-    return f"Word count: {len(text.split())}"
-
-@tool
-def get_example_posts_for_writer() -> str:
-    examples = [
-        {"title": "Researchers Have Developed Next-Generation Plant Immune System", 
-         "content": "Full example content here...", 
-         "structure": "Title -> 2 Paragraphs -> Hashtags"},
-    ]
-    return json.dumps(examples, indent=2)
-
-@tool
-def get_example_posts_for_critic() -> str:
-    examples = [
-        {"title": "Scientists Found 14 Mysterious Creatures", "quality_score": 9.0},
-    ]
-    return json.dumps(examples, indent=2)
-
-# --- 6. WORKFLOW STAGES ENUM ---
-
-class WorkflowStage(Enum):
-    IDLE = "idle"
-    DISCOVERY = "discovery"
-    CHECKPOINT_1 = "checkpoint_1"
-    CURATOR = "curator"
-    CHECKPOINT_2 = "checkpoint_2"
-    WRITER = "writer"
-    CRITIC = "critic"
-    CHECKPOINT_3 = "checkpoint_3"
-    FINALIZE = "finalize"
-    COMPLETE = "complete"
-    ERROR = "error"
-
-# --- 7. ENHANCED STATE ---
-
-class EnhancedAgentState(TypedDict):
-    stage: str
-    search_topic: str
-    candidates: List[Dict]
-    selected_story: Dict
-    draft_summary: str
-    quality_score: float
-    critic_feedback: str
-    retry_count: int
-    error_message: str
-    progress_log: List[str]
-
-# --- 8. AGENT FUNCTIONS WITH PROGRESS TRACKING ---
-
-def run_discovery(state: EnhancedAgentState, progress_callback=None) -> EnhancedAgentState:
-    """Discovery Agent with progress tracking"""
-    try:
-        if progress_callback:
-            progress_callback("🔍 Searching for scientific breakthroughs...")
-        
-        topic = state.get("search_topic", "general science")
-        
-        system_msg = SystemMessage(content=f"""You are the Discovery Agent for Tuesday Trivia.
-        Mission: Find 10-15 recent scientific breakthroughs (2-4 weeks).
-        Tools: search_science_breakthroughs, get_all_previous_posts, check_topic_similarity.
-        Output: List 10-15 unique breakthroughs with Title, Desc, URL, Why interesting.""")
-        
-        user_msg = HumanMessage(content=f"Search for recent breakthroughs in {topic}.")
-        
-        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 = 5
-        steps = 0
-        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']
-                
-                if name == 'search_science_breakthroughs': 
-                    res = search_science_breakthroughs.invoke(args)
-                elif name == 'get_all_previous_posts': 
-                    res = get_all_previous_posts.invoke(args)
-                elif name == 'check_topic_similarity': 
-                    res = check_topic_similarity.invoke(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
-        
-        # Parse candidates from response
-        candidates_text = response.content
-        
-        if progress_callback:
-            progress_callback("✅ Discovery complete!")
-        
-        state["candidates"] = [{"raw": candidates_text}]
-        state["stage"] = WorkflowStage.CHECKPOINT_1.value
-        state["progress_log"].append("Discovery completed successfully")
-        
-        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"""
-    try:
-        if progress_callback:
-            progress_callback("🎯 Ranking and selecting best 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.""")
-        
-        user_msg = HumanMessage(content=f"Evaluate these candidates:\n{candidates_text}")
-        
-        curator_llm = llm_large.bind_tools([])
-        response = curator_llm.invoke([system_msg, user_msg])
-        
-        if progress_callback:
-            progress_callback("✅ Story selected!")
-        
-        state["selected_story"] = {"raw": response.content}
-        state["stage"] = WorkflowStage.CHECKPOINT_2.value
-        state["progress_log"].append("Curation completed")
-        
-        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"""
-    try:
-        if progress_callback:
-            progress_callback("✍️ Writing draft post...")
-        
-        selected_story = state.get("selected_story", {})
-        story_text = selected_story.get("raw", "")
-        retry_count = state.get("retry_count", 0)
-        
-        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.
-        Structure: Title, Para 1 (Hook), Para 2 (Tech/Impact), Hashtags (#TuesdayTrivia #RnDCell #CCA).
-        Tone: Conversational, active voice, no questions. 140-180 words.
-        Use tools to get examples and count words.""")
-        
-        user_msg = HumanMessage(content=f"Write post for:\n{story_text}\n{retry_context}")
-        
-        writer_llm = llm_medium.bind_tools([get_example_posts_for_writer, 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)
-            response = writer_llm.invoke(conversation)
-            conversation.append(response)
-            steps += 1
-
-        if progress_callback:
-            progress_callback("✅ Draft completed!")
-
-        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})")
-        
-        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"""
-    try:
-        if progress_callback:
-            progress_callback("🔍 Evaluating draft quality...")
-        
-        draft = state.get("draft_summary", "")
-        
-        system_msg = SystemMessage(content="""You are the Critic Agent.
-        Evaluate: Structure, Content, Style, Hashtags, Word Count.
-        Output: Quality Score (0-10) and Decision (APPROVED/REJECTED). Approved if score >= 8.""")
-        
-        user_msg = HumanMessage(content=f"Evaluate:\n{draft}")
-        
-        critic_llm = llm_large.bind_tools([get_example_posts_for_critic])
-        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'])
-                conversation.append(ToolMessage(content=str(res), tool_call_id=tool_call['id']))
-            response = critic_llm.invoke(conversation)
-        
-        text = response.content
-        score = 5.0
-        try:
-            if "TOTAL SCORE:" in text:
-                score = float(text.split("TOTAL SCORE:")[1].split("/")[0].strip())
-        except: 
-            pass
-        
-        if progress_callback:
-            progress_callback(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")
-        
-        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:
-        if progress_callback:
-            progress_callback("💾 Saving to database...")
-        
-        draft = state.get("draft_summary", "")
-        score = state.get("quality_score", 0.0)
-        
-        topic = "Scientific Breakthrough"
-        hashtags = ""
-        lines = draft.split('\n')
-        for line in lines:
-            if "#" in line: 
-                hashtags = line
-                break
-        
-        res = save_approved_post.invoke({
-            "topic": topic, 
-            "summary": draft, 
-            "source_url": "N/A", 
-            "quality_score": score, 
-            "hashtags": hashtags
-        })
-        
-        if progress_callback:
-            progress_callback("✅ Post saved successfully!")
-        
-        state["stage"] = WorkflowStage.COMPLETE.value
-        state["progress_log"].append(f"Finalized: {res}")
-        
-        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 ---
-
-def create_initial_state(topic: str) -> EnhancedAgentState:
-    return {
-        "stage": WorkflowStage.IDLE.value,
-        "search_topic": topic,
-        "candidates": [],
-        "selected_story": {},
-        "draft_summary": "",
-        "quality_score": 0.0,
-        "critic_feedback": "",
-        "retry_count": 0,
-        "error_message": "",
-        "progress_log": []
-    }
-
-def start_workflow(topic: str, progress=gr.Progress()):
-    """Start the discovery process"""
-    state = create_initial_state(topic)
-    
-    def update_progress(msg):
-        progress(0.3, desc=msg)
-    
-    state = run_discovery(state, update_progress)
-    
-    if state["stage"] == WorkflowStage.ERROR.value:
-        return (
-            state,
-            f"❌ **Error:** {state['error_message']}",
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=True),  # Show restart
-            "",
-            gr.update(visible=False)
-        )
-    
-    candidates_text = state["candidates"][0]["raw"] if state["candidates"] else "No candidates found"
-    
-    return (
-        state,
-        f"## 🔍 Discovery Results\n\n{candidates_text}",
-        gr.update(visible=True),  # Approve button
-        gr.update(visible=True),  # Reject button
-        gr.update(visible=False),
-        gr.update(visible=False),
-        "",
-        gr.update(visible=False)
-    )
-
-def handle_checkpoint1_approve(state, progress=gr.Progress()):
-    """Handle approval at checkpoint 1"""
-    def update_progress(msg):
-        progress(0.5, desc=msg)
-    
-    state = run_curator(state, update_progress)
-    
-    if state["stage"] == WorkflowStage.ERROR.value:
-        return (
-            state,
-            f"❌ **Error:** {state['error_message']}",
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=True),
-            "",
-            gr.update(visible=False)
-        )
-    
-    story_text = state["selected_story"]["raw"] if state["selected_story"] else "No story selected"
-    
-    return (
-        state,
-        f"## 🎯 Selected Story\n\n{story_text}",
-        gr.update(visible=True),  # Approve button
-        gr.update(visible=True),  # Different story
-        gr.update(visible=False),
-        gr.update(visible=False),
-        "",
-        gr.update(visible=False)
-    )
-
-def handle_checkpoint1_reject(state, progress=gr.Progress()):
-    """Handle rejection at checkpoint 1 - restart discovery"""
-    def update_progress(msg):
-        progress(0.3, desc=msg)
-    
-    state = run_discovery(state, update_progress)
-    
-    if state["stage"] == WorkflowStage.ERROR.value:
-        return (
-            state,
-            f"❌ **Error:** {state['error_message']}",
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=True),
-            "",
-            gr.update(visible=False)
-        )
-    
-    candidates_text = state["candidates"][0]["raw"] if state["candidates"] else "No candidates found"
-    
-    return (
-        state,
-        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=False)
-    )
-
-def handle_checkpoint2_approve(state, progress=gr.Progress()):
-    """Handle approval at checkpoint 2 - proceed to writing"""
-    def update_progress(msg):
-        progress(0.6, desc=msg)
-    
-    state = run_writer(state, update_progress)
-    
-    if state["stage"] == WorkflowStage.ERROR.value:
-        return (
-            state,
-            f"❌ **Error:** {state['error_message']}",
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=True),
-            "",
-            gr.update(visible=False)
-        )
-    
-    progress(0.8, desc="Evaluating draft...")
-    state = run_critic(state, lambda x: progress(0.8, desc=x))
-    
-    draft = state.get("draft_summary", "")
-    score = state.get("quality_score", 0)
-    feedback = state.get("critic_feedback", "")
-    
-    return (
-        state,
-        f"## ✍️ Draft Post\n\n{draft}\n\n---\n\n**Quality Score:** {score}/10\n\n**Feedback:**\n{feedback}",
-        gr.update(visible=True),  # Finalize
-        gr.update(visible=True),  # Edit/Rewrite
-        gr.update(visible=False),
-        gr.update(visible=False),
-        "",
-        gr.update(visible=True)  # Show edit instructions
-    )
-
-def handle_checkpoint2_different(state, progress=gr.Progress()):
-    """Request different story - re-run curator"""
-    def update_progress(msg):
-        progress(0.5, desc=msg)
-    
-    state = run_curator(state, update_progress)
-    
-    if state["stage"] == WorkflowStage.ERROR.value:
-        return (
-            state,
-            f"❌ **Error:** {state['error_message']}",
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=True),
-            "",
-            gr.update(visible=False)
-        )
-    
-    story_text = state["selected_story"]["raw"] if state["selected_story"] else "No story selected"
-    
-    return (
-        state,
-        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=False)
-    )
-
-def handle_checkpoint3_finalize(state, progress=gr.Progress()):
-    """Finalize and save the post"""
-    def update_progress(msg):
-        progress(0.9, desc=msg)
-    
-    state = run_finalize(state, update_progress)
-    
-    draft = state.get("draft_summary", "")
-    
-    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",
-        gr.update(visible=False),
-        gr.update(visible=False),
-        gr.update(visible=False),
-        gr.update(visible=True),  # Show restart
-        "",
-        gr.update(visible=False)
-    )
-
-def handle_checkpoint3_edit(state, edit_instructions, progress=gr.Progress()):
-    """Edit the draft based on instructions"""
-    if not edit_instructions:
-        return (
-            state,
-            "⚠️ Please provide edit instructions",
-            gr.update(visible=True),
-            gr.update(visible=True),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            edit_instructions,
-            gr.update(visible=True)
-        )
-    
-    # Add edit instructions to state for writer to use
-    if "critic_feedback" in state:
-        state["critic_feedback"] += f"\n\nUser edit request: {edit_instructions}"
-    
-    def update_progress(msg):
-        progress(0.6, desc=msg)
-    
-    state = run_writer(state, update_progress)
-    
-    if state["stage"] == WorkflowStage.ERROR.value:
-        return (
-            state,
-            f"❌ **Error:** {state['error_message']}",
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=True),
-            "",
-            gr.update(visible=False)
-        )
-    
-    progress(0.8, desc="Re-evaluating draft...")
-    state = run_critic(state, lambda x: progress(0.8, desc=x))
-    
-    draft = state.get("draft_summary", "")
-    score = state.get("quality_score", 0)
-    feedback = state.get("critic_feedback", "")
-    
-    return (
-        state,
-        f"## ✍️ Revised Draft\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)
-    )
-
-def restart_workflow():
-    """Reset everything to start fresh"""
-    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=False),
-        "",
-        gr.update(visible=False)
-    )
-
-# --- 10. BUILD GRADIO UI ---
-
-css = """
-.output-box {
-    min-height: 400px;
-    max-height: 600px;
-    overflow-y: auto;
-    padding: 20px;
-    border-radius: 8px;
-    background: #f8f9fa;
-}
-.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;
-}
-"""
-
-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.
-    """)
-    
-    # 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(
-                label="Search Topic",
-                placeholder="e.g., quantum computing, biotechnology, space exploration",
-                value="general science"
-            )
-            
-            with gr.Row():
-                start_btn = gr.Button("🚀 Start Discovery", variant="primary", visible=True)
-                restart_btn = gr.Button("🔄 Start New", variant="secondary", visible=False)
-            
-            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)
-            
-            edit_instructions = gr.Textbox(
-                label="Edit Instructions (optional)",
-                placeholder="Provide specific instructions for changes...",
-                visible=False,
-                lines=3
-            )
-            
-            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!
-            
-            **Tips:**
-            - Be specific with topics for better results
-            - Edit instructions help refine drafts
-            - All posts are saved with quality scores
-            """)
-    
-    # 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]
-    )
-    
-    # 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):
-        if s and s.get("stage") == WorkflowStage.CHECKPOINT_1.value:
-            return handle_checkpoint1_reject(s)
-        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):
-        if s and s.get("stage") == WorkflowStage.CHECKPOINT_2.value:
-            return handle_checkpoint2_different(s)
-        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):
-        if not s:
-            return s, "No active workflow", 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)
-        elif stage == WorkflowStage.CHECKPOINT_2.value:
-            return route_checkpoint2_approve(s)
-        elif stage == WorkflowStage.CHECKPOINT_3.value:
-            return route_checkpoint3_finalize(s)
-        
-        return s, "Invalid stage for approval", gr.update(), gr.update(), gr.update(), gr.update(), "", gr.update()
-    
-    def smart_reject(s):
-        if not s:
-            return s, "No active workflow", 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)
-        elif stage == WorkflowStage.CHECKPOINT_2.value:
-            return route_checkpoint2_different(s)
-        elif stage == WorkflowStage.CHECKPOINT_3.value:
-            return route_checkpoint3_edit(s, "")
-        
-        return s, "Invalid stage for rejection", gr.update(), gr.update(), gr.update(), gr.update(), "", gr.update()
-    
-    approve_btn.click(
-        fn=smart_approve,
-        inputs=[state],
-        outputs=[state, output_display, approve_btn, reject_btn, start_btn, restart_btn, edit_instructions, edit_instructions]
-    )
-    
-    reject_btn.click(
-        fn=smart_reject,
-        inputs=[state],
-        outputs=[state, output_display, approve_btn, reject_btn, start_btn, restart_btn, edit_instructions, edit_instructions]
-    )
-    
-    restart_btn.click(
-        fn=restart_workflow,
-        outputs=[state, output_display, approve_btn, reject_btn, start_btn, restart_btn, edit_instructions, edit_instructions]
-    )
-
-if __name__ == "__main__":
+import os
+import json
+import sqlite3
+import random
+import requests
+import asyncio
+from typing import TypedDict, Annotated, List, Dict, Any, Optional
+from datetime import datetime
+from pathlib import Path
+from enum import Enum
+import tempfile
+import shutil
+
+# Third-party imports
+import gradio as gr
+from dotenv import load_dotenv
+from bs4 import BeautifulSoup
+from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, SystemMessage, ToolMessage
+from langchain_mistralai.chat_models import ChatMistralAI
+from langchain_core.tools import tool
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langgraph.graph import StateGraph, END
+from huggingface_hub import HfApi, hf_hub_download, upload_file
+from datasets import Dataset, load_dataset
+
+# --- 1. SETUP & CONFIGURATION ---
+
+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
+
+# Auto-construct repo name if not provided
+if "/" not in HF_DATASET_REPO and HF_USERNAME:
+    HF_DATASET_REPO = f"{HF_USERNAME}/{HF_DATASET_REPO}"
+
+# Local temporary database
+DB_DIR = Path(tempfile.gettempdir()) / "trivia_data"
+DB_DIR.mkdir(exist_ok=True, parents=True)
+DB_PATH = DB_DIR / "trivia_posts.db"
+
+print(f"📁 Local database location: {DB_PATH.absolute()}")
+print(f"☁️  HF Dataset repository: {HF_DATASET_REPO}")
+
+# Initialize HF API
+hf_api = HfApi(token=HF_TOKEN) if HF_TOKEN else None
+
+# --- 2. API KEY ROTATION ---
+
+class APIKeyRotator:
+    """Handles API key rotation for fault tolerance"""
+    
+    def __init__(self, service_name: str):
+        self.service_name = service_name
+        self.keys = self._load_keys()
+        self.current_index = 0
+        
+    def _load_keys(self) -> List[str]:
+        keys = []
+        i = 1
+        while True:
+            key = os.getenv(f"{self.service_name}_API_KEY_{i}")
+            if not key:
+                if i == 1:
+                    key = os.getenv(f"{self.service_name}_API_KEY")
+                    if key:
+                        keys.append(key)
+                break
+            keys.append(key)
+            i += 1
+        
+        if not keys:
+            print(f"⚠️  No API keys found for {self.service_name}. Ensure secrets are set.")
+            return [] 
+        
+        random.shuffle(keys)
+        return keys
+    
+    def get_key(self) -> str:
+        if not self.keys: return ""
+        return self.keys[self.current_index]
+    
+    def rotate(self) -> str:
+        if not self.keys: return ""
+        self.current_index = (self.current_index + 1) % len(self.keys)
+        print(f"🔄 Rotated {self.service_name} API key to index {self.current_index + 1}/{len(self.keys)}")
+        return self.get_key()
+
+mistral_rotator = APIKeyRotator("MISTRAL")
+tavily_rotator = APIKeyRotator("TAVILY")
+
+os.environ["MISTRAL_API_KEY"] = mistral_rotator.get_key()
+os.environ["TAVILY_API_KEY"] = tavily_rotator.get_key()
+
+# --- 3. RESILIENT LLM ---
+
+def create_llm_with_rotation(model: str, temperature: float = 0.2, max_retries: int = 3):
+    class ResilientLLM:
+        def __init__(self, model, temperature):
+            self.model = model
+            self.temperature = temperature
+            
+        def invoke(self, *args, **kwargs):
+            for attempt in range(max_retries):
+                try:
+                    llm = ChatMistralAI(
+                        model=self.model,
+                        temperature=self.temperature,
+                        mistral_api_key=mistral_rotator.get_key()
+                    )
+                    return llm.invoke(*args, **kwargs)
+                except Exception as e:
+                    print(f"❌ API call failed (attempt {attempt + 1}/{max_retries}): {str(e)}")
+                    if attempt < max_retries - 1:
+                        mistral_rotator.rotate()
+                    else:
+                        raise Exception(f"All API keys exhausted after {max_retries} attempts")
+        
+        def bind_tools(self, tools):
+            class ToolBoundLLM:
+                def __init__(self, parent, tools):
+                    self.parent = parent
+                    self.tools = tools
+                
+                def invoke(self, *args, **kwargs):
+                    for attempt in range(max_retries):
+                        try:
+                            llm = ChatMistralAI(
+                                model=self.parent.model,
+                                temperature=self.parent.temperature,
+                                mistral_api_key=mistral_rotator.get_key()
+                            )
+                            bound_llm = llm.bind_tools(self.tools)
+                            return bound_llm.invoke(*args, **kwargs)
+                        except Exception as e:
+                            print(f"❌ API call failed (attempt {attempt + 1}/{max_retries}): {str(e)}")
+                            if attempt < max_retries - 1:
+                                mistral_rotator.rotate()
+                            else:
+                                raise Exception(f"All API keys exhausted after {max_retries} attempts")
+            return ToolBoundLLM(self, tools)
+    
+    return ResilientLLM(model, temperature)
+
+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 ---
+
+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)
+            
+            # 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')
+                ))
+            
+            conn.commit()
+            conn.close()
+            
+            print(f"✅ Synced {len(dataset)} posts from HF Dataset")
+            return True
+            
+        except Exception as e:
+            if "not found" in str(e).lower() or "doesn't exist" in str(e).lower():
+                print(f"ℹ️  Dataset '{HF_DATASET_REPO}' doesn't exist yet, will create on first save")
+                return False
+            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
+            )
+            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
+                )
+                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 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(),))
+    conn.commit()
+    conn.close()
+    
+    # Sync from HF after initializing schema
+    sync_from_hf()
+
+# Initialize immediately
+init_database()
+
+# --- 5. TOOLS ---
+
+@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"
+        )
+        results = search.invoke(query)
+        return json.dumps(results, indent=2)
+    except Exception as e:
+        return f"Error in search: {str(e)}"
+
+@tool
+def fetch_article_content(url: str) -> str:
+    """Fetch the full text content of an article."""
+    try:
+        response = requests.get(url, timeout=10, headers={'User-Agent': 'Mozilla/5.0'})
+        response.raise_for_status()
+        soup = BeautifulSoup(response.content, 'html.parser')
+        for script in soup(["script", "style", "nav", "footer", "header"]):
+            script.decompose()
+        text = soup.get_text()
+        lines = (line.strip() for line in text.splitlines())
+        chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
+        text = ' '.join(chunk for chunk in chunks if chunk)
+        return text[:5000] if len(text) > 5000 else text
+    except Exception as e:
+        return f"Error fetching article: {str(e)}"
+
+@tool
+def get_all_previous_posts() -> str:
+    """Retrieve all previously published posts."""
+    conn = None
+    try:
+        conn = get_db_connection()
+        cursor = conn.cursor()
+        cursor.execute('SELECT id, date, topic, summary, source_url, quality_score FROM posts ORDER BY date DESC')
+        posts = cursor.fetchall()
+        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]})
+        return json.dumps(formatted, indent=2)
+    except Exception as e:
+        return f"Error: {str(e)}"
+    finally:
+        if conn: conn.close()
+
+@tool
+def get_top_quality_posts(limit: int = 5) -> str:
+    """Retrieve top quality posts."""
+    conn = None
+    try:
+        conn = get_db_connection()
+        cursor = conn.cursor()
+        cursor.execute('SELECT id, date, topic, summary, quality_score, hashtags FROM posts WHERE quality_score >= 7 ORDER BY quality_score DESC LIMIT ?', (limit,))
+        posts = cursor.fetchall()
+        if not posts: return "No high-quality example posts found."
+        formatted = []
+        for p in posts:
+            formatted.append({"id": p[0], "date": p[1], "topic": p[2], "summary": p[3], "quality_score": p[4], "hashtags": p[5]})
+        return json.dumps(formatted, indent=2)
+    except Exception as e:
+        return f"Error: {str(e)}"
+    finally:
+        if conn: conn.close()
+
+@tool
+def save_approved_post(topic: str, summary: str, source_url: str, quality_score: float, hashtags: str) -> str:
+    """Save an approved post to the database and sync to HF."""
+    conn = None
+    try:
+        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()))
+        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)}"
+    finally:
+        if conn: 
+            try:
+                conn.close()
+            except:
+                pass
+
+@tool
+def check_topic_similarity(new_topic: str) -> str:
+    """Check topic similarity."""
+    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": []})
+        
+        new_keywords = set(new_topic.lower().split())
+        similar = []
+        for prev_topic, prev_summary in previous:
+            prev_keywords = set(prev_topic.lower().split())
+            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)
+    except Exception as e:
+        return f"Error: {str(e)}"
+    finally:
+        if conn: conn.close()
+
+@tool
+def count_words(text: str) -> str:
+    return f"Word count: {len(text.split())}"
+
+@tool
+def get_example_posts_for_writer() -> str:
+    examples = [
+        {"title": "Researchers Have Developed Next-Generation Plant Immune System", 
+         "content": "Full example content here...", 
+         "structure": "Title -> 2 Paragraphs -> Hashtags"},
+    ]
+    return json.dumps(examples, indent=2)
+
+@tool
+def get_example_posts_for_critic() -> str:
+    examples = [
+        {"title": "Scientists Found 14 Mysterious Creatures", "quality_score": 9.0},
+    ]
+    return json.dumps(examples, indent=2)
+
+# --- 6. WORKFLOW STAGES ENUM ---
+
+class WorkflowStage(Enum):
+    IDLE = "idle"
+    DISCOVERY = "discovery"
+    CHECKPOINT_1 = "checkpoint_1"
+    CURATOR = "curator"
+    CHECKPOINT_2 = "checkpoint_2"
+    WRITER = "writer"
+    CRITIC = "critic"
+    CHECKPOINT_3 = "checkpoint_3"
+    FINALIZE = "finalize"
+    COMPLETE = "complete"
+    ERROR = "error"
+
+# --- 7. ENHANCED STATE ---
+
+class EnhancedAgentState(TypedDict):
+    stage: str
+    search_topic: str
+    candidates: List[Dict]
+    selected_story: Dict
+    draft_summary: str
+    quality_score: float
+    critic_feedback: str
+    retry_count: int
+    error_message: str
+    progress_log: List[str]
+
+# --- 8. AGENT FUNCTIONS WITH PROGRESS TRACKING ---
+
+def run_discovery(state: EnhancedAgentState, progress_callback=None) -> EnhancedAgentState:
+    """Discovery Agent with progress tracking"""
+    try:
+        if progress_callback:
+            progress_callback("🔍 Searching for scientific breakthroughs...")
+        
+        topic = state.get("search_topic", "general science")
+        
+        system_msg = SystemMessage(content=f"""You are the Discovery Agent for Tuesday Trivia.
+        Mission: Find 10-15 recent scientific breakthroughs (2-4 weeks).
+        Tools: search_science_breakthroughs, get_all_previous_posts, check_topic_similarity.
+        Output: List 10-15 unique breakthroughs with Title, Desc, URL, Why interesting.""")
+        
+        user_msg = HumanMessage(content=f"Search for recent breakthroughs in {topic}.")
+        
+        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 = 5
+        steps = 0
+        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']
+                
+                if name == 'search_science_breakthroughs': 
+                    res = search_science_breakthroughs.invoke(args)
+                elif name == 'get_all_previous_posts': 
+                    res = get_all_previous_posts.invoke(args)
+                elif name == 'check_topic_similarity': 
+                    res = check_topic_similarity.invoke(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
+        
+        # Parse candidates from response
+        candidates_text = response.content
+        
+        if progress_callback:
+            progress_callback("✅ Discovery complete!")
+        
+        state["candidates"] = [{"raw": candidates_text}]
+        state["stage"] = WorkflowStage.CHECKPOINT_1.value
+        state["progress_log"].append("Discovery completed successfully")
+        
+        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"""
+    try:
+        if progress_callback:
+            progress_callback("🎯 Ranking and selecting best 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.""")
+        
+        user_msg = HumanMessage(content=f"Evaluate these candidates:\n{candidates_text}")
+        
+        curator_llm = llm_large.bind_tools([])
+        response = curator_llm.invoke([system_msg, user_msg])
+        
+        if progress_callback:
+            progress_callback("✅ Story selected!")
+        
+        state["selected_story"] = {"raw": response.content}
+        state["stage"] = WorkflowStage.CHECKPOINT_2.value
+        state["progress_log"].append("Curation completed")
+        
+        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"""
+    try:
+        if progress_callback:
+            progress_callback("✍️ Writing draft post...")
+        
+        selected_story = state.get("selected_story", {})
+        story_text = selected_story.get("raw", "")
+        retry_count = state.get("retry_count", 0)
+        
+        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.
+        Structure: Title, Para 1 (Hook), Para 2 (Tech/Impact), Hashtags (#TuesdayTrivia #RnDCell #CCA).
+        Tone: Conversational, active voice, no questions. 140-180 words.
+        Use tools to get examples and count words.""")
+        
+        user_msg = HumanMessage(content=f"Write post for:\n{story_text}\n{retry_context}")
+        
+        writer_llm = llm_medium.bind_tools([get_example_posts_for_writer, 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)
+            response = writer_llm.invoke(conversation)
+            conversation.append(response)
+            steps += 1
+
+        if progress_callback:
+            progress_callback("✅ Draft completed!")
+
+        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})")
+        
+        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"""
+    try:
+        if progress_callback:
+            progress_callback("🔍 Evaluating draft quality...")
+        
+        draft = state.get("draft_summary", "")
+        
+        system_msg = SystemMessage(content="""You are the Critic Agent.
+        Evaluate: Structure, Content, Style, Hashtags, Word Count.
+        Output: Quality Score (0-10) and Decision (APPROVED/REJECTED). Approved if score >= 8.""")
+        
+        user_msg = HumanMessage(content=f"Evaluate:\n{draft}")
+        
+        critic_llm = llm_large.bind_tools([get_example_posts_for_critic])
+        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'])
+                conversation.append(ToolMessage(content=str(res), tool_call_id=tool_call['id']))
+            response = critic_llm.invoke(conversation)
+        
+        text = response.content
+        score = 5.0
+        try:
+            if "TOTAL SCORE:" in text:
+                score = float(text.split("TOTAL SCORE:")[1].split("/")[0].strip())
+        except: 
+            pass
+        
+        if progress_callback:
+            progress_callback(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")
+        
+        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:
+        if progress_callback:
+            progress_callback("💾 Saving to database...")
+        
+        draft = state.get("draft_summary", "")
+        score = state.get("quality_score", 0.0)
+        
+        topic = "Scientific Breakthrough"
+        hashtags = ""
+        lines = draft.split('\n')
+        for line in lines:
+            if "#" in line: 
+                hashtags = line
+                break
+        
+        res = save_approved_post.invoke({
+            "topic": topic, 
+            "summary": draft, 
+            "source_url": "N/A", 
+            "quality_score": score, 
+            "hashtags": hashtags
+        })
+        
+        if progress_callback:
+            progress_callback("✅ Post saved successfully!")
+        
+        state["stage"] = WorkflowStage.COMPLETE.value
+        state["progress_log"].append(f"Finalized: {res}")
+        
+        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 ---
+
+def create_initial_state(topic: str) -> EnhancedAgentState:
+    return {
+        "stage": WorkflowStage.IDLE.value,
+        "search_topic": topic,
+        "candidates": [],
+        "selected_story": {},
+        "draft_summary": "",
+        "quality_score": 0.0,
+        "critic_feedback": "",
+        "retry_count": 0,
+        "error_message": "",
+        "progress_log": []
+    }
+
+def start_workflow(topic: str, progress=gr.Progress()):
+    """Start the discovery process"""
+    state = create_initial_state(topic)
+    
+    def update_progress(msg):
+        progress(0.3, desc=msg)
+    
+    state = run_discovery(state, update_progress)
+    
+    if state["stage"] == WorkflowStage.ERROR.value:
+        return (
+            state,
+            f"❌ **Error:** {state['error_message']}",
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=True),  # Show restart
+            "",
+            gr.update(visible=False)
+        )
+    
+    candidates_text = state["candidates"][0]["raw"] if state["candidates"] else "No candidates found"
+    
+    return (
+        state,
+        f"## 🔍 Discovery Results\n\n{candidates_text}",
+        gr.update(visible=True),  # Approve button
+        gr.update(visible=True),  # Reject button
+        gr.update(visible=False),
+        gr.update(visible=False),
+        "",
+        gr.update(visible=False)
+    )
+
+def handle_checkpoint1_approve(state, progress=gr.Progress()):
+    """Handle approval at checkpoint 1"""
+    def update_progress(msg):
+        progress(0.5, desc=msg)
+    
+    state = run_curator(state, update_progress)
+    
+    if state["stage"] == WorkflowStage.ERROR.value:
+        return (
+            state,
+            f"❌ **Error:** {state['error_message']}",
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=True),
+            "",
+            gr.update(visible=False)
+        )
+    
+    story_text = state["selected_story"]["raw"] if state["selected_story"] else "No story selected"
+    
+    return (
+        state,
+        f"## 🎯 Selected Story\n\n{story_text}",
+        gr.update(visible=True),  # Approve button
+        gr.update(visible=True),  # Different story
+        gr.update(visible=False),
+        gr.update(visible=False),
+        "",
+        gr.update(visible=False)
+    )
+
+def handle_checkpoint1_reject(state, progress=gr.Progress()):
+    """Handle rejection at checkpoint 1 - restart discovery"""
+    def update_progress(msg):
+        progress(0.3, desc=msg)
+    
+    state = run_discovery(state, update_progress)
+    
+    if state["stage"] == WorkflowStage.ERROR.value:
+        return (
+            state,
+            f"❌ **Error:** {state['error_message']}",
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=True),
+            "",
+            gr.update(visible=False)
+        )
+    
+    candidates_text = state["candidates"][0]["raw"] if state["candidates"] else "No candidates found"
+    
+    return (
+        state,
+        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=False)
+    )
+
+def handle_checkpoint2_approve(state, progress=gr.Progress()):
+    """Handle approval at checkpoint 2 - proceed to writing"""
+    def update_progress(msg):
+        progress(0.6, desc=msg)
+    
+    state = run_writer(state, update_progress)
+    
+    if state["stage"] == WorkflowStage.ERROR.value:
+        return (
+            state,
+            f"❌ **Error:** {state['error_message']}",
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=True),
+            "",
+            gr.update(visible=False)
+        )
+    
+    progress(0.8, desc="Evaluating draft...")
+    state = run_critic(state, lambda x: progress(0.8, desc=x))
+    
+    draft = state.get("draft_summary", "")
+    score = state.get("quality_score", 0)
+    feedback = state.get("critic_feedback", "")
+    
+    return (
+        state,
+        f"## ✍️ Draft Post\n\n{draft}\n\n---\n\n**Quality Score:** {score}/10\n\n**Feedback:**\n{feedback}",
+        gr.update(visible=True),  # Finalize
+        gr.update(visible=True),  # Edit/Rewrite
+        gr.update(visible=False),
+        gr.update(visible=False),
+        "",
+        gr.update(visible=True)  # Show edit instructions
+    )
+
+def handle_checkpoint2_different(state, progress=gr.Progress()):
+    """Request different story - re-run curator"""
+    def update_progress(msg):
+        progress(0.5, desc=msg)
+    
+    state = run_curator(state, update_progress)
+    
+    if state["stage"] == WorkflowStage.ERROR.value:
+        return (
+            state,
+            f"❌ **Error:** {state['error_message']}",
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=True),
+            "",
+            gr.update(visible=False)
+        )
+    
+    story_text = state["selected_story"]["raw"] if state["selected_story"] else "No story selected"
+    
+    return (
+        state,
+        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=False)
+    )
+
+def handle_checkpoint3_finalize(state, progress=gr.Progress()):
+    """Finalize and save the post"""
+    def update_progress(msg):
+        progress(0.9, desc=msg)
+    
+    state = run_finalize(state, update_progress)
+    
+    draft = state.get("draft_summary", "")
+    
+    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",
+        gr.update(visible=False),
+        gr.update(visible=False),
+        gr.update(visible=False),
+        gr.update(visible=True),  # Show restart
+        "",
+        gr.update(visible=False)
+    )
+
+def handle_checkpoint3_edit(state, edit_instructions, progress=gr.Progress()):
+    """Edit the draft based on instructions"""
+    if not edit_instructions:
+        return (
+            state,
+            "⚠️ Please provide edit instructions",
+            gr.update(visible=True),
+            gr.update(visible=True),
+            gr.update(visible=False),
+            gr.update(visible=False),
+            edit_instructions,
+            gr.update(visible=True)
+        )
+    
+    # Add edit instructions to state for writer to use
+    if "critic_feedback" in state:
+        state["critic_feedback"] += f"\n\nUser edit request: {edit_instructions}"
+    
+    def update_progress(msg):
+        progress(0.6, desc=msg)
+    
+    state = run_writer(state, update_progress)
+    
+    if state["stage"] == WorkflowStage.ERROR.value:
+        return (
+            state,
+            f"❌ **Error:** {state['error_message']}",
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=True),
+            "",
+            gr.update(visible=False)
+        )
+    
+    progress(0.8, desc="Re-evaluating draft...")
+    state = run_critic(state, lambda x: progress(0.8, desc=x))
+    
+    draft = state.get("draft_summary", "")
+    score = state.get("quality_score", 0)
+    feedback = state.get("critic_feedback", "")
+    
+    return (
+        state,
+        f"## ✍️ Revised Draft\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)
+    )
+
+def restart_workflow():
+    """Reset everything to start fresh"""
+    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=False),
+        "",
+        gr.update(visible=False)
+    )
+
+# --- 10. BUILD GRADIO UI ---
+
+css = """
+.output-box {
+    min-height: 400px;
+    max-height: 600px;
+    overflow-y: auto;
+    padding: 20px;
+    border-radius: 8px;
+    background: #f8f9fa;
+}
+.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;
+}
+"""
+
+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.
+    
+    **☁️ Cloud Storage:** Posts are automatically synced to Hugging Face Datasets for persistence.
+    """)
+    
+    # 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(
+                label="Search Topic",
+                placeholder="e.g., quantum computing, biotechnology, space exploration",
+                value="general science"
+            )
+            
+            with gr.Row():
+                start_btn = gr.Button("🚀 Start Discovery", variant="primary", visible=True)
+                restart_btn = gr.Button("🔄 Start New", variant="secondary", visible=False)
+            
+            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)
+            
+            edit_instructions = gr.Textbox(
+                label="Edit Instructions (optional)",
+                placeholder="Provide specific instructions for changes...",
+                visible=False,
+                lines=3
+            )
+            
+            # Add sync status
+            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.
+                """)
+                
+                manual_sync_btn = gr.Button("🔄 Manual Sync to HF", size="sm")
+            
+            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!
+            
+            **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
+            """)
+    
+    # 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."
+    
+    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]
+    )
+    
+    # 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):
+        if s and s.get("stage") == WorkflowStage.CHECKPOINT_1.value:
+            return handle_checkpoint1_reject(s)
+        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):
+        if s and s.get("stage") == WorkflowStage.CHECKPOINT_2.value:
+            return handle_checkpoint2_different(s)
+        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):
+        if not s:
+            return s, "No active workflow", 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)
+        elif stage == WorkflowStage.CHECKPOINT_2.value:
+            return route_checkpoint2_approve(s)
+        elif stage == WorkflowStage.CHECKPOINT_3.value:
+            return route_checkpoint3_finalize(s)
+        
+        return s, "Invalid stage for approval", gr.update(), gr.update(), gr.update(), gr.update(), "", gr.update()
+    
+    def smart_reject(s):
+        if not s:
+            return s, "No active workflow", 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)
+        elif stage == WorkflowStage.CHECKPOINT_2.value:
+            return route_checkpoint2_different(s)
+        elif stage == WorkflowStage.CHECKPOINT_3.value:
+            return route_checkpoint3_edit(s, "")
+        
+        return s, "Invalid stage for rejection", gr.update(), gr.update(), gr.update(), gr.update(), "", gr.update()
+    
+    approve_btn.click(
+        fn=smart_approve,
+        inputs=[state],
+        outputs=[state, output_display, approve_btn, reject_btn, start_btn, restart_btn, edit_instructions, edit_instructions]
+    )
+    
+    reject_btn.click(
+        fn=smart_reject,
+        inputs=[state],
+        outputs=[state, output_display, approve_btn, reject_btn, start_btn, restart_btn, edit_instructions, edit_instructions]
+    )
+    
+    restart_btn.click(
+        fn=restart_workflow,
+        outputs=[state, output_display, approve_btn, reject_btn, start_btn, restart_btn, edit_instructions, edit_instructions]
+    )
+
+if __name__ == "__main__":
     demo.launch()