app-backup.py

import gradio as gr
import os
import json
import random
from datetime import datetime
import pandas as pd
from typing import Dict, List, Tuple, Optional, Generator
import sqlite3
from dataclasses import dataclass, asdict
import hashlib
import time
from enum import Enum
import numpy as np
import threading
import queue
import re

# For LLM API integration
try:
    from openai import OpenAI
except ImportError:
    print("OpenAI library not installed. Install with: pip install openai")
    OpenAI = None

try:
    from gradio_client import Client as GradioClient
except ImportError:
    print("Gradio client not installed. Install with: pip install gradio_client")
    GradioClient = None

# For Gemini API
try:
    from google import genai
    from google.genai import types
except ImportError:
    print("Google GenAI library not installed. Install with: pip install google-genai")
    genai = None
    types = None

# For Claude API
try:
    import anthropic
except ImportError:
    print("Anthropic library not installed. Install with: pip install anthropic")
    anthropic = None

# For Hugging Face Dataset integration
try:
    from huggingface_hub import HfApi, login, create_repo, upload_file, hf_hub_download
    from datasets import Dataset, load_dataset
    import pyarrow.parquet as pq
    import pyarrow as pa
except ImportError:
    print("Hugging Face libraries not installed. Install with: pip install huggingface_hub datasets pyarrow")
    HfApi = None
    Dataset = None

# ==================== Configuration ====================
class Category(Enum):
    STORYTELLING = "storytelling"
    INNOVATION = "innovation"
    BUSINESS = "business"

@dataclass
class Battle:
    id: str
    prompt_id: str
    prompt_text: str
    model_a: str
    model_b: str
    response_a: str
    response_b: str
    winner: Optional[str]
    voter_id: str
    timestamp: datetime
    category: Category
    custom_prompt: bool = False
    language: str = "en"

# ==================== Language Configurations ====================
LANGUAGES = {
    "en": "English",
    "ko": "한국어"
}

UI_TEXT = {
    "en": {
        "title": "🎨 AI Models Creativity Battle Arena",
        "subtitle": "Test cutting-edge AI models in creative challenges",
        "battle_tab": "⚔️ Battle Arena",
        "leaderboard_tab": "🏆 Leaderboard",
        "category_label": "Select Category",
        "custom_prompt_label": "✏️ Custom Challenge (Optional)",
        "custom_prompt_placeholder": "Enter your creative challenge for the models...",
        "new_battle_btn": "🎲 Start New Battle",
        "model_a": "### 🅰️ Model A",
        "model_b": "### 🅱️ Model B",
        "vote_a": "🅰️ Model A is more creative",
        "vote_b": "🅱️ Model B is more creative",
        "vote_complete": "### 🎉 Vote Complete!",
        "winner": "Winner",
        "leaderboard_title": "## 🏆 AI Models Leaderboard",
        "category_filter": "Category Filter",
        "refresh_btn": "🔄 Refresh",
        "language_label": "Language",
        "contact": "Contact: arxivgpt@gmail.com",
        "challenge_task": "### 📝 Challenge Task",
        "category": "Category",
        "prompt": "Challenge",
        "model_identity": "Model Identity",
        "elo_updated": "Scores have been updated!",
        "generating": "🔄 Generating response...",
        "categories": {
            "random": "🎲 Random",
            "storytelling": "📚 Storytelling",
            "innovation": "💡 Innovation",
            "business": "💼 Business"
        },
        "filter_categories": {
            "overall": "Overall",
            "storytelling": "Storytelling",
            "innovation": "Innovation",
            "business": "Business"
        }
    },
    "ko": {
        "title": "🎨 AI 모델 창의성 배틀 아레나",
        "subtitle": "최첨단 AI 모델들의 창의력 대결",
        "battle_tab": "⚔️ 배틀 아레나",
        "leaderboard_tab": "🏆 리더보드",
        "category_label": "카테고리 선택",
        "custom_prompt_label": "✏️ 커스텀 도전과제 (선택사항)",
        "custom_prompt_placeholder": "모델들에게 도전할 창의적인 과제를 입력하세요...",
        "new_battle_btn": "🎲 새로운 배틀 시작",
        "model_a": "### 🅰️ 모델 A",
        "model_b": "### 🅱️ 모델 B",
        "vote_a": "🅰️ 모델 A가 더 창의적이다",
        "vote_b": "🅱️ 모델 B가 더 창의적이다",
        "vote_complete": "### 🎉 투표 완료!",
        "winner": "승자",
        "leaderboard_title": "## 🏆 AI 모델 리더보드",
        "category_filter": "카테고리 필터",
        "refresh_btn": "🔄 새로고침",
        "language_label": "언어",
        "contact": "문의: arxivgpt@gmail.com",
        "challenge_task": "### 📝 도전 과제",
        "category": "카테고리",
        "prompt": "도전과제",
        "model_identity": "모델 정체",
        "elo_updated": "점수가 업데이트되었습니다!",
        "generating": "🔄 응답 생성 중...",
        "categories": {
            "random": "🎲 랜덤",
            "storytelling": "📚 스토리텔링",
            "innovation": "💡 혁신/발명",
            "business": "💼 비즈니스"
        },
        "filter_categories": {
            "overall": "전체",
            "storytelling": "스토리텔링",
            "innovation": "혁신/발명",
            "business": "비즈니스"
        }
    }
}

# ==================== Simplified Prompt Database ====================
PROMPTS = {
    Category.STORYTELLING: {
        "en": [
            {"text": "Write a sci-fi movie proposal with a never-before-explored concept", "difficulty": "high"},
            {"text": "Create a story where the protagonists never meet but fall deeply in love", "difficulty": "high"},
            {"text": "Design a thriller where the twist is revealed in the first scene but still surprises at the end", "difficulty": "high"}
        ],
        "ko": [
            {"text": "한 번도 다뤄지지 않은 소재로 SF 영화 기획안을 작성하세요", "difficulty": "high"},
            {"text": "주인공들이 한 번도 만나지 않지만 깊은 사랑에 빠지는 스토리를 창작하세요", "difficulty": "high"},
            {"text": "첫 장면에서 반전을 공개하지만 마지막에 여전히 충격적인 스릴러를 설계하세요", "difficulty": "high"}
        ]
    },
    Category.INNOVATION: {
        "en": [
            {"text": "Present 5 innovative ideas that could revolutionize the bicycle", "difficulty": "high"},
            {"text": "Propose 5 breakthrough innovations that could transform email communication", "difficulty": "high"},
            {"text": "Design 5 inventions that could make elevators obsolete", "difficulty": "high"}
        ],
        "ko": [
            {"text": "자전거를 혁신할 수 있는 획기적인 발명 아이디어를 5개 제시하세요", "difficulty": "high"},
            {"text": "이메일 커뮤니케이션을 완전히 변화시킬 혁신 아이디어를 5개 제시하세요", "difficulty": "high"},
            {"text": "엘리베이터를 대체할 수 있는 5가지 혁신적 발명을 설계하세요", "difficulty": "high"}
        ]
    },
    Category.BUSINESS: {
        "en": [
            {"text": "Design a business model in robotics/drone sector that could become a unicorn startup", "difficulty": "high"},
            {"text": "Create a one-person SaaS business that could scale to $1M ARR", "difficulty": "high"},
            {"text": "Develop a subscription model that people would happily pay $1000/month for", "difficulty": "high"}
        ],
        "ko": [
            {"text": "로봇/드론 분야에서 유니콘 기업이 될 수 있는 비즈니스 모델을 설계하세요", "difficulty": "high"},
            {"text": "연 매출 10억원을 달성할 수 있는 1인 SaaS 창업 아이템을 기획하세요", "difficulty": "high"},
            {"text": "사람들이 기꺼이 월 100만원을 지불할 만한 구독 비즈니스를 개발하세요", "difficulty": "high"}
        ]
    }
}

# ==================== Database Management ====================
class ArenaDatabase:
    def __init__(self, db_path="ai_models_arena.db", use_hf=True):
        self.db_path = db_path
        self.use_hf = use_hf and HfApi is not None
        self.hf_token = os.getenv("HF_TOKEN")
        self.hf_dataset_name = os.getenv("HF_DATASET_NAME", "ai_models_arena")
        self.hf_username = None
        
        if self.use_hf and self.hf_token:
            try:
                login(token=self.hf_token)
                self.api = HfApi()
                user_info = self.api.whoami()
                self.hf_username = user_info["name"]
                self.hf_repo_id = f"{self.hf_username}/{self.hf_dataset_name}"
                
                # Create or access the dataset repository
                self._init_hf_dataset()
                print(f"✅ Connected to Hugging Face Dataset: {self.hf_repo_id}")
                
                # ⭐ CRITICAL: Try to restore from HF FIRST
                if self._restore_from_hf():
                    print("✅ Successfully restored data from Hugging Face Dataset")
                    return  # ⭐ EXIT HERE if data exists - DO NOT initialize new database
                else:
                    print("📝 No existing data in HF Dataset, will create new database")
                    
            except Exception as e:
                print(f"❌ Failed to connect to Hugging Face: {e}")
                self.use_hf = False
        
        # ⭐ ONLY initialize new database if HF restore failed or HF not available
        print("📝 Initializing new local database")
        self.init_database()
    
    def _init_hf_dataset(self):
        """Initialize Hugging Face dataset repository"""
        try:
            # Try to create the repository (it will fail if it already exists)
            create_repo(
                repo_id=self.hf_repo_id,
                repo_type="dataset",
                private=True,
                exist_ok=True
            )
            print(f"✅ HF Dataset repository ready: {self.hf_repo_id}")
        except Exception as e:
            print(f"Dataset repo creation note: {e}")
    
    def _restore_from_hf(self):
        """⭐ NEW METHOD: Restore complete database from HF - returns True if successful"""
        try:
            print("🔄 Attempting to restore data from Hugging Face...")
            
            # Try to load battles data
            try:
                dataset = load_dataset(self.hf_repo_id, split="train", token=self.hf_token)
            except Exception as e:
                print(f"No existing battles data found: {e}")
                return False
            
            if not dataset or len(dataset) == 0:
                print("Dataset exists but is empty")
                return False
            
            print(f"Found {len(dataset)} battles in HF Dataset")
            
            # Create fresh local database with data from HF
            conn = sqlite3.connect(self.db_path)
            cursor = conn.cursor()
            
            # Create tables
            cursor.execute('''
                CREATE TABLE IF NOT EXISTS battles (
                    id TEXT PRIMARY KEY,
                    prompt_id TEXT,
                    prompt_text TEXT,
                    category TEXT,
                    model_a TEXT,
                    model_b TEXT,
                    response_a TEXT,
                    response_b TEXT,
                    winner TEXT,
                    voter_id TEXT,
                    timestamp DATETIME,
                    custom_prompt INTEGER DEFAULT 0,
                    language TEXT DEFAULT 'en'
                )
            ''')
            
            cursor.execute('''
                CREATE TABLE IF NOT EXISTS model_stats (
                    model_name TEXT PRIMARY KEY,
                    overall_score REAL DEFAULT 5.0,
                    storytelling_score REAL DEFAULT 5.0,
                    innovation_score REAL DEFAULT 5.0,
                    business_score REAL DEFAULT 5.0,
                    total_battles INTEGER DEFAULT 0,
                    wins INTEGER DEFAULT 0,
                    losses INTEGER DEFAULT 0,
                    elo_rating INTEGER DEFAULT 1500
                )
            ''')
            
            # Restore battles data
            battles_df = dataset.to_pandas()
            battles_df.to_sql('battles', conn, if_exists='replace', index=False)
            print(f"✅ Restored {len(battles_df)} battles")
            
            # Try to restore model stats
            stats_restored = False
            try:
                stats_dataset = load_dataset(self.hf_repo_id, split="stats", token=self.hf_token)
                if stats_dataset and len(stats_dataset) > 0:
                    stats_df = stats_dataset.to_pandas()
                    stats_df.to_sql('model_stats', conn, if_exists='replace', index=False)
                    print(f"✅ Restored model stats")
                    stats_restored = True
            except Exception as e:
                print(f"Could not restore stats: {e}")
            
            # If stats not restored, recalculate from battles
            if not stats_restored:
                print("📊 Recalculating stats from battle history...")
                self._recalculate_stats_from_battles(cursor)
            
            conn.commit()
            conn.close()
            
            return True  # Successfully restored
            
        except Exception as e:
            print(f"Failed to restore from HF: {e}")
            return False
    
    def _recalculate_stats_from_battles(self, cursor):
        """Recalculate model stats from battle history"""
        # Initialize all 4 models
        for model in ["GPT-5", "jetXA", "Gemini-2.5-Pro", "Claude-Opus-4.1"]:
            cursor.execute('''
                INSERT OR REPLACE INTO model_stats 
                (model_name, overall_score, storytelling_score, innovation_score, 
                 business_score, total_battles, wins, losses, elo_rating)
                VALUES (?, 5.0, 5.0, 5.0, 5.0, 0, 0, 0, 1500)
            ''', (model,))
        
        # Get all battles with winners
        cursor.execute('''
            SELECT model_a, model_b, winner, category FROM battles WHERE winner IS NOT NULL
        ''')
        
        battles = cursor.fetchall()
        
        # Process each battle
        for model_a, model_b, winner, category in battles:
            # Update win/loss counts
            if winner == model_a:
                cursor.execute('UPDATE model_stats SET wins = wins + 1, total_battles = total_battles + 1 WHERE model_name = ?', (model_a,))
                cursor.execute('UPDATE model_stats SET losses = losses + 1, total_battles = total_battles + 1 WHERE model_name = ?', (model_b,))
                
                # Update category scores
                self._update_category_scores(cursor, model_a, Category(category), True)
                self._update_category_scores(cursor, model_b, Category(category), False)
            else:
                cursor.execute('UPDATE model_stats SET wins = wins + 1, total_battles = total_battles + 1 WHERE model_name = ?', (model_b,))
                cursor.execute('UPDATE model_stats SET losses = losses + 1, total_battles = total_battles + 1 WHERE model_name = ?', (model_a,))
                
                # Update category scores
                self._update_category_scores(cursor, model_b, Category(category), True)
                self._update_category_scores(cursor, model_a, Category(category), False)
        
        # Recalculate ELO ratings
        self._recalculate_elo_from_battles(cursor)
        
        print(f"✅ Recalculated stats from {len(battles)} battles")
    
    def _recalculate_elo_from_battles(self, cursor):
        """Recalculate ELO ratings from battle history"""
        # Reset ELO to 1500
        cursor.execute('UPDATE model_stats SET elo_rating = 1500')
        
        # Get battles in chronological order
        cursor.execute('''
            SELECT model_a, model_b, winner FROM battles 
            WHERE winner IS NOT NULL 
            ORDER BY timestamp
        ''')
        
        battles = cursor.fetchall()
        
        for model_a, model_b, winner in battles:
            # Get current ELO ratings
            cursor.execute('SELECT elo_rating FROM model_stats WHERE model_name = ?', (model_a,))
            elo_a = cursor.fetchone()[0]
            
            cursor.execute('SELECT elo_rating FROM model_stats WHERE model_name = ?', (model_b,))
            elo_b = cursor.fetchone()[0]
            
            # Calculate new ELO
            K = 32
            if winner == model_a:
                expected_a = 1 / (1 + 10**((elo_b - elo_a) / 400))
                new_elo_a = int(elo_a + K * (1 - expected_a))
                new_elo_b = int(elo_b + K * (0 - (1 - expected_a)))
            else:
                expected_b = 1 / (1 + 10**((elo_a - elo_b) / 400))
                new_elo_a = int(elo_a + K * (0 - (1 - expected_b)))
                new_elo_b = int(elo_b + K * (1 - expected_b))
            
            cursor.execute('UPDATE model_stats SET elo_rating = ? WHERE model_name = ?', (new_elo_a, model_a))
            cursor.execute('UPDATE model_stats SET elo_rating = ? WHERE model_name = ?', (new_elo_b, model_b))
    
    def _sync_to_hf(self):
        """Sync local database to Hugging Face with improved error handling"""
        if not self.use_hf:
            print("HF sync disabled")
            return
        
        try:
            conn = sqlite3.connect(self.db_path)
            
            # Export battles
            battles_df = pd.read_sql_query("SELECT * FROM battles", conn)
            
            if len(battles_df) > 0:
                print(f"📤 Syncing {len(battles_df)} battles to HF...")
                
                # Convert to Dataset
                battles_dataset = Dataset.from_pandas(battles_df)
                
                # Push to hub with retry logic
                max_retries = 3
                for attempt in range(max_retries):
                    try:
                        battles_dataset.push_to_hub(
                            self.hf_repo_id,
                            split="train",
                            token=self.hf_token,
                            private=True
                        )
                        print(f"✅ Successfully pushed {len(battles_df)} battles to HF")
                        break
                    except Exception as push_error:
                        if attempt < max_retries - 1:
                            print(f"⚠️ Push attempt {attempt + 1} failed, retrying...")
                            time.sleep(2)  # Wait before retry
                        else:
                            print(f"❌ Failed to push to HF after {max_retries} attempts: {push_error}")
            
            # Also sync model stats for backup
            stats_df = pd.read_sql_query("SELECT * FROM model_stats", conn)
            if len(stats_df) > 0:
                try:
                    stats_dataset = Dataset.from_pandas(stats_df)
                    stats_dataset.push_to_hub(
                        self.hf_repo_id,
                        split="stats",
                        token=self.hf_token,
                        private=True
                    )
                    print(f"✅ Model stats synced to HF")
                except Exception as e:
                    print(f"⚠️ Could not sync stats: {e}")
            
            conn.close()
            
        except Exception as e:
            print(f"❌ Critical error in HF sync: {e}")
    
    def init_database(self):
        """Initialize SQLite database - ONLY called when no existing data"""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS battles (
                id TEXT PRIMARY KEY,
                prompt_id TEXT,
                prompt_text TEXT,
                category TEXT,
                model_a TEXT,
                model_b TEXT,
                response_a TEXT,
                response_b TEXT,
                winner TEXT,
                voter_id TEXT,
                timestamp DATETIME,
                custom_prompt INTEGER DEFAULT 0,
                language TEXT DEFAULT 'en'
            )
        ''')
        
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS model_stats (
                model_name TEXT PRIMARY KEY,
                overall_score REAL DEFAULT 5.0,
                storytelling_score REAL DEFAULT 5.0,
                innovation_score REAL DEFAULT 5.0,
                business_score REAL DEFAULT 5.0,
                total_battles INTEGER DEFAULT 0,
                wins INTEGER DEFAULT 0,
                losses INTEGER DEFAULT 0,
                elo_rating INTEGER DEFAULT 1500
            )
        ''')
        
        conn.commit()
        conn.close()
        
        self._init_models()
    
    def _init_models(self):
        """Initialize all 4 models"""
        models = ["GPT-5", "jetXA", "Gemini-2.5-Pro", "Claude-Opus-4.1"]
        
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        for model in models:
            cursor.execute('''
                INSERT OR IGNORE INTO model_stats (model_name) VALUES (?)
            ''', (model,))
        
        conn.commit()
        conn.close()
    
    def save_battle(self, battle: Battle):
        """Save battle result with proper duplicate prevention and sync"""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        try:
            # First check if this battle already exists
            cursor.execute('SELECT id, winner FROM battles WHERE id = ?', (battle.id,))
            existing = cursor.fetchone()
            
            if existing and existing[1]:
                print(f"⚠️ Battle {battle.id} already has a winner: {existing[1]}")
                conn.close()
                return  # Don't update if already voted
            
            # Insert or update the battle
            cursor.execute('''
                INSERT OR REPLACE INTO battles VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
            ''', (
                battle.id,
                battle.prompt_id,
                battle.prompt_text,
                battle.category.value,
                battle.model_a,
                battle.model_b,
                battle.response_a,
                battle.response_b,
                battle.winner,
                battle.voter_id,
                battle.timestamp.isoformat(),
                1 if battle.custom_prompt else 0,
                battle.language
            ))
            
            if battle.winner:
                winner = battle.winner
                loser = battle.model_b if winner == battle.model_a else battle.model_a
                
                # Only update stats if this is a new vote
                if not existing or not existing[1]:
                    print(f"📊 Updating stats: {winner} wins, {loser} loses")
                    
                    # Update winner stats
                    cursor.execute('''
                        UPDATE model_stats 
                        SET total_battles = total_battles + 1, 
                            wins = wins + 1
                        WHERE model_name = ?
                    ''', (winner,))
                    
                    # Update loser stats
                    cursor.execute('''
                        UPDATE model_stats 
                        SET total_battles = total_battles + 1, 
                            losses = losses + 1
                        WHERE model_name = ?
                    ''', (loser,))
                    
                    # Update category scores
                    self._update_category_scores(cursor, winner, battle.category, True)
                    self._update_category_scores(cursor, loser, battle.category, False)
                    
                    # Update ELO ratings
                    self._update_elo_ratings(cursor, winner, loser)
                    
                    print(f"✅ Stats updated for battle {battle.id}")
            
            conn.commit()
            print(f"💾 Battle {battle.id} saved to local database")
            
        except Exception as e:
            print(f"❌ Error saving battle: {e}")
            conn.rollback()
        finally:
            conn.close()
        
        # Sync to Hugging Face after saving
        self._sync_to_hf()
    
    def _update_category_scores(self, cursor, model, category, is_winner):
        """Update category-specific scores"""
        column_map = {
            Category.STORYTELLING: "storytelling_score",
            Category.INNOVATION: "innovation_score",
            Category.BUSINESS: "business_score"
        }
        
        score_column = column_map.get(category, "overall_score")
        
        cursor.execute(f'SELECT {score_column} FROM model_stats WHERE model_name = ?', (model,))
        result = cursor.fetchone()
        
        if result:
            current_score = result[0]
        else:
            current_score = 5.0
        
        if is_winner:
            new_score = min(10, current_score + 0.2)
        else:
            new_score = max(0, current_score - 0.1)
        
        cursor.execute(f'UPDATE model_stats SET {score_column} = ? WHERE model_name = ?', 
                      (new_score, model))
        
        # Update overall score
        cursor.execute('''
            UPDATE model_stats 
            SET overall_score = (storytelling_score + innovation_score + business_score) / 3.0
            WHERE model_name = ?
        ''', (model,))
    
    def _update_elo_ratings(self, cursor, winner, loser):
        """Update ELO ratings"""
        K = 32
        
        cursor.execute('SELECT elo_rating FROM model_stats WHERE model_name = ?', (winner,))
        winner_elo = cursor.fetchone()[0]
        
        cursor.execute('SELECT elo_rating FROM model_stats WHERE model_name = ?', (loser,))
        loser_elo = cursor.fetchone()[0]
        
        expected_winner = 1 / (1 + 10**((loser_elo - winner_elo) / 400))
        expected_loser = 1 / (1 + 10**((winner_elo - loser_elo) / 400))
        
        new_winner_elo = int(winner_elo + K * (1 - expected_winner))
        new_loser_elo = int(loser_elo + K * (0 - expected_loser))
        
        cursor.execute('UPDATE model_stats SET elo_rating = ? WHERE model_name = ?', 
                      (new_winner_elo, winner))
        cursor.execute('UPDATE model_stats SET elo_rating = ? WHERE model_name = ?', 
                      (new_loser_elo, loser))
    
    def get_leaderboard(self, category: Optional[Category] = None) -> pd.DataFrame:
        """Get leaderboard data"""
        conn = sqlite3.connect(self.db_path)
        
        if category:
            column_map = {
                Category.STORYTELLING: "storytelling_score",
                Category.INNOVATION: "innovation_score",
                Category.BUSINESS: "business_score"
            }
            sort_column = column_map.get(category, "overall_score")
        else:
            sort_column = "overall_score"
        
        query = f'''
            SELECT 
                model_name,
                ROUND(overall_score, 1) as overall_score,
                ROUND(storytelling_score, 1) as storytelling_score,
                ROUND(innovation_score, 1) as innovation_score,
                ROUND(business_score, 1) as business_score,
                total_battles,
                wins,
                CASE 
                    WHEN total_battles > 0 
                    THEN ROUND(100.0 * wins / total_battles, 1)
                    ELSE 0 
                END as win_rate,
                elo_rating
            FROM model_stats
            ORDER BY {sort_column} DESC, elo_rating DESC
        '''
        
        df = pd.read_sql_query(query, conn)
        conn.close()
        
        df.insert(0, 'rank', range(1, len(df) + 1))
        return df
    
    def debug_database_state(self):
        """Debug method to check current database state"""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        # Check battles count
        cursor.execute("SELECT COUNT(*) FROM battles")
        total_battles = cursor.fetchone()[0]
        
        cursor.execute("SELECT COUNT(*) FROM battles WHERE winner IS NOT NULL")
        voted_battles = cursor.fetchone()[0]
        
        # Check model stats
        cursor.execute("SELECT * FROM model_stats ORDER BY elo_rating DESC")
        stats = cursor.fetchall()
        
        conn.close()
        
        print("\n" + "="*50)
        print("📊 DATABASE STATE DEBUG")
        print("="*50)
        print(f"Total battles: {total_battles}")
        print(f"Voted battles: {voted_battles}")
        print("\nModel Stats:")
        print("-"*50)
        for stat in stats:
            print(f"{stat[0]:20} | Battles: {stat[5]:3} | Wins: {stat[6]:3} | ELO: {stat[8]:4}")
        print("="*50 + "\n")
        
        return {
            "total_battles": total_battles,
            "voted_battles": voted_battles,
            "model_stats": stats
        }

# ==================== Fixed LLM Interface with 4 Models ====================
class LLMInterface:
    """Interface for GPT-5, jetXA, Gemini 2.5 Pro, and Claude Opus 4.1 models"""
    
    def __init__(self):
        self.models = ["GPT-5", "jetXA", "Gemini-2.5-Pro", "Claude-Opus-4.1"]
        self.response_cache = {}
        self.cache_enabled = False  # Disable caching by default
        
        # Initialize OpenAI client for GPT-5
        self.openai_client = None
        openai_key = os.getenv("OPENAI_API_KEY")
        if openai_key and OpenAI:
            try:
                self.openai_client = OpenAI(api_key=openai_key)
                print("✅ GPT-5 client initialized")
            except Exception as e:
                print(f"❌ GPT-5 initialization failed: {e}")
        else:
            print("⚠️ GPT-5: No API key or OpenAI library not installed")
        
        # Initialize Gradio client for jetXA
        self.gradio_client = None
        jetxa_space = os.getenv("jetXA_API", "aiqtech/tests")
        hf_token = os.getenv("HF_TOKEN")
        
        if GradioClient:
            connection_attempts = [
                lambda: GradioClient(jetxa_space, hf_token=hf_token) if hf_token else GradioClient(jetxa_space),
                lambda: GradioClient(f"https://huggingface.co/spaces/{jetxa_space}"),
                lambda: GradioClient(f"https://{jetxa_space.replace('/', '-')}.hf.space"),
                lambda: GradioClient(src=jetxa_space),
                lambda: GradioClient("aiqtech/tests")
            ]
            
            for i, attempt in enumerate(connection_attempts, 1):
                try:
                    self.gradio_client = attempt()
                    if hasattr(self.gradio_client, 'view_api'):
                        api_info = self.gradio_client.view_api()
                        print(f"✅ jetXA client initialized successfully using method {i}!")
                        break
                except Exception as e:
                    if i == len(connection_attempts):
                        print(f"⚠️ jetXA: All connection attempts failed. Last error: {e}")
                        print("Will use fallback responses for jetXA")
                    else:
                        continue
        else:
            print("⚠️ jetXA: Gradio client not installed")
        
        # Initialize Gemini client
        self.gemini_client = None
        gemini_key = os.getenv("GEMINI_API_KEY")
        if gemini_key and genai:
            try:
                self.gemini_client = genai.Client(api_key=gemini_key)
                print("✅ Gemini 2.5 Pro client initialized")
            except Exception as e:
                print(f"❌ Gemini initialization failed: {e}")
        else:
            print("⚠️ Gemini: No API key or google-genai library not installed")
        
        # Initialize Claude client
        self.claude_client = None
        claude_key = os.getenv("ANTHROPIC_API_KEY")
        if claude_key and anthropic:
            try:
                self.claude_client = anthropic.Anthropic(api_key=claude_key)
                print("✅ Claude Opus 4.1 client initialized")
            except Exception as e:
                print(f"❌ Claude initialization failed: {e}")
        else:
            print("⚠️ Claude: No API key or anthropic library not installed")
    
    def clear_cache(self):
        """Clear all cached responses"""
        self.response_cache = {}
        print("✅ Cache cleared")
    
    def generate_response_stream(self, model: str, prompt: str, language: str = "en") -> Generator[str, None, None]:
        """Generate streaming response with proper accumulation"""
        
        # Add language and creativity instructions
        if language == "ko":
            instruction = "창의적이고 혁신적인 한국어 답변을 작성해주세요. 독창적이고 상세한 아이디어를 제시하세요."
        else:
            instruction = "Provide a highly creative and innovative response. Be original and detailed."
        
        full_prompt = f"{instruction}\n\n{prompt}"
        
        try:
            if model == "GPT-5":
                # Stream GPT-5 with proper accumulation
                accumulated = ""
                for chunk in self._stream_gpt5(full_prompt):
                    accumulated += chunk
                    yield accumulated  # Always yield the accumulated text
                    
            elif model == "jetXA":
                # Get full response and simulate streaming
                full_response = self._get_jetxa_response(full_prompt)
                
                if full_response:
                    # Format jetXA response with proper spacing
                    formatted_response = self._format_jetxa_response(full_response)
                    
                    # Simulate streaming word by word for jetXA for smoother effect
                    words = formatted_response.split()
                    accumulated = ""
                    
                    # Stream words in small batches for natural effect
                    batch_size = 2  # Stream 2 words at a time
                    for i in range(0, len(words), batch_size):
                        batch = words[i:i+batch_size]
                        for word in batch:
                            if accumulated:
                                accumulated += " "
                            accumulated += word
                        yield accumulated  # Yield accumulated text after each batch
                        time.sleep(0.03)  # Small delay between batches
                else:
                    # Use fallback if jetXA fails
                    fallback = self._generate_fallback(model, prompt, language)
                    # Stream fallback with accumulation
                    words = fallback.split()
                    accumulated = ""
                    for word in words:
                        if accumulated:
                            accumulated += " "
                        accumulated += word
                        yield accumulated
                        time.sleep(0.02)
                        
            elif model == "Gemini-2.5-Pro":
                # Stream Gemini with proper accumulation
                accumulated = ""
                for chunk in self._stream_gemini(full_prompt):
                    accumulated += chunk
                    yield accumulated
                    
            elif model == "Claude-Opus-4.1":
                # Stream Claude with proper accumulation
                accumulated = ""
                for chunk in self._stream_claude(full_prompt):
                    accumulated += chunk
                    yield accumulated
            else:
                # Unknown model - use fallback
                fallback = self._generate_fallback(model, prompt, language)
                # Stream fallback with accumulation
                words = fallback.split()
                accumulated = ""
                for word in words:
                    if accumulated:
                        accumulated += " "
                    accumulated += word
                    yield accumulated
                    time.sleep(0.02)
                    
        except Exception as e:
            print(f"Error streaming {model}: {e}")
            fallback = self._generate_fallback(model, prompt, language)
            yield fallback

    def _stream_gemini(self, prompt: str) -> Generator[str, None, None]:
        """Stream Gemini 2.5 Pro response"""
        if not self.gemini_client:
            fallback = self._generate_fallback("Gemini-2.5-Pro", prompt, "en")
            words = fallback.split()
            for word in words:
                yield word + " "
                time.sleep(0.02)
            return
        
        try:
            contents = [
                types.Content(
                    role="user",
                    parts=[types.Part.from_text(text=prompt)],
                ),
            ]
            
            # 수정된 설정 - max_output_tokens 증가 및 thinking_config 제거
            generate_content_config = types.GenerateContentConfig(
                response_mime_type="text/plain",
                temperature=0.9,  # 창의성을 위해 온도 상승
                max_output_tokens=2048,  # 토큰 수 증가
                top_p=0.95,
                top_k=40,
            )
        
            # 전체 응답을 수집
            full_response = ""
            
            for chunk in self.gemini_client.models.generate_content_stream(
                model="gemini-2.0-flash-exp",  # 또는 "gemini-2.0-flash-thinking-exp-1219"
                contents=contents,
                config=generate_content_config,
            ):
                if chunk.text:
                    full_response += chunk.text
                    yield chunk.text
            
            # 응답이 너무 짧으면 재시도
            if len(full_response) < 100:
                print(f"⚠️ Gemini response too short ({len(full_response)} chars), using fallback")
                fallback = self._generate_fallback("Gemini-2.5-Pro", prompt, "en")
                yield fallback
                    
        except Exception as e:
            print(f"Gemini streaming error: {e}")
            fallback = self._generate_fallback("Gemini-2.5-Pro", prompt, "en")
            yield fallback
    
    def _stream_claude(self, prompt: str) -> Generator[str, None, None]:
        """Stream Claude Opus 4.1 response"""
        if not self.claude_client:
            fallback = self._generate_fallback("Claude-Opus-4.1", prompt, "en")
            words = fallback.split()
            for word in words:
                yield word + " "
                time.sleep(0.02)
            return
        
        try:
            with self.claude_client.messages.stream(
                model="claude-opus-4-1-20250805",
                max_tokens=1500,
                temperature=0.8,
                messages=[
                    {
                        "role": "user",
                        "content": [
                            {
                                "type": "text",
                                "text": prompt
                            }
                        ]
                    }
                ]
            ) as stream:
                for text in stream.text_stream:
                    yield text
                    
        except Exception as e:
            print(f"Claude streaming error: {e}")
            fallback = self._generate_fallback("Claude-Opus-4.1", prompt, "en")
            yield fallback
    
    def _format_jetxa_response(self, text: str) -> str:
        """Format jetXA response with proper spacing and line breaks for better readability"""
        # Clean up the response first
        text = self._clean_markdown_response(text)
        
        # Split into lines
        lines = text.split('\n')
        formatted_lines = []
        
        for i, line in enumerate(lines):
            line = line.strip()
            
            if not line:
                # Keep empty lines for spacing
                formatted_lines.append('')
                continue
            
            # Add extra spacing around headers
            if line.startswith('#'):
                # Add double blank line before headers (except first line)
                if i > 0 and formatted_lines and formatted_lines[-1].strip():
                    formatted_lines.append('')
                    formatted_lines.append('')
                formatted_lines.append(line)
                # Add blank line after major headers
                if line.startswith('# ') or line.startswith('## '):
                    formatted_lines.append('')
            # Add spacing around lists
            elif line.startswith('- ') or line.startswith('* ') or re.match(r'^\d+\. ', line):
                # Add blank line before first list item
                if i > 0 and formatted_lines and formatted_lines[-1].strip() and not (
                    formatted_lines[-1].startswith('- ') or 
                    formatted_lines[-1].startswith('* ') or 
                    re.match(r'^\d+\. ', formatted_lines[-1])
                ):
                    formatted_lines.append('')
                formatted_lines.append(line)
            else:
                formatted_lines.append(line)
        
        # Join with newlines
        result = '\n'.join(formatted_lines)
        
        # Clean up excessive blank lines (max 2 consecutive)
        while '\n\n\n\n' in result:
            result = result.replace('\n\n\n\n', '\n\n')
        while '\n\n\n' in result:
            result = result.replace('\n\n\n', '\n\n')
        
        return result.strip()
    
    def _stream_gpt5(self, prompt: str) -> Generator[str, None, None]:
        """Stream GPT-5 API response - returns chunks only (not accumulated)"""
        if not self.openai_client:
            fallback = self._generate_fallback("GPT-5", prompt, "en")
            words = fallback.split()
            for word in words:
                yield word + " "
                time.sleep(0.02)
            return
        
        try:
            stream = self.openai_client.chat.completions.create(
                model="gpt-4",  # Use gpt-4 as fallback if gpt-5 not available
                messages=[{"role": "user", "content": prompt}],
                max_tokens=1500,
                temperature=0.8,
                stream=True
            )
            
            for chunk in stream:
                if chunk.choices[0].delta.content is not None:
                    yield chunk.choices[0].delta.content  # Yield only the chunk
        except Exception as e:
            print(f"GPT-5 streaming error: {e}")
            fallback = self._generate_fallback("GPT-5", prompt, "en")
            yield fallback
    
    def _get_jetxa_response(self, prompt: str) -> str:
        """Get complete response from jetXA"""
        if not self.gradio_client:
            return ""
        
        try:
            result = self.gradio_client.predict(
                message=prompt,
                history=[],
                use_search=False,
                show_agent_thoughts=False,
                search_count=5,
                api_name="/process_query_optimized"
            )
            
            response_text = ""
            
            if result and isinstance(result, (tuple, list)) and len(result) >= 1:
                chat_history = result[0]
                
                if isinstance(chat_history, list) and len(chat_history) > 0:
                    for msg in reversed(chat_history):
                        if isinstance(msg, dict):
                            content = msg.get('content', '')
                            if content:
                                response_text = str(content)
                                break
                        elif isinstance(msg, (list, tuple)) and len(msg) >= 2:
                            if msg[1]:
                                response_text = str(msg[1])
                                break
                
                if not response_text:
                    for i in range(1, min(3, len(result))):
                        if result[i] and isinstance(result[i], str) and result[i].strip():
                            response_text = result[i]
                            break
            
            if response_text:
                # Clean up any potential formatting issues
                response_text = self._clean_markdown_response(response_text)
            
            return response_text
                
        except Exception as e:
            print(f"jetXA response error: {e}")
            return ""
    
    def _clean_markdown_response(self, text: str) -> str:
        """Clean and fix common markdown formatting issues"""
        # Remove any duplicate markers or broken formatting
        text = text.replace('| ---', '|---')  # Fix table separators
        text = text.replace('---\n---', '---')  # Remove duplicate horizontal rules
        
        # Ensure proper spacing around headers
        lines = text.split('\n')
        cleaned_lines = []
        
        for i, line in enumerate(lines):
            # Fix header formatting
            if line.strip().startswith('#'):
                # Ensure space after # symbols
                if '#' in line and not line.startswith('# '):
                    parts = line.split('#', 1)
                    if len(parts) > 1:
                        hash_count = len(line) - len(line.lstrip('#'))
                        line = '#' * hash_count + ' ' + parts[-1].strip()
                
                # Add blank line before headers (except first line)
                if i > 0 and cleaned_lines and cleaned_lines[-1].strip():
                    cleaned_lines.append('')
            
            # Fix table formatting
            if '|' in line:
                # Ensure proper table separator
                if all(c in ['-', '|', ' '] for c in line.strip()):
                    line = line.replace(' ', '').replace('|-', '|---').replace('-|', '---|')
                    if not line.startswith('|'):
                        line = '|' + line
                    if not line.endswith('|'):
                        line = line + '|'
            
            cleaned_lines.append(line)
        
        return '\n'.join(cleaned_lines)
    
    def _generate_fallback(self, model: str, prompt: str, language: str) -> str:
        """Generate high-quality fallback response with language support and proper markdown"""
        
        # Determine category from prompt
        if any(word in prompt.lower() for word in ["story", "movie", "novel", "plot", "스토리", "영화", "소설"]):
            category = "story"
        elif any(word in prompt.lower() for word in ["innovate", "invent", "revolution", "혁신", "발명", "개발"]):
            category = "innovation"
        else:
            category = "business"
        
        # Korean responses with better markdown formatting
        if language == "ko":
            responses = {
                "story": {
                    "GPT-5": """# 양자 거울

## 시놉시스
한 형사가 도시의 모든 거울이 실제로 **범죄가 예방된 다른 타임라인**으로 통하는 포털임을 발견한다.""",
                    "jetXA": """# 감정 고고학

## 기획 의도
2045년, 고고학자들은 유물을 발굴하지 않는다—그들은 **비극의 장소에 남겨진 압축된 인간 감정**을 발굴한다.""",
                    "Gemini-2.5-Pro": """# 기억의 도서관

## 줄거리
죽은 사람들의 마지막 기억이 책으로 변하는 **사후 도서관**을 발견한 사서의 이야기.""",
                    "Claude-Opus-4.1": """# 시간의 정원사

## 개요
매일 밤 다른 시대로 이동하는 정원을 관리하며 **역사의 순간들을 가꾸는** 정원사의 모험."""
                },
                "innovation": {
                    "GPT-5": """# 🚲 자전거 혁신 5가지

## 1. **중력 무시 바퀴** (Gravity Defiance Wheels)
- **기술**: 전자기 림이 오르막길에서 무게를 거의 0으로 감소""",
                    "jetXA": """# 📧 이메일 혁명 5가지

## 1. **시간 메시징** (Temporal Messaging)
### 핵심 기능
- ⏰ 과거/미래로 이메일 전송""",
                    "Gemini-2.5-Pro": """# 🚲 자전거 미래 혁신

## 1. **AI 균형 시스템**
- 자이로스코프와 AI가 결합되어 절대 넘어지지 않는 자전거""",
                    "Claude-Opus-4.1": """# 📧 이메일 진화

## 1. **감정 전송 시스템**
- 텍스트와 함께 작성자의 감정 상태를 전달하는 기술"""
                },
                "business": {
                    "GPT-5": """# 🚁 NeuralNest - 10억달러 드론 심리 플랫폼

## 사업 개요

### 비전
> **"위기 지역에서 실시간 정신 건강 지원을 제공하는 세계 최초 AI 드론 플랫폼"**""",
                    "jetXA": """# 💾 MemoryBank - 월 100만원 구독 서비스

## 서비스 개요

### 핵심 가치
> **"당신의 모든 기억을 영원히 보존하고 다시 경험하세요"**""",
                    "Gemini-2.5-Pro": """# 🤖 RoboChef - 로봇 요리사 플랫폼

## 비즈니스 모델

### 목표
> **"미슐랭 스타 셰프의 요리를 집에서 재현하는 AI 로봇"**""",
                    "Claude-Opus-4.1": """# 🏢 VirtualOffice - 메타버스 사무실

## 서비스 컨셉

### 미션
> **"물리적 사무실이 필요 없는 완벽한 가상 근무 환경"**"""
                }
            }
        else:
            # English responses
            responses = {
                "story": {
                    "GPT-5": """# The Quantum Mirror

## Synopsis
A detective discovers that every mirror in the city is actually a portal to **alternate timelines where crimes were prevented**.""",
                    "jetXA": """# Emotional Archaeology

## Concept
In 2045, archaeologists don't dig for artifacts—they excavate **compressed human emotions left in places of tragedy**.""",
                    "Gemini-2.5-Pro": """# The Memory Library

## Plot
A librarian discovers a **posthumous library** where dead people's last memories transform into books.""",
                    "Claude-Opus-4.1": """# The Time Gardener

## Overview
Adventures of a gardener who tends to a garden that **shifts to different historical eras** each night."""
                },
                "innovation": {
                    "GPT-5": """# 🚲 5 Bicycle Innovations

## 1. **Gravity Defiance Wheels**
- **Tech**: Electromagnetic rims reduce weight to near-zero when pedaling uphill""",
                    "jetXA": """# 📧 5 Email Revolutionaries

## 1. **Temporal Messaging**
### Core Features
- ⏰ Send emails to past/future""",
                    "Gemini-2.5-Pro": """# 🚲 Future Bicycle Tech

## 1. **AI Balance System**
- Gyroscope + AI creates a bicycle that never falls over""",
                    "Claude-Opus-4.1": """# 📧 Email Evolution

## 1. **Emotion Transfer System**
- Technology that transmits the sender's emotional state with text"""
                },
                "business": {
                    "GPT-5": """# 🚁 NeuralNest - $1B Drone Psychology Platform

## Business Overview

### Vision
> **"World's first AI drone platform providing real-time mental health support in crisis zones"**""",
                    "jetXA": """# 💾 MemoryBank - $1000/month Subscription

## Service Overview

### Core Value
> **"Preserve and re-experience all your memories forever"**""",
                    "Gemini-2.5-Pro": """# 🤖 RoboChef - Robot Chef Platform

## Business Model

### Goal
> **"AI robots that recreate Michelin star chef dishes at home"**""",
                    "Claude-Opus-4.1": """# 🏢 VirtualOffice - Metaverse Workspace

## Service Concept

### Mission
> **"Perfect virtual work environment eliminating need for physical offices"**"""
                }
            }
        
        return responses[category].get(model, responses[category]["GPT-5"])

# ==================== Main Arena Class ====================
class CreativityArena:
    def __init__(self):
        self.db = ArenaDatabase()
        self.llm = LLMInterface()
        self.current_battle = None
    
    def get_random_prompt(self, category: Category, language: str = "en") -> dict:
        """Get random prompt from database"""
        prompts = PROMPTS[category].get(language, PROMPTS[category]["en"])
        return random.choice(prompts)
    
    def start_new_battle_stream(self, category: str, custom_prompt: str = None, language: str = "en"):
        """Start new battle with streaming responses"""
        
        # Select category
        if category == "random":
            category = random.choice(list(Category))
        else:
            category = Category(category)
        
        # Get or set prompt
        if custom_prompt and custom_prompt.strip():
            prompt_text = custom_prompt.strip()
            is_custom = True
        else:
            prompt_data = self.get_random_prompt(category, language)
            prompt_text = prompt_data["text"]
            is_custom = False
        
        # Randomly select 2 models from the 4 available
        models = random.sample(["GPT-5", "jetXA", "Gemini-2.5-Pro", "Claude-Opus-4.1"], 2)
        
        # Create battle structure
        battle = Battle(
            id=hashlib.md5(f"{datetime.now().isoformat()}-{random.randint(0,999999)}".encode()).hexdigest(),
            prompt_id=hashlib.md5(prompt_text.encode()).hexdigest(),
            prompt_text=prompt_text,
            model_a=models[0],
            model_b=models[1],
            response_a="",
            response_b="",
            winner=None,
            voter_id="",
            timestamp=datetime.now(),
            category=category,
            custom_prompt=is_custom,
            language=language
        )
        
        self.current_battle = battle
        
        return {
            "prompt": prompt_text,
            "category": category.value,
            "models": models,
            "battle": battle
        }
    
    def vote(self, choice: str, voter_id: str = None):
        """Process vote with better error handling"""
        if not self.current_battle:
            print("❌ No active battle to vote on")
            return {"error": "No active battle"}
        
        # Ensure we have the complete battle data
        if not self.current_battle.response_a or not self.current_battle.response_b:
            print("⚠️ Battle responses not complete")
            return {"error": "Battle responses not complete"}
        
        # Set the winner
        self.current_battle.winner = self.current_battle.model_a if choice == "A" else self.current_battle.model_b
        self.current_battle.voter_id = voter_id or f"anonymous_{datetime.now().timestamp()}"
        
        print(f"🗳️ Vote recorded: {choice} -> {self.current_battle.winner}")
        
        # Save to database
        self.db.save_battle(self.current_battle)
        
        # Force immediate sync to HF
        self.db._sync_to_hf()
        
        return {
            "model_a": self.current_battle.model_a,
            "model_b": self.current_battle.model_b,
            "winner": self.current_battle.winner
        }
    
    def get_leaderboard(self, category: Optional[Category] = None):
        """Get leaderboard from database"""
        return self.db.get_leaderboard(category)

# ==================== Periodic Sync Function ====================
def periodic_sync(arena):
    """Periodically sync to HF every 30 seconds"""
    while True:
        time.sleep(30)
        try:
            arena.db._sync_to_hf()
            print(f"⏰ Periodic sync completed at {datetime.now()}")
        except Exception as e:
            print(f"⏰ Periodic sync failed: {e}")

# ==================== Gradio Interface ====================
def create_app():
    arena = CreativityArena()
    
    # Updated CSS with pastel colors and proper markdown rendering
    css = """
    .gradio-container {
        background: linear-gradient(135deg, #f5e6ff 0%, #e6f3ff 50%, #ffeef5 100%);
        font-family: 'Inter', sans-serif;
    }
    .main-header {
        background: rgba(255, 255, 255, 0.98);
        border-radius: 20px;
        padding: 2rem;
        text-align: center;
        margin-bottom: 2rem;
        box-shadow: 0 4px 20px rgba(150, 100, 200, 0.15);
        border: 1px solid rgba(200, 180, 220, 0.3);
    }
    .response-container {
        background: rgba(255, 255, 255, 0.95);
        border-radius: 15px;
        padding: 1.5rem;
        min-height: 400px;
        max-height: 800px;
        overflow-y: auto;
        box-shadow: 0 3px 15px rgba(150, 100, 200, 0.1);
        transition: transform 0.3s ease;
        border: 1px solid rgba(200, 180, 220, 0.2);
    }
    .response-container:hover {
        transform: translateY(-3px);
        box-shadow: 0 6px 20px rgba(150, 100, 200, 0.2);
    }
    
    /* Markdown specific styles */
    .markdown-text {
        line-height: 1.6;
        color: #2d3748;
    }
    .markdown-text h1 {
        font-size: 2.5em !important;
        font-weight: bold;
        color: #6b46c1;
        margin-top: 1em;
        margin-bottom: 0.5em;
        border-bottom: 2px solid #e9d8fd;
        padding-bottom: 0.3em;
    }
    .markdown-text h2 {
        font-size: 2em !important;
        font-weight: bold;
        color: #805ad5;
        margin-top: 0.8em;
        margin-bottom: 0.4em;
    }
    .markdown-text h3 {
        font-size: 1.5em !important;
        font-weight: bold;
        color: #9f7aea;
        margin-top: 0.6em;
        margin-bottom: 0.3em;
    }
    """
    
    with gr.Blocks(title="AI Models Battle Arena", theme=gr.themes.Soft(), css=css) as app:
        current_lang = gr.State(value="en")
        
        # Language change handler
        def update_language(lang_value):
            return lang_value
        
        def update_ui_text(lang):
            ui = UI_TEXT[lang]
            return (
                f"""
                <div class="main-header">
                    <h1 style="color: #6b46c1; font-size: 2.5rem;">{ui['title']}</h1>
                    <p style="color: #805ad5; font-size: 1.2rem;">{ui['subtitle']}</p>
                </div>
                """,
                ui['leaderboard_title'],
                gr.update(label=ui['category_label']),
                gr.update(label=ui['custom_prompt_label']),
                gr.update(placeholder=ui['custom_prompt_placeholder']),
                gr.update(value=ui['new_battle_btn']),
                ui['model_a'],
                ui['model_b'],
                gr.update(value=ui['vote_a']),
                gr.update(value=ui['vote_b']),
                gr.update(label=ui['category_filter']),
                gr.update(value=ui['refresh_btn']),
                gr.update(choices=[
                    (ui['categories']['random'], "random"),
                    (ui['categories']['storytelling'], "storytelling"),
                    (ui['categories']['innovation'], "innovation"),
                    (ui['categories']['business'], "business")
                ]),
                gr.update(choices=[
                    (ui['filter_categories']['overall'], "overall"),
                    (ui['filter_categories']['storytelling'], "storytelling"),
                    (ui['filter_categories']['innovation'], "innovation"),
                    (ui['filter_categories']['business'], "business")
                ])
            )
        
        # Header
        with gr.Row():
            with gr.Column(scale=10):
                header_html = gr.HTML(f"""
                    <div class="main-header">
                        <h1 style="color: #6b46c1; font-size: 2.5rem;">🎨 AI Models Creativity Battle Arena</h1>
                        <p style="color: #805ad5; font-size: 1.2rem;">Test cutting-edge AI models in creative challenges</p>
                        <p style="color: #9f7aea; font-size: 1rem;">GPT-5 vs jetXA vs Gemini 2.5 Pro vs Claude Opus 4.1</p>
                    </div>
                """)
            with gr.Column(scale=1):
                language_select = gr.Dropdown(
                    choices=[("English", "en"), ("한국어", "ko")],
                    value="en",
                    label="Language",
                    interactive=True,
                    elem_classes="category-select"
                )
        
        with gr.Tabs(elem_classes="tab-nav") as tabs:
            # Battle Arena Tab
            with gr.TabItem("⚔️ Battle Arena", id="battle_tab") as battle_tab:
                with gr.Row():
                    with gr.Column(scale=1):
                        category_select = gr.Dropdown(
                            choices=[
                                ("🎲 Random", "random"),
                                ("📚 Storytelling", "storytelling"),
                                ("💡 Innovation", "innovation"),
                                ("💼 Business", "business")
                            ],
                            value="random",
                            label="Select Category",
                            interactive=True,
                            elem_classes="category-select"
                        )
                        
                        custom_prompt_accordion = gr.Accordion("✏️ Custom Challenge (Optional)", open=False)
                        with custom_prompt_accordion:
                            custom_prompt_input = gr.Textbox(
                                label="",
                                placeholder="Enter your creative challenge...",
                                lines=3
                            )
                        
                        new_battle_btn = gr.Button(
                            "🎲 Start New Battle",
                            variant="primary",
                            size="lg",
                            elem_classes="vote-button"
                        )
                    
                    with gr.Column(scale=3):
                        prompt_display = gr.Markdown("")
                
                with gr.Row():
                    with gr.Column():
                        model_a_label = gr.Markdown("### 🅰️ Model A")
                        response_a = gr.Markdown(
                            "", 
                            elem_classes=["response-container", "markdown-text"],
                            sanitize_html=False,
                            line_breaks=True,
                            latex_delimiters=[
                                {"left": "$", "right": "$", "display": True},
                                {"left": "$", "right": "$", "display": False}
                            ]
                        )
                        model_a_reveal = gr.Textbox(label="Model Identity", visible=False)
                    
                    with gr.Column():
                        model_b_label = gr.Markdown("### 🅱️ Model B")
                        response_b = gr.Markdown(
                            "", 
                            elem_classes=["response-container", "markdown-text"],
                            sanitize_html=False,
                            line_breaks=True,
                            latex_delimiters=[
                                {"left": "$", "right": "$", "display": True},
                                {"left": "$", "right": "$", "display": False}
                            ]
                        )
                        model_b_reveal = gr.Textbox(label="Model Identity", visible=False)
                
                with gr.Row():
                    vote_a_btn = gr.Button("🅰️ Model A is more creative", size="lg", variant="primary", elem_classes="vote-button")
                    vote_b_btn = gr.Button("🅱️ Model B is more creative", size="lg", variant="primary", elem_classes="vote-button")
                
                vote_result = gr.Markdown("")
                battle_state = gr.State({})
            
            # Leaderboard Tab
            with gr.TabItem("🏆 Leaderboard", id="leaderboard_tab") as leaderboard_tab:
                leaderboard_title = gr.Markdown("## 🏆 AI Models Leaderboard")
                
                category_filter = gr.Radio(
                    choices=[
                        ("Overall", "overall"),
                        ("Storytelling", "storytelling"),
                        ("Innovation", "innovation"),
                        ("Business", "business")
                    ],
                    value="overall",
                    label="Category Filter",
                    elem_classes="category-select"
                )
                
                leaderboard_display = gr.Dataframe(
                    headers=["Rank", "Model", "Overall", "Story", "Innovation", "Business", "Battles", "Win%", "ELO"],
                    datatype=["number", "str", "number", "number", "number", "number", "number", "number", "number"]
                )
                
                refresh_btn = gr.Button("🔄 Refresh", variant="secondary")
        
        # Footer
        footer_html = gr.HTML("""
            <div class="footer">
                <p>Testing GPT-5, jetXA, Gemini 2.5 Pro, and Claude Opus 4.1 in creative challenges | Contact: arxivgpt@gmail.com</p>
            </div>
        """)
        
        # Event handlers with streaming support
        def start_battle_stream(category, custom_prompt, lang):
            # Clear cache for fresh responses if needed
            arena.llm.clear_cache()
            
            battle_info = arena.start_new_battle_stream(category, custom_prompt, lang)
            
            ui = UI_TEXT[lang]
            category_display = ui["categories"].get(battle_info['category'], battle_info['category'])
            
            prompt_text = f"""
{ui['challenge_task']}

**{ui['category']}**: {category_display}

**{ui['prompt']}**:
> {battle_info['prompt']}
"""
            
            # Initialize with loading state
            initial_response = ui['generating']
            
            # Start streaming in separate threads
            response_a_queue = queue.Queue()
            response_b_queue = queue.Queue()
            response_a_final = ""
            response_b_final = ""
            done_a = False
            done_b = False
            
            def stream_model_a():
                nonlocal response_a_final, done_a
                try:
                    for chunk in arena.llm.generate_response_stream(
                        battle_info['models'][0], 
                        battle_info['prompt'], 
                        lang
                    ):
                        # chunk is already accumulated text
                        response_a_queue.put(('update', chunk))  # Add type marker
                        response_a_final = chunk
                    battle_info['battle'].response_a = response_a_final
                except Exception as e:
                    print(f"Error in stream_model_a: {e}")
                    response_a_final = arena.llm._generate_fallback(
                        battle_info['models'][0], 
                        battle_info['prompt'], 
                        lang
                    )
                    response_a_queue.put(('update', response_a_final))
                    battle_info['battle'].response_a = response_a_final
                finally:
                    response_a_queue.put(('done', None))  # Signal completion
                    done_a = True
            
            def stream_model_b():
                nonlocal response_b_final, done_b
                try:
                    for chunk in arena.llm.generate_response_stream(
                        battle_info['models'][1], 
                        battle_info['prompt'], 
                        lang
                    ):
                        # chunk is already accumulated text
                        response_b_queue.put(('update', chunk))  # Add type marker
                        response_b_final = chunk
                    battle_info['battle'].response_b = response_b_final
                except Exception as e:
                    print(f"Error in stream_model_b: {e}")
                    response_b_final = arena.llm._generate_fallback(
                        battle_info['models'][1], 
                        battle_info['prompt'], 
                        lang
                    )
                    response_b_queue.put(('update', response_b_final))
                    battle_info['battle'].response_b = response_b_final
                finally:
                    response_b_queue.put(('done', None))  # Signal completion
                    done_b = True
            
            thread_a = threading.Thread(target=stream_model_a)
            thread_b = threading.Thread(target=stream_model_b)
            
            thread_a.start()
            thread_b.start()
            
            # Yield updates for both responses
            response_a_text = initial_response
            response_b_text = initial_response
            last_update_time = time.time()
            stream_a_done = False
            stream_b_done = False
            
            while not (stream_a_done and stream_b_done):
                updated = False
                current_time = time.time()
                
                # Process all updates from model A
                try:
                    while True:
                        msg_type, content = response_a_queue.get_nowait()
                        if msg_type == 'done':
                            stream_a_done = True
                        elif msg_type == 'update':
                            response_a_text = content
                            updated = True
                except queue.Empty:
                    pass
                
                # Process all updates from model B
                try:
                    while True:
                        msg_type, content = response_b_queue.get_nowait()
                        if msg_type == 'done':
                            stream_b_done = True
                        elif msg_type == 'update':
                            response_b_text = content
                            updated = True
                except queue.Empty:
                    pass
                
                # Always yield updates more frequently for better streaming effect
                if updated or (current_time - last_update_time) > 0.05:  # Update every 50ms
                    yield (
                        prompt_text,
                        response_a_text,
                        response_b_text,
                        gr.update(visible=False),
                        gr.update(visible=False),
                        "",
                        battle_info
                    )
                    last_update_time = current_time
                
                time.sleep(0.02)  # Smaller sleep for more responsive updates
            
            # Final update with complete responses
            yield (
                prompt_text,
                response_a_final if response_a_final else initial_response,
                response_b_final if response_b_final else initial_response,
                gr.update(visible=False),
                gr.update(visible=False),
                "",
                battle_info
            )
        
        def process_vote(choice, state, lang):
            if not state or 'battle' not in state:
                print("❌ No battle in state")
                return (
                    gr.update(),
                    gr.update(),
                    "Error: No active battle"
                )
            
            # Ensure the battle object is properly set
            battle_obj = state['battle']
            arena.current_battle = battle_obj
            
            print(f"🎯 Processing vote: Choice={choice}, Battle ID={battle_obj.id}")
            
            # Process the vote
            result = arena.vote(choice)
            
            if "error" in result:
                return (
                    gr.update(),
                    gr.update(),
                    f"Error: {result['error']}"
                )
            
            ui = UI_TEXT[lang]
            
            winner_emoji = "🏆" if result['winner'] == result['model_a'] else "🥈"
            loser_emoji = "🥈" if winner_emoji == "🏆" else "🏆"
            
            result_text = f"""
{ui['vote_complete']}

**{ui['winner']}**: {winner_emoji} **{result['winner']}**

**Model A**: {result['model_a']} {winner_emoji if choice == "A" else loser_emoji}  
**Model B**: {result['model_b']} {winner_emoji if choice == "B" else loser_emoji}

{ui['elo_updated']}
"""
            
            # Debug: Check database state after vote
            arena.db.debug_database_state()
            
            return (
                gr.update(value=result['model_a'], visible=True),
                gr.update(value=result['model_b'], visible=True),
                result_text
            )
        
        def update_leaderboard(category):
            df = arena.get_leaderboard(
                Category(category) if category != "overall" else None
            )
            return df[['rank', 'model_name', 'overall_score', 'storytelling_score', 
                      'innovation_score', 'business_score', 'total_battles', 'win_rate', 'elo_rating']]
        
        # Update UI when language changes
        language_select.change(
            fn=update_language,
            inputs=[language_select],
            outputs=[current_lang]
        ).then(
            fn=update_ui_text,
            inputs=[current_lang],
            outputs=[
                header_html,
                leaderboard_title,
                category_select,
                custom_prompt_accordion,
                custom_prompt_input,
                new_battle_btn,
                model_a_label,
                model_b_label,
                vote_a_btn,
                vote_b_btn,
                category_filter,
                refresh_btn,
                category_select,
                category_filter
            ]
        )
        
        # Connect events with streaming
        new_battle_btn.click(
            fn=start_battle_stream,
            inputs=[category_select, custom_prompt_input, current_lang],
            outputs=[prompt_display, response_a, response_b, model_a_reveal, model_b_reveal, vote_result, battle_state]
        )
        
        vote_a_btn.click(
            fn=lambda s, l: process_vote("A", s, l),
            inputs=[battle_state, current_lang],
            outputs=[model_a_reveal, model_b_reveal, vote_result]
        )
        
        vote_b_btn.click(
            fn=lambda s, l: process_vote("B", s, l),
            inputs=[battle_state, current_lang],
            outputs=[model_a_reveal, model_b_reveal, vote_result]
        )
        
        category_filter.change(
            fn=update_leaderboard,
            inputs=[category_filter],
            outputs=[leaderboard_display]
        )
        
        refresh_btn.click(
            fn=update_leaderboard,
            inputs=[category_filter],
            outputs=[leaderboard_display]
        )
        
        # Initialize on load
        app.load(
            fn=lambda: update_leaderboard("overall"),
            outputs=[leaderboard_display]
        )
    
    return app

# ==================== Main ====================
if __name__ == "__main__":
    print("="*50)
    print("🚀 AI Models Creativity Battle Arena")
    print("="*50)
    print("\n📋 Environment Setup:")
    print("1. Set OPENAI_API_KEY for GPT-5")
    print("2. Set GEMINI_API_KEY for Gemini 2.5 Pro")
    print("3. Set ANTHROPIC_API_KEY for Claude Opus 4.1")
    print("4. jetXA will use 'aiqtech/tests' by default")
    print("5. Set HF_TOKEN for persistent data storage (REQUIRED)")
    print("6. Optional: Set HF_DATASET_NAME (default: ai_models_arena)")
    print("\n⚠️  Without HF_TOKEN, data will be lost on server restart!")
    print("\n" + "="*50 + "\n")
    
    # Check for required API keys
    if not os.getenv("HF_TOKEN"):
        print("⚠️  WARNING: HF_TOKEN not set - data will not persist!")
        print("Set it with: export HF_TOKEN='your_token_here'")
        print("")
    
    if not os.getenv("OPENAI_API_KEY"):
        print("⚠️  GPT-5: No API key found - will use fallback responses")
    
    if not os.getenv("GEMINI_API_KEY"):
        print("⚠️  Gemini: No API key found - will use fallback responses")
    
    if not os.getenv("ANTHROPIC_API_KEY"):
        print("⚠️  Claude: No API key found - will use fallback responses")
    
    print("\n🎯 Starting arena with 4 models: GPT-5, jetXA, Gemini 2.5 Pro, Claude Opus 4.1")
    print("="*50 + "\n")
    
    # Create app
    app = create_app()
    
    # Start periodic sync in background (optional)
    arena = CreativityArena()
    sync_thread = threading.Thread(target=lambda: periodic_sync(arena), daemon=True)
    sync_thread.start()
    print("✅ Background sync thread started (every 30 seconds)")
    
    # Launch app
    app.launch()