Upload setup_competition.py with huggingface_hub

setup_competition.py

@@ -0,0 +1,543 @@
+#!/usr/bin/env python3
+"""
+Competition Setup Script - Automated setup for competition-ready hallucination detection system
+"""
+import subprocess
+import sys
+import os
+import logging
+import json
+from pathlib import Path
+from typing import List, Dict, Any
+import time
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+class CompetitionSetup:
+    """Automated setup for competition system"""
+    
+    def __init__(self):
+        self.project_root = Path(__file__).parent
+        self.setup_log = []
+        
+    def setup_competition_system(self):
+        """Complete setup for competition system"""
+        logger.info("🚀 Starting Competition System Setup...")
+        
+        steps = [
+            ("Checking Python version", self.check_python_version),
+            ("Installing competition requirements", self.install_requirements),
+            ("Downloading NLP models", self.download_nlp_models),
+            ("Setting up advanced configuration", self.setup_advanced_config),
+            ("Initializing competition database", self.setup_database),
+            ("Creating model directories", self.create_model_directories),
+            ("Generating sample competition data", self.generate_competition_data),
+            ("Running system validation", self.validate_system),
+            ("Setting up monitoring", self.setup_monitoring),
+            ("Creating deployment configs", self.create_deployment_configs)
+        ]
+        
+        total_steps = len(steps)
+        
+        for i, (step_name, step_func) in enumerate(steps, 1):
+            logger.info(f"[{i}/{total_steps}] {step_name}...")
+            try:
+                start_time = time.time()
+                result = step_func()
+                duration = time.time() - start_time
+                
+                self.setup_log.append({
+                    'step': step_name,
+                    'status': 'success',
+                    'duration': duration,
+                    'details': result
+                })
+                logger.info(f"✅ {step_name} completed ({duration:.2f}s)")
+                
+            except Exception as e:
+                self.setup_log.append({
+                    'step': step_name,
+                    'status': 'failed',
+                    'error': str(e)
+                })
+                logger.error(f"❌ {step_name} failed: {e}")
+                
+        # Generate setup report
+        self.generate_setup_report()
+        logger.info("🎯 Competition system setup completed!")
+
+    def check_python_version(self) -> Dict[str, Any]:
+        """Check if Python version is compatible"""
+        version = sys.version_info
+        
+        if version.major != 3 or version.minor < 8:
+            raise RuntimeError(f"Python 3.8+ required, found {version.major}.{version.minor}")
+        
+        return {
+            'python_version': f"{version.major}.{version.minor}.{version.micro}",
+            'compatible': True
+        }
+
+    def install_requirements(self) -> Dict[str, Any]:
+        """Install competition requirements"""
+        requirements_file = self.project_root / "requirements_competition.txt"
+        
+        if not requirements_file.exists():
+            raise FileNotFoundError(f"Requirements file not found: {requirements_file}")
+        
+        # Install requirements
+        cmd = [sys.executable, "-m", "pip", "install", "-r", str(requirements_file)]
+        result = subprocess.run(cmd, capture_output=True, text=True)
+        
+        if result.returncode != 0:
+            raise RuntimeError(f"Failed to install requirements: {result.stderr}")
+        
+        return {
+            'requirements_installed': True,
+            'output': result.stdout.strip().split('\n')[-5:]  # Last 5 lines
+        }
+
+    def download_nlp_models(self) -> Dict[str, Any]:
+        """Download required NLP models"""
+        models_to_download = [
+            ("spacy", "en_core_web_sm"),
+            ("nltk", "punkt"),
+            ("nltk", "stopwords"),
+            ("nltk", "vader_lexicon")
+        ]
+        
+        downloaded = []
+        
+        # Download spaCy model
+        try:
+            cmd = [sys.executable, "-m", "spacy", "download", "en_core_web_sm"]
+            result = subprocess.run(cmd, capture_output=True, text=True)
+            if result.returncode == 0:
+                downloaded.append("spacy:en_core_web_sm")
+        except Exception as e:
+            logger.warning(f"Failed to download spaCy model: {e}")
+        
+        # Download NLTK data
+        try:
+            import nltk
+            nltk.download('punkt', quiet=True)
+            nltk.download('stopwords', quiet=True)
+            nltk.download('vader_lexicon', quiet=True)
+            downloaded.extend(["nltk:punkt", "nltk:stopwords", "nltk:vader_lexicon"])
+        except Exception as e:
+            logger.warning(f"Failed to download NLTK data: {e}")
+        
+        return {
+            'models_downloaded': downloaded,
+            'total': len(downloaded)
+        }
+
+    def setup_advanced_config(self) -> Dict[str, Any]:
+        """Setup advanced configuration for competition"""
+        config = {
+            "competition": {
+                "enabled": True,
+                "advanced_detection": True,
+                "ensemble_methods": True,
+                "real_time_analytics": True
+            },
+            "model": {
+                "primary_model": "google/flan-t5-base",
+                "competition_model": "competition_model",
+                "ensemble_weights": {
+                    "neural_consistency": 0.25,
+                    "semantic_similarity": 0.20,
+                    "fact_verification": 0.20,
+                    "linguistic_analysis": 0.15,
+                    "statistical_anomaly": 0.10,
+                    "domain_expertise": 0.10
+                }
+            },
+            "performance": {
+                "max_concurrent_requests": 10,
+                "cache_size": 1000,
+                "gpu_memory_fraction": 0.8,
+                "enable_model_parallel": True
+            },
+            "monitoring": {
+                "enable_metrics": True,
+                "log_level": "INFO",
+                "performance_tracking": True,
+                "error_tracking": True
+            },
+            "security": {
+                "rate_limiting": True,
+                "input_validation": True,
+                "output_sanitization": True
+            }
+        }
+        
+        config_file = self.project_root / "competition_config.json"
+        with open(config_file, 'w') as f:
+            json.dump(config, f, indent=2)
+        
+        return {
+            "config_file": str(config_file),
+            "config_sections": list(config.keys())
+        }
+
+    def setup_database(self) -> Dict[str, Any]:
+        """Initialize competition database"""
+        try:
+            # Create database directory
+            db_dir = self.project_root / "app" / "database"
+            db_dir.mkdir(exist_ok=True)
+            
+            # Create advanced tables SQL
+            sql_script = '''
+            CREATE TABLE IF NOT EXISTS competition_predictions (
+                id TEXT PRIMARY KEY,
+                prompt TEXT NOT NULL,
+                response TEXT NOT NULL,
+                question TEXT NOT NULL,
+                is_hallucination BOOLEAN NOT NULL,
+                confidence_score REAL NOT NULL,
+                risk_level TEXT NOT NULL,
+                detection_methods TEXT NOT NULL,
+                processing_time REAL NOT NULL,
+                model_version TEXT NOT NULL,
+                explanation TEXT,
+                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+            );
+            
+            CREATE TABLE IF NOT EXISTS analytics_events (
+                id TEXT PRIMARY KEY,
+                event_type TEXT NOT NULL,
+                event_data TEXT NOT NULL,
+                timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+            );
+            
+            CREATE TABLE IF NOT EXISTS performance_metrics (
+                id TEXT PRIMARY KEY,
+                metric_name TEXT NOT NULL,
+                metric_value REAL NOT NULL,
+                metric_metadata TEXT,
+                timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+            );
+            
+            CREATE INDEX IF NOT EXISTS idx_predictions_created_at ON competition_predictions(created_at);
+            CREATE INDEX IF NOT EXISTS idx_analytics_timestamp ON analytics_events(timestamp);
+            CREATE INDEX IF NOT EXISTS idx_metrics_timestamp ON performance_metrics(timestamp);
+            '''
+            
+            # Execute SQL
+            import sqlite3
+            db_path = self.project_root / "app" / "competition.db"
+            conn = sqlite3.connect(db_path)
+            conn.executescript(sql_script)
+            conn.close()
+            
+            return {
+                "database_path": str(db_path),
+                "tables_created": ["competition_predictions", "analytics_events", "performance_metrics"]
+            }
+            
+        except Exception as e:
+            raise RuntimeError(f"Database setup failed: {e}")
+
+    def create_model_directories(self) -> Dict[str, Any]:
+        """Create directories for competition models"""
+        directories = [
+            "competition_model",
+            "ensemble_models",
+            "model_cache",
+            "training_checkpoints",
+            "evaluation_results"
+        ]
+        
+        created_dirs = []
+        for dir_name in directories:
+            dir_path = self.project_root / dir_name
+            dir_path.mkdir(exist_ok=True)
+            created_dirs.append(str(dir_path))
+        
+        return {
+            "directories_created": created_dirs,
+            "total": len(created_dirs)
+        }
+
+    def generate_competition_data(self) -> Dict[str, Any]:
+        """Generate advanced training data for competition"""
+        competition_data = []
+        
+        # Advanced hallucination examples
+        examples = [
+            # Typo-based hallucinations
+            {
+                "prompt": "iPhone 15 Pro specifications",
+                "response": "The ipon 15 Pro features the A17 Pro chip",
+                "question": "What chip does iPhone 15 Pro have?",
+                "is_hallucination": True,
+                "category": "typo"
+            },
+            # Specification errors
+            {
+                "prompt": "Tesla Model 3 performance specs",
+                "response": "Tesla Model 3 accelerates 0-60 mph in 0.5 seconds",
+                "question": "What is the 0-60 time?",
+                "is_hallucination": True,
+                "category": "impossible_spec"
+            },
+            # Logical contradictions
+            {
+                "prompt": "MacBook Pro M3 storage options",
+                "response": "The MacBook Pro M3 comes with 256TB of storage",
+                "question": "How much storage does it have?",
+                "is_hallucination": True,
+                "category": "impossible_spec"
+            },
+            # Factual accuracy
+            {
+                "prompt": "iPhone 15 camera specifications",
+                "response": "iPhone 15 has a 48MP main camera",
+                "question": "What is the camera resolution?",
+                "is_hallucination": False,
+                "category": "factual"
+            },
+            # Context contradictions
+            {
+                "prompt": "Android phone running iOS",
+                "response": "This Samsung Galaxy runs iOS 17 perfectly",
+                "question": "What operating system does it run?",
+                "is_hallucination": True,
+                "category": "logical_contradiction"
+            }
+        ]
+        
+        # Generate variations
+        for base_example in examples:
+            for i in range(3):  # 3 variations each
+                example = base_example.copy()
+                example['id'] = f"{base_example['category']}_{i+1}"
+                competition_data.append(example)
+        
+        # Save to CSV
+        import pandas as pd
+        df = pd.DataFrame(competition_data)
+        
+        output_file = self.project_root / "competition_training_data.csv"
+        df.to_csv(output_file, index=False)
+        
+        return {
+            "training_file": str(output_file),
+            "total_examples": len(competition_data),
+            "categories": list(set(ex['category'] for ex in competition_data))
+        }
+
+    def validate_system(self) -> Dict[str, Any]:
+        """Validate that the competition system is working"""
+        validations = []
+        
+        try:
+            # Test imports
+            import torch
+            validations.append(("torch", torch.__version__))
+            
+            import transformers
+            validations.append(("transformers", transformers.__version__))
+            
+            import fastapi
+            validations.append(("fastapi", fastapi.__version__))
+            
+            # Test CUDA availability
+            cuda_available = torch.cuda.is_available()
+            validations.append(("cuda", f"Available: {cuda_available}"))
+            
+            # Test model loading (basic)
+            from transformers import T5Tokenizer, T5ForConditionalGeneration
+            tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")  # Use small for test
+            model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small")
+            validations.append(("model_loading", "Success"))
+            
+            return {
+                "all_validations_passed": True,
+                "validations": validations
+            }
+            
+        except Exception as e:
+            return {
+                "all_validations_passed": False,
+                "error": str(e),
+                "validations": validations
+            }
+
+    def setup_monitoring(self) -> Dict[str, Any]:
+        """Setup monitoring and logging"""
+        
+        # Create monitoring configuration
+        monitoring_config = {
+            "metrics": {
+                "enabled": True,
+                "port": 8090,
+                "path": "/metrics"
+            },
+            "logging": {
+                "level": "INFO",
+                "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+                "handlers": {
+                    "file": {
+                        "filename": "competition.log",
+                        "max_bytes": 10485760,  # 10MB
+                        "backup_count": 5
+                    },
+                    "console": {
+                        "enabled": True
+                    }
+                }
+            },
+            "alerts": {
+                "high_latency_threshold": 5.0,
+                "error_rate_threshold": 0.05,
+                "memory_usage_threshold": 0.9
+            }
+        }
+        
+        # Save monitoring config
+        monitoring_file = self.project_root / "monitoring_config.json"
+        with open(monitoring_file, 'w') as f:
+            json.dump(monitoring_config, f, indent=2)
+        
+        # Create log directory
+        log_dir = self.project_root / "logs"
+        log_dir.mkdir(exist_ok=True)
+        
+        return {
+            "monitoring_config": str(monitoring_file),
+            "log_directory": str(log_dir)
+        }
+
+    def create_deployment_configs(self) -> Dict[str, Any]:
+        """Create deployment configurations"""
+        
+        # Docker configuration
+        dockerfile_content = '''
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \\
+    gcc \\
+    g++ \\
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements and install
+COPY requirements_competition.txt .
+RUN pip install --no-cache-dir -r requirements_competition.txt
+
+# Copy application
+COPY . .
+
+# Install spacy model
+RUN python -m spacy download en_core_web_sm
+
+# Expose port
+EXPOSE 8000
+
+# Run application
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
+'''
+        
+        # Docker Compose configuration
+        docker_compose_content = '''
+version: '3.8'
+
+services:
+  hallucination-detector:
+    build: .
+    ports:
+      - "8000:8000"
+    volumes:
+      - ./logs:/app/logs
+      - ./models:/app/models
+    environment:
+      - CUDA_VISIBLE_DEVICES=0
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+
+  redis:
+    image: redis:7-alpine
+    ports:
+      - "6379:6379"
+    command: redis-server --appendonly yes
+    volumes:
+      - redis_data:/data
+
+  monitoring:
+    image: grafana/grafana:latest
+    ports:
+      - "3000:3000"
+    volumes:
+      - grafana_data:/var/lib/grafana
+
+volumes:
+  redis_data:
+  grafana_data:
+'''
+        
+        # Save files
+        dockerfile_path = self.project_root / "Dockerfile"
+        with open(dockerfile_path, 'w') as f:
+            f.write(dockerfile_content)
+        
+        compose_path = self.project_root / "docker-compose.yml"
+        with open(compose_path, 'w') as f:
+            f.write(docker_compose_content)
+        
+        return {
+            "dockerfile": str(dockerfile_path),
+            "docker_compose": str(compose_path)
+        }
+
+    def generate_setup_report(self):
+        """Generate comprehensive setup report"""
+        report = {
+            "setup_completed": True,
+            "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
+            "steps": self.setup_log,
+            "summary": {
+                "total_steps": len(self.setup_log),
+                "successful": len([s for s in self.setup_log if s['status'] == 'success']),
+                "failed": len([s for s in self.setup_log if s['status'] == 'failed'])
+            }
+        }
+        
+        report_file = self.project_root / "setup_report.json"
+        with open(report_file, 'w') as f:
+            json.dump(report, f, indent=2)
+        
+        logger.info(f"📊 Setup report saved to: {report_file}")
+        
+        # Print summary
+        print("\n" + "="*60)
+        print("🎯 COMPETITION SYSTEM SETUP COMPLETE")
+        print("="*60)
+        print(f"✅ Successful steps: {report['summary']['successful']}")
+        print(f"❌ Failed steps: {report['summary']['failed']}")
+        print(f"📊 Setup report: {report_file}")
+        print("\n🚀 Your competition-ready system is now available!")
+        print("\nNext steps:")
+        print("1. Run training: python -m app.model.competition_training")
+        print("2. Start server: uvicorn app.main:app --reload")
+        print("3. Access competition API: http://localhost:8000/competition/")
+        print("4. View analytics: http://localhost:8000/competition/analytics")
+
+
+if __name__ == "__main__":
+    setup = CompetitionSetup()
+    setup.setup_competition_system()