🚀 feat: Add comprehensive GAIA evaluation system and batch testing infrastructure

- Add GAIAEvaluator with performance analysis, metrics, and visualizations
- Add improved batch testing system with async processing support
- Support detailed question analysis and comparative evaluation
- Include test session logging and performance tracking

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

app/gaia_evaluator.py

@@ -0,0 +1,740 @@
+#!/usr/bin/env python3
+"""
+GAIA Evaluator
+A comprehensive evaluation system for analyzing GAIA agent performance across different dimensions.
+"""
+
+import json
+import logging
+from pathlib import Path
+from typing import Dict, List, Any, Optional, Tuple
+import statistics
+from datetime import datetime
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+
+from answer_validator import AnswerValidator
+
+
+class GAIAEvaluator:
+    """
+    A comprehensive evaluation system for GAIA benchmark performance analysis.
+    Provides detailed metrics, visualizations, and comparative analysis.
+    """
+    
+    def __init__(self, 
+                 results_dir: Optional[str] = None,
+                 validation_file: Optional[str] = "gaia_validation_metadata.jsonl"):
+        """
+        Initialize the GAIA evaluator.
+        
+        Args:
+            results_dir: Directory containing test results (None to provide later)
+            validation_file: Path to validation metadata file
+        """
+        self.logger = logging.getLogger("GAIAEvaluator")
+        self.results_dir = Path(results_dir) if results_dir else None
+        self.validation_file = Path(validation_file) if validation_file else None
+        self.validator = AnswerValidator()
+        
+        # Performance metrics
+        self.metrics = {}
+        self.question_details = {}
+        self.validation_data = {}
+        
+        # Load validation data if provided
+        if self.validation_file and self.validation_file.exists():
+            self._load_validation_data()
+    
+    def _load_validation_data(self) -> None:
+        """Load validation data from JSONL file."""
+        self.logger.info(f"Loading validation data from {self.validation_file}")
+        
+        try:
+            with open(self.validation_file, 'r') as f:
+                for line in f:
+                    try:
+                        entry = json.loads(line)
+                        question_id = entry.get('question_id')
+                        if question_id:
+                            self.validation_data[question_id] = entry
+                    except json.JSONDecodeError:
+                        self.logger.warning(f"Could not parse line in validation file: {line[:50]}...")
+        except Exception as e:
+            self.logger.error(f"Error loading validation data: {e}")
+    
+    def set_results_directory(self, results_dir: str) -> None:
+        """Set or update the results directory."""
+        self.results_dir = Path(results_dir)
+    
+    def load_results(self, results_file: Optional[str] = None) -> Dict:
+        """
+        Load test results from the specified file or search for it.
+        
+        Args:
+            results_file: Specific results file to load (None to search in results_dir)
+            
+        Returns:
+            Dict of loaded results
+        """
+        if results_file:
+            file_path = Path(results_file)
+        elif self.results_dir:
+            # Find the most recent results.json file
+            json_files = list(self.results_dir.glob("**/results.json"))
+            if not json_files:
+                self.logger.error(f"No results.json files found in {self.results_dir}")
+                return {}
+            
+            # Sort by modification time, newest first
+            file_path = sorted(json_files, key=lambda x: x.stat().st_mtime, reverse=True)[0]
+        else:
+            self.logger.error("No results directory or file specified")
+            return {}
+        
+        try:
+            self.logger.info(f"Loading results from {file_path}")
+            with open(file_path, 'r') as f:
+                results = json.load(f)
+                return results
+        except Exception as e:
+            self.logger.error(f"Error loading results: {e}")
+            return {}
+    
+    def evaluate(self, results: Dict = None) -> Dict:
+        """
+        Evaluate GAIA test results with comprehensive metrics.
+        
+        Args:
+            results: Test results dict (None to load from file)
+            
+        Returns:
+            Dict of evaluation metrics
+        """
+        if not results:
+            results = self.load_results()
+            if not results:
+                return {}
+        
+        # Calculate basic metrics
+        total_questions = len(results)
+        correct_answers = 0
+        partial_answers = 0
+        incorrect_answers = 0
+        errors = 0
+        timeouts = 0
+        
+        classification_accuracy = 0
+        total_classified = 0
+        
+        processing_times = []
+        confidence_scores = []
+        
+        # Analyze each question
+        question_metrics = {}
+        for question_id, data in results.items():
+            # Extract validation status
+            validation = data.get('validation', {})
+            validation_status = validation.get('validation_status', 'error')
+            
+            # Basic counters
+            if validation_status == 'correct':
+                correct_answers += 1
+            elif validation_status == 'partial':
+                partial_answers += 1
+            elif validation_status == 'incorrect':
+                incorrect_answers += 1
+            elif validation_status == 'error':
+                errors += 1
+            elif validation_status == 'timeout':
+                timeouts += 1
+                
+            # Track processing time
+            if 'processing_time' in data:
+                processing_times.append(data['processing_time'])
+                
+            # Track confidence scores
+            if 'confidence_score' in validation:
+                confidence_scores.append(validation['confidence_score'])
+                
+            # Track classification accuracy
+            if 'classification' in data:
+                classification_data = data['classification']
+                total_classified += 1
+                if classification_data.get('is_correct', False):
+                    classification_accuracy += 1
+                    
+            # Store detailed metrics per question
+            question_metrics[question_id] = {
+                'validation_status': validation_status,
+                'processing_time': data.get('processing_time'),
+                'confidence_score': validation.get('confidence_score'),
+                'classification': data.get('classification', {}).get('classification'),
+                'is_classification_correct': data.get('classification', {}).get('is_correct', False),
+                'tools_used': data.get('tools_used', []),
+                'steps_count': len(data.get('steps', [])),
+            }
+        
+        # Calculate derived metrics
+        accuracy = (correct_answers / total_questions) * 100 if total_questions > 0 else 0
+        success_rate = ((correct_answers + partial_answers) / total_questions) * 100 if total_questions > 0 else 0
+        classification_accuracy_pct = (classification_accuracy / total_classified) * 100 if total_classified > 0 else 0
+        
+        avg_processing_time = statistics.mean(processing_times) if processing_times else 0
+        median_processing_time = statistics.median(processing_times) if processing_times else 0
+        
+        avg_confidence = statistics.mean(confidence_scores) if confidence_scores else 0
+        
+        # Store metrics
+        self.metrics = {
+            'total_questions': total_questions,
+            'correct_answers': correct_answers,
+            'partial_answers': partial_answers,
+            'incorrect_answers': incorrect_answers,
+            'errors': errors,
+            'timeouts': timeouts,
+            'accuracy': accuracy,
+            'success_rate': success_rate,
+            'classification_accuracy': classification_accuracy_pct,
+            'avg_processing_time': avg_processing_time,
+            'median_processing_time': median_processing_time,
+            'avg_confidence_score': avg_confidence,
+        }
+        
+        self.question_details = question_metrics
+        
+        return self.metrics
+    
+    def visualize_performance(self, output_dir: Optional[str] = None) -> None:
+        """
+        Generate visualizations of performance metrics.
+        
+        Args:
+            output_dir: Directory to save visualizations (None to use results_dir)
+        """
+        if not self.metrics:
+            self.logger.error("No metrics available. Run evaluate() first.")
+            return
+            
+        if not output_dir:
+            output_dir = self.results_dir
+        
+        output_path = Path(output_dir)
+        output_path.mkdir(exist_ok=True)
+        
+        # Set the style
+        sns.set(style="whitegrid")
+        plt.rcParams.update({'font.size': 12})
+        
+        # Create visualizations
+        self._create_accuracy_chart(output_path)
+        self._create_timing_chart(output_path)
+        self._create_question_type_chart(output_path)
+        self._create_confidence_distribution(output_path)
+        
+    def _create_accuracy_chart(self, output_path: Path) -> None:
+        """Create accuracy breakdown chart."""
+        categories = ['Correct', 'Partial', 'Incorrect', 'Error', 'Timeout']
+        values = [
+            self.metrics['correct_answers'],
+            self.metrics['partial_answers'],
+            self.metrics['incorrect_answers'],
+            self.metrics['errors'],
+            self.metrics['timeouts']
+        ]
+        
+        plt.figure(figsize=(10, 6))
+        colors = ['#2ecc71', '#f39c12', '#e74c3c', '#7f8c8d', '#95a5a6']
+        
+        ax = plt.bar(categories, values, color=colors)
+        
+        for i, v in enumerate(values):
+            plt.text(i, v + 0.1, str(v), ha='center')
+        
+        plt.title('Accuracy Breakdown')
+        plt.ylabel('Number of Questions')
+        plt.tight_layout()
+        plt.savefig(output_path / 'accuracy_breakdown.png', dpi=300)
+        plt.close()
+
+    def _create_timing_chart(self, output_path: Path) -> None:
+        """Create timing analysis chart."""
+        if not self.question_details:
+            return
+            
+        # Extract times and statuses
+        times = []
+        statuses = []
+        labels = []
+        
+        for q_id, details in self.question_details.items():
+            if details.get('processing_time'):
+                times.append(details['processing_time'])
+                statuses.append(details['validation_status'])
+                labels.append(q_id)
+        
+        if not times:
+            return
+            
+        # Convert to dataframe
+        df = pd.DataFrame({
+            'Question': labels,
+            'Time (s)': times,
+            'Status': statuses
+        })
+        
+        # Sort by time
+        df = df.sort_values('Time (s)', ascending=False)
+        
+        plt.figure(figsize=(12, 8))
+        
+        # Color mapping
+        color_map = {
+            'correct': '#2ecc71',
+            'partial': '#f39c12',
+            'incorrect': '#e74c3c',
+            'error': '#7f8c8d',
+            'timeout': '#95a5a6'
+        }
+        
+        sns.barplot(x='Time (s)', y='Question', hue='Status', data=df, 
+                   palette=color_map, dodge=False)
+        
+        plt.title('Processing Time by Question')
+        plt.tight_layout()
+        plt.savefig(output_path / 'processing_times.png', dpi=300)
+        plt.close()
+
+    def _create_question_type_chart(self, output_path: Path) -> None:
+        """Create question type performance chart."""
+        if not self.question_details:
+            return
+            
+        # Group by classification type
+        question_types = {}
+        
+        for q_id, details in self.question_details.items():
+            q_type = details.get('classification', 'unknown')
+            if q_type not in question_types:
+                question_types[q_type] = {
+                    'total': 0,
+                    'correct': 0,
+                    'partial': 0,
+                    'incorrect': 0,
+                    'other': 0
+                }
+                
+            question_types[q_type]['total'] += 1
+            
+            status = details.get('validation_status')
+            if status == 'correct':
+                question_types[q_type]['correct'] += 1
+            elif status == 'partial':
+                question_types[q_type]['partial'] += 1
+            elif status == 'incorrect':
+                question_types[q_type]['incorrect'] += 1
+            else:
+                question_types[q_type]['other'] += 1
+        
+        # Convert to dataframe
+        types = []
+        statuses = []
+        counts = []
+        
+        for q_type, stats in question_types.items():
+            for status, count in stats.items():
+                if status != 'total':
+                    types.append(q_type)
+                    statuses.append(status)
+                    counts.append(count)
+        
+        df = pd.DataFrame({
+            'Question Type': types,
+            'Status': statuses,
+            'Count': counts
+        })
+        
+        plt.figure(figsize=(12, 8))
+        
+        # Create grouped bar chart
+        sns.barplot(x='Question Type', y='Count', hue='Status', data=df)
+        
+        plt.title('Performance by Question Type')
+        plt.tight_layout()
+        plt.savefig(output_path / 'question_type_performance.png', dpi=300)
+        plt.close()
+
+    def _create_confidence_distribution(self, output_path: Path) -> None:
+        """Create confidence score distribution chart."""
+        if not self.question_details:
+            return
+            
+        # Extract confidence scores and statuses
+        scores = []
+        statuses = []
+        
+        for details in self.question_details.values():
+            conf_score = details.get('confidence_score')
+            if conf_score is not None:
+                scores.append(conf_score)
+                statuses.append(details['validation_status'])
+        
+        if not scores:
+            return
+            
+        # Create dataframe
+        df = pd.DataFrame({
+            'Confidence Score': scores,
+            'Status': statuses
+        })
+        
+        plt.figure(figsize=(10, 6))
+        
+        # Create histogram with KDE
+        sns.histplot(data=df, x='Confidence Score', hue='Status', kde=True)
+        
+        plt.title('Confidence Score Distribution')
+        plt.tight_layout()
+        plt.savefig(output_path / 'confidence_distribution.png', dpi=300)
+        plt.close()
+
+    def generate_report(self, output_file: Optional[str] = None) -> str:
+        """
+        Generate a comprehensive evaluation report.
+        
+        Args:
+            output_file: Path to save the report (None for no saving)
+            
+        Returns:
+            HTML report as string
+        """
+        if not self.metrics:
+            self.logger.error("No metrics available. Run evaluate() first.")
+            return ""
+            
+        # Create report HTML
+        report = f"""
+        <html>
+        <head>
+            <title>GAIA Performance Evaluation Report</title>
+            <style>
+                body {{ font-family: Arial, sans-serif; margin: 20px; }}
+                h1 {{ color: #2c3e50; }}
+                h2 {{ color: #3498db; }}
+                .metric-card {{ 
+                    background-color: #f8f9fa;
+                    border-radius: 8px;
+                    padding: 15px;
+                    margin-bottom: 20px;
+                    box-shadow: 0 2px 5px rgba(0,0,0,0.1);
+                }}
+                .metric-title {{ font-weight: bold; margin-bottom: 8px; }}
+                .metric-value {{ font-size: 24px; color: #2c3e50; }}
+                .good {{ color: #2ecc71; }}
+                .medium {{ color: #f39c12; }}
+                .poor {{ color: #e74c3c; }}
+                table {{ border-collapse: collapse; width: 100%; }}
+                th, td {{ padding: 12px; text-align: left; border-bottom: 1px solid #ddd; }}
+                th {{ background-color: #f2f2f2; }}
+                tr:hover {{background-color: #f5f5f5;}}
+                .chart-container {{ margin-top: 30px; margin-bottom: 30px; }}
+                .chart {{ max-width: 100%; height: auto; }}
+            </style>
+        </head>
+        <body>
+            <h1>GAIA Performance Evaluation Report</h1>
+            <p>Generated on {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>
+            
+            <div class="metric-card">
+                <h2>Summary Metrics</h2>
+                <div class="metric-row">
+                    <div class="metric-title">Accuracy</div>
+                    <div class="metric-value {self._get_color_class(self.metrics['accuracy'])}">
+                        {self.metrics['accuracy']:.2f}%
+                    </div>
+                </div>
+                <div class="metric-row">
+                    <div class="metric-title">Success Rate (Correct + Partial)</div>
+                    <div class="metric-value {self._get_color_class(self.metrics['success_rate'])}">
+                        {self.metrics['success_rate']:.2f}%
+                    </div>
+                </div>
+                <div class="metric-row">
+                    <div class="metric-title">Classification Accuracy</div>
+                    <div class="metric-value {self._get_color_class(self.metrics['classification_accuracy'])}">
+                        {self.metrics['classification_accuracy']:.2f}%
+                    </div>
+                </div>
+                <div class="metric-row">
+                    <div class="metric-title">Average Processing Time</div>
+                    <div class="metric-value">
+                        {self.metrics['avg_processing_time']:.2f} seconds
+                    </div>
+                </div>
+            </div>
+            
+            <div class="metric-card">
+                <h2>Accuracy Breakdown</h2>
+                <table>
+                    <tr>
+                        <th>Metric</th>
+                        <th>Count</th>
+                        <th>Percentage</th>
+                    </tr>
+                    <tr>
+                        <td>Correct Answers</td>
+                        <td>{self.metrics['correct_answers']}</td>
+                        <td>{(self.metrics['correct_answers'] / self.metrics['total_questions'] * 100):.2f}%</td>
+                    </tr>
+                    <tr>
+                        <td>Partial Answers</td>
+                        <td>{self.metrics['partial_answers']}</td>
+                        <td>{(self.metrics['partial_answers'] / self.metrics['total_questions'] * 100):.2f}%</td>
+                    </tr>
+                    <tr>
+                        <td>Incorrect Answers</td>
+                        <td>{self.metrics['incorrect_answers']}</td>
+                        <td>{(self.metrics['incorrect_answers'] / self.metrics['total_questions'] * 100):.2f}%</td>
+                    </tr>
+                    <tr>
+                        <td>Errors</td>
+                        <td>{self.metrics['errors']}</td>
+                        <td>{(self.metrics['errors'] / self.metrics['total_questions'] * 100):.2f}%</td>
+                    </tr>
+                    <tr>
+                        <td>Timeouts</td>
+                        <td>{self.metrics['timeouts']}</td>
+                        <td>{(self.metrics['timeouts'] / self.metrics['total_questions'] * 100):.2f}%</td>
+                    </tr>
+                </table>
+            </div>
+            
+            <!-- Include charts if available -->
+            <div class="chart-container">
+                <h2>Performance Visualizations</h2>
+                <img class="chart" src="accuracy_breakdown.png" alt="Accuracy Breakdown" />
+                <img class="chart" src="processing_times.png" alt="Processing Times" />
+                <img class="chart" src="question_type_performance.png" alt="Question Type Performance" />
+                <img class="chart" src="confidence_distribution.png" alt="Confidence Distribution" />
+            </div>
+            
+            <!-- Detailed results table -->
+            <div class="metric-card">
+                <h2>Detailed Question Results</h2>
+                <table>
+                    <tr>
+                        <th>Question ID</th>
+                        <th>Status</th>
+                        <th>Processing Time (s)</th>
+                        <th>Confidence</th>
+                        <th>Classification</th>
+                    </tr>
+                    {self._generate_question_rows()}
+                </table>
+            </div>
+        </body>
+        </html>
+        """
+        
+        # Save if output file provided
+        if output_file:
+            try:
+                with open(output_file, 'w') as f:
+                    f.write(report)
+                self.logger.info(f"Report saved to {output_file}")
+            except Exception as e:
+                self.logger.error(f"Error saving report: {e}")
+                
+        return report
+    
+    def _get_color_class(self, value: float) -> str:
+        """Get CSS class based on value."""
+        if value >= 80:
+            return "good"
+        elif value >= 60:
+            return "medium"
+        else:
+            return "poor"
+    
+    def _generate_question_rows(self) -> str:
+        """Generate HTML table rows for question details."""
+        rows = ""
+        for q_id, details in self.question_details.items():
+            status = details.get('validation_status', 'unknown')
+            proc_time = f"{details.get('processing_time', 'N/A'):.2f}" if details.get('processing_time') else 'N/A'
+            confidence = f"{details.get('confidence_score', 'N/A'):.2f}" if details.get('confidence_score') is not None else 'N/A'
+            classification = details.get('classification', 'unknown')
+            
+            # Get status class
+            status_class = ""
+            if status == 'correct':
+                status_class = "good"
+            elif status == 'partial':
+                status_class = "medium"
+            elif status in ('incorrect', 'error', 'timeout'):
+                status_class = "poor"
+                
+            rows += f"""
+            <tr>
+                <td>{q_id}</td>
+                <td class="{status_class}">{status}</td>
+                <td>{proc_time}</td>
+                <td>{confidence}</td>
+                <td>{classification}</td>
+            </tr>
+            """
+        return rows
+    
+    def compare_runs(self, results_files: List[str], labels: List[str]) -> Dict:
+        """
+        Compare metrics across multiple test runs.
+        
+        Args:
+            results_files: List of results files to compare
+            labels: Labels for each run
+            
+        Returns:
+            Dict with comparison data
+        """
+        if len(results_files) != len(labels):
+            self.logger.error("Number of result files must match number of labels")
+            return {}
+            
+        comparison_data = {
+            'runs': {},
+            'metrics': ['accuracy', 'success_rate', 'classification_accuracy', 
+                       'avg_processing_time', 'correct_answers', 'partial_answers',
+                       'incorrect_answers', 'errors', 'timeouts']
+        }
+        
+        for i, (file_path, label) in enumerate(zip(results_files, labels)):
+            # Create a temporary evaluator to analyze this run
+            temp_evaluator = GAIAEvaluator(validation_file=self.validation_file)
+            results = temp_evaluator.load_results(file_path)
+            metrics = temp_evaluator.evaluate(results)
+            
+            if metrics:
+                comparison_data['runs'][label] = metrics
+        
+        return comparison_data
+    
+    def visualize_comparison(self, comparison_data: Dict, output_dir: str) -> None:
+        """
+        Create visualizations comparing multiple runs.
+        
+        Args:
+            comparison_data: Data from compare_runs method
+            output_dir: Directory to save visualizations
+        """
+        if not comparison_data or not comparison_data.get('runs'):
+            self.logger.error("No comparison data available")
+            return
+            
+        output_path = Path(output_dir)
+        output_path.mkdir(exist_ok=True)
+        
+        # Set style
+        sns.set(style="whitegrid")
+        plt.rcParams.update({'font.size': 12})
+        
+        # Get run labels and metrics
+        run_labels = list(comparison_data['runs'].keys())
+        all_metrics = comparison_data['metrics']
+        
+        # Create bar chart for key metrics
+        key_metrics = ['accuracy', 'success_rate', 'classification_accuracy']
+        
+        # Extract data
+        metric_values = {metric: [] for metric in key_metrics}
+        
+        for run_label in run_labels:
+            run_data = comparison_data['runs'][run_label]
+            for metric in key_metrics:
+                metric_values[metric].append(run_data.get(metric, 0))
+        
+        # Create grouped bar chart
+        plt.figure(figsize=(12, 8))
+        x = np.arange(len(run_labels))
+        width = 0.25
+        
+        for i, metric in enumerate(key_metrics):
+            plt.bar(x + i*width - width, metric_values[metric], width, label=metric.replace('_', ' ').title())
+        
+        plt.xlabel('Test Run')
+        plt.ylabel('Percentage')
+        plt.title('Key Metrics Comparison')
+        plt.xticks(x, run_labels)
+        plt.legend()
+        plt.tight_layout()
+        plt.savefig(output_path / 'metrics_comparison.png', dpi=300)
+        plt.close()
+        
+        # Create processing time comparison
+        times = [comparison_data['runs'][label].get('avg_processing_time', 0) for label in run_labels]
+        
+        plt.figure(figsize=(10, 6))
+        plt.bar(run_labels, times)
+        plt.xlabel('Test Run')
+        plt.ylabel('Average Processing Time (s)')
+        plt.title('Processing Time Comparison')
+        plt.tight_layout()
+        plt.savefig(output_path / 'processing_time_comparison.png', dpi=300)
+        plt.close()
+
+
+if __name__ == "__main__":
+    import argparse
+    
+    # Configure logging
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
+        handlers=[logging.StreamHandler()]
+    )
+    
+    # Parse arguments
+    parser = argparse.ArgumentParser(description="GAIA Benchmark Evaluation Tool")
+    parser.add_argument("--results_dir", type=str, help="Directory containing test results")
+    parser.add_argument("--results_file", type=str, help="Specific results file to evaluate")
+    parser.add_argument("--validation_file", type=str, default="gaia_validation_metadata.jsonl", 
+                       help="Path to validation metadata file")
+    parser.add_argument("--output_dir", type=str, help="Directory to save evaluation outputs")
+    parser.add_argument("--report_file", type=str, help="Path to save HTML report")
+    parser.add_argument("--compare", action="store_true", help="Compare multiple test runs")
+    parser.add_argument("--compare_files", type=str, nargs="+", help="List of files to compare")
+    parser.add_argument("--compare_labels", type=str, nargs="+", help="Labels for comparison runs")
+    
+    args = parser.parse_args()
+    
+    # Initialize evaluator
+    evaluator = GAIAEvaluator(
+        results_dir=args.results_dir,
+        validation_file=args.validation_file
+    )
+    
+    # Handle comparison mode
+    if args.compare and args.compare_files and args.compare_labels:
+        comparison_data = evaluator.compare_runs(args.compare_files, args.compare_labels)
+        if comparison_data and args.output_dir:
+            evaluator.visualize_comparison(comparison_data, args.output_dir)
+            print(f"Comparison visualizations saved to {args.output_dir}")
+    else:
+        # Regular evaluation
+        if args.results_file:
+            results = evaluator.load_results(args.results_file)
+        else:
+            results = evaluator.load_results()
+            
+        if results:
+            metrics = evaluator.evaluate(results)
+            print(f"Evaluation metrics: {metrics}")
+            
+            if args.output_dir:
+                evaluator.visualize_performance(args.output_dir)
+                print(f"Performance visualizations saved to {args.output_dir}")
+                
+            if args.report_file:
+                evaluator.generate_report(args.report_file)
+                print(f"Report saved to {args.report_file}")