"""
Pipeline Deployment Dialog

This dialog handles the conversion of .mflow pipeline data to executable format
and deployment to Kneron dongles using the InferencePipeline system.

Main Components:
    - Pipeline conversion using MFlowConverter
    - Topology analysis and optimization
    - Dongle status monitoring
    - Real-time deployment progress
    - Error handling and troubleshooting

Usage:
    from ui.dialogs.deployment import DeploymentDialog
    
    dialog = DeploymentDialog(pipeline_data, parent=self)
    dialog.exec_()
"""

import os
import sys
import json
import threading
import traceback
from typing import Dict, Any, List, Optional
from PyQt5.QtWidgets import (
    QDialog, QVBoxLayout, QHBoxLayout, QLabel, QTextEdit, QPushButton,
    QProgressBar, QTabWidget, QWidget, QFormLayout, QLineEdit, QSpinBox,
    QCheckBox, QGroupBox, QScrollArea, QTableWidget, QTableWidgetItem,
    QHeaderView, QMessageBox, QSplitter, QFrame
)
from PyQt5.QtCore import Qt, QThread, pyqtSignal, QTimer
from PyQt5.QtGui import QFont, QColor, QPalette, QImage, QPixmap

# Import our converter and pipeline system
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'core', 'functions'))

try:
    from ...core.functions.mflow_converter import MFlowConverter, PipelineConfig
    CONVERTER_AVAILABLE = True
except ImportError as e:
    print(f"Warning: MFlow converter not available: {e}")
    CONVERTER_AVAILABLE = False

try:
    from ...core.functions.Multidongle import MultiDongle
    from ...core.functions.InferencePipeline import InferencePipeline
    from ...core.functions.workflow_orchestrator import WorkflowOrchestrator
    # from workflow_orchestrator import WorkflowOrchestrator
    PIPELINE_AVAILABLE = True
except ImportError as e:
    print(f"Warning: Pipeline system not available: {e}")
    PIPELINE_AVAILABLE = False


class DeploymentWorker(QThread):
    """Worker thread for pipeline deployment to avoid blocking UI."""
    
    # Signals
    progress_updated = pyqtSignal(int, str)  # progress, message
    topology_analyzed = pyqtSignal(dict)  # topology analysis results
    conversion_completed = pyqtSignal(object)  # PipelineConfig object
    deployment_started = pyqtSignal()
    deployment_completed = pyqtSignal(bool, str)  # success, message
    error_occurred = pyqtSignal(str)
    frame_updated = pyqtSignal('PyQt_PyObject')  # For live view
    result_updated = pyqtSignal(dict)  # For inference results
    
    def __init__(self, pipeline_data: Dict[str, Any]):
        super().__init__()
        self.pipeline_data = pipeline_data
        self.should_stop = False
        self.orchestrator = None
        
    def run(self):
        """Main deployment workflow."""
        try:
            # Step 1: Convert .mflow to pipeline config
            self.progress_updated.emit(10, "Converting pipeline configuration...")
            
            if not CONVERTER_AVAILABLE:
                self.error_occurred.emit("MFlow converter not available. Please check installation.")
                return
                
            converter = MFlowConverter()
            config = converter._convert_mflow_to_config(self.pipeline_data)
            
            # Emit topology analysis results
            self.topology_analyzed.emit({
                'total_stages': len(config.stage_configs),
                'pipeline_name': config.pipeline_name,
                'input_config': config.input_config,
                'output_config': config.output_config
            })
            
            self.progress_updated.emit(30, "Pipeline conversion completed")
            self.conversion_completed.emit(config)
            
            if self.should_stop:
                return
            
            # Step 2: Validate configuration
            self.progress_updated.emit(40, "Validating pipeline configuration...")
            is_valid, errors = converter.validate_config(config)
            
            if not is_valid:
                error_msg = "Configuration validation failed:\n" + "\n".join(errors)
                self.error_occurred.emit(error_msg)
                return
                
            self.progress_updated.emit(60, "Configuration validation passed")
            
            if self.should_stop:
                return
            
            # Step 3: Initialize pipeline (if dongle system available)
            self.progress_updated.emit(70, "Initializing inference pipeline...")
            
            if not PIPELINE_AVAILABLE:
                self.progress_updated.emit(100, "Pipeline configuration ready (dongle system not available)")
                self.deployment_completed.emit(True, "Pipeline configuration prepared successfully. Dongle system not available for actual deployment.")
                return
            
            # Create InferencePipeline instance
            try:
                pipeline = converter.create_inference_pipeline(config)
                
                self.progress_updated.emit(80, "Initializing workflow orchestrator...")
                self.deployment_started.emit()
                
                # Create and start the orchestrator
                self.orchestrator = WorkflowOrchestrator(pipeline, config.input_config, config.output_config)
                self.orchestrator.set_frame_callback(self.frame_updated.emit)
                
                # Set up both GUI and terminal result callbacks
                def combined_result_callback(result_dict):
                    # Print to terminal
                    self._print_terminal_results(result_dict)
                    # Emit for GUI
                    self.result_updated.emit(result_dict)
                
                self.orchestrator.set_result_callback(combined_result_callback)
                self.orchestrator.start()
                
                self.progress_updated.emit(100, "Pipeline deployed successfully!")
                self.deployment_completed.emit(True, f"Pipeline '{config.pipeline_name}' deployed with {len(config.stage_configs)} stages")
                
                # Keep running until stop is requested
                while not self.should_stop:
                    self.msleep(100)  # Sleep for 100ms and check again
                
            except Exception as e:
                self.error_occurred.emit(f"Pipeline deployment failed: {str(e)}")
                
        except Exception as e:
            self.error_occurred.emit(f"Deployment error: {str(e)}")
    
    def stop(self):
        """Stop the deployment process."""
        self.should_stop = True
        if self.orchestrator:
            self.orchestrator.stop()
    
    def _print_terminal_results(self, result_dict):
        """Print inference results to terminal with detailed formatting."""
        try:
            from datetime import datetime
            
            # Header with timestamp
            timestamp = datetime.fromtimestamp(result_dict.get('timestamp', 0)).strftime("%H:%M:%S.%f")[:-3]
            pipeline_id = result_dict.get('pipeline_id', 'Unknown')
            
            print(f"\n🔥 INFERENCE RESULT [{timestamp}]")
            print(f"   Pipeline ID: {pipeline_id}")
            print("   " + "="*50)
            
            # Stage results
            stage_results = result_dict.get('stage_results', {})
            if stage_results:
                for stage_id, result in stage_results.items():
                    print(f"   📊 Stage: {stage_id}")
                    
                    if isinstance(result, tuple) and len(result) == 2:
                        # Handle tuple results (result_string, probability)
                        result_string, probability = result
                        print(f"      ✅ Result: {result_string}")
                        print(f"      📈 Probability: {probability:.3f}")
                        
                        # Add confidence level
                        if probability > 0.8:
                            confidence = "🟢 Very High"
                        elif probability > 0.6:
                            confidence = "🟡 High"
                        elif probability > 0.4:
                            confidence = "🟠 Medium"
                        else:
                            confidence = "🔴 Low"
                        print(f"      🎯 Confidence: {confidence}")
                        
                    elif isinstance(result, dict):
                        # Handle dict results
                        for key, value in result.items():
                            if key == 'probability':
                                print(f"      📈 {key.title()}: {value:.3f}")
                            elif key == 'result':
                                print(f"      ✅ {key.title()}: {value}")
                            elif key == 'confidence':
                                print(f"      🎯 {key.title()}: {value}")
                            elif key == 'fused_probability':
                                print(f"      🔀 Fused Probability: {value:.3f}")
                            elif key == 'individual_probs':
                                print(f"      📋 Individual Probabilities:")
                                for prob_key, prob_value in value.items():
                                    print(f"         {prob_key}: {prob_value:.3f}")
                            else:
                                print(f"      📝 {key}: {value}")
                    else:
                        # Handle other result types
                        print(f"      📝 Raw Result: {result}")
                    
                    print()  # Blank line between stages
            else:
                print("   ⚠️  No stage results available")
            
            # Processing time if available
            metadata = result_dict.get('metadata', {})
            if 'total_processing_time' in metadata:
                processing_time = metadata['total_processing_time']
                print(f"   ⏱️  Processing Time: {processing_time:.3f}s")
                
                # Add FPS calculation
                if processing_time > 0:
                    fps = 1.0 / processing_time
                    print(f"   🚄 Theoretical FPS: {fps:.2f}")
            
            # Additional metadata
            if metadata:
                interesting_keys = ['dongle_count', 'stage_count', 'queue_sizes', 'error_count']
                for key in interesting_keys:
                    if key in metadata:
                        print(f"   📋 {key.replace('_', ' ').title()}: {metadata[key]}")
            
            print("   " + "="*50)
            
        except Exception as e:
            print(f"❌ Error printing terminal results: {e}")


class DeploymentDialog(QDialog):
    """Main deployment dialog with comprehensive deployment management."""
    
    def __init__(self, pipeline_data: Dict[str, Any], parent=None):
        super().__init__(parent)
        self.pipeline_data = pipeline_data
        self.deployment_worker = None
        self.pipeline_config = None
        
        self.setWindowTitle("Deploy Pipeline to Dongles")
        self.setMinimumSize(800, 600)
        self.setup_ui()
        self.apply_theme()
        
    def setup_ui(self):
        """Setup the dialog UI."""
        layout = QVBoxLayout(self)
        
        # Header
        header_label = QLabel("Pipeline Deployment")
        header_label.setFont(QFont("Arial", 16, QFont.Bold))
        header_label.setAlignment(Qt.AlignCenter)
        layout.addWidget(header_label)
        
        # Main content with tabs
        self.tab_widget = QTabWidget()
        
        # Overview tab
        self.overview_tab = self.create_overview_tab()
        self.tab_widget.addTab(self.overview_tab, "Overview")
        
        # Topology tab
        self.topology_tab = self.create_topology_tab()
        self.tab_widget.addTab(self.topology_tab, "Topology Analysis")
        
        # Configuration tab
        self.config_tab = self.create_configuration_tab()
        self.tab_widget.addTab(self.config_tab, "Configuration")
        
        # Deployment tab
        self.deployment_tab = self.create_deployment_tab()
        self.tab_widget.addTab(self.deployment_tab, "Deployment")

        # Live View tab
        self.live_view_tab = self.create_live_view_tab()
        self.tab_widget.addTab(self.live_view_tab, "Live View")
        
        layout.addWidget(self.tab_widget)
        
        # Progress bar
        self.progress_bar = QProgressBar()
        self.progress_bar.setVisible(False)
        layout.addWidget(self.progress_bar)
        
        # Status label
        self.status_label = QLabel("Ready to deploy")
        self.status_label.setAlignment(Qt.AlignCenter)
        layout.addWidget(self.status_label)
        
        # Buttons
        button_layout = QHBoxLayout()
        
        self.analyze_button = QPushButton("Analyze Pipeline")
        self.analyze_button.clicked.connect(self.analyze_pipeline)
        button_layout.addWidget(self.analyze_button)
        
        self.deploy_button = QPushButton("Deploy to Dongles")
        self.deploy_button.clicked.connect(self.start_deployment)
        self.deploy_button.setEnabled(False)
        button_layout.addWidget(self.deploy_button)
        
        self.stop_button = QPushButton("Stop Inference")
        self.stop_button.clicked.connect(self.stop_deployment)
        self.stop_button.setEnabled(False)
        self.stop_button.setVisible(False)
        button_layout.addWidget(self.stop_button)
        
        button_layout.addStretch()
        
        self.close_button = QPushButton("Close")
        self.close_button.clicked.connect(self.accept)
        button_layout.addWidget(self.close_button)
        
        layout.addLayout(button_layout)
        
        # Populate initial data
        self.populate_overview()
        
    def create_overview_tab(self) -> QWidget:
        """Create pipeline overview tab."""
        widget = QWidget()
        layout = QVBoxLayout(widget)
        
        # Pipeline info
        info_group = QGroupBox("Pipeline Information")
        info_layout = QFormLayout(info_group)
        
        self.name_label = QLabel()
        self.description_label = QLabel()
        self.nodes_label = QLabel()
        self.connections_label = QLabel()
        
        info_layout.addRow("Name:", self.name_label)
        info_layout.addRow("Description:", self.description_label)
        info_layout.addRow("Nodes:", self.nodes_label)
        info_layout.addRow("Connections:", self.connections_label)
        
        layout.addWidget(info_group)
        
        # Nodes table
        nodes_group = QGroupBox("Pipeline Nodes")
        nodes_layout = QVBoxLayout(nodes_group)
        
        self.nodes_table = QTableWidget()
        self.nodes_table.setColumnCount(3)
        self.nodes_table.setHorizontalHeaderLabels(["Name", "Type", "Status"])
        self.nodes_table.horizontalHeader().setStretchLastSection(True)
        nodes_layout.addWidget(self.nodes_table)
        
        layout.addWidget(nodes_group)
        
        return widget
    
    def create_topology_tab(self) -> QWidget:
        """Create topology analysis tab."""
        widget = QWidget()
        layout = QVBoxLayout(widget)
        
        # Analysis results
        self.topology_text = QTextEdit()
        self.topology_text.setReadOnly(True)
        self.topology_text.setFont(QFont("Consolas", 10))
        self.topology_text.setText("Click 'Analyze Pipeline' to see topology analysis...")
        
        layout.addWidget(self.topology_text)
        
        return widget
    
    def create_configuration_tab(self) -> QWidget:
        """Create configuration tab."""
        widget = QWidget()
        layout = QVBoxLayout(widget)
        
        scroll_area = QScrollArea()
        scroll_content = QWidget()
        scroll_layout = QVBoxLayout(scroll_content)
        
        # Stage configurations will be populated after analysis
        self.config_content = QLabel("Run pipeline analysis to see stage configurations...")
        self.config_content.setAlignment(Qt.AlignCenter)
        scroll_layout.addWidget(self.config_content)
        
        scroll_area.setWidget(scroll_content)
        scroll_area.setWidgetResizable(True)
        layout.addWidget(scroll_area)
        
        return widget
    
    def create_deployment_tab(self) -> QWidget:
        """Create deployment monitoring tab."""
        widget = QWidget()
        layout = QVBoxLayout(widget)
        
        # Deployment log
        log_group = QGroupBox("Deployment Log")
        log_layout = QVBoxLayout(log_group)
        
        self.deployment_log = QTextEdit()
        self.deployment_log.setReadOnly(True)
        self.deployment_log.setFont(QFont("Consolas", 9))
        log_layout.addWidget(self.deployment_log)
        
        layout.addWidget(log_group)
        
        # Dongle status (placeholder)
        status_group = QGroupBox("Dongle Status")
        status_layout = QVBoxLayout(status_group)
        
        self.dongle_status = QLabel("No dongles detected")
        self.dongle_status.setAlignment(Qt.AlignCenter)
        status_layout.addWidget(self.dongle_status)
        
        layout.addWidget(status_group)
        
        return widget

    def create_live_view_tab(self) -> QWidget:
        """Create the live view tab for real-time output."""
        widget = QWidget()
        layout = QHBoxLayout(widget)

        # Video display
        video_group = QGroupBox("Live Video Feed")
        video_layout = QVBoxLayout(video_group)
        self.live_view_label = QLabel("Live view will appear here after deployment.")
        self.live_view_label.setAlignment(Qt.AlignCenter)
        self.live_view_label.setMinimumSize(640, 480)
        video_layout.addWidget(self.live_view_label)
        layout.addWidget(video_group, 2)

        # Inference results
        results_group = QGroupBox("Inference Results")
        results_layout = QVBoxLayout(results_group)
        self.results_text = QTextEdit()
        self.results_text.setReadOnly(True)
        results_layout.addWidget(self.results_text)
        layout.addWidget(results_group, 1)

        return widget
    
    def populate_overview(self):
        """Populate overview tab with pipeline data."""
        self.name_label.setText(self.pipeline_data.get('project_name', 'Untitled'))
        self.description_label.setText(self.pipeline_data.get('description', 'No description'))
        
        nodes = self.pipeline_data.get('nodes', [])
        connections = self.pipeline_data.get('connections', [])
        
        self.nodes_label.setText(str(len(nodes)))
        self.connections_label.setText(str(len(connections)))
        
        # Populate nodes table
        self.nodes_table.setRowCount(len(nodes))
        for i, node in enumerate(nodes):
            self.nodes_table.setItem(i, 0, QTableWidgetItem(node.get('name', 'Unknown')))
            self.nodes_table.setItem(i, 1, QTableWidgetItem(node.get('type', 'Unknown')))
            self.nodes_table.setItem(i, 2, QTableWidgetItem("Ready"))
    
    def analyze_pipeline(self):
        """Analyze pipeline topology and configuration."""
        if not CONVERTER_AVAILABLE:
            QMessageBox.warning(self, "Analysis Error", 
                              "Pipeline analyzer not available. Please check installation.")
            return
            
        try:
            self.status_label.setText("Analyzing pipeline...")
            self.analyze_button.setEnabled(False)
            
            # Create converter and analyze
            converter = MFlowConverter()
            config = converter._convert_mflow_to_config(self.pipeline_data)
            self.pipeline_config = config
            
            # Update topology tab
            analysis_text = f"""Pipeline Analysis Results:

Name: {config.pipeline_name}
Description: {config.description}
Total Stages: {len(config.stage_configs)}

Input Configuration:
{json.dumps(config.input_config, indent=2)}

Output Configuration:
{json.dumps(config.output_config, indent=2)}

Stage Configurations:
"""
            
            for i, stage_config in enumerate(config.stage_configs, 1):
                analysis_text += f"\nStage {i}: {stage_config.stage_id}\n"
                analysis_text += f"  Port IDs: {stage_config.port_ids}\n"
                analysis_text += f"  Model Path: {stage_config.model_path}\n"
                analysis_text += f"  SCPU Firmware: {stage_config.scpu_fw_path}\n"
                analysis_text += f"  NCPU Firmware: {stage_config.ncpu_fw_path}\n"
                analysis_text += f"  Upload Firmware: {stage_config.upload_fw}\n"
                analysis_text += f"  Max Queue Size: {stage_config.max_queue_size}\n"
            
            self.topology_text.setText(analysis_text)
            
            # Update configuration tab
            self.update_configuration_tab(config)
            
            # Validate configuration
            is_valid, errors = converter.validate_config(config)
            
            if is_valid:
                self.status_label.setText("Pipeline analysis completed successfully")
                self.deploy_button.setEnabled(True)
                self.tab_widget.setCurrentIndex(1)  # Switch to topology tab
            else:
                error_msg = "Configuration validation failed:\n" + "\n".join(errors)
                QMessageBox.warning(self, "Validation Error", error_msg)
                self.status_label.setText("Pipeline analysis failed validation")
                
        except Exception as e:
            QMessageBox.critical(self, "Analysis Error", 
                               f"Failed to analyze pipeline: {str(e)}")
            self.status_label.setText("Pipeline analysis failed")
        finally:
            self.analyze_button.setEnabled(True)
    
    def update_configuration_tab(self, config: 'PipelineConfig'):
        """Update configuration tab with detailed stage information."""
        # Clear existing content
        scroll_content = QWidget()
        scroll_layout = QVBoxLayout(scroll_content)
        
        for i, stage_config in enumerate(config.stage_configs, 1):
            stage_group = QGroupBox(f"Stage {i}: {stage_config.stage_id}")
            stage_layout = QFormLayout(stage_group)
            
            # Create read-only fields for stage configuration
            model_path_edit = QLineEdit(stage_config.model_path)
            model_path_edit.setReadOnly(True)
            stage_layout.addRow("Model Path:", model_path_edit)
            
            scpu_fw_edit = QLineEdit(stage_config.scpu_fw_path)
            scpu_fw_edit.setReadOnly(True)
            stage_layout.addRow("SCPU Firmware:", scpu_fw_edit)
            
            ncpu_fw_edit = QLineEdit(stage_config.ncpu_fw_path)
            ncpu_fw_edit.setReadOnly(True)
            stage_layout.addRow("NCPU Firmware:", ncpu_fw_edit)
            
            port_ids_edit = QLineEdit(str(stage_config.port_ids))
            port_ids_edit.setReadOnly(True)
            stage_layout.addRow("Port IDs:", port_ids_edit)
            
            queue_size_spin = QSpinBox()
            queue_size_spin.setValue(stage_config.max_queue_size)
            queue_size_spin.setReadOnly(True)
            stage_layout.addRow("Queue Size:", queue_size_spin)
            
            upload_fw_check = QCheckBox()
            upload_fw_check.setChecked(stage_config.upload_fw)
            upload_fw_check.setEnabled(False)
            stage_layout.addRow("Upload Firmware:", upload_fw_check)
            
            scroll_layout.addWidget(stage_group)
        
        # Update the configuration tab
        config_tab_layout = self.config_tab.layout()
        old_scroll_area = config_tab_layout.itemAt(0).widget()
        config_tab_layout.removeWidget(old_scroll_area)
        old_scroll_area.deleteLater()
        
        new_scroll_area = QScrollArea()
        new_scroll_area.setWidget(scroll_content)
        new_scroll_area.setWidgetResizable(True)
        config_tab_layout.addWidget(new_scroll_area)
    
    def start_deployment(self):
        """Start the deployment process."""
        if not self.pipeline_config:
            QMessageBox.warning(self, "Deployment Error", 
                              "Please analyze the pipeline first.")
            return
        
        # Switch to deployment tab
        self.tab_widget.setCurrentIndex(3)
        
        # Setup UI for deployment
        self.progress_bar.setVisible(True)
        self.progress_bar.setValue(0)
        self.deploy_button.setEnabled(False)
        self.close_button.setText("Cancel")
        
        # Clear deployment log
        self.deployment_log.clear()
        self.deployment_log.append("Starting pipeline deployment...")
        
        # Create and start deployment worker
        self.deployment_worker = DeploymentWorker(self.pipeline_data)
        self.deployment_worker.progress_updated.connect(self.update_progress)
        self.deployment_worker.topology_analyzed.connect(self.update_topology_results)
        self.deployment_worker.conversion_completed.connect(self.on_conversion_completed)
        self.deployment_worker.deployment_started.connect(self.on_deployment_started)
        self.deployment_worker.deployment_completed.connect(self.on_deployment_completed)
        self.deployment_worker.error_occurred.connect(self.on_deployment_error)
        self.deployment_worker.frame_updated.connect(self.update_live_view)
        self.deployment_worker.result_updated.connect(self.update_inference_results)
        
        self.deployment_worker.start()
    
    def stop_deployment(self):
        """Stop the current deployment/inference."""
        if self.deployment_worker and self.deployment_worker.isRunning():
            reply = QMessageBox.question(self, "Stop Inference",
                                       "Are you sure you want to stop the inference?",
                                       QMessageBox.Yes | QMessageBox.No)
            if reply == QMessageBox.Yes:
                self.deployment_log.append("Stopping inference...")
                self.status_label.setText("Stopping inference...")
                
                # Disable stop button immediately to prevent multiple clicks
                self.stop_button.setEnabled(False)
                
                self.deployment_worker.stop()
                
                # Wait for worker to finish in a separate thread to avoid blocking UI
                def wait_for_stop():
                    if self.deployment_worker.wait(5000):  # Wait up to 5 seconds
                        self.deployment_log.append("Inference stopped successfully.")
                    else:
                        self.deployment_log.append("Warning: Inference may not have stopped cleanly.")
                    
                    # Update UI on main thread
                    self.stop_button.setVisible(False)
                    self.deploy_button.setEnabled(True)
                    self.close_button.setText("Close")
                    self.progress_bar.setVisible(False)
                    self.status_label.setText("Inference stopped")
                    self.dongle_status.setText("Pipeline stopped")
                
                import threading
                threading.Thread(target=wait_for_stop, daemon=True).start()
    
    def update_progress(self, value: int, message: str):
        """Update deployment progress."""
        self.progress_bar.setValue(value)
        self.status_label.setText(message)
        self.deployment_log.append(f"[{value}%] {message}")
    
    def update_topology_results(self, results: Dict):
        """Update topology analysis results."""
        self.deployment_log.append(f"Topology Analysis: {results['total_stages']} stages detected")
    
    def on_conversion_completed(self, config):
        """Handle conversion completion."""
        self.deployment_log.append("Pipeline conversion completed successfully")
    
    def on_deployment_started(self):
        """Handle deployment start."""
        self.deployment_log.append("Connecting to dongles...")
        self.dongle_status.setText("Initializing dongles...")
        
        # Show stop button and hide deploy button
        self.stop_button.setEnabled(True)
        self.stop_button.setVisible(True)
        self.deploy_button.setEnabled(False)
    
    def on_deployment_completed(self, success: bool, message: str):
        """Handle deployment completion."""
        self.progress_bar.setValue(100)
        
        if success:
            self.deployment_log.append(f"SUCCESS: {message}")
            self.status_label.setText("Deployment completed successfully!")
            self.dongle_status.setText("Pipeline running on dongles")
            # Keep stop button visible for successful deployment
            self.stop_button.setEnabled(True)
            self.stop_button.setVisible(True)
            QMessageBox.information(self, "Deployment Success", message)
        else:
            self.deployment_log.append(f"FAILED: {message}")
            self.status_label.setText("Deployment failed")
            # Hide stop button for failed deployment
            self.stop_button.setEnabled(False)
            self.stop_button.setVisible(False)
            self.deploy_button.setEnabled(True)
            
        self.close_button.setText("Close")
        self.progress_bar.setVisible(False)
    
    def on_deployment_error(self, error: str):
        """Handle deployment error."""
        self.deployment_log.append(f"ERROR: {error}")
        self.status_label.setText("Deployment failed")
        QMessageBox.critical(self, "Deployment Error", error)
        
        # Hide stop button and show deploy button on error
        self.stop_button.setEnabled(False)
        self.stop_button.setVisible(False)
        self.deploy_button.setEnabled(True)
        self.close_button.setText("Close")
        self.progress_bar.setVisible(False)

    def update_live_view(self, frame):
        """Update the live view with a new frame."""
        try:
            # Convert the OpenCV frame to a QImage
            height, width, channel = frame.shape
            bytes_per_line = 3 * width
            q_image = QImage(frame.data, width, height, bytes_per_line, QImage.Format_RGB888).rgbSwapped()

            # Display the QImage in the QLabel
            self.live_view_label.setPixmap(QPixmap.fromImage(q_image))
        except Exception as e:
            print(f"Error updating live view: {e}")
    
    def update_inference_results(self, result_dict):
        """Update the inference results display."""
        try:
            import json
            from datetime import datetime
            
            # Format the results for display
            timestamp = datetime.fromtimestamp(result_dict.get('timestamp', 0)).strftime("%H:%M:%S.%f")[:-3]
            stage_results = result_dict.get('stage_results', {})
            
            result_text = f"[{timestamp}] Pipeline ID: {result_dict.get('pipeline_id', 'Unknown')}\n"
            
            # Display results from each stage
            for stage_id, result in stage_results.items():
                result_text += f"  {stage_id}:\n"
                if isinstance(result, tuple) and len(result) == 2:
                    # Handle tuple results (probability, result_string)
                    probability, result_string = result
                    result_text += f"    Result: {result_string}\n"
                    result_text += f"    Probability: {probability:.3f}\n"
                elif isinstance(result, dict):
                    # Handle dict results
                    for key, value in result.items():
                        if key == 'probability':
                            result_text += f"    Probability: {value:.3f}\n"
                        else:
                            result_text += f"    {key}: {value}\n"
                else:
                    result_text += f"    {result}\n"
            
            result_text += "-" * 50 + "\n"
            
            # Append to results display (keep last 100 lines)
            current_text = self.results_text.toPlainText()
            lines = current_text.split('\n')
            if len(lines) > 100:
                lines = lines[-50:]  # Keep last 50 lines
                current_text = '\n'.join(lines)
            
            self.results_text.setPlainText(current_text + result_text)
            
            # Auto-scroll to bottom
            scrollbar = self.results_text.verticalScrollBar()
            scrollbar.setValue(scrollbar.maximum())
            
        except Exception as e:
            print(f"Error updating inference results: {e}")
    
    def apply_theme(self):
        """Apply consistent theme to the dialog."""
        self.setStyleSheet("""
            QDialog {
                background-color: #1e1e2e;
                color: #cdd6f4;
            }
            QTabWidget::pane {
                border: 1px solid #45475a;
                background-color: #313244;
            }
            QTabWidget::tab-bar {
                alignment: center;
            }
            QTabBar::tab {
                background-color: #45475a;
                color: #cdd6f4;
                padding: 8px 16px;
                margin-right: 2px;
                border-top-left-radius: 4px;
                border-top-right-radius: 4px;
            }
            QTabBar::tab:selected {
                background-color: #89b4fa;
                color: #1e1e2e;
            }
            QTabBar::tab:hover {
                background-color: #585b70;
            }
            QGroupBox {
                font-weight: bold;
                border: 2px solid #45475a;
                border-radius: 5px;
                margin-top: 1ex;
                padding-top: 5px;
            }
            QGroupBox::title {
                subcontrol-origin: margin;
                left: 10px;
                padding: 0 10px 0 10px;
            }
            QPushButton {
                background-color: #45475a;
                color: #cdd6f4;
                border: 1px solid #6c7086;
                border-radius: 4px;
                padding: 8px 16px;
                font-weight: bold;
            }
            QPushButton:hover {
                background-color: #585b70;
            }
            QPushButton:pressed {
                background-color: #313244;
            }
            QPushButton:disabled {
                background-color: #313244;
                color: #6c7086;
            }
            QTextEdit, QLineEdit {
                background-color: #313244;
                color: #cdd6f4;
                border: 1px solid #45475a;
                border-radius: 4px;
                padding: 4px;
            }
            QTableWidget {
                background-color: #313244;
                alternate-background-color: #45475a;
                color: #cdd6f4;
                border: 1px solid #45475a;
            }
            QProgressBar {
                background-color: #313244;
                border: 1px solid #45475a;
                border-radius: 4px;
                text-align: center;
            }
            QProgressBar::chunk {
                background-color: #a6e3a1;
                border-radius: 3px;
            }
        """)
    
    def closeEvent(self, event):
        """Handle dialog close event."""
        if self.deployment_worker and self.deployment_worker.isRunning():
            reply = QMessageBox.question(self, "Cancel Deployment",
                                       "Deployment is in progress. Are you sure you want to cancel?",
                                       QMessageBox.Yes | QMessageBox.No)
            if reply == QMessageBox.Yes:
                self.deployment_worker.stop()
                self.deployment_worker.wait(3000)  # Wait up to 3 seconds
                event.accept()
            else:
                event.ignore()
        else:
            event.accept()