fix model mappings,dash updates, trading

web/tensorboard_component.py

@@ -0,0 +1,173 @@
+#!/usr/bin/env python3
+"""
+TensorBoard Component for Dashboard
+
+This module provides a Dash component that embeds TensorBoard in the dashboard.
+"""
+
+import dash
+from dash import html, dcc
+import dash_bootstrap_components as dbc
+import logging
+from typing import Optional, Dict, Any
+
+logger = logging.getLogger(__name__)
+
+def create_tensorboard_tab(tensorboard_url: str = "http://localhost:6006") -> html.Div:
+    """
+    Create a dashboard tab that embeds TensorBoard
+    
+    Args:
+        tensorboard_url: URL of the TensorBoard server
+        
+    Returns:
+        html.Div: Dash component containing TensorBoard iframe
+    """
+    return html.Div([
+        dbc.Alert([
+            html.I(className="fas fa-chart-line me-2"),
+            "TensorBoard Training Visualization",
+            html.A(
+                "Open in New Window",
+                href=tensorboard_url,
+                target="_blank",
+                className="ms-2 btn btn-sm btn-primary"
+            )
+        ], color="info", className="mb-3"),
+        
+        # TensorBoard iframe
+        html.Iframe(
+            src=tensorboard_url,
+            style={
+                'width': '100%',
+                'height': '800px',
+                'border': 'none'
+            }
+        ),
+        
+        # Training metrics summary
+        html.Div([
+            html.H5("Training Metrics Summary", className="mt-3"),
+            html.Div(id="training-metrics-summary", className="mt-2")
+        ], className="mt-3")
+    ])
+
+def create_training_metrics_card() -> dbc.Card:
+    """
+    Create a card displaying key training metrics
+    
+    Returns:
+        dbc.Card: Dash Bootstrap card component
+    """
+    return dbc.Card([
+        dbc.CardHeader([
+            html.I(className="fas fa-brain me-2"),
+            "Training Metrics"
+        ]),
+        dbc.CardBody([
+            dbc.Row([
+                dbc.Col([
+                    html.H6("Model Status"),
+                    html.Div(id="model-training-status", children="Initializing...")
+                ], width=6),
+                dbc.Col([
+                    html.H6("Training Progress"),
+                    dbc.Progress(id="training-progress-bar", value=0, className="mb-2"),
+                    html.Div(id="training-progress-text", children="0%")
+                ], width=6)
+            ], className="mb-3"),
+            
+            dbc.Row([
+                dbc.Col([
+                    html.H6("Loss"),
+                    html.Div(id="training-loss-value", children="N/A")
+                ], width=4),
+                dbc.Col([
+                    html.H6("Reward"),
+                    html.Div(id="training-reward-value", children="N/A")
+                ], width=4),
+                dbc.Col([
+                    html.H6("State Quality"),
+                    html.Div(id="training-state-quality", children="N/A")
+                ], width=4)
+            ], className="mb-3"),
+            
+            dbc.Row([
+                dbc.Col([
+                    html.A(
+                        dbc.Button([
+                            html.I(className="fas fa-chart-line me-2"),
+                            "Open TensorBoard"
+                        ], color="primary", size="sm", className="w-100"),
+                        href="http://localhost:6006",
+                        target="_blank"
+                    )
+                ], width=12)
+            ])
+        ])
+    ], className="mb-3")
+
+def create_tensorboard_status_indicator(tensorboard_url: str = "http://localhost:6006") -> html.Div:
+    """
+    Create a status indicator for TensorBoard
+    
+    Args:
+        tensorboard_url: URL of the TensorBoard server
+        
+    Returns:
+        html.Div: Dash component showing TensorBoard status
+    """
+    return html.Div([
+        dbc.Button([
+            html.I(className="fas fa-chart-line me-2"),
+            "TensorBoard"
+        ], 
+        id="tensorboard-status-button",
+        color="success",
+        size="sm",
+        href=tensorboard_url,
+        target="_blank",
+        external_link=True,
+        className="ms-2")
+    ], id="tensorboard-status-container")
+
+def update_training_metrics_card(metrics: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Update training metrics card with latest data
+    
+    Args:
+        metrics: Dictionary of training metrics
+        
+    Returns:
+        Dict: Dictionary of Dash component updates
+    """
+    # Extract metrics
+    training_active = metrics.get("training_active", False)
+    loss = metrics.get("loss", None)
+    reward = metrics.get("reward", None)
+    state_quality = metrics.get("state_quality", None)
+    progress = metrics.get("progress", 0)
+    
+    # Format values
+    loss_str = f"{loss:.4f}" if loss is not None else "N/A"
+    reward_str = f"{reward:.4f}" if reward is not None else "N/A"
+    state_quality_str = f"{state_quality:.1%}" if state_quality is not None else "N/A"
+    progress_str = f"{progress:.1%}"
+    
+    # Determine status
+    if training_active:
+        status = "Training Active"
+        status_class = "text-success"
+    else:
+        status = "Training Inactive"
+        status_class = "text-warning"
+    
+    # Return updates
+    return {
+        "model-training-status": html.Span(status, className=status_class),
+        "training-progress-bar": progress * 100,
+        "training-progress-text": progress_str,
+        "training-loss-value": loss_str,
+        "training-reward-value": reward_str,
+        "training-state-quality": state_quality_str
+    }