Update App.py

App.py

@@ -1,158 +1,167 @@
-import gradio as gr
-import json
-import pandas as pd
-from Engine import Engine
-
-def run_study(mode, benchmark_func, optimizers, dim, dataset, epochs, batch_size, lr, use_sa, sa_temp, sa_cooling_rate):
-    if not optimizers:
-        raise gr.Error("Please select at least one optimizer.")
-    if mode == "Benchmark Optimization" and not benchmark_func:
-        raise gr.Error("Please select a benchmark function.")
-    if mode == "ML Task Training" and not dataset:
-        raise gr.Error("Please select a dataset.")
-
-    config = {
-        'mode': 'benchmark' if mode == 'Benchmark Optimization' else 'ml_task',
-        'benchmark_func': benchmark_func,
-        'optimizers': optimizers,
-        'dim': int(dim),
-        'dataset': dataset,
-        'epochs': int(epochs),
-        'batch_size': int(batch_size),
-        'lr': float(lr),
-        'use_sa': use_sa if 'AzureSky' in optimizers else None,
-        'sa_temp': float(sa_temp) if 'AzureSky' in optimizers and use_sa else None,
-        'sa_cooling_rate': float(sa_cooling_rate) if 'AzureSky' in optimizers and use_sa else None,
-        'max_iter': 100
-    }
-    runner = Engine()
-    results = runner.run(config)
-
-    if config['mode'] == 'benchmark':
-        metrics_df = pd.DataFrame(results['metrics'], index=config['optimizers'])
-        return results['plot'], None, metrics_df, results['metrics'], json.dumps(results, indent=2), "Study completed successfully."
-    else:
-        metrics_df = pd.DataFrame(results['metrics'], index=config['optimizers'])
-        return results['plot_acc'], results['plot_loss'], metrics_df, results['metrics'], json.dumps(results, indent=2), "Study completed successfully."
-
-def export_results(results_json):
-    return results_json, "results.json"
-
-def toggle_azure_settings(optimizers):
-    return gr.update(visible='AzureSky' in optimizers)
-
-with gr.Blocks(theme=gr.themes.Soft(), title="Nexa R&D Studio", css="""
-    .gr-button { margin-top: 10px; }
-    .gr-box { border-radius: 8px; }
-    .status-message { color: green; font-weight: bold; }
-""") as app:
-    gr.Markdown("""
-        # Nexa R&D Studio
-        A visual research tool for comparing and evaluating optimizers on benchmark functions and ML tasks.
-        Select a mode, configure your study, and analyze results with interactive plots and metrics.
-    """)
-
-    with gr.Tabs() as tabs:
-        with gr.TabItem("Study Configuration"):
-            mode = gr.Radio(
-                ['Benchmark Optimization', 'ML Task Training'],
-                label='Study Mode',
-                value='Benchmark Optimization',
-                info='Choose between optimizing benchmark functions or training on ML datasets.'
-            )
-
-            with gr.Row():
-                with gr.Column():
-                    optimizers = gr.CheckboxGroup(
-                        ['AzureSky', 'Adam', 'SGD', 'AdamW', 'RMSprop'],
-                        label='Optimizers',
-                        info='Select optimizers to compare. AzureSky includes a Simulated Annealing option.'
-                    )
-                    with gr.Accordion("AzureSky Ablation Settings", open=False, visible=False) as azure_settings:
-                        use_sa = gr.Checkbox(
-                            label='Enable Simulated Annealing (AzureSky)',
-                            value=True,
-                            info='Toggle Simulated Annealing for AzureSky optimizer.'
-                        )
-                        sa_temp = gr.Number(
-                            label='Initial SA Temperature',
-                            value=1.0,
-                            minimum=0.1,
-                            info='Controls exploration in Simulated Annealing (higher = more exploration).'
-                        )
-                        sa_cooling_rate = gr.Number(
-                            label='SA Cooling Rate',
-                            value=0.95,
-                            minimum=0.1,
-                            maximum=0.99,
-                            info='Rate at which SA temperature decreases (closer to 1 = slower cooling).'
-                        )
-
-                with gr.Column():
-                    with gr.Group(visible=True) as benchmark_tab:
-                        benchmark_func = gr.Dropdown(
-                            ['Himmelblau', 'Ackley', 'Adjiman', 'Brent'],
-                            label='Benchmark Function',
-                            info='Select a mathematical function to optimize.'
-                        )
-                        dim = gr.Number(
-                            label='Dimensionality',
-                            value=2,
-                            minimum=2,
-                            info='Number of dimensions for the benchmark function.'
-                        )
-                    with gr.Group(visible=False) as ml_task_tab:
-                        dataset = gr.Dropdown(
-                            ['MNIST', 'CIFAR-10'],
-                            label='Dataset',
-                            info='Select a dataset for ML training.'
-                        )
-                        epochs = gr.Number(
-                            label='Epochs',
-                            value=10,
-                            minimum=1,
-                            info='Number of training epochs.'
-                        )
-                        batch_size = gr.Number(
-                            label='Batch Size',
-                            value=32,
-                            minimum=1,
-                            info='Number of samples per training batch.'
-                        )
-                        lr = gr.Number(
-                            label='Learning Rate',
-                            value=0.001,
-                            minimum=0,
-                            info='Learning rate for optimizers.'
-                        )
-
-            run_button = gr.Button('Run Study', variant='primary')
-
-        with gr.TabItem("Results"):
-            status_message = gr.Markdown("Configure and run a study to view results.", elem_classes=["status-message"])
-            with gr.Row():
-                plot1 = gr.Plot(label='Main Plot (Benchmark or Accuracy)')
-                plot2 = gr.Plot(label='Loss Plot (ML Mode)')
-            metrics_df = gr.Dataframe(label='Metrics Table', headers=['Optimizer'] + [
-                'distance', 'final_loss', 'convergence_rate',
-                'final_train_acc', 'final_val_acc', 'generalization_gap',
-                'final_train_loss', 'final_val_loss', 'best_epoch'
-            ])
-            metrics_json = gr.JSON(label='Detailed Metrics')
-            export_data = gr.State()
-            export_button = gr.Button('Export Results as JSON')
-            export_file = gr.File(label='Download Results')
-
-    def toggle_tabs(mode):
-        return gr.update(visible=mode == 'Benchmark Optimization'), gr.update(visible=mode == 'ML Task Training')
-
-    mode.change(toggle_tabs, inputs=mode, outputs=[benchmark_tab, ml_task_tab])
-    optimizers.change(toggle_azure_settings, inputs=optimizers, outputs=azure_settings)
-    run_button.click(
-        run_study,
-        inputs=[mode, benchmark_func, optimizers, dim, dataset, epochs, batch_size, lr, use_sa, sa_temp, sa_cooling_rate],
-        outputs=[plot1, plot2, metrics_df, metrics_json, export_data, status_message]
-    )
-    export_button.click(export_results, inputs=[export_data], outputs=[export_file, gr.File()])
-
-app.launch()
+import gradio as gr
+import json
+import pandas as pd
+import os
+from Engine import Engine
+
+def run_study(mode, benchmark_func, optimizers, dim, dataset, epochs, batch_size, lr, use_sa, sa_temp, sa_cooling_rate):
+    # Ensure optimizers is a list
+    if not isinstance(optimizers, list):
+        optimizers = [optimizers] if optimizers else []
+    
+    if not optimizers:
+        raise gr.Error("Please select at least one optimizer.")
+    if mode == "Benchmark Optimization" and not benchmark_func:
+        raise gr.Error("Please select a benchmark function.")
+    if mode == "ML Task Training" and not dataset:
+        raise gr.Error("Please select a dataset.")
+
+    config = {
+        'mode': 'benchmark' if mode == 'Benchmark Optimization' else 'ml_task',
+        'benchmark_func': benchmark_func,
+        'optimizers': optimizers,
+        'dim': int(dim),
+        'dataset': dataset,
+        'epochs': int(epochs) if epochs else 10,
+        'batch_size': int(batch_size) if batch_size else 32,
+        'lr': float(lr) if lr else 0.001,
+        'use_sa': use_sa if 'AzureSky' in optimizers else None,
+        'sa_temp': float(sa_temp) if 'AzureSky' in optimizers and use_sa else None,
+        'sa_cooling_rate': float(sa_cooling_rate) if 'AzureSky' in optimizers and use_sa else None,
+        'max_iter': 100
+    }
+    runner = Engine()
+    results = runner.run(config)
+
+    if config['mode'] == 'benchmark':
+        metrics_df = pd.DataFrame(results['metrics'], index=config['optimizers'])
+        return results['plot'], None, metrics_df, results['metrics'], json.dumps(results, indent=2), "Study completed successfully."
+    else:
+        metrics_df = pd.DataFrame(results['metrics'], index=config['optimizers'])
+        return results['plot_acc'], results['plot_loss'], metrics_df, results['metrics'], json.dumps(results, indent=2), "Study completed successfully."
+
+def export_results(results_json):
+    return results_json, "results.json"
+
+def toggle_azure_settings(optimizers):
+    # Handle case where optimizers is a single value or None
+    optimizers = [optimizers] if isinstance(optimizers, str) else optimizers or []
+    return gr.update(visible='AzureSky' in optimizers)
+
+with gr.Blocks(theme=gr.themes.Soft(), title="Nexa R&D Studio", css="""
+    .gr-button { margin-top: 10px; }
+    .gr-box { border-radius: 8px; }
+    .status-message { color: green; font-weight: bold; }
+""") as app:
+    gr.Markdown("""
+        # Nexa R&D Studio
+        A visual research tool for comparing and evaluating optimizers on benchmark functions and ML tasks.
+        Select a mode, configure your study, and analyze results with interactive plots and metrics.
+    """)
+
+    with gr.Tabs() as tabs:
+        with gr.TabItem("Study Configuration"):
+            mode = gr.Radio(
+                ['Benchmark Optimization', 'ML Task Training'],
+                label='Study Mode',
+                value='Benchmark Optimization',
+                info='Choose between optimizing benchmark functions or training on ML datasets.'
+            )
+
+            with gr.Row():
+                with gr.Column():
+                    optimizers = gr.CheckboxGroup(
+                        ['AzureSky', 'Adam', 'SGD', 'AdamW', 'RMSprop'],
+                        label='Optimizers',
+                        info='Select optimizers to compare. AzureSky includes a Simulated Annealing option.'
+                    )
+                    with gr.Accordion("AzureSky Ablation Settings", open=False, visible=False) as azure_settings:
+                        use_sa = gr.Checkbox(
+                            label='Enable Simulated Annealing (AzureSky)',
+                            value=True,
+                            info='Toggle Simulated Annealing for AzureSky optimizer.'
+                        )
+                        sa_temp = gr.Number(
+                            label='Initial SA Temperature',
+                            value=1.0,
+                            minimum=0.1,
+                            info='Controls exploration in Simulated Annealing (higher = more exploration).'
+                        )
+                        sa_cooling_rate = gr.Number(
+                            label='SA Cooling Rate',
+                            value=0.95,
+                            minimum=0.1,
+                            maximum=0.99,
+                            info='Rate at which SA temperature decreases (closer to 1 = slower cooling).'
+                        )
+
+                with gr.Column():
+                    with gr.Group(visible=True) as benchmark_tab:
+                        benchmark_func = gr.Dropdown(
+                            ['Himmelblau', 'Ackley', 'Adjiman', 'Brent'],
+                            label='Benchmark Function',
+                            info='Select a mathematical function to optimize.'
+                        )
+                        dim = gr.Number(
+                            label='Dimensionality',
+                            value=2,
+                            minimum=2,
+                            info='Number of dimensions for the benchmark function.'
+                        )
+                    with gr.Group(visible=False) as ml_task_tab:
+                        dataset = gr.Dropdown(
+                            ['MNIST', 'CIFAR-10'],
+                            label='Dataset',
+                            info='Select a dataset for ML training.'
+                        )
+                        epochs = gr.Number(
+                            label='Epochs',
+                            value=10,
+                            minimum=1,
+                            info='Number of training epochs.'
+                        )
+                        batch_size = gr.Number(
+                            label='Batch Size',
+                            value=32,
+                            minimum=1,
+                            info='Number of samples per training batch.'
+                        )
+                        lr = gr.Number(
+                            label='Learning Rate',
+                            value=0.001,
+                            minimum=0,
+                            info='Learning rate for optimizers.'
+                        )
+
+            run_button = gr.Button('Run Study', variant='primary')
+
+        with gr.TabItem("Results"):
+            status_message = gr.Markdown("Configure and run a study to view results.", elem_classes=["status-message"])
+            with gr.Row():
+                plot1 = gr.Plot(label='Main Plot (Benchmark or Accuracy)')
+                plot2 = gr.Plot(label='Loss Plot (ML Mode)')
+            metrics_df = gr.Dataframe(label='Metrics Table', headers=['Optimizer'] + [
+                'distance', 'final_loss', 'convergence_rate',
+                'final_train_acc', 'final_val_acc', 'generalization_gap',
+                'final_train_loss', 'final_val_loss', 'best_epoch'
+            ])
+            metrics_json = gr.JSON(label='Detailed Metrics')
+            export_data = gr.State()
+            export_button = gr.Button('Export Results as JSON')
+            export_file = gr.File(label='Download Results')
+
+    def toggle_tabs(mode):
+        return gr.update(visible=mode == 'Benchmark Optimization'), gr.update(visible=mode == 'ML Task Training')
+
+    mode.change(toggle_tabs, inputs=mode, outputs=[benchmark_tab, ml_task_tab])
+    optimizers.change(toggle_azure_settings, inputs=optimizers, outputs=azure_settings)
+    run_button.click(
+        run_study,
+        inputs=[mode, benchmark_func, optimizers, dim, dataset, epochs, batch_size, lr, use_sa, sa_temp, sa_cooling_rate],
+        outputs=[plot1, plot2, metrics_df, metrics_json, export_data, status_message]
+    )
+    export_button.click(export_results, inputs=[export_data], outputs=[export_file, gr.File()])
+
+# Configure launch based on environment
+is_huggingface = os.getenv("HF_SPACE") is not None
+app.launch(share=is_huggingface, server_name="0.0.0.0" if is_huggingface else None)