Update App.py

App.py

@@ -1,13 +1,11 @@
 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 []
+    optimizers = [optimizers] if isinstance(optimizers, str) else optimizers or []
     
     if not optimizers:
         raise gr.Error("Please select at least one optimizer.")
@@ -17,10 +15,10 @@ def run_study(mode, benchmark_func, optimizers, dim, dataset, epochs, batch_size
         raise gr.Error("Please select a dataset.")
 
     config = {
-        'mode': 'benchmark' if mode == 'Benchmark Optimization' else 'ml_task',
+        'mode': 'benchmark' if mode == "Benchmark Optimization" else 'ml_task',
         'benchmark_func': benchmark_func,
         'optimizers': optimizers,
-        'dim': int(dim),
+        'dim': int(dim) if dim else 2,
         'dataset': dataset,
         'epochs': int(epochs) if epochs else 10,
         'batch_size': int(batch_size) if batch_size else 32,
@@ -35,16 +33,17 @@ def run_study(mode, benchmark_func, optimizers, dim, dataset, epochs, batch_size
 
     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."
+        return results['plot'], None, metrics_df, 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."
+        return results['plot_acc'], results['plot_loss'], metrics_df, json.dumps(results, indent=2), "Study completed successfully."
 
 def export_results(results_json):
-    return results_json, "results.json"
+    with open("results.json", "w") as f:
+        f.write(results_json)
+    return "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)
 
@@ -59,7 +58,7 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Nexa R&D Studio", css="""
         Select a mode, configure your study, and analyze results with interactive plots and metrics.
     """)
 
-    with gr.Tabs() as tabs:
+    with gr.Tabs():
         with gr.TabItem("Study Configuration"):
             mode = gr.Radio(
                 ['Benchmark Optimization', 'ML Task Training'],
@@ -140,28 +139,31 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Nexa R&D Studio", css="""
             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_df = gr.Dataframe(label='Metrics Table')
             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)
+    mode.change(
+        fn=toggle_tabs,
+        inputs=mode,
+        outputs=[benchmark_tab, ml_task_tab]
+    )
+    optimizers.change(
+        fn=toggle_azure_settings,
+        inputs=optimizers,
+        outputs=azure_settings
+    )
     run_button.click(
-        run_study,
+        fn=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]
+        outputs=[plot1, plot2, metrics_df, metrics_json, status_message]
+    )
+    export_button.click(
+        fn=export_results,
+        inputs=metrics_json,
+        outputs=export_file
     )
-    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=True, server_name="0.0.0.0" if is_huggingface else None)
+# Launch without share parameter for Hugging Face Spaces
+app.launch()