Merge branch 'main' of hf.co:spaces/elvis-hf/mlp_visualizer

README.md

@@ -5,8 +5,8 @@ colorFrom: yellow
 colorTo: gray
 sdk: gradio
 app_file: frontends/gradio/main.py
-sdk_version: 6.3.0
+sdk_version: 6.5.1
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

backend/src/manager.py

@@ -37,6 +37,7 @@ class OptimizerOptions:
 class Manager:
     def __init__(self) -> None:
         self._dataset: Dataset | None = None
+        self._true_dataset: Dataset | None = None
 
         self._architecture: str | None = None
         self._model: nn.Module | None = None
@@ -188,7 +189,7 @@ class Manager:
         else:
             test_dataset = self._true_dataset
 
-        if test_dataset is not None and self._model is not None:
+        if test_dataset.x and test_dataset.y and self._model is not None:
             test_predictions = generate_test_predictions(
                 test_dataset,
                 self._model,

frontends/gradio/main.py

@@ -48,7 +48,7 @@ def handle_set_dataset(
     has_header: bool,
     xcol: int,
     ycol: int,
-) -> PlotData:
+) -> Figure:
     options = {
         "dataset_type": dataset_type,
         "function": function,
@@ -62,17 +62,16 @@ def handle_set_dataset(
         "xcol": xcol,
         "ycol": ycol,
     }
-    plot_data = manager.handle_set_dataset(options)
-
-    return plot_data
+    manager.handle_set_dataset(options)
+    return generate_plot(manager.get_plot_data())
 
 
 def handle_set_architecture(
     manager: Manager,
     architecture_str: str,
-) -> PlotData:
-    plot_data = manager.handle_set_architecture(architecture_str)
-    return plot_data
+) -> Figure:
+    manager.handle_set_architecture(architecture_str)
+    return generate_plot(manager.get_plot_data())
 
 
 def handle_set_optimizer(
@@ -84,7 +83,7 @@ def handle_set_optimizer(
     momentum: float,
     weight_decay: float,
     batch_size: int,
-) -> PlotData:
+) -> Figure:
     options = {
         "optimizer_type": optimizer,
         "learning_rate": learning_rate,
@@ -94,23 +93,24 @@ def handle_set_optimizer(
         "weight_decay": weight_decay,
         "batch_size": batch_size,
     }
-    plot_data = manager.handle_set_optimizer(options)
-    return plot_data
+    manager.handle_reset_model()
+    manager.handle_set_optimizer(options)
+    return generate_plot(manager.get_plot_data())
 
 
 def handle_train_step(
     manager: Manager,
     step_increment: int,
-) -> PlotData:
-    plot_data = manager.handle_train_step(step_increment)
-    return plot_data
+) -> Figure:
+    manager.handle_train_step(step_increment)
+    return generate_plot(manager.get_plot_data())
     
 
 def handle_reset_model(
     manager: Manager,
-) -> PlotData:
-    plot_data = manager.handle_reset_model()
-    return plot_data
+) -> Figure:
+    manager.handle_reset_model()
+    return generate_plot(manager.get_plot_data())
 
 
 def generate_plot(data: PlotData) -> Figure:
@@ -224,43 +224,40 @@ def launch():
         gr.HTML("<div style='text-align:left; font-size:40px; font-weight: bold;'>MLP visualizer</div>")
 
         manager = gr.State(Manager())
-        handle_set_dataset(
-            manager.value,
-            default_dataset_type,
-            default_function,
-            default_data_xmin,
-            default_data_xmax,
-            default_sigma,
-            default_nsample,
-            default_sample_method,
-            default_csv_file,
-            default_has_header,
-            default_xcol,
-            default_ycol,
+        manager.value.handle_set_dataset(
+            {
+                "dataset_type": default_dataset_type,
+                "function": default_function,
+                "xmin": default_data_xmin,
+                "xmax": default_data_xmax,
+                "sigma": default_sigma,
+                "nsample": default_nsample,
+                "sample_method": default_sample_method,
+                "csv_path": default_csv_file,
+                "has_header": default_has_header,
+                "xcol": default_xcol,
+                "ycol": default_ycol,
+            }
         )
-        handle_set_architecture(
-            manager.value,
+        manager.value.handle_set_architecture(
             ARCHITECTURE_PRESETS[default_architecture_preset],
         )
-        handle_set_optimizer(
-            manager.value,
-            default_optimizer,
-            default_learning_rate,
-            default_beta1,
-            default_beta2,
-            default_momentum,
-            default_weight_decay,
-            default_batch_size,
-        )
-
-        plot_data = gr.State(
-            manager.value.get_plot_data()
+        manager.value.handle_set_optimizer(
+            {
+                "optimizer_type": default_optimizer,
+                "learning_rate": default_learning_rate,
+                "beta1": default_beta1,
+                "beta2": default_beta2,
+                "momentum": default_momentum,
+                "weight_decay": default_weight_decay,
+                "batch_size": default_batch_size,
+            }
         )
         
         with gr.Row():
             with gr.Column(scale=2):
                 plot = gr.Plot(
-                    value=generate_plot(plot_data.value),
+                    value=generate_plot(manager.value.get_plot_data()),
                 )
 
             with gr.Tab("Data"):
@@ -375,10 +372,6 @@ def launch():
                         xcol,
                         ycol,
                     ],
-                    outputs=[plot_data],
-                ).then(
-                    fn=generate_plot,
-                    inputs=[plot_data],
                     outputs=[plot],
                 )
 
@@ -418,10 +411,6 @@ def launch():
                         manager,
                         architecture,
                     ],
-                    outputs=[plot_data],
-                ).then(
-                    fn=generate_plot,
-                    inputs=[plot_data],
                     outputs=[plot],
                 )
 
@@ -486,10 +475,6 @@ def launch():
 
                 update_optimizer_button = gr.Button("Update Optimizer")
                 update_optimizer_button.click(
-                    fn=manager.value.handle_reset_model,
-                    inputs=[],
-                    outputs=[plot_data],
-                ).then(
                     fn=handle_set_optimizer,
                     inputs=[
                         manager, 
@@ -501,10 +486,6 @@ def launch():
                         weight_decay,
                         batch_size,
                     ],
-                    outputs=[plot_data],
-                ).then(
-                    fn=generate_plot,
-                    inputs=[plot_data],
                     outputs=[plot],
                 )
 
@@ -518,10 +499,6 @@ def launch():
                 train_button.click(
                     fn=handle_train_step,
                     inputs=[manager, step_increment],
-                    outputs=[plot_data],
-                ).then(
-                    fn=generate_plot,
-                    inputs=[plot_data],
                     outputs=[plot],
                 )
 
@@ -529,10 +506,6 @@ def launch():
                 reset_button.click(
                     fn=handle_reset_model,
                     inputs=[manager],
-                    outputs=[plot_data],
-                ).then(
-                    fn=generate_plot,
-                    inputs=[plot_data],
                     outputs=[plot],
                 )