Update app.py

app.py

@@ -430,37 +430,35 @@ with gr.Blocks(title="CNN Trainer and Tester") as demo:
         "Tab 1 trains a simple CNN. Tab 2 loads a saved model and tests it on uploaded images or random test samples."
     )
 
-    with gr.Tabs():
-        with gr.Tab("Train"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    dataset_name = gr.Dropdown(
-                        choices=["MNIST", "FashionMNIST"],
-                        value="MNIST",
-                        label="Dataset"
-                    )
-                    conv1_channels = gr.Slider(8, 64, value=16, step=8, label="Conv1 Channels")
-                    conv2_channels = gr.Slider(16, 128, value=32, step=16, label="Conv2 Channels")
-                    kernel_size = gr.Dropdown(choices=[3, 5], value=3, label="Kernel Size")
-                    dropout = gr.Slider(0.0, 0.7, value=0.2, step=0.05, label="Dropout")
-                    fc_dim = gr.Slider(32, 256, value=128, step=32, label="FC Hidden Dimension")
-                    learning_rate = gr.Number(value=0.001, label="Learning Rate")
-                    batch_size = gr.Dropdown(choices=[32, 64, 128, 256], value=64, label="Batch Size")
-                    epochs = gr.Slider(1, 10, value=3, step=1, label="Epochs")
-                    model_tag = gr.Textbox(label="Model Tag", placeholder="e.g. mnist_demo")
-                    train_btn = gr.Button("Start Training", variant="primary")
-
-                with gr.Column(scale=1):
-                    train_status = gr.Textbox(label="Training Status", lines=10)
-                    train_plot = gr.LinePlot(
-                        x="epoch",
-                        y="value",
-                        color="metric",
-                        title="Training Curves",
-                        y_title="Value",
-                        x_title="Epoch",
-
-                    )
+with gr.Tab("Train"):
+    with gr.Row():
+        with gr.Column(scale=1):
+            dataset_name = gr.Dropdown(
+                choices=["MNIST", "FashionMNIST"],
+                value="MNIST",
+                label="Dataset"
+            )
+            conv1_channels = gr.Slider(8, 64, value=16, step=8, label="Conv1 Channels")
+            conv2_channels = gr.Slider(16, 128, value=32, step=16, label="Conv2 Channels")
+            kernel_size = gr.Dropdown(choices=[3, 5], value=3, label="Kernel Size")
+            dropout = gr.Slider(0.0, 0.7, value=0.2, step=0.05, label="Dropout")
+            fc_dim = gr.Slider(32, 256, value=128, step=32, label="FC Hidden Dimension")
+            learning_rate = gr.Number(value=0.001, label="Learning Rate")
+            batch_size = gr.Dropdown(choices=[32, 64, 128, 256], value=64, label="Batch Size")
+            epochs = gr.Slider(1, 10, value=3, step=1, label="Epochs")
+            model_tag = gr.Textbox(label="Model Tag", placeholder="e.g. mnist_demo")
+            train_btn = gr.Button("Start Training", variant="primary")
+
+        with gr.Column(scale=1):
+            train_status = gr.Textbox(label="Training Status", lines=10)
+            train_plot = gr.LinePlot(
+                x="epoch",
+                y="value",
+                color="metric",
+                title="Training Curves",
+                y_title="Value",
+                x_title="Epoch",
+            )
 
         with gr.Tab("Test"):
             with gr.Row():