try different layout

app.py

@@ -176,41 +176,56 @@ with demo:
     gr.Markdown(
         """
         # TensorFlow XLA Text Generation Benchmark
-        Pick a tab for the type of generation (or other information), and then select a model from the dropdown menu.
-        You can also omit results from TensorFlow Eager Execution, if you wish to better compare the performance of
+        Instructions:
+        1. Pick a tab for the type of generation (or other information);
+        2. Select a model from the dropdown menu;
+        3. Optionally omit results from TensorFlow Eager Execution, if you wish to better compare the performance of
         PyTorch to TensorFlow with XLA.
         """
     )
     with gr.Tabs():
         with gr.TabItem("Greedy Search"):
-            gr.Markdown(
-                """
-                ### Greedy Search benchmark parameters
-                - `max_new_tokens = 64`;
-                - `pad_to_multiple_of = 64` for Tensorflow XLA models. Others do not pad (input prompts between 2 and 33 tokens).
-                """
-            )
-            with gr.Row():
-                model_selector = gr.Dropdown(
-                    choices=["DistilGPT2", "GPT2", "OPT-1.3B", "GPTJ-6B", "T5 Small", "T5 Base", "T5 Large", "T5 3B"],
-                    value="T5 Small",
-                    label="Model",
-                    interactive=True,
-                )
-                eager_enabler = gr.Radio(
-                    ["Yes", "No"],
-                    value="Yes",
-                    label="Plot TF Eager Execution?",
-                    interactive=True
-                )
             plot_fn = functools.partial(get_plot, generate_type="Greedy Search")
-            plot = gr.Image(value=plot_fn("T5 Small", "Yes"))  # Show plot when the gradio app is initialized
+            with gr.Row():
+                with gr.Column():
+                    model_selector = gr.Dropdown(
+                        choices=["DistilGPT2", "GPT2", "OPT-1.3B", "GPTJ-6B", "T5 Small", "T5 Base", "T5 Large", "T5 3B"],
+                        value="T5 Small",
+                        label="Model",
+                        interactive=True,
+                    )
+                    eager_enabler = gr.Radio(
+                        ["Yes", "No"],
+                        value="Yes",
+                        label="Plot TF Eager Execution?",
+                        interactive=True
+                    )
+                    gr.Markdown(
+                        """
+                        ### Greedy Search benchmark parameters
+                        - `max_new_tokens = 64`;
+                        - `pad_to_multiple_of = 64` for Tensorflow XLA models. Others do not pad (input prompts between 2 and 33 tokens).
+                        """
+                    )
+                plot = gr.Image(value=plot_fn("T5 Small", "Yes"))  # Show plot when the gradio app is initialized
             model_selector.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot)
             eager_enabler.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot)
         with gr.TabItem("Sample"):
             plot_fn = functools.partial(get_plot, generate_type="Sample")
             with gr.Row():
                 with gr.Column():
+                    model_selector = gr.Dropdown(
+                        choices=["DistilGPT2", "GPT2", "OPT-1.3B", "GPTJ-6B", "T5 Small", "T5 Base", "T5 Large", "T5 3B"],
+                        value="T5 Small",
+                        label="Model",
+                        interactive=True,
+                    )
+                    eager_enabler = gr.Radio(
+                        ["Yes", "No"],
+                        value="Yes",
+                        label="Plot TF Eager Execution?",
+                        interactive=True
+                    )
                     gr.Markdown(
                         """
                         ### Sample benchmark parameters
@@ -220,6 +235,13 @@ with demo:
                         - `pad_to_multiple_of = 64` for Tensorflow XLA models. Others do not pad (input prompts between 2 and 33 tokens).
                         """
                     )
+                plot = gr.Image(value=plot_fn("T5 Small", "Yes"))  # Show plot when the gradio app is initialized
+            model_selector.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot)
+            eager_enabler.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot)
+        with gr.TabItem("Beam Search"):
+            plot_fn = functools.partial(get_plot, generate_type="Beam Search")
+            with gr.Row():
+                with gr.Column():
                     model_selector = gr.Dropdown(
                         choices=["DistilGPT2", "GPT2", "OPT-1.3B", "GPTJ-6B", "T5 Small", "T5 Base", "T5 Large", "T5 3B"],
                         value="T5 Small",
@@ -232,35 +254,17 @@ with demo:
                         label="Plot TF Eager Execution?",
                         interactive=True
                     )
+                    gr.Markdown(
+                        """
+                        ### Beam Search benchmark parameters
+                        - `max_new_tokens = 256`;
+                        - `num_beams = 16`;
+                        - `pad_to_multiple_of = 64` for Tensorflow XLA models. Others do not pad (input prompts between 2 and 33 tokens).
+                        """
+                    )
                 plot = gr.Image(value=plot_fn("T5 Small", "Yes"))  # Show plot when the gradio app is initialized
             model_selector.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot)
             eager_enabler.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot)
-        with gr.TabItem("Beam Search"):
-            gr.Markdown(
-                """
-                ### Beam Search benchmark parameters
-                - `max_new_tokens = 256`;
-                - `num_beams = 16`;
-                - `pad_to_multiple_of = 64` for Tensorflow XLA models. Others do not pad (input prompts between 2 and 33 tokens).
-                """
-            )
-            with gr.Row():
-                model_selector = gr.Dropdown(
-                    choices=["DistilGPT2", "GPT2", "OPT-1.3B", "GPTJ-6B", "T5 Small", "T5 Base", "T5 Large", "T5 3B"],
-                    value="T5 Small",
-                    label="Model",
-                    interactive=True,
-                )
-                eager_enabler = gr.Radio(
-                    ["Yes", "No"],
-                    value="Yes",
-                    label="Plot TF Eager Execution?",
-                    interactive=True
-                )
-            plot_fn = functools.partial(get_plot, generate_type="Beam Search")
-            plot = gr.Image(value=plot_fn("T5 Small", "Yes"))  # Show plot when the gradio app is initialized
-            model_selector.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot)
-            eager_enabler.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot)
         with gr.TabItem("Benchmark Information"):
             gr.Dataframe(
                 headers=["Parameter", "Value"],