try different layout

app.py

@@ -149,7 +149,6 @@ def get_plot(model_name, plot_eager, generate_type):
     if plot_eager == "No":
         df = df[df["framework"] != "TF (Eager Execition)"]
 
-    plt.figure(figsize=(200 / FIG_DPI, 200 / FIG_DPI))
     g = sns.catplot(
         data=df,
         kind="bar",
@@ -205,34 +204,35 @@ with demo:
                     interactive=True
                 )
             plot_fn = functools.partial(get_plot, generate_type="Greedy Search")
-            plot = gr.Image(value=plot_fn("T5 Small", "Yes"), shape=[1, 1])  # Show plot when the gradio app is initialized
+            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"):
-            gr.Markdown(
-                """
-                ### Sample benchmark parameters
-                - `max_new_tokens = 128`;
-                - `temperature = 2.0`;
-                - `top_k = 50`;
-                - `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="Sample")
-            plot = gr.Image(value=plot_fn("T5 Small", "Yes"))  # Show plot when the gradio app is initialized
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown(
+                        """
+                        ### Sample benchmark parameters
+                        - `max_new_tokens = 128`;
+                        - `temperature = 2.0`;
+                        - `top_k = 50`;
+                        - `pad_to_multiple_of = 64` for Tensorflow XLA models. Others do not pad (input prompts between 2 and 33 tokens).
+                        """
+                    )
+                    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 = 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"):