minor layout adjustments

app.py

@@ -63,29 +63,29 @@ with app:
             )
             gr.Markdown(
                 """
-                #### App usage:
+                #### App usage
                 The model is intented to be used for **semantic search**: It encodes the search query (entered in the textbox on the right) in a dense vector space and finds semantic neighbours, i.e., token which frequently occur within similar contexts in the underlying training data.
                 The model allows for two use cases:
                 1. *Single Search:* The input query consists of a single word. When provided a bi-, tri-, or even fourgram, the quality of the model output depends on the presence of the query token in the model's vocabulary. N-grams should be concated by an underscore (e.g., "machine_learning" or "artifical_intelligence").
                 2. *Multi Search:* The input query may consist of several words or n-grams, seperated by comma, semi-colon or newline. It then computes the average vector over all inputs and performs semantic search based on the average input token.
 
-                ##### Examples
+                #### Examples
                 - transformation
                 - climate_change
                 - risk, political_risk, uncertainty
                 """
             )
         with gr.Column():
-            text_in = gr.Textbox(lines=1, placeholder="Insert search query")
+            text_in = gr.Textbox(lines=1, placeholder="Insert text", label="Search Query")
             with gr.Row():
                 n = gr.Slider(value=50, minimum=5, maximum=250, step=5, label="Number of Neighbours")
-                compute_bt = gr.Button("Compute")
+                compute_bt = gr.Button("Start\nSearch")
             df_out = gr.Dataframe(interactive=False)
-            f_out = gr.File(interactive=False)
+            f_out = gr.File(interactive=False, label="Download")
     gr.Markdown(
         """
         <div style='text-align: center;'>Call2Vec by X and Y</center></div>
-        <img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=simonschoe.call2vec&left_color=green&right_color=red" />'
+        <p class="aligncenter"><img 'id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=simonschoe.call2vec&left_color=green&right_color=blue" /></p>
         """
     )
     compute_bt.click(semantic_search, inputs=[text_in, n], outputs=[df_out, f_out, text_in])