update descriptions

app.py

@@ -534,18 +534,20 @@ def search_hub(query: str, min_score=None, min_model_card_length=None):
 
 with gr.Blocks() as demo:
     with gr.Tab("Search"):
-        gr.Markdown("#  🤗 Hub model search with metadata quality filters")
+        gr.HTML(
+            """      
+<h1 style="text-align: center;">MetaRefine </h1>
+<p style="text-align: center;">&#x2728; <em>Hub model search results by metadata quality.</em> &#x2728;</p>
+"""
+        )
         gr.Markdown(
-            """This search tool relies on the full-text search API.
-                Your search is passed to this API and the returned models are assessed for metadata quality.
-                If you don't specify any minimum requirements you will get back your results with metadata quality info
-                for each result. The results are ordered by:
-                
-                - Metadata quality i.e. a model with 80% metadata quality will rank higher than one with 75%
-                - Original search order i.e. if two models have the same metadata quality the one that appeared first in the original search will rank higher.
-
-                If there is interest in this app I will expose more options for filtering and sorting results.
-                    """
+            """This app enables you to perform full-text searches on the Hugging Face Hub for machine learning models. 
+            You can search by keyword or phrase and filter results by metadata quality. 
+            Optionally, you can set a minimum model card length or metadata quality score to refine your results. 
+            Models are ranked based on metadata quality, with higher scores receiving priority. 
+            In case of equal scores, the original search order determines the ranking. 
+            More filtering and sorting options may be added based on user interest!
+            """
         )
         with gr.Row():
             with gr.Column():
@@ -559,9 +561,12 @@ with gr.Blocks() as demo:
                     mim_model_card_length = gr.Number(
                         100, label="Minimum model card length"
                     )
-                    min_metadata_score = gr.Slider(0, 100, 50, label="Minimum metadata score")
-        filter_results = gr.Markdown("Filter results vs original search")
-        results_markdown = gr.Markdown("Search results")
+                    min_metadata_score = gr.Slider(
+                        0, 100, 50, label="Minimum metadata score"
+                    )
+        gr.Markdown("## Search results")
+        filter_results = gr.Markdown()
+        results_markdown = gr.Markdown()
         button.click(
             search_hub,
             [query, min_metadata_score, mim_model_card_length],