Update main.py

main.py

@@ -20,8 +20,7 @@ vectors = numpy.stack(data['embedding'].to_list(), axis=0)
 
 index.add(vectors)
 
-def search(query):
-    k=5
+def search(query, k):
     query = numpy.expand_dims(model.encode(query), axis=0)
     _, I = top_five = index.search(query, k)
     top_five = data.loc[I[0]]
@@ -30,16 +29,18 @@ def search(query):
     for i in range(k):
         search_results += str(i+1) + ". "
         search_results += '"' + top_five["bibtex"].values[i]["title"] + '" '
-        search_results += "_" + top_five["bibtex"].values[i]["citation"] + "_"
+        search_results += top_five["bibtex"].values[i]["citation"]
         if top_five["pub_url"].values[i] is not None:
-            search_results += " [Full Paper](" + top_five["pub_url"].values[i] + ")"
+            search_results += " [Paper](" + top_five["pub_url"].values[i] + ")"
         search_results += "\n"
     return search_results
 
 with gradio.Blocks() as demo:
-    query = gradio.Textbox(placeholder="Enter search terms...")
-    btn = gradio.Button("Search")
-    results = gradio.Markdown()
-    btn.click(fn=search, inputs=[query], outputs=results)
+    with gradio.Group():
+        query = gradio.Textbox(placeholder="Enter search terms...", show_label=False, lines=1, max_lines=1)
+        with gradio.Accordion("Settings", open=False):
+            k = gradio.Number(5.0, label="Number of results", precision=0)
+        results = gradio.Markdown()
+        query.change(fn=search, inputs=[query, k], outputs=results)
 
 demo.launch(debug=True)