use case

app.py

@@ -7,8 +7,13 @@ client = chromadb.PersistentClient(path="chroma.db")
 
 db = client.get_collection(name="banks")
 
-
-
+'''
+https://dash.elfsight.com
+'''
+counter="""
+<script src="https://static.elfsight.com/platform/platform.js" data-use-service-core defer></script>
+<div class="elfsight-app-5f3e8eb9-9103-490e-9999-e20aa4157dc7" data-elfsight-app-lazy></div>
+"""
 
 def similar(issue):
   global db
@@ -16,14 +21,23 @@ def similar(issue):
   return docs
 
 
-
-iface = gr.Interface(fn=similar, inputs="text", outputs="text", title="NLP Leads Generation", description="""
+'''
+https://www.gradio.app/docs/interface
+'''
+iface = gr.Interface(fn=similar, inputs="text", outputs="text",
+                     title="Enhancing Customer Engagement and Operational Efficiency with Semantic Similarity Document Search (SSDS)",
+                     examples=[["having bad client experience"],
+  ["having credit card problem"],
+  ["late payment fee"],
+  ["credit score dropping"]]
+                     description="""
   Data Scientist: Kevin Wong, objectdeveloper@gmail.com, 416-903-7937
   ============
   open source ml bank dataset
 https://www.kaggle.com/datasets/trainingdatapro/20000-customers-reviews-on-banks/?select=Banks.csv
 
   Using Sentence Embedding to inject Public ML Banks Text Dataset @ https://github.com/kevinwkc/analytics/blob/master/ai/vectorDB.py""",
+                       css=counter,
                      article="""
       
 Description:
@@ -59,5 +73,7 @@ Future Improvement
 ============
 tuning the distance for use case
  
+ <script src="https://static.elfsight.com/platform/platform.js" data-use-service-core defer></script>
+<div class="elfsight-app-5f3e8eb9-9103-490e-9999-e20aa4157dc7" data-elfsight-app-lazy></div>
  """)
 iface.launch()