update ui

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 *.sqlite3 filter=lfs diff=lfs merge=lfs -text
+chroma.* filter=lfs diff=lfs merge=lfs -text

app.py

@@ -10,11 +10,22 @@ db = client.get_collection(name="banks")
 
 
 
-def greet(name):
+def greet(issue):
   global db
-  docs = db.query(query_texts=name, n_results=5)
+  docs = db.query(query_texts=issue, n_results=5)
   return docs
 
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text", title="Leads Generation", description="service, sales, point of failure, product, trend")
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text", title="Leads Generation", description="""Using Sentence Embedding to inject Public ML Banks Text Dataset @ https://github.com/kevinwkc/analytics/blob/master/ai/vectorDB.py""",
+ article="""
+ put in the issue regarding service, sales, point of failure, product, trend to find out what customer talking about
+ 
+ some ideas
+ ----------
+ having bad client experience
+ having credit card problem
+ late payment fee
+ credit score dropping
+ """)
 iface.launch()