use case
Browse files
app.py
CHANGED
|
@@ -10,33 +10,42 @@ db = client.get_collection(name="banks")
|
|
| 10 |
|
| 11 |
|
| 12 |
|
| 13 |
-
def
|
| 14 |
global db
|
| 15 |
docs = db.query(query_texts=issue, n_results=5)
|
| 16 |
return docs
|
| 17 |
|
| 18 |
|
| 19 |
|
| 20 |
-
iface = gr.Interface(fn=
|
| 21 |
Data Scientist: Kevin Wong
|
|
|
|
|
|
|
|
|
|
|
|
|
| 22 |
Using Sentence Embedding to inject Public ML Banks Text Dataset @ https://github.com/kevinwkc/analytics/blob/master/ai/vectorDB.py""",
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
Marketing Leads
|
| 34 |
-
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
Sentiments
|
| 38 |
-
|
| 39 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 40 |
|
| 41 |
""")
|
| 42 |
iface.launch()
|
|
|
|
| 10 |
|
| 11 |
|
| 12 |
|
| 13 |
+
def similar(issue):
|
| 14 |
global db
|
| 15 |
docs = db.query(query_texts=issue, n_results=5)
|
| 16 |
return docs
|
| 17 |
|
| 18 |
|
| 19 |
|
| 20 |
+
iface = gr.Interface(fn=similar, inputs="text", outputs="text", title="NLP Leads Generation", description="""
|
| 21 |
Data Scientist: Kevin Wong
|
| 22 |
+
============
|
| 23 |
+
open source ml bank dataset
|
| 24 |
+
https://www.kaggle.com/datasets/trainingdatapro/20000-customers-reviews-on-banks/?select=Banks.csv
|
| 25 |
+
|
| 26 |
Using Sentence Embedding to inject Public ML Banks Text Dataset @ https://github.com/kevinwkc/analytics/blob/master/ai/vectorDB.py""",
|
| 27 |
+
article="""
|
| 28 |
+
|
| 29 |
+
Description:
|
| 30 |
+
=======
|
| 31 |
+
In today's dynamic financial landscape, the Semantic Similarity Document Search (SSDS) capability is a practical innovation to improve client experience, marketing leads, and sentiment analysis. As a Data Scientist with a decades in the financial industry, I see the value of SSDS in action.
|
| 32 |
+
|
| 33 |
+
Client Experience:
|
| 34 |
+
------
|
| 35 |
+
When a client faces a bad experience, SSDS helps us swiftly locate relevant documents to understand and address their concerns, be it credit card issues, late payment fees, or credit score drops.
|
| 36 |
+
|
| 37 |
+
Marketing Leads:
|
| 38 |
+
------
|
| 39 |
+
To enhance marketing strategies, SSDS identifies market trends and consumer preferences, such as the demand for low-interest credit cards. It's a treasure trove for refining our product offerings.
|
| 40 |
+
|
| 41 |
+
Sentiments:
|
| 42 |
+
------
|
| 43 |
+
SSDS tracks customer sentiment, empowering us to swiftly respond to upset customers. It ensures we address their issues promptly, enhancing trust and loyalty.
|
| 44 |
+
With no need for jargon, SSDS delivers tangible value to our fintech operations. It's about staying agile, informed, and customer-centric in a rapidly changing financial world.
|
| 45 |
+
|
| 46 |
+
Future Improvement
|
| 47 |
+
============
|
| 48 |
+
tuning the distance for use case
|
| 49 |
|
| 50 |
""")
|
| 51 |
iface.launch()
|