fine tune LLM

app.py

@@ -30,9 +30,14 @@ FINE TUNE LLM LIKE SCORE
 '''
 from fastai.text.all import *
 
-import platform
+
 import pathlib
 p=pathlib.Path('./banks_txt_like.pkl').resolve()
+
+'''
+NotImplementedError: cannot instantiate ‘WindowsPath’ on your system
+'''
+import platform
 plt = platform.system()
 if plt == 'Windows':
   pathlib.PosixPath = pathlib.WindowsPath
@@ -63,7 +68,10 @@ with gr.Blocks() as demo:
 https://www.kaggle.com/datasets/trainingdatapro/20000-customers-reviews-on-banks/?select=Banks.csv
   """)
   with gr.Tab("Semantic Similarity Document Search (SSDS)"):
-    in_similar = gr.Textbox(placeholder="having credit card problem")
+    in_similar = gr.Textbox(placeholder="having credit card problem",
+                            label="Issue",
+                            info="issue you want to explore about"
+                            )
     out_similar = gr.JSON()
 
     btn_similar = gr.Button("Find Similar Verbatim")
@@ -108,11 +116,20 @@ Using Sentence Embedding to inject Public ML Banks Text Dataset @ https://github
       """)
 
   with gr.Tab("Fine Tune LLM"):
-    in_like = gr.Textbox(placeholder="having credit card problem")
-    out_like = gr.Textbox(placeholder="like score")
+    in_like = gr.Textbox(placeholder="having credit card problem"   ,                                             label="Issue",
+                            info="issue you want to explore about")
+    out_like = gr.Textbox(placeholder="like score in range [2 to 248] from fine tuning data",
+                          info="like score")
 
     btn_like = gr.Button("Find Like Score")
     btn_like.click(fn=like, inputs=in_like, outputs=out_like)
+    gr.Markdown("""
+As a Data Scientist with a decades of financial industry experience, I recognize the paramount importance of staying closely tuned to our customer's needs and opinions. In this use case statement, we outline how fine-tuning a Language Model (LLM) on a custom dataset can provide valuable insights into customer sentiment across crucial areas such as service, sales, point of failure, product, and emerging trends.
+
+Objective:
+---------
+Our aim is to extract meaningful insights from customer interactions to improve our services, products, and overall customer experience. This analysis will help us understand what our customers are discussing and how they feel about different aspects of our business.
+    """)
 
 demo.launch()