Update pages/1_Classification.py

pages/1_Classification.py

@@ -5,6 +5,7 @@ import pandas as pd
 from transformers import pipeline
 #for the pre-processing of uploaded data
 import string 
+#adding the css presentation from the styles file in utils
 from utils.styles import load_css
 load_css()
 
@@ -16,7 +17,7 @@ def load_classifier():
         model="SuchithTissera/text_analytics_model",
         truncation=True
     )
-
+#adding stop word based on nlk library - look at limitations for the reason
 stop_words = {'most', "i've", 't', "we'll", 'mustn', 'we', 'into', 'they', 'on', "they'll", 
               'haven', 'after', 'had', 'myself', 'she', 'too', 'won', 'over', 'any', "you've", 
               'not', 'me', "they're", "wouldn't", 'other', 'have', 'once', 'yourselves', 'doing', 
@@ -35,6 +36,7 @@ stop_words = {'most', "i've", 't', "we'll", 'mustn', 'we', 'into', 'they', 'on',
               'a', 'same', 'which', 'being', 'himself', 'under', 'through', 'when', "mightn't", 'ours', 'above', 'were', 
               'd', 'does', 'such', 'doesn', 'itself', 'this', 'i', 'their', 'with', 'him'}
 
+#constructing the stopwork function
 def remove_stopwords(text):
     return " ".join([word for word in str(text).lower().split() if word not in stop_words])
 
@@ -58,6 +60,7 @@ st.markdown("""
 </div>
 """, unsafe_allow_html=True)
 
+#dividing the page
 st.divider()
 
 #uploading the file 
@@ -66,6 +69,7 @@ st.markdown(
     unsafe_allow_html=True
 )
 
+#saving the csv file in a variable
 uploaded_file = st.file_uploader("", type=["csv"])
 
 #loop to carry out the transformation
@@ -113,6 +117,7 @@ if uploaded_file is not None:
                 file_name="output.csv",
                 mime="text/csv"
             )
-
+    #exception handling if errors happpen
     except Exception as e:
-        st.error(f"Error reading or processing file: {e}")
+        st.error(f"Error reading or processing file: {e}")
+