minor change

app.py

@@ -1,48 +1,48 @@
+# import essential libraraies
 import streamlit as st
 import pandas as pd
+from transformers import pipeline #for pre-trained model
+# for graphing
 import plotly.express as px
 import matplotlib.pyplot as plt
 import plotly.graph_objs as go
 from wordcloud import WordCloud
-from transformers import pipeline
 
 # Load the pre-trained sentiment analysis model
 sentiment_model = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
 
-# Define the Streamlit app's user interface
-# Set page title and favicon
-st.set_page_config(page_title="Hotel Reviews Sentiment", page_icon=":hotel:",layout='wide')
 
-# Add image and heading
-st.image("Header.png", use_column_width=True)
+# Define user interface
 
+# Set page title
+st.set_page_config(page_title="Hotel Reviews Sentiment", page_icon=":hotel:",layout='wide')
+# Add a header
+st.image("Header.png", use_column_width=True)
+# Define the format of the file to be uploaded
 st.write("<br>", unsafe_allow_html=True)
 file_format_link = "https://drive.google.com/file/d/1B6Glpna9kZsakgjpWStfhbxI20EoGsnv/view?usp=sharing"
 message = f"⚠️ㅤPlease stick to the given layout when uploading the file. You can download the sample file layout from [here]({file_format_link})."
 st.write(message)
+# Uploading the file
+file = st.file_uploader("",type=["csv"])
 
-file = st.file_uploader(" ",type=["csv"])
-
-# Define the app's functionality
 if file is not None:
     # Read the CSV file into a Pandas DataFrame
     df = pd.read_csv(file)
-    st.markdown(f"<h5 style='margin-top:40px'>Total reviews: {len(df)} </h5>", unsafe_allow_html=True)
+    # Print total number of reviews to analyse
+    st.markdown(f"<h5 style='font-family: sans-serif;margin-top:40px'>Total reviews: {len(df)} </h5>", unsafe_allow_html=True)
     
-    # Write the total number of records
     st.markdown(
         f'<div style="background-color: #4AA6DD; color: #ffffff; padding: 6px; font-size: 20px; font-weight: bold; text-align: center; border-radius: 1rem;margin-top: 10px"> Distribution of Reviews </div>',
         unsafe_allow_html=True
     )
-
-    # Apply the sentiment analysis model to each review and store the results in a new column
+    # Apply the sentiment analysis model
     df["Sentiment"] = df["Review"].apply(lambda x: sentiment_model(x)[0]["label"])
 
+    # Building the dashboard
+    
     # Generate pie chart
-    # Define custom colors
     colors = ['#30C3C4', '#D1DDDE']
-
-    # Generate pie chart
     sentiment_counts = df["Sentiment"].value_counts()
     fig = px.pie(sentiment_counts, values=sentiment_counts.values, names=sentiment_counts.index,
                  color_discrete_sequence=colors)
@@ -51,10 +51,8 @@ if file is not None:
     # Create word clouds for positive and negative reviews
     positive_reviews = " ".join(df[df["Sentiment"] == "POSITIVE"]["Review"].tolist())
     negative_reviews = " ".join(df[df["Sentiment"] == "NEGATIVE"]["Review"].tolist())
-
-    # Center-align the word clouds
+    # Diplay wordcloud in two columns 
     col1, col2 = st.columns(2)
-    
     with col1:
         st.markdown(
             f'<div style="background-color: #4AA6DD; color: #ffffff; padding: 6px; font-size: 20px; font-weight: bold; text-align: center; margin-bottom: 40px; border-radius: 1rem">Positive Reviews</div>',
@@ -62,7 +60,6 @@ if file is not None:
         )
         wc_pos = WordCloud(width=800, height=600, background_color="white", colormap="winter").generate(positive_reviews)
         st.image(wc_pos.to_array(),use_column_width=True)
-    
     with col2:
         st.markdown(
             f'<div style="background-color: #4AA6DD; color: #ffffff; padding: 6px; font-size: 20px; font-weight: bold; text-align: center; margin-bottom: 40px;border-radius: 1rem">Negative Reviews</div>',
@@ -71,23 +68,20 @@ if file is not None:
         wc_neg = WordCloud(width=800, height=600, background_color="white", colormap="winter").generate(negative_reviews)
         st.image(wc_neg.to_array(),use_column_width=True)
 
-    # Display the sentiment of each review as cards
+    # Display the sentiment of each review as a dataframe
     st.markdown(
         f'<div style="background-color: #4AA6DD; color: #ffffff; padding: 6px; font-size: 20px; font-weight: bold; text-align: center; margin-top: 60px; border-radius: 1rem"> Reviews in depth </div>',
         unsafe_allow_html=True
     )
-
-    # Add the selectbox to filter sentiments
+    # Add a filter for sentiments
     filter_sentiment = st.selectbox("", ["ALL", "POSITIVE", "NEGATIVE"])
-    
-    # Filter the dataframe based on the selected sentiment
+    # Filter the dataframe
     if filter_sentiment != "ALL":
         df = df[df['Sentiment'] == filter_sentiment]
-    
-    # Set the max number of rows to display at a time
+    # Max number of rows to display at a time
     max_rows = 10
     
-    # Create HTML table with no border and centered text
+    # Table generation
     table_html = (df.style
                   .set_properties(**{'text-align': 'left'})
                   .set_table_styles([{'selector': 'th', 'props': [('border', '0px')]},
@@ -95,22 +89,23 @@ if file is not None:
                   .set_table_attributes('style="position: sticky; top: 0;"')
                   .to_html(index=False, escape=False))
     
-    # Wrap the table inside a div with a fixed height and scrollable content
+    # Scrollable content
     st.write(f'<div style="height: {max_rows*30}px; overflow-y: scroll;">{table_html}</div>', unsafe_allow_html=True,header=True,sticky_header=True)
 
+    #save output as csv
     def convert_df(df):
         # IMPORTANT: Cache the conversion to prevent computation on every rerun
         return df.to_csv().encode('utf-8')
-    
     csv = convert_df(df)
     
-    # Add some space between the download button and the table
+    # Download button
     st.write("<br>", unsafe_allow_html=True)
-    
     st.download_button(
         label="Download data as CSV",
         data=csv,
         file_name='Review Sentiments.csv'
     )
+
+# Footnote
 st.write("<br>", unsafe_allow_html=True)
 st.caption('<div style="text-align:center; background-color:#CFEDFF;padding: 6px">crafted with ❤️</div>', unsafe_allow_html=True)