Update app.py

app.py

@@ -30,21 +30,21 @@ if file is not None:
     )
 
     # Apply the sentiment analysis model to each review and store the results in a new column
-    df["sentiment"] = df["review"].apply(lambda x: sentiment_model(x)[0]["label"])
+    df["Sentiment"] = df["Review"].apply(lambda x: sentiment_model(x)[0]["label"])
 
     # Generate pie chart
     # Define custom colors
     colors = ['#FFC845', '#52565E']
 
     # Generate pie chart
-    sentiment_counts = df["sentiment"].value_counts()
+    sentiment_counts = df["Sentiment"].value_counts()
     fig = px.pie(sentiment_counts, values=sentiment_counts.values, names=sentiment_counts.index,
                  color_discrete_sequence=colors)
     st.plotly_chart(fig)
 
     # 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())
+    positive_reviews = " ".join(df[df["Sentiment"] == "POSITIVE"]["Review"].tolist())
+    negative_reviews = " ".join(df[df["Sentiment"] == "NEGATIVE"]["Review"].tolist())
 
     st.markdown(
         f'<div style="background-color: #FFC845; color: #ffffff; padding: 6px; font-size: 20px; font-family: Open-Sans; font-weight: bold; text-align: center; margin-bottom: 40px;"> Causes behind Positive Reviews</div>',
@@ -66,28 +66,19 @@ if file is not None:
         unsafe_allow_html=True
     )
 
-
-    # Define sentiment colors
-    sentiment_colors = {'POSITIVE': '#ffc845', 'NEGATIVE': '#52565e'}
-    
-    # Map sentiment to colours and apply to review column
-    df['sentiment'] = df['sentiment'].apply(lambda x: f'<span style="color: {sentiment_colors[x]}">{x}</span>')
-    
     # Create HTML table with no border and centered text
     table_html = (df.style
-                  .set_properties(**{'text-align': 'left'})
+                  .set_properties(**{'text-align': 'left','font-size': '14px'})
                   .set_table_styles([{'selector': 'th', 'props': [('border', '0px')]},
                                      {'selector': 'td', 'props': [('border', '0px')]}])
-                  .background_gradient(subset=['sentiment'], cmap=sentiment_colors)
                   .to_html(escape=False))
     
-    # Add the title of the table and the selectbox to filter sentiments
-    st.title("Sentiment Analysis Results")
-    filter_sentiment = st.selectbox("Filter Sentiment", ["All"] + list(sentiment_colors.keys()))
+    # Add the selectbox to filter sentiments
+    filter_sentiment = st.selectbox("Filter Sentiments", ["All"] + list(sentiment_colors.keys()))
     
     # Filter the dataframe based on the selected sentiment
     if filter_sentiment != "All":
-        df = df[df['sentiment'].str.contains(filter_sentiment)]
+        df = df[df['Sentiment'].str.contains(filter_sentiment)]
     
     # Display the table and the selectbox widget beside the title
     st.write(table_html, unsafe_allow_html=True)