Changes DV - all charts are altered

app.py

@@ -50,7 +50,7 @@ st.sidebar.markdown("[View Raw Data Source](https://www.kaggle.com/datasets/joeb
 
 # Main Content
 st.title("Music Data Analysis Dashboard")
-st.markdown("Explore trends and insights from a diverse music dataset.")
+# st.markdown("Explore trends and insights from a diverse music dataset.")
 
 # Call Analysis Functions Based on Selection with updated explanations
 if analysis_option == "Popularity Trends Over Time":

functions/visualizations.py

@@ -10,442 +10,459 @@ import numpy as np
 from collections import Counter
 
 
-# def generate_popularity_trends(df):
-#     st.header("Popularity Trends Over Time")
-#     tab1, tab2, tab3 = st.tabs(["Average Popularity", "Individual Songs", "Top 10 Songs"])
+def generate_popularity_trends(df):
+    st.header("Popularity Trends Over Time")
+    tab1, tab2, tab3 = st.tabs(["Average Popularity", "Individual Songs", "Top 10 Songs"])
     
-#     with tab1:
-#         st.markdown("**Average Popularity by Decade:** This chart shows how the average popularity of songs has changed over different decades.")
-#         if 'Decade' in df.columns:
-#             top_decades = df.groupby('Decade')['Popularity'].mean().reset_index().nlargest(10, 'Popularity')
+    with tab1:
+        st.markdown("**Average Popularity by Decade:** This chart shows how the average popularity of songs has changed over different decades.")
+        if 'Decade' in df.columns:
+            top_decades = df.groupby('Decade')['Popularity'].mean().reset_index().nlargest(10, 'Popularity')
             
-#             fig1 = go.Figure()
-#             fig1.add_trace(go.Scatter(
-#                 x=top_decades['Decade'],
-#                 y=top_decades['Popularity'],
-#                 mode='lines+markers',
-#                 fill='tonexty',
-#                 line=dict(color='royalblue', width=3),
-#                 marker=dict(size=8, color='darkblue', line=dict(width=2, color='white')),
-#                 name='Popularity',
-#                 hovertext=top_decades['Decade']
-#             ))
-#             fig1.update_layout(
-#                 title='Top 10 Decades by Average Popularity',
-#                 xaxis_title='Decade',
-#                 yaxis_title='Average Popularity Score',
-#                 template='plotly_white',
-#                 width=900,
-#                 height=450
-#             )
-#             st.plotly_chart(fig1)
-#         else:
-#             st.error("Cannot plot: 'Decade' column missing.")
+            fig1 = go.Figure()
+            fig1.add_trace(go.Scatter(
+                x=top_decades['Decade'],
+                y=top_decades['Popularity'],
+                mode='lines+markers',
+                fill='tonexty',
+                line=dict(color='royalblue', width=3),
+                marker=dict(size=8, color='darkblue', line=dict(width=2, color='white')),
+                name='Popularity',
+                hovertext=top_decades['Decade']
+            ))
+            fig1.update_layout(
+                title='Top 10 Decades by Average Popularity',
+                xaxis_title='Decade',
+                yaxis_title='Average Popularity Score',
+                template='plotly_white',
+                width=900,
+                height=450
+            )
+            st.plotly_chart(fig1)
+        else:
+            st.error("Cannot plot: 'Decade' column missing.")
     
-#     with tab2:
-#         st.markdown("**Top 10 Individual Songs:** This scatter plot highlights the popularity of the top 10 most popular songs over time.")
-#         if 'Year' in df.columns:
-#             top_songs = df.nlargest(10, 'Popularity')
-#             fig2 = px.scatter(
-#                 top_songs, x='Year', y='Popularity',
-#                 color='Popularity',
-#                 size='Popularity',
-#                 color_continuous_scale='viridis',
-#                 title='Top 10 Individual Songs by Popularity',
-#                 hover_data=['Track Name', 'Artist Name(s)', 'Year']
-#             )
-#             fig2.update_layout(
-#                 xaxis_title='Release Year',
-#                 yaxis_title='Popularity Score',
-#                 template='plotly_white',
-#                 width=900,
-#                 height=500
-#             )
-#             st.plotly_chart(fig2)
-#         else:
-#             st.error("Cannot plot: 'Year' column missing.")
+    with tab2:
+        st.markdown("**Top 10 Individual Songs:** This scatter plot highlights the popularity of the top 10 most popular songs over time.")
+        if 'Year' in df.columns:
+            top_songs = df.nlargest(10, 'Popularity')
+            fig2 = px.scatter(
+                top_songs, x='Year', y='Popularity',
+                color='Popularity',
+                size='Popularity',
+                color_continuous_scale='viridis',
+                title='Top 10 Individual Songs by Popularity',
+                hover_data=['Track Name', 'Artist Name(s)', 'Year']
+            )
+            fig2.update_layout(
+                xaxis_title='Release Year',
+                yaxis_title='Popularity Score',
+                template='plotly_white',
+                width=900,
+                height=500
+            )
+            st.plotly_chart(fig2)
+        else:
+            st.error("Cannot plot: 'Year' column missing.")
     
-#     with tab3:
-#         st.markdown("**Top 10 Most Popular Songs:** This bar chart displays the top 10 songs based on their popularity scores.")
-#         if 'Track Name' in df.columns and 'Popularity' in df.columns:
-#             top_songs = df.nlargest(10, 'Popularity')[['Track Name', 'Artist Name(s)', 'Popularity']]
-#             fig3 = px.bar(
-#                 top_songs, y='Track Name', x='Popularity',
-#                 orientation='h', color='Popularity',
-#                 color_continuous_scale='deep',
-#                 title='Top 10 Most Popular Songs',
-#                 labels={'Track Name': 'Song Title', 'Popularity': 'Popularity Score'},
-#                 hover_data=['Track Name', 'Artist Name(s)']
-#             )
-#             fig3.update_layout(
-#                 xaxis_title='Popularity Score',
-#                 yaxis_title='Song Title',
-#                 template='plotly_white',
-#                 width=900,
-#                 height=500
-#             )
-#             st.plotly_chart(fig3)
-#         else:
-#             st.error("Cannot plot: 'Track Name' or 'Popularity' column missing.")
+    with tab3:
+        st.markdown("**Top 10 Most Popular Songs:** This bar chart displays the top 10 songs based on their popularity scores.")
+        if 'Track Name' in df.columns and 'Popularity' in df.columns:
+            top_songs = df.nlargest(10, 'Popularity')[['Track Name', 'Artist Name(s)', 'Popularity']]
+            fig3 = px.bar(
+                top_songs, y='Track Name', x='Popularity',
+                orientation='h', color='Popularity',
+                color_continuous_scale='deep',
+                title='Top 10 Most Popular Songs',
+                labels={'Track Name': 'Song Title', 'Popularity': 'Popularity Score'},
+                hover_data=['Track Name', 'Artist Name(s)']
+            )
+            fig3.update_layout(
+                xaxis_title='Popularity Score',
+                yaxis_title='Song Title',
+                template='plotly_white',
+                width=900,
+                height=500
+            )
+            st.plotly_chart(fig3)
+        else:
+            st.error("Cannot plot: 'Track Name' or 'Popularity' column missing.")
+
 
+def generate_audio_features(df): 
+    st.header("Audio Features Analysis")
 
-# def generate_audio_features(df):
-#     st.header("Audio Features Analysis")
-#     feature = st.selectbox(
-#         "Select Feature", ['Danceability', 'Energy', 'Tempo', 'Loudness']
-#     )
-#     tab1, tab2 = st.tabs(["Distribution", "By Decade"])
+    feature = st.selectbox(
+        "Select Feature", ['Danceability', 'Energy', 'Tempo', 'Loudness']
+    )
     
-#     with tab1:
-#         st.markdown(f"**Top 20 {feature} Values:** This histogram displays the distribution of the top 20 songs based on {feature}.")
-#         top_features = df.nlargest(20, feature)
-#         fig = px.histogram(
-#             top_features, x=feature, nbins=20,
-#             color='Decade' if 'Decade' in df.columns else None,
-#             barmode='overlay',
-#             opacity=0.7,
-#             title=f'Top 20 Songs by {feature}',
-#             color_discrete_sequence=px.colors.qualitative.Set2,
-#             hover_data=['Track Name', 'Artist Name(s)']
-#         )
-#         st.plotly_chart(fig)
+    tab1, tab2 = st.tabs(["Distribution", "By Decade"])
     
-#     with tab2:
-#         st.markdown(f"**{feature} by Decade:** This box plot compares the top 20 {feature} values across different decades.")
-#         if 'Decade' in df.columns:
-#             top_features = df.nlargest(20, feature)
-#             fig2 = px.box(top_features, x='Decade', y=feature,
-#                           color='Decade',
-#                           title=f'Top 20 {feature} Values by Decade',
-#                           color_discrete_sequence=px.colors.qualitative.Pastel,
-#                           hover_data=['Track Name', 'Artist Name(s)']
-#                           )
-#             st.plotly_chart(fig2)
-#         else:
-#             st.error("Cannot plot: 'Decade' column missing.")
-# def generate_genre_analysis(df):
-#     st.header("Genre & Artist Analysis")
-#     tab1, tab2, tab3 = st.tabs(["Top Genres", "Genre Distribution", "Artist Popularity"])
+    with tab1:
+        st.markdown(f"**Top 20 {feature} Values:** This bar chart displays the distribution of the top 20 songs based on {feature}.")
+        top_features = df.nlargest(20, feature)
+
+        fig = px.bar(
+            top_features, x='Track Name', y=feature, 
+            color='Decade' if 'Decade' in df.columns else None,
+            title=f'Top 20 Songs by {feature}',
+            color_discrete_sequence=px.colors.qualitative.Set2,
+            hover_data=['Track Name', 'Artist Name(s)']
+        )
+        fig.update_layout(xaxis_tickangle=-45, template='plotly_white')
+        st.plotly_chart(fig)
+    
+    with tab2:
+        st.markdown(f"**{feature} by Decade:** This line chart compares the top {feature} trends over different decades.")
+
+        if 'Decade' in df.columns:
+            avg_feature_by_decade = df.groupby('Decade')[feature].mean().reset_index()
+
+            fig2 = px.line(
+                avg_feature_by_decade, x='Decade', y=feature,
+                title=f'Average {feature} by Decade',
+                markers=True,
+                color_discrete_sequence=['red'],
+                hover_data=['Decade']
+            )
+            fig2.update_layout(template='plotly_white', width=800, height=400)
+            st.plotly_chart(fig2)
+        else:
+            st.error("Cannot plot: 'Decade' column missing.")
+
+def generate_genre_analysis(df):
+    st.header("Genre & Artist Analysis")
+    tab1, tab2, tab3 = st.tabs(["Top Genres", "Genre Distribution", "Artist Popularity"])
     
-#     with tab1:
-#         st.markdown("**Top Genres in Top 10 Songs:** Displays the most common genres among the top 10 most popular songs.")
-#         top_songs = df.nlargest(10, 'Popularity')
-#         top_genres = top_songs.explode('Genres')['Genres'].value_counts().reset_index()
-#         fig1 = px.bar(
-#             top_genres, x='count', y='Genres',
-#             orientation='h', color='count',
-#             color_continuous_scale='viridis',
-#             title='Top Genres in Top 10 Songs',
-#             labels={'count': 'Number of Songs', 'Genres': 'Genre Name'},
-#             hover_data=['Genres', 'count']
-#         )
-#         fig1.update_layout(template='plotly_white', width=900, height=500)
-#         st.plotly_chart(fig1)
+    with tab1:
+        st.markdown("**Top Genres in Top 10 Songs:** Displays the most common genres among the top 10 most popular songs.")
+        top_songs = df.nlargest(10, 'Popularity')
+        top_genres = top_songs.explode('Genres')['Genres'].value_counts().reset_index()
+        fig1 = px.bar(
+            top_genres, x='count', y='Genres',
+            orientation='h', color='count',
+            color_continuous_scale='viridis',
+            title='Top Genres in Top 10 Songs',
+            labels={'count': 'Number of Songs', 'Genres': 'Genre Name'},
+            hover_data=['Genres', 'count']
+        )
+        fig1.update_layout(template='plotly_white', width=900, height=500)
+        st.plotly_chart(fig1)
     
-#     with tab2:
-#         st.markdown("**Genre Distribution in Top 10 Songs:** Shows how different genres contribute to the top 10 songs.")
-#         genre_song_data = top_songs.explode('Genres')
-#         fig2 = px.bar(
-#             genre_song_data, x='Track Name', y='Popularity', color='Genres',
-#             title='Genre Distribution in Top 10 Songs',
-#             labels={'Track Name': 'Song Title', 'Popularity': 'Popularity Score', 'Genres': 'Genre'},
-#             barmode='stack',
-#             hover_data=['Track Name', 'Genres']
-#         )
-#         fig2.update_layout(template='plotly_white', width=900, height=500)
-#         st.plotly_chart(fig2)
+    with tab2:
+        st.markdown("**Genre Distribution in Top 10 Songs:** Shows how different genres contribute to the top 10 songs.")
+        genre_song_data = top_songs.explode('Genres')
+        fig2 = px.bar(
+            genre_song_data, x='Track Name', y='Popularity', color='Genres',
+            title='Genre Distribution in Top 10 Songs',
+            labels={'Track Name': 'Song Title', 'Popularity': 'Popularity Score', 'Genres': 'Genre'},
+            barmode='stack',
+            hover_data=['Track Name', 'Genres']
+        )
+        fig2.update_layout(template='plotly_white', width=900, height=500)
+        st.plotly_chart(fig2)
     
-#     with tab3:
-#         st.markdown("**Artist Popularity in Top 10 Songs:** Visualizes the most popular artists in the top 10 songs with their song count and names.")
-#         artist_popularity = top_songs.groupby('Artist Name(s)').agg({'Popularity': 'sum', 'Track Name': lambda x: list(x)}).reset_index().sort_values(by='Popularity', ascending=False)
-#         artist_popularity['Song Count'] = artist_popularity['Track Name'].apply(len)
-#         fig3 = px.bar(
-#             artist_popularity, x='Popularity', y='Artist Name(s)',
-#             orientation='h', color='Popularity',
-#             color_continuous_scale='blues',
-#             title='Artist Popularity in Top 10 Songs',
-#             labels={'Artist Name(s)': 'Artist Name', 'Popularity': 'Total Popularity Score', 'Song Count': 'Number of Songs'},
-#             hover_data={'Artist Name(s)': True, 'Popularity': True, 'Song Count': True, 'Track Name': True}
-#         )
-#         fig3.update_layout(template='plotly_white', width=900, height=500)
-#         st.plotly_chart(fig3)
+    with tab3:
+        st.markdown("**Artist Popularity in Top 10 Songs:** Visualizes the most popular artists in the top 10 songs with their song count and names.")
+        artist_popularity = top_songs.groupby('Artist Name(s)').agg({'Popularity': 'sum', 'Track Name': lambda x: list(x)}).reset_index().sort_values(by='Popularity', ascending=False)
+        artist_popularity['Song Count'] = artist_popularity['Track Name'].apply(len)
+        fig3 = px.bar(
+            artist_popularity, x='Popularity', y='Artist Name(s)',
+            orientation='h', color='Popularity',
+            color_continuous_scale='blues',
+            title='Artist Popularity in Top 10 Songs',
+            labels={'Artist Name(s)': 'Artist Name', 'Popularity': 'Total Popularity Score', 'Song Count': 'Number of Songs'},
+            hover_data={'Artist Name(s)': True, 'Popularity': True, 'Song Count': True, 'Track Name': True}
+        )
+        fig3.update_layout(template='plotly_white', width=900, height=500)
+        st.plotly_chart(fig3)
 
-# def generate_explicit_trends(df):
-#     st.header("Explicit Content Trends")
-#     st.markdown("**Explicit vs Non-Explicit Songs Over Time:** This line chart shows how the number of explicit and non-explicit songs has changed over different decades.")
-#     if 'Decade' in df.columns and 'Explicit' in df.columns:
-#         explicit_trends = df.groupby(['Decade', 'Explicit']).size().reset_index(name='Count')
-#         fig = px.line(
-#             explicit_trends, x='Decade', y='Count', color='Explicit',
-#             markers=True, line_shape='linear',
-#             title='Explicit vs Non-Explicit Songs Over Time',
-#             labels={'Decade': 'Decade', 'Count': 'Number of Songs', 'Explicit': 'Song Type'},
-#             color_discrete_map={True: 'purple', False: 'green'}
-#         )
-#         fig.update_layout(template='plotly_white', width=900, height=500)
-#         st.plotly_chart(fig)
-#     else:
-#         st.error("Cannot plot: 'Decade' or 'Explicit' column missing.")
+def generate_explicit_trends(df):
+    st.header("Explicit Content Trends")
+    st.markdown("**Explicit vs Non-Explicit Songs Over Time:** This line chart shows how the number of explicit and non-explicit songs has changed over different decades.")
+    if 'Decade' in df.columns and 'Explicit' in df.columns:
+        explicit_trends = df.groupby(['Decade', 'Explicit']).size().reset_index(name='Count')
+        fig = px.line(
+            explicit_trends, x='Decade', y='Count', color='Explicit',
+            markers=True, line_shape='linear',
+            title='Explicit vs Non-Explicit Songs Over Time',
+            labels={'Decade': 'Decade', 'Count': 'Number of Songs', 'Explicit': 'Song Type'},
+            color_discrete_map={True: 'purple', False: 'green'}
+        )
+        fig.update_layout(template='plotly_white', width=900, height=500)
+        st.plotly_chart(fig)
+    else:
+        st.error("Cannot plot: 'Decade' or 'Explicit' column missing.")
 
-# def generate_album_insights(df):
-#     st.header("Album & Label Insights")
-#     tab1, tab2 = st.tabs(["Top Labels", "Album Popularity"])
+def generate_album_insights(df):
+    st.header("Album & Label Insights")
+    tab1, tab2 = st.tabs(["Top Labels", "Album Popularity"])
     
-#     with tab1:
-#         st.markdown("**Top Record Labels:** Displays the most dominant record labels based on the number of songs they have released.")
-#         if 'Label' in df.columns:
-#             top_labels = df['Label'].value_counts().nlargest(10).reset_index()
-#             fig9 = px.sunburst(
-#                 top_labels, path=['Label'], values='count',
-#                 title='Top Record Labels by Song Count',
-#                 color='count', color_continuous_scale='blues',
-#                 labels={'Label': 'Record Label', 'count': 'Number of Songs'}
-#             )
-#             fig9.update_layout(template='plotly_white', width=900, height=500)
-#             st.plotly_chart(fig9)
-#         else:
-#             st.error("Cannot plot: 'Label' column missing.")
+    with tab1:
+        st.markdown("**Top Record Labels:** Displays the most dominant record labels based on the number of songs they have released.")
+        if 'Label' in df.columns:
+            top_labels = df['Label'].value_counts().nlargest(10).reset_index()
+            fig9 = px.sunburst(
+                top_labels, path=['Label'], values='count',
+                title='Top Record Labels by Song Count',
+                color='count', color_continuous_scale='blues',
+                labels={'Label': 'Record Label', 'count': 'Number of Songs'}
+            )
+            fig9.update_layout(template='plotly_white', width=900, height=500)
+            st.plotly_chart(fig9)
+        else:
+            st.error("Cannot plot: 'Label' column missing.")
     
-#     with tab2:
-#         st.markdown("**Album Popularity:** Compares the popularity of albums based on the number of songs and their average popularity score.")
-#         if 'Album Name' in df.columns and 'Popularity' in df.columns:
-#             album_pop = df.groupby('Album Name')['Popularity'].agg(['mean', 'count']).reset_index()
-#             album_pop = album_pop.sort_values(by=['mean', 'count'], ascending=[False, False]).nlargest(10, 'mean')
-#             fig10 = px.strip(
-#                 album_pop, x='mean', y='Album Name',
-#                 color='count',
-#                 title='Top 10 Albums by Popularity',
-#                 labels={'Album Name': 'Album', 'mean': 'Average Popularity Score', 'count': 'Number of Songs'},
-#                 hover_data={'Album Name': True, 'count': True, 'mean': True},
-#                 color_discrete_sequence=px.colors.qualitative.Pastel
-#             )
-#             fig10.update_layout(template='plotly_white', width=900, height=500)
-#             st.plotly_chart(fig10)
-#         else:
-#             st.error("Cannot plot: 'Album Name' or 'Popularity' column missing.")
+    with tab2:
+        st.markdown("**Album Popularity:** Compares the popularity of albums based on the number of songs and their average popularity score.")
+        if 'Album Name' in df.columns and 'Popularity' in df.columns:
+            album_pop = df.groupby('Album Name')['Popularity'].agg(['mean', 'count']).reset_index()
+            album_pop = album_pop.sort_values(by=['mean', 'count'], ascending=[False, False]).nlargest(10, 'mean')
+            fig10 = px.strip(
+                album_pop, x='mean', y='Album Name',
+                color='count',
+                title='Top 10 Albums by Popularity',
+                labels={'Album Name': 'Album', 'mean': 'Average Popularity Score', 'count': 'Number of Songs'},
+                hover_data={'Album Name': True, 'count': True, 'mean': True},
+                color_discrete_sequence=px.colors.qualitative.Pastel
+            )
+            fig10.update_layout(template='plotly_white', width=900, height=500)
+            st.plotly_chart(fig10)
+        else:
+            st.error("Cannot plot: 'Album Name' or 'Popularity' column missing.")
 
 
-# def generate_tempo_mood(df):
-#     st.header("Tempo & Mood Analysis")
-#     tab1, tab2 = st.tabs(["Tempo Trends", "Mood Scatter"])
-#     with tab1:
-#         st.markdown("**Tempo Trends:** Tracks tempo changes.")
-#         if 'Year' in df.columns and 'Tempo' in df.columns:
-#             tempo_by_year = df.groupby('Year')['Tempo'].mean().reset_index()
-#             fig11 = px.line(tempo_by_year, x='Year', y='Tempo', title='Average Tempo Over Time', color_discrete_sequence=['orange'])
-#             fig11.update_layout(template='plotly_white', width=800, height=400)
-#             st.plotly_chart(fig11)
-#         else:
-#             st.error("Cannot plot: 'Year' or 'Tempo' column missing.")
-#     with tab2:
-#         st.markdown("**Mood Analysis (Valence & Energy):** Categorizes songs based on mood and energy.")
-#         if 'Valence' in df.columns and 'Energy' in df.columns:
-#             top_songs = df.nlargest(10, 'Popularity')
-#             mood_by_valence = top_songs.groupby('Valence')['Energy'].mean().reset_index()
-#             fig12 = px.bar(
-#                 mood_by_valence, x='Valence', y='Energy',
-#                 title='Average Energy Levels by Valence (Mood Analysis)',
-#                 color='Energy', color_continuous_scale='plasma'
-#             )
-#             fig12.update_layout(template='plotly_white', width=900, height=500)
-#             st.plotly_chart(fig12)
-#         else:
-#             st.error("Cannot plot: 'Valence' or 'Energy' column missing.")
-# def generate_top_artists_songs(df):
-#     st.header("Top Artists and Songs")
-#     tab1, tab2 = st.tabs(["Top Artists", "Top Songs"])
+def generate_tempo_mood(df):
+    st.header("Tempo & Mood Analysis")
+    tab1, tab2 = st.tabs(["Tempo Trends", "Mood Scatter"])
+    with tab1:
+        st.markdown("**Tempo Trends:** Tracks tempo changes.")
+        if 'Year' in df.columns and 'Tempo' in df.columns:
+            tempo_by_year = df.groupby('Year')['Tempo'].mean().reset_index()
+            fig11 = px.line(tempo_by_year, x='Year', y='Tempo', title='Average Tempo Over Time', color_discrete_sequence=['orange'])
+            fig11.update_layout(template='plotly_white', width=800, height=400)
+            st.plotly_chart(fig11)
+        else:
+            st.error("Cannot plot: 'Year' or 'Tempo' column missing.")
+    with tab2:
+        st.markdown("**Mood Analysis (Valence & Energy):** Categorizes songs based on mood and energy.")
+        if 'Valence' in df.columns and 'Energy' in df.columns:
+            top_songs = df.nlargest(10, 'Popularity')
+            mood_by_valence = top_songs.groupby('Valence')['Energy'].mean().reset_index()
+            fig12 = px.bar(
+                mood_by_valence, x='Valence', y='Energy',
+                title='Average Energy Levels by Valence (Mood Analysis)',
+                color='Energy', color_continuous_scale='plasma'
+            )
+            fig12.update_layout(template='plotly_white', width=900, height=500)
+            st.plotly_chart(fig12)
+        else:
+            st.error("Cannot plot: 'Valence' or 'Energy' column missing.")
+def generate_top_artists_songs(df):
+    st.header("Top Artists and Songs")
+    tab1, tab2 = st.tabs(["Top Artists", "Top Songs"])
     
-#     with tab1:
-#         st.markdown("**Most Featured Artists:** Shows top artists.")
-#         if 'Artist Name(s)' in df.columns:
-#             top_artists = df['Artist Name(s)'].value_counts().nlargest(10).reset_index()
-#             fig13 = px.bar(
-#                 top_artists, x='count', y='Artist Name(s)',
-#                 orientation='h',
-#                 title='Most Featured Artists',
-#                 color='count', color_continuous_scale='greens'
-#             )
-#             fig13.update_layout(template='plotly_white', width=900, height=500)
-#             st.plotly_chart(fig13)
-#         else:
-#             st.error("Cannot plot: 'Artist Name(s)' column missing.")
+    with tab1:
+        st.markdown("**Most Featured Artists:** Shows top artists.")
+        if 'Artist Name(s)' in df.columns:
+            top_artists = df['Artist Name(s)'].value_counts().nlargest(10).reset_index()
+            fig13 = px.bar(
+                top_artists, x='count', y='Artist Name(s)',
+                orientation='h',
+                title='Most Featured Artists',
+                color='count', color_continuous_scale='greens'
+            )
+            fig13.update_layout(template='plotly_white', width=900, height=500)
+            st.plotly_chart(fig13)
+        else:
+            st.error("Cannot plot: 'Artist Name(s)' column missing.")
     
-#     with tab2:
-#         st.markdown("**Top 10 Songs:** Lists top songs.")
-#         if 'Track Name' in df.columns and 'Popularity' in df.columns:
-#             top_songs = df.nlargest(10, 'Popularity')[['Track Name', 'Popularity']]
-#             fig14 = px.pie(
-#                 top_songs, values='Popularity', names='Track Name',
-#                 title='Top 10 Songs by Popularity', color_discrete_sequence=px.colors.qualitative.Set3
-#             )
-#             fig14.update_layout(template='plotly_white', width=900, height=500)
-#             st.plotly_chart(fig14)
-#         else:
-#             st.error("Cannot plot: 'Track Name' or 'Popularity' column missing.")
+    with tab2:
+        st.markdown("**Top 10 Songs:** Lists top songs.")
+        if 'Track Name' in df.columns and 'Popularity' in df.columns:
+            top_songs = df.nlargest(10, 'Popularity')[['Track Name', 'Popularity']]
+            fig14 = px.pie(
+                top_songs, values='Popularity', names='Track Name',
+                title='Top 10 Songs by Popularity', color_discrete_sequence=px.colors.qualitative.Set3
+            )
+            fig14.update_layout(template='plotly_white', width=900, height=500)
+            st.plotly_chart(fig14)
+        else:
+            st.error("Cannot plot: 'Track Name' or 'Popularity' column missing.")
 
 
-# def generate_album_release_trends(df):
-#     st.header("Album Release Trends")
-#     tab1, tab2 = st.tabs(["Albums per Year", "Artist-Year Heatmap"])
-#     with tab1:
-#         st.markdown("**Albums per Year:** Tracks release patterns.")
-#         if 'Year' in df.columns:
-#             albums_per_year = df['Year'].value_counts().sort_index().reset_index()
-#             fig15 = px.line(albums_per_year, x='Year', y='count', title='Number of Albums Released per Year', color_discrete_sequence=['purple'])
-#             fig15.update_layout(template='plotly_white', width=800, height=400)
-#             st.plotly_chart(fig15)
-#         else:
-#             st.error("Cannot plot: 'Year' column missing.")
-#     with tab2:
-#         st.markdown("**Songs by Artists and Years:** Visualizes trends.")
-#         if 'Artist Name(s)' in df.columns and 'Year' in df.columns:
-#             # Filter to only show the top 10 most featured artists
-#             top_artists = df['Artist Name(s)'].value_counts().nlargest(10).index
-#             filtered_df = df[df['Artist Name(s)'].isin(top_artists)]
+def generate_album_release_trends(df):
+    st.header("Album Release Trends")
+    tab1, tab2 = st.tabs(["Albums per Year", "Artist-Year Heatmap"])
+    with tab1:
+        st.markdown("**Albums per Year:** Tracks release patterns.")
+        if 'Year' in df.columns:
+            albums_per_year = df['Year'].value_counts().sort_index().reset_index()
+            fig15 = px.line(albums_per_year, x='Year', y='count', title='Number of Albums Released per Year', color_discrete_sequence=['purple'])
+            fig15.update_layout(template='plotly_white', width=800, height=400)
+            st.plotly_chart(fig15)
+        else:
+            st.error("Cannot plot: 'Year' column missing.")
+    with tab2:
+        st.markdown("**Songs by Artists and Years:** Visualizes trends.")
+        if 'Artist Name(s)' in df.columns and 'Year' in df.columns:
+            # Filter to only show the top 10 most featured artists
+            top_artists = df['Artist Name(s)'].value_counts().nlargest(10).index
+            filtered_df = df[df['Artist Name(s)'].isin(top_artists)]
             
-#             # Grouping data
-#             artist_year = filtered_df.groupby(['Year', 'Artist Name(s)']).size().reset_index(name='Count')
+            # Grouping data
+            artist_year = filtered_df.groupby(['Year', 'Artist Name(s)']).size().reset_index(name='Count')
 
-#             # Create a grouped bar chart
-#             fig16 = px.bar(
-#                 artist_year, x='Year', y='Count', color='Artist Name(s)',
-#                 title='Songs Released by Top Artists Over the Years',
-#                 labels={'Count': 'Number of Songs', 'Year': 'Year'},
-#                 barmode='group',  # Grouped bars for each artist per year
-#                 color_discrete_sequence=px.colors.qualitative.Set2
-#             )
-#             fig16.update_layout(width=900, height=500)
-#             st.plotly_chart(fig16)
-#         else:
-#             st.error("Cannot plot: 'Artist Name(s)' or 'Year' column missing.")
-# def generate_duration_analysis(df):
-#     st.header("Track Duration Analysis")
-#     tab1, tab2 = st.tabs(["Distribution", "By Decade"])
+            # Create a grouped bar chart
+            fig16 = px.bar(
+                artist_year, x='Year', y='Count', color='Artist Name(s)',
+                title='Songs Released by Top Artists Over the Years',
+                labels={'Count': 'Number of Songs', 'Year': 'Year'},
+                barmode='group',  # Grouped bars for each artist per year
+                color_discrete_sequence=px.colors.qualitative.Set2
+            )
+            fig16.update_layout(width=900, height=500)
+            st.plotly_chart(fig16)
+        else:
+            st.error("Cannot plot: 'Artist Name(s)' or 'Year' column missing.")
+def generate_duration_analysis(df):
+    st.header("Track Duration Analysis")
+    tab1, tab2 = st.tabs(["Distribution", "By Decade"])
 
-#     # Filter out tracks longer than 900,000ms (15 minutes)
-#     df = df[df['Track Duration (ms)'] <= 900000]
+    # Filter out tracks longer than 900,000ms (15 minutes)
+    df = df[df['Track Duration (ms)'] <= 900000]
 
-#     with tab1:
-#         st.markdown("**Track Duration Distribution:** Illustrates how track durations vary, helping identify common song lengths.")
-#         if 'Track Duration (ms)' in df.columns:
-#             fig17 = px.histogram(
-#                 df, x='Track Duration (ms)',
-#                 title='Track Duration Distribution (Filtered)',
-#                 nbins=50,
-#                 color_discrete_sequence=['orange']
-#             )
-#             fig17.update_layout(template='plotly_white', width=800, height=400)
-#             st.plotly_chart(fig17)
-#         else:
-#             st.error("Cannot plot: 'Track Duration (ms)' column missing.")
+    with tab1:
+        st.markdown("**Track Duration Distribution:** Illustrates how track durations vary, helping identify common song lengths.")
+        if 'Track Duration (ms)' in df.columns:
+            fig17 = px.histogram(
+                df, x='Track Duration (ms)',
+                title='Track Duration Distribution (Filtered)',
+                nbins=50,
+                color_discrete_sequence=['orange']
+            )
+            fig17.update_layout(template='plotly_white', width=800, height=400)
+            st.plotly_chart(fig17)
+        else:
+            st.error("Cannot plot: 'Track Duration (ms)' column missing.")
     
-#     with tab2:
-#         st.markdown("**Duration by Decade:** Compares the evolution of average track durations across decades, showing historical trends.")
-#         if 'Decade' in df.columns and 'Track Duration (ms)' in df.columns:
-#             fig18 = px.pie(
-#                 df.groupby('Decade')['Track Duration (ms)'].mean().reset_index(),
-#                 names='Decade', values='Track Duration (ms)',
-#                 title='Average Track Duration by Decade',
-#                 color_discrete_sequence=px.colors.qualitative.Set2
-#             )
-#             fig18.update_layout(template='plotly_white', width=800, height=400)
-#             st.plotly_chart(fig18)
-#         else:
-#             st.error("Cannot plot: 'Decade' or 'Track Duration (ms)' column missing.")
+    with tab2:
+        st.markdown("**Duration by Decade:** Compares the evolution of average track durations across decades, showing historical trends.")
+        if 'Decade' in df.columns and 'Track Duration (ms)' in df.columns:
+            fig18 = px.pie(
+                df.groupby('Decade')['Track Duration (ms)'].mean().reset_index(),
+                names='Decade', values='Track Duration (ms)',
+                title='Average Track Duration by Decade',
+                color_discrete_sequence=px.colors.qualitative.Set2
+            )
+            fig18.update_layout(template='plotly_white', width=800, height=400)
+            st.plotly_chart(fig18)
+        else:
+            st.error("Cannot plot: 'Decade' or 'Track Duration (ms)' column missing.")
 
 
-# def generate_streaming_insights(df):
-#     st.header("Streaming and Engagement Insights")
-#     tab1, tab2 = st.tabs(["Popularity vs Duration", "Time Signature"])
-    
-#     with tab1:
-#         st.markdown("**Popularity vs Duration:** Examines how track length influences popularity trends.")
-#         if 'Track Duration (ms)' in df.columns and 'Popularity' in df.columns:
-#             df['Duration (minutes)'] = df['Track Duration (ms)'] / 60000
-#             fig19 = px.box(
-#                 df, x=pd.cut(df['Duration (minutes)'], bins=[0, 2, 4, 6, 8, 10, 15], labels=['0-2', '2-4', '4-6', '6-8', '8-10', '10+']),
-#                 y='Popularity',
-#                 title='Popularity Distribution Across Track Durations',
-#                 color_discrete_sequence=['blue']
-#             )
-#             fig19.update_layout(template='plotly_white', width=800, height=400, xaxis_title='Track Duration (Minutes)')
-#             st.plotly_chart(fig19)
-#         else:
-#             st.error("Cannot plot: 'Track Duration (ms)' or 'Popularity' column missing.")
+
+def generate_streaming_insights(df):
+    st.header("Streaming and Engagement Insights")
+    tab1, tab2 = st.tabs(["Popularity vs Duration", "Time Signature"])
     
-#     with tab2:
-#         st.markdown("**Popularity by Time Signature:** Analyzes the average popularity of songs across different time signatures.")
-#         if 'Time Signature' in df.columns and 'Popularity' in df.columns:
-#             pop_by_time = df.groupby('Time Signature')['Popularity'].mean().reset_index()
-#             fig20 = px.bar(
-#                 pop_by_time, x='Time Signature', y='Popularity',
-#                 title='Average Popularity by Time Signature',
-#                 color='Popularity',
-#                 color_continuous_scale='purples'
-#             )
-#             fig20.update_layout(template='plotly_white', width=800, height=400)
-#             st.plotly_chart(fig20)
-#         else:
-#             st.error("Cannot plot: 'Time Signature' or 'Popularity' column missing.")
+    with tab1:
+        st.markdown("**Popularity vs Track Duration:** This line chart shows the trend of song popularity based on their duration.")
+        
+        if 'Track Duration (ms)' in df.columns and 'Popularity' in df.columns:
+            df['Duration (minutes)'] = df['Track Duration (ms)'] / 60000
+            duration_bins = pd.cut(df['Duration (minutes)'], bins=[0, 2, 4, 6, 8, 10, 15], labels=['0-2', '2-4', '4-6', '6-8', '8-10', '10+'])
+            avg_popularity = df.groupby(duration_bins)['Popularity'].mean().reset_index()
 
+            fig1 = px.line(
+                avg_popularity, 
+                x='Duration (minutes)', 
+                y='Popularity',
+                title='Popularity vs. Track Duration',
+                markers=True,  # Adds points to the line
+                line_shape='spline',  # Smoothens the line
+                color_discrete_sequence=['blue']
+            )
+            fig1.update_layout(template='plotly_white', xaxis_title='Track Duration (Minutes)', yaxis_title='Average Popularity')
+            st.plotly_chart(fig1)
+        else:
+            st.error("Cannot plot: 'Track Duration (ms)' or 'Popularity' column missing.")
+
+    with tab2:
+        st.markdown("**Popularity by Time Signature:** This bar chart compares the average popularity of songs based on their time signatures.")
 
-# def generate_feature_comparisons(df):
-#     st.header("Feature Comparisons Across Decades")
-#     tab1, tab2 = st.tabs(["Feature Comparison", "Loudness Trends"])
-#     with tab1:
-#         st.markdown("**Feature Comparison:** Compares features across decades.")
-#         if 'Decade' in df.columns:
-#             features_by_decade = df.groupby('Decade')[['Danceability', 'Energy', 'Valence']].mean().reset_index()
-#             fig21 = px.bar(features_by_decade.melt(id_vars='Decade'), x='Decade', y='value', color='variable',
-#                            barmode='group', title='Feature Comparison by Decade', color_discrete_sequence=px.colors.qualitative.Pastel)
-#             fig21.update_layout(template='plotly_white', width=800, height=400)
-#             st.plotly_chart(fig21)
-#         else:
-#             st.error("Cannot plot: 'Decade' column missing.")
-#     with tab2:
-#         st.markdown("**Loudness Over Time:** Tracks loudness trends.")
-#         if 'Year' in df.columns and 'Loudness' in df.columns:
-#             loudness_by_year = df.groupby('Year')['Loudness'].mean().reset_index()
-#             fig22 = px.line(loudness_by_year, x='Year', y='Loudness', title='Average Loudness Over Time', color_discrete_sequence=['green'])
-#             fig22.update_layout(template='plotly_white', width=800, height=400)
-#             st.plotly_chart(fig22)
-#         else:
-#             st.error("Cannot plot: 'Year' or 'Loudness' column missing.")
+        if 'Time Signature' in df.columns and 'Popularity' in df.columns:
+            pop_by_time = df.groupby('Time Signature')['Popularity'].mean().reset_index()
+            fig2 = px.bar(
+                pop_by_time, 
+                x='Time Signature', 
+                y='Popularity',
+                title='Average Popularity by Time Signature',
+                color='Popularity',
+                color_continuous_scale='purples'
+            )
+            fig2.update_layout(template='plotly_white', xaxis_title='Time Signature', yaxis_title='Average Popularity')
+            st.plotly_chart(fig2)
+        else:
+            st.error("Cannot plot: 'Time Signature' or 'Popularity' column missing.")
 
-# def generate_top_artists_songs(df):
-#     st.header("Top Artists and Songs")
-#     tab1, tab2 = st.tabs(["Top Artists", "Top Songs"])
+def generate_feature_comparisons(df):
+    st.header("Feature Comparisons Across Decades")
+    tab1, tab2 = st.tabs(["Feature Comparison", "Loudness Trends"])
+    with tab1:
+        st.markdown("**Feature Comparison:** Compares features across decades.")
+        if 'Decade' in df.columns:
+            features_by_decade = df.groupby('Decade')[['Danceability', 'Energy', 'Valence']].mean().reset_index()
+            fig21 = px.bar(features_by_decade.melt(id_vars='Decade'), x='Decade', y='value', color='variable',
+                           barmode='group', title='Feature Comparison by Decade', color_discrete_sequence=px.colors.qualitative.Pastel)
+            fig21.update_layout(template='plotly_white', width=800, height=400)
+            st.plotly_chart(fig21)
+        else:
+            st.error("Cannot plot: 'Decade' column missing.")
+    with tab2:
+        st.markdown("**Loudness Over Time:** Tracks loudness trends.")
+        if 'Year' in df.columns and 'Loudness' in df.columns:
+            loudness_by_year = df.groupby('Year')['Loudness'].mean().reset_index()
+            fig22 = px.line(loudness_by_year, x='Year', y='Loudness', title='Average Loudness Over Time', color_discrete_sequence=['green'])
+            fig22.update_layout(template='plotly_white', width=800, height=400)
+            st.plotly_chart(fig22)
+        else:
+            st.error("Cannot plot: 'Year' or 'Loudness' column missing.")
+
+def generate_top_artists_songs(df):
+    st.header("Top Artists and Songs")
+    tab1, tab2 = st.tabs(["Top Artists", "Top Songs"])
     
-#     with tab1:
-#         st.markdown("**Most Featured Artists:** Displays the top 10 artists with the highest song counts, highlighting their dominance in the dataset.")
-#         if 'Artist Name(s)' in df.columns:
-#             top_artists = df['Artist Name(s)'].value_counts().nlargest(10).reset_index()
-#             top_artists.columns = ['Artist Name(s)', 'Count']
-#             fig13 = px.sunburst(
-#                 top_artists, path=['Artist Name(s)'], values='Count',
-#                 title='Most Featured Artists',
-#                 color='Count',
-#                 color_continuous_scale='greens'
-#             )
-#             fig13.update_layout(template='plotly_white', width=900, height=500)
-#             st.plotly_chart(fig13)
-#         else:
-#             st.error("Cannot plot: 'Artist Name(s)' column missing.")
+    with tab1:
+        st.markdown("**Most Featured Artists:** Displays the top 10 artists with the highest song counts, highlighting their dominance in the dataset.")
+        if 'Artist Name(s)' in df.columns:
+            top_artists = df['Artist Name(s)'].value_counts().nlargest(10).reset_index()
+            top_artists.columns = ['Artist Name(s)', 'Count']
+            fig13 = px.sunburst(
+                top_artists, path=['Artist Name(s)'], values='Count',
+                title='Most Featured Artists',
+                color='Count',
+                color_continuous_scale='greens'
+            )
+            fig13.update_layout(template='plotly_white', width=900, height=500)
+            st.plotly_chart(fig13)
+        else:
+            st.error("Cannot plot: 'Artist Name(s)' column missing.")
     
-#     with tab2:
-#         st.markdown("**Songs by Artists and Years:** Analyzes song release trends across different years, focusing on the top artists.")
-#         if 'Artist Name(s)' in df.columns and 'Year' in df.columns:
-#             artist_year = df.groupby(['Artist Name(s)', 'Year']).size().reset_index(name='Count')
-#             fig16 = px.sunburst(
-#                 artist_year, path=['Year', 'Artist Name(s)'], values='Count',
-#                 title='Songs Released by Artists Over the Years',
-#                 color='Count',
-#                 color_continuous_scale=px.colors.qualitative.Set2
-#             )
-#             fig16.update_layout(width=900, height=500)
-#             st.plotly_chart(fig16)
-#         else:
-#             st.error("Cannot plot: 'Artist Name(s)' or 'Year' column missing.")
+    with tab2:
+        st.markdown("**Songs by Artists and Years:** Analyzes song release trends across different years, focusing on the top artists.")
+        if 'Artist Name(s)' in df.columns and 'Year' in df.columns:
+            artist_year = df.groupby(['Artist Name(s)', 'Year']).size().reset_index(name='Count')
+            fig16 = px.sunburst(
+                artist_year, path=['Year', 'Artist Name(s)'], values='Count',
+                title='Songs Released by Artists Over the Years',
+                color='Count',
+                color_continuous_scale=px.colors.qualitative.Set2
+            )
+            fig16.update_layout(width=900, height=500)
+            st.plotly_chart(fig16)
+        else:
+            st.error("Cannot plot: 'Artist Name(s)' or 'Year' column missing.")
 
 def generate_network_analysis(df):
     st.header("Network Analysis")
@@ -455,11 +472,11 @@ def generate_network_analysis(df):
     df.columns = df.columns.str.strip()
     
     with tab1:
-        st.markdown("**Top Collaborating Artists:** Shows which artists collaborate the most.")
-        if 'Artists' in df.columns:
-            df['Artists'] = df['Artists'].astype(str).str.split(', ')
+        st.markdown("**Top Collaborating Artists:** This chart highlights artists who frequently collaborate with each other.")
+        if 'Artist Name(s)' in df.columns:
+            df['Artist Name(s)'] = df['Artist Name(s)'].astype(str).str.split(', ')
             collaborations = []
-            for artists in df['Artists']:
+            for artists in df['Artist Name(s)']:
                 collaborations.extend(combinations(sorted(artists), 2))
             
             collab_counts = Counter(collaborations)
@@ -477,10 +494,10 @@ def generate_network_analysis(df):
             plt.title("Top 20 Artist Collaborations")
             st.pyplot(plt)
         else:
-            st.error("Cannot plot: 'Artists' column missing. Available columns: " + ", ".join(df.columns))
+            st.error("Cannot plot: 'Artist Name(s)' column missing. Available columns: " + ", ".join(df.columns))
     
     with tab2:
-        st.markdown("**Genre Crossover:** Displays genre relationships using a Chord Diagram.")
+        st.markdown("**Genre Crossover:** This chart shows how different music genres are connected and often blend together.")
         if 'Genres' in df.columns:
             df['Genres'] = df['Genres'].astype(str).str.split(', ')
             genre_pairs = []