Update app.py

app.py

@@ -129,7 +129,7 @@ def create_genre_wordcloud(conn):
     plt.imshow(wordcloud, interpolation='bilinear')
     plt.axis('off')
     plt.title('Top Genres in IMDb Dataset')
-    st.pyplot()
+    st.pyplot(plt.gcf())  # Pass the current figure explicitly to st.pyplot()
 
 # Function to find best movie of each genre by numVotes * averageRating
 def find_best_movies_by_genre(conn):
@@ -157,15 +157,27 @@ def find_best_movies_by_genre(conn):
 # Main function to orchestrate the dashboard
 def main():
     # Load data from SQLite database
-    db_file = 'imdb_data.db'  # Adjust path as needed
+    db_file = 'imdb_data/imdb_data.db'  # Adjust path as needed
     conn = load_data(db_file)
 
     # Fetch and display summary info
     total_movies, total_years, avg_rating = fetch_summary_info(conn)
-    summary_info = f"Total Movies: {total_movies}\nTotal Years: {total_years}\nAverage Rating: {avg_rating:.2f}"
+    
+    # Display summary information in three columns
     st.write("# IMDb Dashboard")
     st.write("## Summary Information")
-    st.markdown(summary_info)
+
+    # Layout the summary information in three columns with big bold numbers
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.subheader("Total Movies")
+        st.markdown(f"**{total_movies}**")
+    with col2:
+        st.subheader("Total Years")
+        st.markdown(f"**{total_years}**")
+    with col3:
+        st.subheader("Average Rating")
+        st.markdown(f"**{avg_rating:.2f}**")
 
     # Display global map of total films per region
     df_movie_region = pd.read_csv('movie_region.csv')  # Replace with your actual CSV loading