app.py

import sqlite3
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import streamlit as st
from wordcloud import WordCloud
import matplotlib.pyplot as plt
from collections import Counter
import numpy as np

# Function to load data from SQLite database
def load_data(db_file):
    conn = sqlite3.connect(db_file)
    return conn

# Function to fetch genre movie releases by year
def fetch_genre_movie_releases(conn):
    query = r'''
    SELECT startYear, genres
    FROM title_basics
    WHERE titleType = 'movie' AND startYear != '\N' AND genres != '\N'
    '''
    df = pd.read_sql_query(query, conn)

    # Split genres and explode to separate rows
    df['genres'] = df['genres'].str.split(',')
    df = df.explode('genres')

    # Convert startYear to numeric
    df['startYear'] = pd.to_numeric(df['startYear'])

    # Group by startYear and genre, count the number of movies
    genre_counts = df.groupby(['startYear', 'genres']).size().reset_index(name='count')

    return genre_counts

# Function to fetch data for filled line chart of movie release years
def fetch_movie_release_years(conn):
    query_release_years = r'''
    SELECT startYear, COUNT(*) as count
    FROM title_basics
    WHERE titleType = 'movie' AND startYear != '\N'
    GROUP BY startYear
    ORDER BY startYear
    '''
    df_release_years = pd.read_sql_query(query_release_years, conn)
    return df_release_years

# Function to fetch data and create box plot of average rating by first_genre
def fetch_and_plot_average_rating_by_genre(conn):
    query = r'''
    SELECT tb.tconst, tb.primaryTitle, tr.averageRating, tb.genres
    FROM title_basics tb
    JOIN title_ratings tr ON tb.tconst = tr.tconst
    WHERE tb.titleType = 'movie' AND tb.genres IS NOT NULL AND tb.genres != '\N'
    '''
    df = pd.read_sql_query(query, conn)

    # Function to extract the first genre from the genres list
    def extract_first_genre(genres):
        if genres:
            return genres.split(',')[0].strip()
        else:
            return None

    # Apply the function to extract the first genre
    df['first_genre'] = df['genres'].apply(extract_first_genre)

    # Drop rows where first_genre is None (shouldn't be necessary if genres column is clean)
    df = df.dropna(subset=['first_genre'])

    # Create a box plot of average rating by first_genre
    fig = px.box(df, x='first_genre', y='averageRating',
                 labels={'first_genre': 'Genre', 'averageRating': 'Average Rating'},
                 title='Average Rating of Movies by First Genre')

    return fig

# Function to create word cloud of genres
def create_genre_wordcloud(conn):
    query = r'''
    SELECT genres
    FROM title_basics
    WHERE titleType = 'movie' AND genres IS NOT NULL AND genres != '\N'
    '''
    df = pd.read_sql_query(query, conn)

    # Process genres
    genres = df['genres'].str.split(',', expand=True).stack().replace('\\N', pd.NA).dropna().reset_index(drop=True)
    genre_counts = Counter(genres)

    # Generate the word cloud
    wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(genre_counts)

    # Display the word cloud
    plt.figure(figsize=(10, 5))
    plt.imshow(wordcloud, interpolation='bilinear')
    plt.axis('off')
    plt.title('Top Genres in IMDb Dataset')
    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):
    query = r'''
    SELECT tb.tconst, tb.primaryTitle, tb.startYear, tb.genres, tr.averageRating, tr.numVotes
    FROM title_basics tb
    JOIN title_ratings tr ON tb.tconst = tr.tconst
    WHERE tb.titleType = 'movie' AND tb.genres IS NOT NULL AND tb.genres != '\N'
    '''
    df = pd.read_sql_query(query, conn)

    # Split genres and select the first genre for each movie
    df['genre'] = df['genres'].str.split(',', expand=True)[0]

    # Calculate score based on numVotes * averageRating
    df['score'] = df['numVotes'] * df['averageRating']

    # Get the best movie (highest score) for each genre
    idx = df.groupby('genre')['score'].idxmax()
    best_movies_by_genre = df.loc[idx, ['genre', 'primaryTitle', 'startYear', 'averageRating', 'numVotes', 'score']] \
        .sort_values(by='score', ascending=False).reset_index(drop=True)

    return best_movies_by_genre

# Function to plot stacked area chart of genre movie releases by year using Plotly Express
def plot_stacked_genre_movie_releases(genre_counts):
    fig = px.area(genre_counts, x='startYear', y='count', color='genres',
                  title='Stacked Genre Movie Releases by Year',
                  labels={'startYear': 'Year', 'count': 'Number of Movies', 'genres': 'Genre'},
                  line_group='genres',  # This groups lines by genre
                  hover_name='genres',  # This sets the genre as the hover label
                  hover_data={'count': ':.0f'})  # Format hover data as integer

    fig.update_layout(xaxis_tickmode='linear',  # Ensure x-axis ticks are shown in a linear manner
                      xaxis_range=[2000, 2025])  # Adjust x-axis range if needed

    return fig

# Function to plot filled line chart of movie release years using Plotly Graph Objects
def plot_movie_release_years(df_release_years):
    fig = go.Figure(data=go.Scatter(x=df_release_years['startYear'], y=df_release_years['count'], fill='tozeroy'))
    fig.update_layout(title='Movie Release Years',
                      xaxis_title='Year',
                      yaxis_title='Number of Movies Released')
    return fig

# Function to plot global map of total films per region using Plotly Express
def plot_global_map():
    df = pd.read_csv('movie_region.csv')
    # Country code to name mapping
    country_mapping = {
        'AF': 'Afghanistan', 'AX': 'Åland Islands', 'AL': 'Albania', 'DZ': 'Algeria', 'AS': 'American Samoa',
        'AD': 'Andorra', 'AO': 'Angola', 'AI': 'Anguilla', 'AQ': 'Antarctica', 'AG': 'Antigua and Barbuda',
        'AR': 'Argentina', 'AM': 'Armenia', 'AW': 'Aruba', 'AU': 'Australia', 'AT': 'Austria',
        'AZ': 'Azerbaijan', 'BS': 'Bahamas', 'BH': 'Bahrain', 'BD': 'Bangladesh', 'BB': 'Barbados',
        'BY': 'Belarus', 'BE': 'Belgium', 'BZ': 'Belize', 'BJ': 'Benin', 'BM': 'Bermuda',
        'BT': 'Bhutan', 'BO': 'Bolivia', 'BA': 'Bosnia and Herzegovina', 'BW': 'Botswana', 'BR': 'Brazil',
        'BN': 'Brunei Darussalam', 'BG': 'Bulgaria', 'BF': 'Burkina Faso', 'BI': 'Burundi', 'KH': 'Cambodia',
        'CM': 'Cameroon', 'CA': 'Canada', 'CV': 'Cape Verde', 'KY': 'Cayman Islands', 'CF': 'Central African Republic',
        'TD': 'Chad', 'CL': 'Chile', 'CN': 'China', 'CO': 'Colombia', 'KM': 'Comoros',
        'CG': 'Congo', 'CD': 'Congo, Democratic Republic of the', 'CR': 'Costa Rica', 'HR': 'Croatia', 'CU': 'Cuba',
        'CY': 'Cyprus', 'CZ': 'Czech Republic', 'DK': 'Denmark', 'DJ': 'Djibouti', 'DM': 'Dominica',
        'DO': 'Dominican Republic', 'EC': 'Ecuador', 'EG': 'Egypt', 'SV': 'El Salvador', 'GQ': 'Equatorial Guinea',
        'ER': 'Eritrea', 'EE': 'Estonia', 'ET': 'Ethiopia', 'FJ': 'Fiji', 'FI': 'Finland',
        'FR': 'France', 'GA': 'Gabon', 'GM': 'Gambia', 'GE': 'Georgia', 'DE': 'Germany',
        'GH': 'Ghana', 'GR': 'Greece', 'GD': 'Grenada', 'GT': 'Guatemala', 'GN': 'Guinea',
        'GW': 'Guinea-Bissau', 'GY': 'Guyana', 'HT': 'Haiti', 'HN': 'Honduras', 'HK': 'Hong Kong',
        'HU': 'Hungary', 'IS': 'Iceland', 'IN': 'India', 'ID': 'Indonesia', 'IR': 'Iran, Islamic Republic of',
        'IQ': 'Iraq', 'IE': 'Ireland', 'IL': 'Israel', 'IT': 'Italy', 'JM': 'Jamaica',
        'JP': 'Japan', 'JO': 'Jordan', 'KZ': 'Kazakhstan', 'KE': 'Kenya', 'KP': 'Korea, Democratic People\'s Republic of',
        'KR': 'Korea, Republic of', 'KW': 'Kuwait', 'KG': 'Kyrgyzstan', 'LA': 'Lao People\'s Democratic Republic',
        'LV': 'Latvia', 'LB': 'Lebanon', 'LS': 'Lesotho', 'LR': 'Liberia', 'LY': 'Libya',
        'LT': 'Lithuania', 'LU': 'Luxembourg', 'MO': 'Macao', 'MK': 'Macedonia, the Former Yugoslav Republic of',
        'MG': 'Madagascar', 'MW': 'Malawi', 'MY': 'Malaysia', 'MV': 'Maldives', 'ML': 'Mali',
        'MT': 'Malta', 'MR': 'Mauritania', 'MU': 'Mauritius', 'MX': 'Mexico', 'MD': 'Moldova, Republic of',
        'MN': 'Mongolia', 'ME': 'Montenegro', 'MA': 'Morocco', 'MZ': 'Mozambique', 'MM': 'Myanmar',
        'NA': 'Namibia', 'NP': 'Nepal', 'NL': 'Netherlands', 'NZ': 'New Zealand', 'NI': 'Nicaragua',
        'NE': 'Niger', 'NG': 'Nigeria', 'NO': 'Norway', 'OM': 'Oman', 'PK': 'Pakistan',
        'PW': 'Palau', 'PA': 'Panama', 'PG': 'Papua New Guinea', 'PY': 'Paraguay', 'PE': 'Peru',
        'PH': 'Philippines', 'PL': 'Poland', 'PT': 'Portugal', 'QA': 'Qatar', 'RO': 'Romania',
        'RU': 'Russian Federation', 'RW': 'Rwanda', 'WS': 'Samoa', 'SA': 'Saudi Arabia', 'SN': 'Senegal',
        'RS': 'Serbia', 'SL': 'Sierra Leone', 'SG': 'Singapore', 'SK': 'Slovakia', 'SI': 'Slovenia',
        'SB': 'Solomon Islands', 'ZA': 'South Africa', 'ES': 'Spain', 'LK': 'Sri Lanka', 'SD': 'Sudan',
        'SR': 'Suriname', 'SZ': 'Swaziland', 'SE': 'Sweden', 'CH': 'Switzerland', 'SY': 'Syrian Arab Republic',
        'TW': 'Taiwan, Province of China', 'TJ': 'Tajikistan', 'TZ': 'Tanzania, United Republic of', 'TH': 'Thailand',
        'TL': 'Timor-Leste', 'TG': 'Togo', 'TO': 'Tonga', 'TT': 'Trinidad and Tobago', 'TN': 'Tunisia',
        'TR': 'Turkey', 'TM': 'Turkmenistan', 'UG': 'Uganda', 'UA': 'Ukraine', 'AE': 'United Arab Emirates',
        'GB': 'United Kingdom', 'US': 'United States', 'UY': 'Uruguay', 'UZ': 'Uzbekistan', 'VU': 'Vanuatu',
        'VE': 'Venezuela, Bolivarian Republic of', 'VN': 'Viet Nam', 'ZM': 'Zambia', 'ZW': 'Zimbabwe'
    }

    # Map country codes to country names
    df['region'] = df['region'].map(country_mapping)

    # Group by country and count the number of films
    df_grouped = df.groupby('region').size().reset_index(name='total_films')

    # Apply log transformation to handle outliers
    df_grouped['log_total_films'] = np.log1p(df_grouped['total_films'])

    # Create a choropleth map with the log-transformed data
    fig = px.choropleth(df_grouped, locations='region', locationmode='country names',
                        color='log_total_films', hover_name='region',
                        color_continuous_scale='Plasma',  # Change the color scheme here
                        labels={'log_total_films': 'Total Films (log scale)'})

    # Update layout of the map
    fig.update_layout(title='Total Films by Country (Log Scale)',
                      geo=dict(showframe=False, showcoastlines=False,
                               projection_type='equirectangular'))

    return fig

# Function to fetch summary info
def fetch_summary_info(conn):
    # Fetch total count of movies
    query_total_movies = r'''
        SELECT COUNT(*) as total_movies
        FROM title_basics
        WHERE titleType = 'movie'
    '''
    total_movies = pd.read_sql_query(query_total_movies, conn).iloc[0]['total_movies']

    # Fetch total count of years
    query_total_years = r'''
        SELECT COUNT(DISTINCT startYear) as total_years
        FROM title_basics
        WHERE titleType = 'movie' AND startYear IS NOT NULL AND startYear != '\N'
    '''
    total_years = pd.read_sql_query(query_total_years, conn).iloc[0]['total_years']

    # Fetch average rating of movies
    query_avg_rating = r'''
        SELECT AVG(averageRating) as avg_rating
        FROM title_ratings
    '''
    avg_rating = pd.read_sql_query(query_avg_rating, conn).iloc[0]['avg_rating']

    return total_movies, total_years, avg_rating

# Main Streamlit app
def run_app():
    st.title('IMDb Movie Data Analysis')

    # Load data from SQLite database
    conn = load_data('imdb_data.db')

    fig_global_map = plot_global_map()
    st.plotly_chart(fig_global_map)

    # Fetch genre movie releases data
    genre_counts = fetch_genre_movie_releases(conn)

    # Plot genre movie releases by year (stacked area chart)
    fig_genre_movie_releases = plot_stacked_genre_movie_releases(genre_counts)
    st.plotly_chart(fig_genre_movie_releases)

    # Fetch movie release years data
    df_release_years = fetch_movie_release_years(conn)

    # Plot movie release years (filled line chart)
    fig_movie_release_years = plot_movie_release_years(df_release_years)
    st.plotly_chart(fig_movie_release_years)

    # Fetch and plot average rating by genre (box plot)
    fig_avg_rating_by_genre = fetch_and_plot_average_rating_by_genre(conn)
    st.plotly_chart(fig_avg_rating_by_genre)

    # Create and display word cloud of genres
    create_genre_wordcloud(conn)

    # Find and display best movies by genre
    best_movies_by_genre = find_best_movies_by_genre(conn)
    st.subheader('Best Movies by Genre')
    st.dataframe(best_movies_by_genre)

    # Fetch summary info
    total_movies, total_years, avg_rating = fetch_summary_info(conn)
    
    # Display summary info
    st.subheader('Summary Info')
    st.metric(label='Total Movies', value=total_movies)
    st.metric(label='Total Years of Movie Data', value=total_years)
    st.metric(label='Average Movie Rating', value=f'{avg_rating:.2f}')

    # Close database connection
    conn.close()

if __name__ == '__main__':
    run_app()