app.py

import streamlit as st
import pandas as pd
import plotly.express as px
import seaborn as sns
import matplotlib.pyplot as plt
import time
from wordcloud import WordCloud

# Load Data
df = pd.read_csv('Data/Time-Wasters on Social Media.csv')

# Custom CSS Styling
def local_css():
    st.markdown("""
    <style>
        .main {background-color: #f5f7fa;}
        h1 {color: #003366; text-align: center;}
        h3 {color: #666666; text-align: center;}
        .stButton>button {background-color: #003366; color: white; font-size: 18px;}
    </style>
    """, unsafe_allow_html=True)
local_css()

# Sidebar Navigation
st.sidebar.image('assets/logo.png', width=200)
st.sidebar.title("Navigation")
page = st.sidebar.radio("Go to", ['Home', 'Time Wasters', 'Engagement Levels', 'Addiction Levels'])

# Session Timer (Tracks time spent on dashboard)
start_time = time.time()
if 'start_time' not in st.session_state:
    st.session_state['start_time'] = start_time
elapsed_time = time.time() - st.session_state['start_time']
st.sidebar.metric("Time Spent Here", f"{int(elapsed_time)} sec")

# Home Page
if page == 'Home':
    st.title("📊 Welcome to the Time-Wasters Analytics Dashboard")
    st.markdown("""
    ### What you will explore:
    1. **Time-Wasting Trends on Social Media**
    2. **Engagement Levels & Productivity Loss**
    3. **Social Media Addiction Insights**
    """)
    st.image('assets/dashboard_preview.png')

# Time Wasters Analysis
elif page == 'Time Wasters':
    st.title("📱 Time Wasters on Social Media")
    col1, col2 = st.columns(2)
    col1.metric("Total Users", len(df['UserID'].unique()))
    col2.metric("Total Time Spent", int(df['Total Time Spent'].sum()))
    
    # Filters
    selected_country = st.selectbox("Select Country", df['Location'].unique())
    selected_gender = st.selectbox("Select Gender", df['Gender'].unique())
    selected_platform = st.selectbox("Select Platform", df['Platform'].unique())
    age_range = st.slider("Select Age Range", int(df['Age'].min()), int(df['Age'].max()), (20, 40))
    
    # Filter Data
    filtered_data = df[(df['Location'] == selected_country) &
                       (df['Gender'] == selected_gender) &
                       (df['Age'].between(*age_range)) &
                       (df['Platform'] == selected_platform)]
    
    avg_addiction_level = filtered_data['Addiction Level'].mean()
    st.subheader(f"Average Addiction Level: {avg_addiction_level:.2f}")
    
    # Animated Bar Chart
    fig = px.histogram(filtered_data, x='Addiction Level', nbins=10, color_discrete_sequence=['teal'], animation_frame='Age')
    st.plotly_chart(fig)

# Engagement Levels
elif page == 'Engagement Levels':
    st.title("🎯 Engagement Levels on Social Media")
    selected_country = st.selectbox("Select Country", df['Location'].unique(), key='engage_country')
    selected_platform = st.selectbox("Select Platform", df['Platform'].unique(), key='engage_platform')
    
    filtered_data = df[(df['Location'] == selected_country) & (df['Platform'] == selected_platform)]
    avg_engagement = filtered_data['Engagement'].mean()
    st.subheader(f"Average Engagement: {avg_engagement:.2f}")
    
    # Word Cloud of Watch Reasons
    text = ' '.join(filtered_data['Watch Reason'].dropna())
    wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)
    st.image(wordcloud.to_array())
    
    # Engagement Scatter Plot
    fig = px.scatter(filtered_data, x='Engagement', y='ProductivityLoss', color='Platform', size='Total Time Spent')
    st.plotly_chart(fig)

# Addiction Levels
elif page == 'Addiction Levels':
    st.title("⚠️ Social Media Addiction Levels")
    selected_country = st.selectbox("Select Country", df['Location'].unique(), key='addict_country')
    selected_platform = st.selectbox("Select Platform", df['Platform'].unique(), key='addict_platform')
    
    filtered_data = df[(df['Location'] == selected_country) & (df['Platform'] == selected_platform)]
    avg_addiction = filtered_data['Addiction Level'].mean()
    st.subheader(f"Average Addiction Level: {avg_addiction:.2f}")
    
    # Addiction vs Age Line Chart
    fig, ax = plt.subplots()
    sns.lineplot(data=filtered_data, x='Age', y='Addiction Level', marker='o', ax=ax)
    ax.set_title("Addiction Level vs Age")
    st.pyplot(fig)
    
    # Heatmap
    st.subheader("Engagement & Addiction Heatmap")
    heatmap_data = df.pivot_table(index='Location', columns='Platform', values='Addiction Level', aggfunc='mean')
    fig, ax = plt.subplots(figsize=(10,6))
    sns.heatmap(heatmap_data, cmap='coolwarm', annot=True, ax=ax)
    st.pyplot(fig)

st.sidebar.write("© 2025 Social Media Analytics Dashboard")