Update app.py

app.py

@@ -1,219 +1,115 @@
 import streamlit as st
 import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
+import plotly.express as px
 import seaborn as sns
-
-# Streamlit App
-st.set_page_config(page_title="Social Media Usage Dashboard", layout="wide")
+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", ["Overview", "Time Analysis", "Demographics", "Behavior Analysis"])
-
-# Load dataset
-df = pd.read_csv("Dataset.csv")
-
-# Display the selected page
-if page == "Overview":
-    st.title("📊 Overview: Social Media Usage Insights")
-    st.write("This dashboard provides key insights into how people waste time on social media.")
-
-    # Sidebar Filters
-    st.sidebar.subheader("🔹 Filters")
-    gender_filter = st.sidebar.multiselect("Select Gender:", options=df["Gender"].unique(), default=df["Gender"].unique())
-    platform_filter = st.sidebar.multiselect("Select Platform:", options=df["Platform"].unique(), default=df["Platform"].unique())
-
-    # Apply Filters
-    filtered_df = df[(df["Gender"].isin(gender_filter)) & (df["Platform"].isin(platform_filter))]
-
-    # Refresh Button
-    if st.sidebar.button("Reset Filters"):
-        gender_filter = df["Gender"].unique()
-        platform_filter = df["Platform"].unique()
-
-    # Display Key Metrics
-    col1, col2, col3 = st.columns(3)
-
-    with col1:
-        total_time = filtered_df["Total Time Spent"].sum()
-        st.metric(label="⏳ Total Time Wasted", value=f"{total_time} minutes")
-
-    with col2:
-        most_used_platform = filtered_df["Platform"].mode()[0]
-        st.metric(label="📱 Most Used Platform", value=most_used_platform)
-
-    with col3:
-        avg_session_time = filtered_df["Total Time Spent"].mean()
-        st.metric(label="🕒 Avg. Session Time", value=f"{avg_session_time:.2f} minutes")
-
-    # Visualizations
-    st.subheader("📌 Time Wasted by Gender")
-    fig, ax = plt.subplots()
-    filtered_df.groupby("Gender")["Total Time Spent"].sum().plot(kind="pie", autopct="%1.1f%%", ax=ax)
-    ax.set_ylabel("")
-    st.pyplot(fig)
-
-    st.subheader("📌 Most Used Social Media Platforms")
-    fig, ax = plt.subplots()
-    sns.countplot(x="Platform", data=filtered_df, order=filtered_df["Platform"].value_counts().index, palette="viridis", ax=ax)
-    plt.xticks(rotation=45)
-    ax.set_title("Most Used Social Media Platforms")
-    st.pyplot(fig)
-
-    st.subheader("📌 Time Spent Distribution")
+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.histplot(filtered_df["Total Time Spent"], bins=20, kde=True, color="blue", ax=ax)
-    ax.set_title("Distribution of Time Spent on Social Media")
+    sns.lineplot(data=filtered_data, x='Age', y='Addiction Level', marker='o', ax=ax)
+    ax.set_title("Addiction Level vs Age")
     st.pyplot(fig)
-
-    # Interactive Button
-    if st.button("📈 Show More Insights"):
-        st.write("More analysis coming soon! 🚀")
-
-    # Footer
-    st.markdown("**🔹 Use the sidebar to filter data by gender and platform.**")
-
-
-elif page == "Time Analysis":
-    st.title("⏳ Time-Based Trends")
-    st.write("Analyze how people waste time on social media at different times of the day.")
-
-    # Sidebar Filters
-    st.sidebar.subheader("🔹 Filters")
-    gender_filter = st.sidebar.multiselect("Select Gender:", options=df["Gender"].unique(), default=df["Gender"].unique())
-    platform_filter = st.sidebar.multiselect("Select Platform:", options=df["Platform"].unique(), default=df["Platform"].unique())
-
-    # Apply Filters
-    filtered_df = df[(df["Gender"].isin(gender_filter)) & (df["Platform"].isin(platform_filter))]
-
-    # Extract Watch Hour from Watch Time
-    filtered_df["Watch Hour"] = pd.to_datetime(filtered_df["Watch Time"], errors="coerce").dt.hour
-
-    # Toggle insights
-    show_insights = st.checkbox("📊 Show Key Insights")
-
-    # Key Insights
-    if show_insights:
-        peak_hour = filtered_df.groupby("Watch Hour")["Total Time Spent"].sum().idxmax()
-        most_active_platform = filtered_df["Platform"].mode()[0]
-
-        col1, col2 = st.columns(2)
-        with col1:
-            st.metric(label="⏰ Peak Hour of Usage", value=f"{peak_hour}:00")
-        with col2:
-            st.metric(label="📱 Most Active Platform", value=most_active_platform)
-
-    # Line Chart: Time Spent Per Hour
-    st.subheader("📌 Time Spent by Hour of the Day")
-    fig, ax = plt.subplots()
-    sns.lineplot(x="Watch Hour", y="Total Time Spent", data=filtered_df, marker="o", ax=ax)
-    ax.set_title("Social Media Usage by Hour")
-    ax.set_xticks(range(0, 24))
-    st.pyplot(fig)
-
-    # Heatmap: Hour vs. Platform
-    st.subheader("📌 Time Spent by Hour & Platform")
-    pivot_table = filtered_df.pivot_table(values="Total Time Spent", index="Watch Hour", columns="Platform", aggfunc="sum")
-
-    fig, ax = plt.subplots(figsize=(8,6))
-    sns.heatmap(pivot_table, cmap="coolwarm", annot=True, fmt=".1f", ax=ax)
-    st.pyplot(fig)
-
-    # Interactive Button
-    if st.button("🔍 More Insights Coming Soon!"):
-        st.write("Stay tuned for deeper analysis! 🚀")
-
-elif page == "Demographics":
-    st.title("🧑‍🤝‍🧑 Demographics Analysis")
-    st.write("Understand how different demographics engage with social media.")
-
-    # Sidebar Filters
-    st.sidebar.subheader("🔹 Filters")
-    gender_filter = st.sidebar.multiselect("Select Gender:", options=df["Gender"].unique(), default=df["Gender"].unique())
-    age_filter = st.sidebar.slider("Select Age Range:", int(df["Age"].min()), int(df["Age"].max()), (int(df["Age"].min()), int(df["Age"].max())))
-    location_filter = st.sidebar.multiselect("Select Location:", options=df["Location"].unique(), default=df["Location"].unique())
-
-    # Apply Filters
-    filtered_df = df[(df["Gender"].isin(gender_filter)) & 
-                     (df["Age"].between(age_filter[0], age_filter[1])) & 
-                     (df["Location"].isin(location_filter))]
-
-    # Toggle insights
-    show_insights = st.checkbox("📊 Show Key Insights")
-
-    # Key Insights
-    if show_insights:
-        most_active_age_group = filtered_df.groupby("Age")["Total Time Spent"].sum().idxmax()
-        most_active_location = filtered_df.groupby("Location")["Total Time Spent"].sum().idxmax()
-
-        col1, col2 = st.columns(2)
-        with col1:
-            st.metric(label="📅 Most Active Age Group", value=f"{most_active_age_group} years")
-        with col2:
-            st.metric(label="📍 Most Active Location", value=most_active_location)
-
-    # Bar Chart: Time Spent by Age Group
-    st.subheader("📌 Time Spent by Age Group")
-    fig, ax = plt.subplots()
-    sns.barplot(x="Age", y="Total Time Spent", data=filtered_df, palette="coolwarm", ax=ax)
-    ax.set_title("Social Media Usage by Age Group")
-    st.pyplot(fig)
-
-    # Pie Chart: Time Wasted by Location
-    st.subheader("📌 Time Wasted by Location")
-    fig, ax = plt.subplots()
-    filtered_df.groupby("Location")["Total Time Spent"].sum().plot(kind="pie", autopct="%1.1f%%", ax=ax)
-    ax.set_ylabel("")
-    st.pyplot(fig)
-
-    # Interactive Button
-    if st.button("🔍 More Insights Coming Soon!"):
-        st.write("Stay tuned for deeper demographic analysis! 🚀")
-elif page == "Behavior Analysis":
-    st.title("🧠 Behavior Analysis")
-    st.write("Understand how social media affects engagement, addiction, and productivity.")
-
-    # Sidebar Filters
-    st.sidebar.subheader("🔹 Filters")
-    gender_filter = st.sidebar.multiselect("Select Gender:", options=df["Gender"].unique(), default=df["Gender"].unique())
-    platform_filter = st.sidebar.multiselect("Select Platform:", options=df["Platform"].unique(), default=df["Platform"].unique())
-    engagement_filter = st.sidebar.slider("Select Engagement Level:", int(df["Engagement"].min()), int(df["Engagement"].max()), (int(df["Engagement"].min()), int(df["Engagement"].max())))
-
-    # Apply Filters
-    filtered_df = df[(df["Gender"].isin(gender_filter)) & 
-                     (df["Platform"].isin(platform_filter)) & 
-                     (df["Engagement"].between(engagement_filter[0], engagement_filter[1]))]
-
-    # Toggle insights
-    show_insights = st.checkbox("📊 Show Key Insights")
-
-    # Key Insights
-    if show_insights:
-        highest_engagement_platform = filtered_df.groupby("Platform")["Engagement"].mean().idxmax()
-        most_addictive_platform = filtered_df.groupby("Platform")["Addiction Level"].mean().idxmax()
-
-        col1, col2 = st.columns(2)
-        with col1:
-            st.metric(label="🔥 Highest Engagement Platform", value=highest_engagement_platform)
-        with col2:
-            st.metric(label="⚠️ Most Addictive Platform", value=most_addictive_platform)
-
-    # Bar Chart: Engagement Levels by Platform
-    st.subheader("📌 Engagement Levels by Platform")
-    fig, ax = plt.subplots()
-    sns.barplot(x="Platform", y="Engagement", data=filtered_df, palette="viridis", ax=ax)
-    ax.set_title("Average Engagement Level per Platform")
-    plt.xticks(rotation=45)
-    st.pyplot(fig)
-
-    # Scatter Plot: Addiction Level vs. Productivity Loss
-    st.subheader("📌 Addiction Level vs. Productivity Loss")
-    fig, ax = plt.subplots()
-    sns.scatterplot(x="Addiction Level", y="ProductivityLoss", hue="Platform", data=filtered_df, palette="coolwarm", ax=ax)
-    ax.set_title("Impact of Addiction on Productivity Loss")
+    
+    # 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)
 
-    # Interactive Button
-    if st.button("🔍 More Insights Coming Soon!"):
-        st.write("Stay tuned for deeper behavior analysis! 🚀")
+st.sidebar.write("© 2025 Social Media Analytics Dashboard")