Pages/1_Time_Wasters.py

import streamlit as st
import pandas as pd
import plotly.express as px
import seaborn as sns
import matplotlib.pyplot as plt

st.title('Time Wasters on Social Media')


df = pd.read_csv('Time-Wasters on Social Media.csv')

st.subheader("Country-wise Addiction Levels by Demographics")
 
col1,col2=st.columns(2)
st.write("")
st.markdown("<hr>",unsafe_allow_html=True)
st.write("")
with col1:
    st.metric("Total Users",len(df['UserID'].unique().tolist()))
with col2:
    st.metric("Total Time Spent", int(df['Total Time Spent'].sum()))


selected_country = st.selectbox("Select Country", df['Location'].unique().tolist())

selected_gender = st.selectbox("Select Gender", df['Gender'].unique().tolist())

min_age, max_age = int(df['Age'].min()), int(df['Age'].max())
selected_age_range = st.slider("Select Age Range", min_age, max_age, (min_age, max_age))

selected_platform = st.selectbox("Select Platform", df['Platform'].unique().tolist())


filtered_data = df[
    (df['Location'] == selected_country) &
    (df['Gender'] == selected_gender) &
    (df['Age'] >= selected_age_range[0]) &
    (df['Age'] <= selected_age_range[1]) &
    (df['Platform'] == selected_platform)
]

# st.write(filtered_data)


avg_addiction_level = filtered_data['Addiction Level'].mean()

st.subheader(f"Average Addiction Level in {selected_country} for {selected_gender} users aged {selected_age_range[0]} to {selected_age_range[1]} on {selected_platform}:")
st.write(f"*{avg_addiction_level:.2f}*")

st.subheader("Addiction Level Distribution")

plt.figure(figsize=(10, 6))
sns.histplot(data=filtered_data, x='Addiction Level', kde=True, bins=10, color='teal')
plt.title(f"Addiction Level Distribution in {selected_country} for {selected_gender} users")
plt.xlabel('Addiction Level')
plt.ylabel('Frequency')

st.pyplot(plt)

#Time Spenditure Analysis
# platform=st.multiselect("Select Platforms",df['Platform'].unique().tolist())

# filter1_df= df[
#     (df['Platform'] == platform) & 
#     (df['Frequency']) & 
#     (df['Total Time Spent'])
# ]

# time_spent_on_platform=filter1_df[["Platform","Frequency","Total Time Spent"]].groupby(['Platform','Frequency']).sum().reset_index()
# pivot_time_spent_on_platform = pd.pivot(time_spent_on_platform,index='Frequency',columns='Platform',values='Total Time Spent')
# pivot_time_spent_on_platform.reset_index(inplace=True)
# pivot_time_spent_on_platform.set_index("Frequency", inplace=True)
# fig1,ax1=plt.subplots(figsize=(9, 7))
# ax1.plot(pivot_time_spent_on_platform,linestyle='--',marker='o')
# plt.xlabel('Frequency')
# plt.ylabel('Total Time Spent')
# plt.title('Multiline Line Plot from Pivot Table')
# plt.legend(df['Platform'],title='Platform',loc=2)
# ax1.grid(True)
# st.pyplot(fig1)
# st.write("")
# st.write("")
# st.markdown("<hr>",unsafe_allow_html=True)
# st.write("")
# st.write("")

watch_reason_counts = filtered_data['Watch Reason'].value_counts()

st.subheader("Most Common Watch Reasons")
st.write(watch_reason_counts)
st.subheader("Country-wise Engagement Levels")

avg_engagement_by_country = df.groupby('Location')['Engagement'].mean().sort_values(ascending=False)

st.bar_chart(avg_engagement_by_country)

st.subheader("Addiction Level vs Age")

selected_country = st.selectbox("Select Country", df['Location'].unique(), key='country_select_addiction')
selected_platform = st.selectbox("Select Platform", df['Platform'].unique(), key='platform_select_addiction')

filtered_data = df[(df['Location'] == selected_country) & (df['Platform'] == selected_platform)]

plt.figure(figsize=(10, 6))
sns.lineplot(data=filtered_data, x='Age', y='Addiction Level', marker='o')
plt.title(f'Addiction Level vs Age in {selected_country} on {selected_platform}')
plt.xlabel('Age')
plt.ylabel('Addiction Level')
st.pyplot(plt)

st.subheader("Engagement Levels by Platform")

age_range = st.slider("Select Age Range", int(df['Age'].min()), int(df['Age'].max()), (20, 40), key='age_slider_platform')

filtered_data = df[(df['Age'] >= age_range[0]) & (df['Age'] <= age_range[1])]

plt.figure(figsize=(10, 6))
sns.boxplot(data=filtered_data, x='Platform', y='Engagement', palette='Set3')
plt.title(f'Engagement Levels for Age Group {age_range[0]} to {age_range[1]}')
st.pyplot(plt)

st.subheader("Gender-wise Comparison of Productivity Loss")

selected_platform = st.selectbox("Select Platform", df['Platform'].unique(), key='platform_select_prod_loss')

filtered_data = df[df['Platform'] == selected_platform]

gender_prod_loss = filtered_data.groupby('Gender')['ProductivityLoss'].mean()

st.bar_chart(gender_prod_loss)

st.subheader("Engagement vs Productivity Loss")

plt.figure(figsize=(10, 6))
sns.scatterplot(data=df, x='Engagement', y='ProductivityLoss', hue='Platform', palette='viridis', s=100)
plt.title('Engagement vs Productivity Loss across Platforms')
st.pyplot(plt)


st.subheader("Watch Reasons per Demographic")

selected_country = st.selectbox("Select Country", df['Location'].unique(), key='country_select_watch_reason')
age_range = st.slider("Select Age Range", int(df['Age'].min()), int(df['Age'].max()), (20, 40), key='age_slider_watch_reason')

filtered_data = df[(df['Location'] == selected_country) & (df['Age'] >= age_range[0]) & (df['Age'] <= age_range[1])]

watch_reasons = filtered_data['Watch Reason'].value_counts()
st.bar_chart(watch_reasons)


# import streamlit as st
# import pandas as pd
# import plotly.express as px
# import seaborn as sns
# import matplotlib.pyplot as plt

# # Custom CSS for Stunning UI
# st.markdown(
#     """
#     <style>
#         /* Background Gradient */
#         body {
#             background: linear-gradient(to right, #0f2027, #203a43, #2c5364);
#             color: white;
#         }
        
#         /* Main Title Styling */
#         .main-title {
#             text-align: center;
#             color: #ffffff;
#             font-size: 60px;
#             font-weight: bold;
#             text-shadow: 3px 3px 15px rgba(255,255,255,0.3);
#         }
        
#         /* Subtitle Styling */
#         .subtitle {
#             text-align: center;
#             color: #d1d1d1;
#             font-size: 38px;
#             font-style: italic;
#         }
        
#         /* Glassmorphism Card Effect */
#         .content-box {
#             background: rgba(255, 255, 255, 0.15);
#             padding: 25px;
#             border-radius: 20px;
#             backdrop-filter: blur(12px);
#             box-shadow: 4px 6px 15px rgba(0,0,0,0.2);
#             margin: 25px;
#         }
        
#         /* List Styling */
#         .list-item {
#             color: #FFD700;
#             font-size: 28px;
#             padding: 8px;
#             transition: transform 0.3s, color 0.3s;
#         }
#         .list-item:hover {
#             color: #FF4500;
#             transform: scale(1.08);
#         }
        
#         /* Stylish Buttons */
#         .stButton>button {
#             background: linear-gradient(to right, #ff7e5f, #feb47b);
#             color: white;
#             font-size: 20px;
#             border-radius: 10px;
#             padding: 14px 22px;
#             transition: all 0.3s ease-in-out;
#             border: none;
#         }
#         .stButton>button:hover {
#             background: linear-gradient(to right, #feb47b, #ff7e5f);
#             transform: scale(1.12);
#         }
#     </style>
#     """,
#     unsafe_allow_html=True
# )

# # Display Title
# st.markdown("<h1 class='main-title'> Welcome to the Time-Wasters on Social Media Analytics</h1>", unsafe_allow_html=True)

# st.write("")

# # Display Subtitle
# st.markdown("<h3 class='subtitle'> Dive into the insights of Social Media Engagement and Addiction.</h3>", unsafe_allow_html=True)

# st.write("")

# # Content Section with Glassmorphism
# st.markdown(
#     """
#     <div class='content-box'>
#         <h4 style='color: #ffffff; font-size: 40px;'>🔍 What You Will Discover:</h4>
#         <ol>
#             <li class='list-item'> Analysis of Users and their activity over Social Media</li>
#             <li class='list-item'> Engagement Levels of Users over Social Media</li>
#             <li class='list-item'> Addiction Levels of Users over Social Media</li>
#         </ol>
#     </div>
#     """, 
#     unsafe_allow_html=True
# )

# # Call-to-Action Button
# if st.button(" Explore Dashboard "):
#     st.switch_page("dashboard.py")  # Ensure 'dashboard.py' exists as the main dashboard page

# # --- KEEPING PLOTTING AND FUNCTIONALITY UNTOUCHED BELOW ---

# st.title('Time Wasters on Social Media')

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

# st.subheader("Country-wise Addiction Levels by Demographics")

# col1, col2 = st.columns(2)
# with col1:
#     st.metric("Total Users", len(df['UserID'].unique().tolist()))
# with col2:
#     st.metric("Total Time Spent", int(df['Total Time Spent'].sum()))

# selected_country = st.selectbox("Select Country", df['Location'].unique().tolist())
# selected_gender = st.selectbox("Select Gender", df['Gender'].unique().tolist())
# min_age, max_age = int(df['Age'].min()), int(df['Age'].max())
# selected_age_range = st.slider("Select Age Range", min_age, max_age, (min_age, max_age))
# selected_platform = st.selectbox("Select Platform", df['Platform'].unique().tolist())

# filtered_data = df[
#     (df['Location'] == selected_country) &
#     (df['Gender'] == selected_gender) &
#     (df['Age'] >= selected_age_range[0]) &
#     (df['Age'] <= selected_age_range[1]) &
#     (df['Platform'] == selected_platform)
# ]

# avg_addiction_level = filtered_data['Addiction Level'].mean()
# st.subheader(f"Average Addiction Level in {selected_country} for {selected_gender} users aged {selected_age_range[0]} to {selected_age_range[1]} on {selected_platform}:")
# st.write(f"*{avg_addiction_level:.2f}*")

# st.subheader("Addiction Level Distribution")
# plt.figure(figsize=(10, 6))
# sns.histplot(data=filtered_data, x='Addiction Level', kde=True, bins=10, color='teal')
# st.pyplot(plt)

# watch_reason_counts = filtered_data['Watch Reason'].value_counts()
# st.subheader("Most Common Watch Reasons")
# st.write(watch_reason_counts)
# st.subheader("Country-wise Engagement Levels")
# avg_engagement_by_country = df.groupby('Location')['Engagement'].mean().sort_values(ascending=False)
# st.bar_chart(avg_engagement_by_country)

# st.subheader("Addiction Level vs Age")
# selected_country = st.selectbox("Select Country", df['Location'].unique(), key='country_select_addiction')
# selected_platform = st.selectbox("Select Platform", df['Platform'].unique(), key='platform_select_addiction')
# filtered_data = df[(df['Location'] == selected_country) & (df['Platform'] == selected_platform)]
# sns.lineplot(data=filtered_data, x='Age', y='Addiction Level', marker='o')
# st.pyplot(plt)
# import streamlit as st
# import pandas as pd
# import plotly.express as px
# import seaborn as sns
# import matplotlib.pyplot as plt

# st.title('Time Wasters on Social Media')


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

# st.subheader("Country-wise Addiction Levels by Demographics")
 
# col1,col2=st.columns(2)
# st.write("")
# st.markdown("<hr>",unsafe_allow_html=True)
# st.write("")
# with col1:
#     st.metric("Total Users",len(df['UserID'].unique().tolist()))
# with col2:
#     st.metric("Total Time Spent", int(df['Total Time Spent'].sum()))


# selected_country = st.selectbox("Select Country", df['Location'].unique().tolist())

# selected_gender = st.selectbox("Select Gender", df['Gender'].unique().tolist())

# min_age, max_age = int(df['Age'].min()), int(df['Age'].max())
# selected_age_range = st.slider("Select Age Range", min_age, max_age, (min_age, max_age))

# selected_platform = st.selectbox("Select Platform", df['Platform'].unique().tolist())


# filtered_data = df[
#     (df['Location'] == selected_country) &
#     (df['Gender'] == selected_gender) &
#     (df['Age'] >= selected_age_range[0]) &
#     (df['Age'] <= selected_age_range[1]) &
#     (df['Platform'] == selected_platform)
# ]

# # st.write(filtered_data)


# avg_addiction_level = filtered_data['Addiction Level'].mean()

# st.subheader(f"Average Addiction Level in {selected_country} for {selected_gender} users aged {selected_age_range[0]} to {selected_age_range[1]} on {selected_platform}:")
# st.write(f"*{avg_addiction_level:.2f}*")

# st.subheader("Addiction Level Distribution")

# plt.figure(figsize=(10, 6))
# sns.histplot(data=filtered_data, x='Addiction Level', kde=True, bins=10, color='teal')
# plt.title(f"Addiction Level Distribution in {selected_country} for {selected_gender} users")
# plt.xlabel('Addiction Level')
# plt.ylabel('Frequency')

# st.pyplot(plt)

# #Time Spenditure Analysis
# # platform=st.multiselect("Select Platforms",df['Platform'].unique().tolist())

# # filter1_df= df[
# #     (df['Platform'] == platform) & 
# #     (df['Frequency']) & 
# #     (df['Total Time Spent'])
# # ]

# # time_spent_on_platform=filter1_df[["Platform","Frequency","Total Time Spent"]].groupby(['Platform','Frequency']).sum().reset_index()
# # pivot_time_spent_on_platform = pd.pivot(time_spent_on_platform,index='Frequency',columns='Platform',values='Total Time Spent')
# # pivot_time_spent_on_platform.reset_index(inplace=True)
# # pivot_time_spent_on_platform.set_index("Frequency", inplace=True)
# # fig1,ax1=plt.subplots(figsize=(9, 7))
# # ax1.plot(pivot_time_spent_on_platform,linestyle='--',marker='o')
# # plt.xlabel('Frequency')
# # plt.ylabel('Total Time Spent')
# # plt.title('Multiline Line Plot from Pivot Table')
# # plt.legend(df['Platform'],title='Platform',loc=2)
# # ax1.grid(True)
# # st.pyplot(fig1)
# # st.write("")
# # st.write("")
# # st.markdown("<hr>",unsafe_allow_html=True)
# # st.write("")
# # st.write("")

# watch_reason_counts = filtered_data['Watch Reason'].value_counts()

# st.subheader("Most Common Watch Reasons")
# st.write(watch_reason_counts)
# st.subheader("Country-wise Engagement Levels")

# avg_engagement_by_country = df.groupby('Location')['Engagement'].mean().sort_values(ascending=False)

# st.bar_chart(avg_engagement_by_country)

# st.subheader("Addiction Level vs Age")

# selected_country = st.selectbox("Select Country", df['Location'].unique(), key='country_select_addiction')
# selected_platform = st.selectbox("Select Platform", df['Platform'].unique(), key='platform_select_addiction')

# filtered_data = df[(df['Location'] == selected_country) & (df['Platform'] == selected_platform)]

# plt.figure(figsize=(10, 6))
# sns.lineplot(data=filtered_data, x='Age', y='Addiction Level', marker='o')
# plt.title(f'Addiction Level vs Age in {selected_country} on {selected_platform}')
# plt.xlabel('Age')
# plt.ylabel('Addiction Level')
# st.pyplot(plt)

# st.subheader("Engagement Levels by Platform")

# age_range = st.slider("Select Age Range", int(df['Age'].min()), int(df['Age'].max()), (20, 40), key='age_slider_platform')

# filtered_data = df[(df['Age'] >= age_range[0]) & (df['Age'] <= age_range[1])]

# plt.figure(figsize=(10, 6))
# sns.boxplot(data=filtered_data, x='Platform', y='Engagement', palette='Set3')
# plt.title(f'Engagement Levels for Age Group {age_range[0]} to {age_range[1]}')
# st.pyplot(plt)

# st.subheader("Gender-wise Comparison of Productivity Loss")

# selected_platform = st.selectbox("Select Platform", df['Platform'].unique(), key='platform_select_prod_loss')

# filtered_data = df[df['Platform'] == selected_platform]

# gender_prod_loss = filtered_data.groupby('Gender')['ProductivityLoss'].mean()

# st.bar_chart(gender_prod_loss)

# st.subheader("Engagement vs Productivity Loss")

# plt.figure(figsize=(10, 6))
# sns.scatterplot(data=df, x='Engagement', y='ProductivityLoss', hue='Platform', palette='viridis', s=100)
# plt.title('Engagement vs Productivity Loss across Platforms')
# st.pyplot(plt)


# st.subheader("Watch Reasons per Demographic")

# selected_country = st.selectbox("Select Country", df['Location'].unique(), key='country_select_watch_reason')
# age_range = st.slider("Select Age Range", int(df['Age'].min()), int(df['Age'].max()), (20, 40), key='age_slider_watch_reason')

# filtered_data = df[(df['Location'] == selected_country) & (df['Age'] >= age_range[0]) & (df['Age'] <= age_range[1])]

watch_reasons = filtered_data['Watch Reason'].value_counts()
# st.bar_chart(watch_reasons)