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Add Sleep Health and Lifestyle Analysis example
VijayaSriRam Mar 20, 2025
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Modify Readme.md file
VijayaSriRam Mar 20, 2025
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36 changes: 36 additions & 0 deletions community_gallery/sleep-health-analysis/README.md
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# Sleep Health and Lifestyle Analysis

## Dataset Source
This project uses the Sleep Health and Lifestyle Dataset from Kaggle, which contains information about sleep patterns, BMI categories, and sleep disorders across different categories.
Reference Link: https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data

## What This App Does
This application provides:
- An overview of sleep health metrics across the dataset
- Interactive filtering by sleep duration using a slider
- Visualizations showing the relationship between gender, BMI category, and sleep disorders
- A data table displaying the filtered dataset records

## How to Run and Deploy

## How to Run and Deploy

1. Install preswald. You can create a new virtual environment or install other requirements as needed.
- pip install preswald

2. Download the dataset from the provided source.
(https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data)

3. You can execute the hello.py file using: preswald run to initialize and run the application locally.

4. To deploy the application, execute the following code in your terminal:
preswald deploy --target structured --github YOUR_GITHUB_USERNAME --api-key YOUR_API_KEY

Note: You need to replace "YOUR_GITHUB_USERNAME" with your Github Profile username (all lowercase) and "YOUR_API_KEY" with the API key generated from "app.preswald.com"



## Live Demo
[Sleep Health Dashboard](https://coding-assessment-466795-3ewu6miz-ndjz2ws6la-ue.a.run.app)

Note: This app is developed taking the 30-minute time limitation into account. The visual dashboard and the storyline can further be improved further with more time.
70 changes: 70 additions & 0 deletions community_gallery/sleep-health-analysis/hello.py
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from preswald import text, plotly, connect, get_df, table, slider
import pandas as pd
import plotly.express as px

text("# Sleep Health and Lifestyle Dashboard")
text("This dashboard will help you understand the relationship between sleep and lifestyle factors based on gender, BMI category and sleep disorders.")

# Load the CSV
connect()
df = get_df('sleep_health')

#Overview of the data
total_participants = len(df)
male_participants = len(df[df['Gender'] == 'Male'])
female_participants = len(df[df['Gender'] == 'Female'])
disorder_count = len(df[df['Sleep Disorder'] != 'None'])

#Display of summary statistics
text("## Sleep Health at a Glance")
text(f"**Total Participants:** {total_participants} | **Males:** {male_participants} | **Females:** {female_participants} | **Suffering from Sleep Disorders:** {disorder_count}")

#Sleep Duration Breakdown
min_sleep = float(df['Sleep Duration'].min())
avg_sleep = float(df['Sleep Duration'].mean())
max_sleep = float(df['Sleep Duration'].max())

#Filtering data based on sleep duration
text("## Filter by Sleep Duration")
sleep_threshold = slider("Minimum Sleep Duration (hours)",
min_val=min_sleep,
max_val=max_sleep,
default=min_sleep)

# Filter data based on slider value
filtered_df = df[df['Sleep Duration'] >= sleep_threshold]
text(f"Showing {len(filtered_df)} records with Sleep Duration ≥ {sleep_threshold:.2f} hours")

#Gender vs BMI Category
bmi_by_gender = filtered_df.groupby(['Gender', 'BMI Category']).size().reset_index(name='Count')
fig1 = px.bar(
bmi_by_gender,
x = 'Gender',
y = 'Count',
color = 'BMI Category',
title = 'BMI Category by Gender',
labels = {'Count': 'Number of People', 'BMI Category': 'BMI Category Type'},
barmode = 'group'
)

#Display the graph
plotly(fig1)

#Gender vs Sleep Disorders
disorder_by_gender = filtered_df.groupby(['Gender', 'Sleep Disorder']).size().reset_index(name='Count')
fig2 = px.bar(
disorder_by_gender,
x='Gender',
y='Count',
color='Sleep Disorder',
title='Sleep Disorders by Gender',
labels={'Count': 'Number of People', 'Sleep Disorder': 'Sleep Disorder Type'},
barmode='group'
)

#Display the graph
plotly(fig2)

#Dataset as table
text("## Dataset overview")
table(filtered_df.head(100), title="Sleep Health and Lifestyle Dataset")
24 changes: 24 additions & 0 deletions community_gallery/sleep-health-analysis/preswald.toml
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[project]
title = "Preswald Project"
version = "0.1.0"
port = 8501
slug = "coding-assessment-466795" # Required: Unique identifier for your project
entrypoint = "hello.py" # Required: Main script to run when executing preswald run

[branding]
name = "Preswald Project"
logo = "images/logo.png"
favicon = "images/favicon.ico"
primaryColor = "#F89613"

[data.sample_csv]
type = "csv"
path = "data/sample.csv"

[logging]
level = "INFO" # Options: DEBUG, INFO, WARNING, ERROR, CRITICAL
format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"

[data.sleep_health]
path = "data/Sleep_Health.csv"
type = "csv"
23 changes: 23 additions & 0 deletions community_gallery/sleep-health-analysis/pyproject.toml
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[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[project]
name = "preswald-app"
version = "0.1.0"
description = "A Preswald application"
requires-python = ">=3.8"
dependencies = [
"preswald"
]

[tool.hatch.build.targets.wheel]
packages = ["."]

[tool.black]
line-length = 88
target-version = ['py38']

[tool.isort]
profile = "black"
multi_line_output = 3