This is my code:
import streamlit as st
from streamlit_option_menu import option_menu
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
import altair as alt
from PIL import Image
from plotly.subplots import make_subplots
Set Page
st.set_page_config(
page_title=“Multipage App”,
page_icon=“
”,
layout=“wide”,
)
VALID_USERNAME = “123”
VALID_PASSWORD = “123”
#Nevigative Bar
selected = option_menu(
menu_title=None,
options=[“Home”,“Annual Report”,“Risk Calculated”], #Pages
icons= [“1-square-fill”,“2-square-fill”,“3-square-fill”],
default_index=0,
orientation=“horizontal”
)
if selected == “Annual Report”:
username_placeholder = st.empty()
password_placeholder = st.empty()
username = username_placeholder.text_input(“Username”)
password = password_placeholder.text_input(“Password”, type=“password”)
login_button_placeholder = st.empty()
if login_button_placeholder.button(“Log in”):
if username == VALID_USERNAME and password == VALID_PASSWORD:
username_placeholder.empty()
password_placeholder.empty()
login_button_placeholder.empty()
alt.themes.enable("default")
st.title("Report of Annual Health Examinations")
st.write(" : Report of annual health examinations is a Dashboard that shows details about the health examination results of employees in the company from 2561 to 2563.")
#Data to USE
df_61 = pd.read_excel("DataToStreamlit.xlsx", sheet_name="61")
df_62 = pd.read_excel("DataToStreamlit.xlsx", sheet_name="62")
df_63 = pd.read_excel("DataToStreamlit.xlsx", sheet_name="63")
dfs = pd.concat([df_61, df_62, df_63], ignore_index=True)
#multi selection
st.sidebar.markdown("<h1 style='text-align: center; font-size: 20px;'>Health Examination</h1>", unsafe_allow_html=True)
st.sidebar.image('Online Doctor-cuate (1).png')
CompanyCode_selection = st.sidebar.multiselect(
"**Select Company Code :**",
options=dfs["Company_Code"].unique(),
default=dfs["Company_Code"].unique(),
)
Employee_selection = st.sidebar.multiselect(
"**Select Type Employee :**",
options=dfs["TYPE_employee"].unique(),
default=dfs["TYPE_employee"].unique(),
)
Sex_selection = st.sidebar.multiselect(
"**Select Sex :**",
options=dfs["Sex"].unique(),
default=dfs["Sex"].unique(),
)
filter = dfs[(dfs["Company_Code"].isin(CompanyCode_selection)) &
(dfs["TYPE_employee"].isin(Employee_selection)) &
(dfs["Sex"].isin(Sex_selection))]
#compute sum employee
count_employ61 = filter[filter['Year'] == 2561]['No'].count()
count_employ62 = filter[filter['Year'] == 2562]['No'].count()
count_employ63 = filter[filter['Year'] == 2563]['No'].count()
total1, total2, total3 = st.columns(3, gap="large")
with total1:
st.info('Total Employee : 2561')
st.metric(label='Persons', value=f"{count_employ61:,.0f}")
with total2:
st.info('Total Employee : 2562')
st.metric(label='Persons', value=f"{count_employ62:,.0f}")
with total3:
st.info('Total Employee : 2563')
st.metric(label='Persons', value=f"{count_employ63:,.0f}")
col1, col2 = st.columns([2, 1])
with col2:
Year_selection = st.slider(
"Select Year:",
min_value=min(dfs["Year"]),
max_value=max(dfs["Year"]),
value=(min(dfs["Year"]), max(dfs["Year"]))
)
filtered_data = dfs[dfs["Year"].between(*Year_selection) & (dfs["Company_Code"].isin(CompanyCode_selection))&
(dfs["TYPE_employee"].isin(Employee_selection)) &
(dfs["Sex"].isin(Sex_selection))]
#-------Chart for each Examination
data_examination = filtered_data[['PE','BLOODPRESSURE','PULSE','BMI','CHO','TRI','HDL','LDL','FBS','UricAcid',
'สรุปการทำงานของตับ', 'ALK.PHOSPHATASE', 'สรุปทำงานไต', 'AFP', 'CEA',
'ผลเลือดจากรพ.', 'สรุปผลปัสสาวะ', 'ZincinBlood', 'NickelinBlood', 'ManganeseinBlood',
'LeadinBlood', 'ChromiuminUrine', 'TolueneinUrine', 'AcetoneinUrine',
'n-HexaneinUrine', 'StyreneinUrine', 'ThinnerinUrine', 'XyleneinUrine',
'MethanolinUrine', 'MEKinUrine', 'MuscleHand', 'MuscleLeg', 'Ear', 'Eye',
'ColourBlindness', 'Spiro', 'X-RAY', 'EKG', 'EST', 'ECHO', 'US']]
exam_selection = st.selectbox(
"**Select Examination :**",
options=data_examination.columns)
if not exam_selection:
st.warning("Please select at least one examination.")
else:
examination_filter = data_examination[exam_selection]
st.image('Doctors-cuate.png', use_column_width=True)
count_exam = examination_filter[examination_filter == 1].groupby(by=filtered_data['Year']).count()
with col1:
st.write(" ")
st.write("#### The Examination Health Results")
st.write(": The examination health results by the values displayed in each section only consider employees who have passed each type of health examination.")
exam_chart = px.bar(count_exam,
text=count_exam,
color_discrete_sequence=['#E13838'],
template='plotly_white')
exam_chart.update_xaxes(title='Year', tickvals=['2561', '2562', '2563'])
exam_chart.update_yaxes(title='Number of Abnormal Cases')
st.plotly_chart(exam_chart, use_container_width=True)
#compute sum employee
count_exemploy61 = examination_filter[(filter['Year'] == 2561) & ((examination_filter == 0) | (examination_filter == 1))].count()
count_exemploy62 = examination_filter[(filter['Year'] == 2562) & ((examination_filter == 0) | (examination_filter == 1))].count()
count_exemploy63 = examination_filter[(filter['Year'] == 2563) & ((examination_filter == 0) | (examination_filter == 1))].count()
total1, total2, total3 = st.columns(3, gap="large")
with total1:
st.info('Number of Employees : 2561')
st.metric(label=': Undergoing Health Examinations', value=f"{count_exemploy61:,.0f}")
with total2:
st.info('Number of Employees : 2562')
st.metric(label=': Undergoing Health Examinations', value=f"{count_exemploy62:,.0f}")
with total3:
st.info('Number of Employees : 2563')
st.metric(label=': Undergoing Health Examinations', value=f"{count_exemploy63:,.0f}")
col1, col2, col3 = st.columns(3)
with col1:
exam_piechart61 = examination_filter[(filter['Year'] == 2561) & ((examination_filter == 0) | (examination_filter == 1))]
exam_pie61 = px.pie(exam_piechart61,
names=exam_piechart61,
title='Percent of Abnormal Cases Examination : 2561',
hole=0.65,
color_discrete_sequence=['#E13838', '#0E4C8A'])
st.plotly_chart(exam_pie61, use_container_width=True)
with col2:
exam_piechart62 = examination_filter[(filter['Year'] == 2562) & ((examination_filter == 0) | (examination_filter == 1))]
exam_pie62 = px.pie(exam_piechart62,
names=exam_piechart62,
title='Percent of Abnormal Cases Examination : 2562',
hole=0.65,
color_discrete_sequence=['#E13838', '#0E4C8A'])
st.plotly_chart(exam_pie62, use_container_width=True)
with col3:
exam_piechart63 = examination_filter[(filter['Year'] == 2563) & ((examination_filter == 0) | (examination_filter == 1))]
exam_pie63 = px.pie(exam_piechart63,
names=exam_piechart63,
title='Percent of Abnormal Cases Examination : 2563',
hole=0.65,
color_discrete_sequence=['#E13838', '#0E4C8A'])
st.plotly_chart(exam_pie63, use_container_width=True)
#-----Personal Results
st.markdown('#### Personal Health Examination Results')
st.write(': The results of personal health examinations in the company.')
ID_data = dfs[dfs["Year"].between(*Year_selection) & (dfs["Company_Code"].isin(CompanyCode_selection))&
(dfs["TYPE_employee"].isin(Employee_selection)) &
(dfs["Sex"].isin(Sex_selection))]
col1, col2 = st.columns([2, 1])
with col1:
try:
id_selection = st.selectbox(
"**Search ID :**",
options=ID_data['ID']
)
if not id_selection:
st.warning("Please Search ID.")
else:
id_filter = ID_data.loc[ID_data['ID'] == id_selection]
st.write(id_filter)
except KeyError:
st.warning("No data available for the selected year range.")
with col2:
st.write(' ')
st.write(' ')
st.write(' ')
st.write(' ')
st.write(' ')
st.write(' ')
st.write(' ')
st.write(' ')
st.write(' ')
st.image('Doctor-cuate.png', use_column_width=True)
#-----NCDs
st.markdown('#### The Risk Factors that cause NCDs')
st.write(': The results of health examinations that are risk factors that cause chronic non-communicable diseases (NCDs).')
#-------Chart for each NCDs Examination
data_ncds = filtered_data[['BLOODPRESSURE', 'BMI', 'CHO', 'TRI', 'HDL', 'LDL', 'FBS']]
ncds_selection = st.selectbox(
"**Select NCDs Examination :**",
options=data_ncds.columns)
if not ncds_selection:
st.warning("Please select at least one examination.")
else:
ncds_filter = data_ncds[ncds_selection]
count_ncds = ncds_filter[ncds_filter == 1].groupby(by=filtered_data['Year']).count()
#compute
percent_ncds61 = (ncds_filter[(ncds_filter == 1) & (filtered_data['Year'] == 2561)].sum() / ncds_filter[filtered_data['Year'] == 2561].count()) * 100
percent_ncds62 = (ncds_filter[(ncds_filter == 1) & (filtered_data['Year'] == 2562)].sum() / ncds_filter[filtered_data['Year'] == 2562].count()) * 100
percent_ncds63 = (ncds_filter[(ncds_filter == 1) & (filtered_data['Year'] == 2563)].sum() / ncds_filter[filtered_data['Year'] == 2563].count()) * 100
total1, total2, total3 = st.columns(3, gap="large")
with total1:
st.metric(label='Percent of Abnomal Case : 2561', value=f"{percent_ncds61:,.2f} %")
with total2:
st.metric(label='Percent of Abnomal Case : 2562', value=f"{percent_ncds62:,.2f} %")
with total3:
st.metric('Percent of Abnomal Case : 2563', value=f"{percent_ncds63:,.2f} %")
col1, col2 = st.columns(2)
with col1:
ncds_chart = px.bar(count_ncds,
text=count_ncds,
color_discrete_sequence=['#E13838'],
template='plotly_white',
title = 'Number of Abnormal Case')
ncds_chart.update_xaxes(title='Year', tickvals=['2561', '2562', '2563'])
ncds_chart.update_yaxes(title='Number of Abnormal Cases')
st.plotly_chart(ncds_chart, use_container_width=True)
with col2:
percent_ncds = ncds_filter[ncds_filter == 1].groupby(by=filtered_data['Year']).size() / ncds_filter.groupby(by=filtered_data['Year']).size() * 100
ncdsline_chart = px.line(
percent_ncds,
title='Percentage of Abnormal Case')
ncdsline_chart.update_xaxes(title='Year', tickvals=['2561', '2562', '2563'])
ncdsline_chart.update_yaxes(title='Percentage of Abnormal Cases', range=[0, 100])
st.plotly_chart(ncdsline_chart, use_container_width=True)
st.button("Log out")
else:
st.error("Invalid username or password")