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import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
# Title and Description
st.title('Patient Data Dashboard')
st.write("""
This dashboard provides an overview of patient health metrics for better monitoring and decision-making.
""")
# Load Patient Data (Example DataFrame)
df = pd.read_csv('patient_data.csv')
# Sidebar for Patient Selection
st.sidebar.header('Select Patient')
patient_id = st.sidebar.selectbox('Patient ID', df['patient_id'].unique())
# Filter Data for Selected Patient
patient_data = df[df['patient_id'] == patient_id]
# Display Patient Profile
st.header('Patient Profile')
st.write(f"Name: {patient_data['name'].values[0]}")
st.write(f"Age: {patient_data['age'].values[0]}")
st.write(f"Gender: {patient_data['gender'].values[0]}")
st.write(f"Medical History: {patient_data['medical_history'].values[0]}")
# Visualize Vital Signs
st.header('Vital Signs Over Time')
fig, ax = plt.subplots()
ax.plot(patient_data['date'], patient_data['heart_rate'], label='Heart Rate')
ax.plot(patient_data['date'], patient_data['blood_pressure'], label='Blood Pressure')
ax.set_xlabel('Date')
ax.set_ylabel('Value')
ax.legend()
st.pyplot(fig)
# Interactive Plotly Chart
st.header('Blood Glucose Levels')
fig = px.line(patient_data, x='date', y='blood_glucose', title='Blood Glucose Over Time')
st.plotly_chart(fig)
# Alerts and Notifications
st.header('Alerts')
if patient_data['heart_rate'].values[-1] > 100:
st.error('High heart rate detected!')
if patient_data['blood_pressure'].values[-1] > 140:
st.error('High blood pressure detected!')
# Download Button for Patient Data
st.download_button(
label="Download Patient Data as CSV",
data=patient_data.to_csv().encode('utf-8'),
file_name=f'patient_{patient_id}_data.csv',
mime='text/csv',
)
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