Create app.py

app.py

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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',
+)