import streamlit as st import pandas as pd import numpy as np import matplotlib.pyplot as plt # Function to simulate WBC activity over time def simulate_wbc_activity(days): # Creating a simple simulation for WBC activity # This can be replaced with more complex logic based on real data neutrophils = np.random.normal(20, 5, days).clip(0, 50) lymphocytes = np.random.normal(15, 4, days).clip(0, 40) monocytes = np.random.normal(10, 3, days).clip(0, 30) eosinophils = np.random.normal(5, 2, days).clip(0, 20) return pd.DataFrame({ "Day": range(1, days + 1), "Neutrophils": neutrophils, "Lymphocytes": lymphocytes, "Monocytes": monocytes, "Eosinophils": eosinophils }) # Streamlit UI st.title("WBC Simulation in Post-Surgery Hematoma and Infection") st.markdown(""" ## Understanding White Blood Cells (WBC) In the event of a hematoma and infection post-surgery, different types of WBCs play crucial roles. - **Neutrophils 🧦**: First responders to infection sites. - **Lymphocytes 🧡**: Provide long-term immunity. - **Monocytes 🧜**: Transform into macrophages to engulf pathogens. - **Eosinophils 🧝**: Combat multicellular parasites and allergic reactions. """) # User input for simulation days days = st.slider("Select number of days for the simulation", 1, 30, 7) # Simulating WBC activity wbc_data = simulate_wbc_activity(days) # Plotting the WBC activity st.line_chart(wbc_data.set_index("Day")) st.markdown(""" ## Simulation Insights The graph above simulates the activity of different WBC types over time post-surgery. - **Peak in Neutrophils**: Indicates the body's immediate response to infection. - **Lymphocytes Trend**: Shows the development of immunity over time. - **Monocytes and Eosinophils**: Fluctuate based on the severity of the infection and allergic reactions. """) st.markdown(""" ### Note This is a simplified simulation intended for educational purposes and does not represent actual medical data. """)