app.py

import os
import sys
import streamlit as st
from SIR_model import main as run_sir_model
import matplotlib
import plotly.graph_objs as go
import plotly.express as px
matplotlib.use("agg")  # Use the non-interactive Agg backend

# Add Graphviz bin directory to PATH
graphviz_bin_dir = os.path.join(sys.prefix, "bin")
os.environ["PATH"] += os.pathsep + graphviz_bin_dir


# Streamlit app title
st.title('SIR Model Simulation')

# Introduction
st.write('This application simulates and visualizes the SIR model for infectious disease spread.')



# Custom CSS to inject for Streamlit buttons
css = """
<style>
.stButton > button {
  align-items: center;
  background-color: #FCFCFD;
  border: 0;
  border-radius: 4px;
  box-shadow: rgba(45, 35, 66, 0.4) 0 2px 4px,
              rgba(45, 35, 66, 0.3) 0 7px 13px -3px,
              #D6D6E7 0 -3px 0 inset;
  color: #36395A;
  cursor: pointer;
  display: inline-flex;
  font-family: "JetBrains Mono", monospace;
  font-size: 18px;
  height: 48px;
  justify-content: center;
  line-height: 1;
  padding: 0 16px;
  transition: box-shadow .15s, transform .15s;
  user-select: none;
  -webkit-user-select: none;
  touch-action: manipulation;
  will-change: box-shadow, transform;
}

.stButton > button:focus {
  box-shadow: #D6D6E7 0 0 0 1.5px inset,
              rgba(45, 35, 66, 0.4) 0 2px 4px,
              rgba(45, 35, 66, 0.3) 0 7px 13px -3px,
              #D6D6E7 0 -3px 0 inset;
}

.stButton > button:hover {
  box-shadow: rgba(45, 35, 66, 0.4) 0 4px 8px,
              rgba(45, 35, 66, 0.3) 0 7px 13px -3px,
              #D6D6E7 0 -3px 0 inset;
  transform: translateY(-2px);
}

.stButton > button:active {
  box-shadow: #D6D6E7 0 3px 7px inset;
  transform: translateY(2px);
}
</style>
"""

st.markdown(css, unsafe_allow_html=True)






# Create an expander for model parameters
with st.expander("Model Parameters", expanded=True):
    # Create a 2-column layout
    col1, col2 = st.columns(2)
    # Place number input and sliders in the first column
    with col1:
        total_population = st.number_input("Total Population", value=1000, min_value=1)
        initial_infected = st.number_input("Initial Infected Population", value=1, min_value=0)
        initial_recovered = st.number_input("Initial Recovered Population", value=0, min_value=0)
    # Place the other sliders in the second column
    with col2:
        beta = st.slider("Infection Rate (β)", min_value=0.0, max_value=1.0, value=0.3)
        gamma = st.slider("Recovery Rate (γ)", min_value=0.0, max_value=1.0, value=0.1)

st.write('The SIR model divides the population into three categories: Susceptible (S), Infected (I), and Recovered (R). The model simulates how an infectious disease spreads and is managed within a population over time.')

# Buttons to control the simulation
run_model_button = st.button('Run Model and Generate Plots')
generate_graph_button = st.button('Generate Computation Graph')

if run_model_button:
    # Call the modified run_sir_model function with user input and slider values
    fig, gif_path = run_sir_model(total_population, initial_infected, initial_recovered, beta, gamma, mode="run")
    st.write("Static Population Model Output:")
    st.pyplot(fig)
    st.write("Animated Population Model Output:")  # Display the GIF
    st.image(gif_path, caption='SIR Model Animation')
    st.success('Model executed successfully with user input and slider values.')

if generate_graph_button:
    # Call the modified run_sir_model function with "graph" mode
    image_path = run_sir_model(total_population, initial_infected, initial_recovered, beta, gamma, mode="graph")
    st.image(image_path, caption="SIR Model Graph")
    st.success('Graph generated successfully with user input and slider values.')