app.py

import gradio as gr
from cadCAD.configuration.utils import config_sim
from cadCAD.configuration import Experiment

from cadCAD.engine import ExecutionMode, ExecutionContext
from cadCAD.engine import Executor

from cadCAD import configs
import pandas as pd
import numpy as np

from seir_model import *

N = 10000 #Total Population
initial_state = {
    "S": N-10,
    "E": 10,
    "I":0,
    "R":0
}
sys_params = {
    "infection_rate":[1],
    "recovery_rate":[1/14], 
    "exposure_rate":[1/3]
}

partial_state_update_blocks = [
    {
        "policies":{
            "expsoed_growth":p_exposed,
            "infected_growth":p_infected,
            "recovered_growth":p_recovered,
            
        },
        "variables":{
            "S":s_susceptible,
            "E":s_exposed,
            "I":s_infected,
            "R":s_recovered,
        }
     
    }

]

del configs[:]

timesteps = 100
sim_config = config_sim({
  "N":1,
  "T":range(timesteps),
  "M":sys_params  
})

experiment = Experiment()
experiment.append_configs(
    sim_configs=sim_config,
    initial_state=initial_state,
    partial_state_update_blocks=partial_state_update_blocks
)

exec_context = ExecutionContext()
simulation = Executor(exec_context=exec_context, configs=experiment.configs)


  
def plot_seir(name):
    raw_result, tensor_fields, sessions = simulation.execute()
    result = pd.DataFrame(raw_result)
    pd.options.plotting.backend = "plotly"
    fig = result.plot(
    kind = "line",
    x = "timestep",
    y= ["S","E","I", "R"])
    fig.update_layout(title = name,
            xaxis_title="Time (Days)",
            yaxis_title="People")
    return fig

graph = gr.Plot()
iface = gr.Interface(fn=plot_seir, inputs="text", outputs=graph)
iface.launch()