Delete streamlit_app_asdm.py

streamlit_app_asdm.py

@@ -1,57 +0,0 @@
-import pandas as pd
-import streamlit as st
-import plotly.graph_objects as go
-from ASDM.ASDM import Structure
-
-def load_model(model_path):
-    try:
-        model = Structure(from_xmile=model_path)
-    except FileNotFoundError:
-        st.error(f"File {model_path} not found.")
-        return None
-    return model
-
-def run_simulation(model, simulation_time, re_investment):
-    model.sim_specs['initial_time'] = 0
-    model.sim_specs['current_time'] = 0
-    model.sim_specs['dt'] = 1
-    model.sim_specs['simulation_time'] = simulation_time
-    model.sim_specs['time_units'] = 'Months'
-
-    model.clear_last_run()
-
-    model.aux_equations['percentageOfSavingsSpentOnCessation'] = str(re_investment)
-
-    model.simulate()
-
-    results = model.export_simulation_result()
-    results_df = pd.DataFrame.from_dict(results)
-
-    columns_to_plot = ["Current_smokers", "Ex_smokers", "Ex_smokers_starting_again"]
-    return results_df['Months'], results_df[columns_to_plot]
-
-st.title('Smoking Cessation')
-
-st.markdown("""
-This simulation estimates the effects of various reinvestment levels in a smoking cessation service within a population of 900 smokers. 
-By varying the proportion of savings that are reinvested into the service, we can observe different outcomes in terms of current smokers, ex-smokers, 
-and ex-smokers who start smoking again over time.
-""")
-            
-st.subheader('Slide the Slider to Vary Re-Investment Levels')
-
-model = load_model('models/smoking cessation demo.stmx')
-
-if model is not None:
-    re_investment = st.slider("Proportion of Savings Spent on Cessation", 0, 100, 45)
-    simulation_time = st.slider("Select the number of months to simulate:", min_value=1, max_value=36, value=24)
-
-    x_values, y_values = run_simulation(model, simulation_time, re_investment)
-
-    st.subheader('Effects of Re-Investment on Smoking Levels')
-
-    fig = go.Figure()
-    for column in y_values.columns:
-        fig.add_trace(go.Scatter(x=x_values, y=y_values[column], mode='lines', name=column))
-    fig.update_layout(xaxis_title='Months', yaxis_title='Number of Smokers', autosize=False, width=800, height=500)
-    st.plotly_chart(fig)