app.py

import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
import optuna

st.title("Optimization tool")

func_user = st.selectbox("Choose a function:", ("cuadratic", "sine", "gaussian"))
# ("sin(x/10)", "(x-2)^2", "exp-(x-4)^2")


def func_cuadratic(x):
    return (x - 2) ** 2


def func_sine(x):
    return np.sin(x / 10)


def func_gauss(x):
    return np.exp(-((x - 4) ** 2))


if func_user == "cuadratic":
    st.latex(r"(x - 2)^2")
    func_to_use = func_cuadratic

elif func_user == "sine":
    st.latex(r"sin({ x \over 10 })")
    func_to_use = func_sine

else:
    st.latex(r"e^{-(x-4)^2}")
    func_to_use = func_gauss


opt_user = st.selectbox("Choose the optimization direction:", ("minimize", "maximize"))

x_low = st.number_input("Please, give me the lower bound:", value=-10)
x_upp = st.number_input("Please, give me the upper bound:", value=10)


def objective(trial):
    x = trial.suggest_float("x", x_low, x_upp)
    return func_to_use(x)


study = optuna.create_study(direction=opt_user)
study.optimize(objective, n_trials=500)

x_opt = study.best_params["x"]
y_opt = func_to_use(x_opt)

st.write(f"The critical point found is ({x_opt:,.4f}, {y_opt:,.4f}).")

x_to_use = np.linspace(x_low, x_upp)

plt.title("Plot of critical point within interval given by user")
plt.xlabel("x axis")
plt.ylabel("y axis")
plt.plot(x_to_use, func_to_use(x_to_use))
plt.scatter(x_opt, func_to_use(x_opt), c="red")
plt.annotate(
    f"({x_opt:,.2f}, {y_opt:,.2f})",
    (x_opt, y_opt),
    textcoords="offset points",
    ha="center",
)

st.pyplot(plt)