Create app.py

app.py

@@ -0,0 +1,70 @@
+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)