Update app.py

app.py

@@ -1,54 +1,46 @@
 import streamlit as st
 import numpy as np
-import matplotlib.pyplot as plt
 import optuna
+from optuna.samplers import TPESampler
+import matplotlib.pyplot as plt
 
 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
-
+    return (x - 2)**2
 
 def func_sine(x):
     return np.sin(x / 10)
 
-
 def func_gauss(x):
-    return np.exp(-((x - 4) ** 2))
-
+    return np.exp(-(x - 4)**2)
 
 if func_user == "cuadratic":
-    st.latex(r"(x - 2)^2")
+    st.latex(r"(x-2)^2")
     func_to_use = func_cuadratic
-
 elif func_user == "sine":
-    st.latex(r"sin({ x \over 10 })")
+    st.latex(r"sin(\frac{x}{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)
-
+x_low = st.number_input("Please, give me the lower bound:")
+x_upp = st.number_input("Please, give me the upper bound:")
 
 def objective(trial):
-    x = trial.suggest_float("x", x_low, x_upp)
+    x = trial.suggest_float('x', x_low, x_upp)
     return func_to_use(x)
 
+direction = "minimize" if opt_user == "minimize" else "maximize"
+study = optuna.create_study(direction=direction, sampler=TPESampler())
+study.optimize(objective, n_trials=100, show_progress_bar=True)
 
-study = optuna.create_study(direction=opt_user)
-study.optimize(objective, n_trials=500)
-
-x_opt = study.best_params["x"]
+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}).")