How to find the 'peak' of a polynomial regression line in Matplotlib-CodePudding

Is it possible to find the peak (vertex?) values (x,y) of a polynomial regression line that was computed using Matplotlib?

I've included my basic setup below (of course with fuller data sets), as well as a screenshot of the actual regression line question.

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LinearRegression

degree=6

setX={'Fixation Duration': {0:1,1:2,2:3}}
setY={'Fixation Occurrences': {0:1,1:2,2:3}}    


X_gall=pd.DataFrame.from_dict(setX)
Y_gall=pd.DataFrame.from_dict(setY)

X_seqGall = np.linspace(X_gall.min(),X_gall.max(),300).reshape(-1,1)

polyregGall=make_pipeline(PolynomialFeatures(degree),LinearRegression())
polyregGall.fit(X_gall,Y_gall)

plt.scatter(X_gall,Y_gall, c="#1E4174", s=100.0, alpha=0.4)
plt.plot(X_seqGall,polyregGall.predict(X_seqGall),color="#1E4174", linewidth=4)

plt.show()

Now you can access the line data:

lines = poly[0].axes.lines

for line in lines:
    max_y = np.max(line.get_ydata())
    print(f"Maximum y is: {max_y}")

    x_of_max_y = line.get_xdata()[np.argmax(line.get_ydata())]
    print(f"x value of maximum y is: {x_of_max_y}")

Output:

Maximum y is: 3.1515605364361114
x value of maximum y is: 2.8127090301003346