Python 将预先训练好的tensorflow回归模型集成到spring boot后端

我使用GoogleColab创建了一个模型，我想将它集成到我用SpringBoot（后端）和react（前端）编写的项目中。我只想从前端传递参数

import numpy as np
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import pandas as pd
import matplotlib.pyplot as plt

D = pd.read_csv("nat64_2_merge.csv")
x_data = np.matrix(D.Time.values)
y_data = np.matrix(D.Length.values)

plt.plot(x_data.T, y_data.T, 'x') # The 'x' means that data points will be marked with an x
plt.xlabel('Time')
plt.ylabel('Length')
plt.title('Relationship between Time and Length')
plt.show()

x = tf.placeholder(tf.float32, shape=(1, None))

a = tf.get_variable("a", shape=())
b = tf.get_variable("b", shape=())

y_predicted = a*x + b

y = tf.placeholder(tf.float32, shape=(1, None))

L = tf.reduce_sum((y_predicted - y)**2)

optimizer = tf.train.AdamOptimizer(learning_rate=0.2).minimize(L)

session = tf.Session()

session.run(tf.global_variables_initializer())

for t in range(10000):
    _, current_loss, current_a, current_b = session.run([optimizer, L, a, b], feed_dict={
        x: x_data,
        y: y_data
    })
    print("t = %g, loss = %g, a = %g, b = %g" % (t, current_loss, current_a, current_b))

x_test_data = np.matrix(np.linspace(0, 600)) //here I add parameters then create prediction line

y_test_data = session.run(y_predicted, feed_dict={
    x: x_test_data
})

plt.plot(x_data.T, y_data.T, 'x')
plt.plot(x_test_data.T, y_test_data.T)
plt.xlabel('Time')
plt.ylabel('Length')
plt.title('Time and Length linear regression')
plt.show()

在最后一行中，我想将该绘图放到前端，并将其显示给最终用户

提前谢谢