如何重新配置​​html和flask代码以加载引导表一次，然后在每个新的字典项后重新加载数据表？

Question

我正在使用flask和bootstrap来编写涉及张量流的网站。 目前，用户将代码输入表单，然后在计算完所有数据后，烧瓶和html代码加载页面;为表单页面造成巨大的缓冲时间。所以，我希望表加载一次 - 空 - 然后在用户点击表（或在设定的时间或新数据之后）使用表中的新计算数据时重新加载。

以下是所有相关的烧瓶代码:(以下代码从multiperceptron页面获取数字并使用这些数字运行张量流神经网络，然后将目前看到的图像数量和训练精度数字添加到a字典）

@app.route('/MultiPerceptron')
def MultiPerceptron():
    return render_template("MultiPerceptron.html")
@app.route('/MultiPerceptronForm', methods=["POST"])
def MultiPerceptronForm():
    #setting up of the tensor flow computational graph...
    #the tensor flow session which creates the data that I will add to the data table
    MultiPerceptResult=dict()
    # Launch the graph
    with tf.Session() as sess:
        sess.run(init)

        # Training cycle
        for epoch in range(training_epochs):
            avg_cost = 0.
            total_batch = int(mnist.train.num_examples/batch_size)
            images_seen_per_epoch = total_batch * batch_size
            # Loop over all batches
            for i in range(total_batch):
                batch_x, batch_y = mnist.train.next_batch(batch_size)
                # Run optimization op (backprop) and cost op (to get loss value)
                _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,
                                                              y: batch_y})
                # Compute average loss
                avg_cost += c / total_batch
            # Display logs per epoch step
            if epoch % display_step == 0:
                images_seen = epoch * images_seen_per_epoch
                print("Epoch:", '%04d' % (epoch+1), "cost=", \
                    "{:.9f}".format(avg_cost))
                # Test model
                correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
                # Calculate accuracy
                accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
                print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
                #adding the new data to the dictionary****
                MultiPerceptResult[images_seen] = accuracy.eval({x: mnist.test.images, y: mnist.test.labels})

    return render_template('MultiPerceptronForm.html', MultiPerceptResult=MultiPerceptResult)

以下是所有相关的html代码:(以下的html代码正在使用烧瓶代码生成的字典，并通过它迭代创建一个包含所有数据的表格）

<table class="table table-hover">
        <thead>
            <tr>
                <th>Images Seen</th>
                <th>Training Accuracy</th>
            </tr>
        </thead>
        <tbody>
        {% for key, value in MultiPerceptResult.items() %}
            <tr>
                <th> {{ key }} </th>
                <td> {{ value }} </td>
            </tr>
        {% endfor %}
        </tbody>
    </table>