bcrypt 导致 POST 请求 500？

Question

我不知道为什么，但我开始在 Vercel 上收到这些错误，但在 LocalHost 上没有问题，即使执行 import os import numpy as np from tensorflow.keras.utils import to_categorical from tensorflow.keras.datasets import fashion_mnist from tensorflow.keras.layers import * from tensorflow.keras.activations import * from tensorflow.keras.models import * from tensorflow.keras.optimizers import * from tensorflow.keras.initializers import * # load and preprocess dataset # Dataset (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data() # Cast to np.float32 x_train = x_train.astype(np.float32) y_train = y_train.astype(np.float32) x_test = x_test.astype(np.float32) y_test = y_test.astype(np.float32) # Dataset variables train_size = x_train.shape[0] test_size = x_test.shape[0] num_timesteps = 784 num_features = 10 num_classes = 10 img_shape = (28, 28, 1,1) # Compute the categorical classes y_train = to_categorical(y_train, num_classes=10) y_test = to_categorical(y_test, num_classes=10) # Reshape the input data #x_train = x_train.reshape(train_size, num_timesteps, num_features, 1) #x_test = x_test.reshape(test_size, num_timesteps, num_features,1) x_train = np.expand_dims(x_train, axis=-1) x_test = np.expand_dims(x_test, axis=-1) # Model params lr = 0.001 optimizer = Adam(lr=lr) epochs = 10 batch_size = 256 units = 50 return_sequences = True print(img_shape) # Define the DNN model = Sequential() # first CONV => RELU => CONV => RELU => POOL layer set model.add(TimeDistributed(Conv2D(filters=32, kernel_size=3, padding="same", input_shape=img_shape))) model.add(TimeDistributed(Activation("relu"))) model.add(TimeDistributed(BatchNormalization())) model.add(TimeDistributed(Conv2D(filters=32, kernel_size=3, padding="same"))) model.add(TimeDistributed(Activation("relu"))) model.add(TimeDistributed(BatchNormalization())) model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2)))) model.add(TimeDistributed(Dropout(0.25))) # second CONV => RELU => CONV => RELU => POOL layer set model.add(TimeDistributed(Conv2D(filters=64, kernel_size=3, padding="same"))) model.add(TimeDistributed(Activation("relu"))) model.add(TimeDistributed(BatchNormalization())) model.add(TimeDistributed(Conv2D(filters=64, kernel_size=3, padding="same"))) model.add(TimeDistributed(Activation("relu"))) model.add(TimeDistributed(BatchNormalization())) model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2)))) model.add(TimeDistributed(Dropout(0.25))) # LSTM => RELU => FC => SOFTMAX (output) model.add(LSTM(units=units, return_sequences=return_sequences)) model.add(Activation("relu")) model.add(Dense(units=num_classes)) model.add(Activation("softmax")) #model.summary() # Compile and train (fit) the model, afterwards evaluate the model model.compile( loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"]) model.fit( x=x_train, y=y_train, epochs=epochs, batch_size=batch_size, validation_data=[x_test, y_test]) score = model.evaluate( x_test, y_test, verbose=0) print("Score: ", score)

我已经确定我在 Next.js 中使用 CORS

还有我的api代码

yarn build && yarn start

我一评论

import bcrypt from 'bcrypt'
import { NextApiRequest, NextApiResponse } from 'next'
import { connectPrisma } from 'utils/connectPrisma'

export default async (req: NextApiRequest, res: NextApiResponse) => {
  if (req.method === 'POST') {
    const { email, password } = req.body

    if (!email || !password) return res.status(422).json({ error: 'Please complete all fields' })

    try {
      const { client } = await connectPrisma()
      const user = await client.user.findFirst({ where: { email } })

      if (user) {
        return res.status(422).json({ error: `User already exists with that email` })
      }

      const hashedPassword = await bcrypt.hash(password, 8)
      await client.user.create({ data: { email: email, password: hashedPassword } })

      res.status(201).json({ message: 'Success test' })
    } catch {
      res.status(500).json({ error: 'Unable to insert user' })
    }
    return
  }

  return res.status(500).json({ error: 'Invalid request' })
}

我可以做 POST 请求