关于我的问题，我已经看到许多类似的主题，但是我找不到解决方案。因此，我想构建一个简单的Web服务。我已经训练并保存了深度学习模型。拥有request.py文件，客户端可以从中发送一些数据，服务器将返回相关的预测。问题是当我执行request.py时收到以下错误
<Response [500]> <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 3.2 Final//EN">
<title>500 Internal Server Error</title>
<h1>Internal Server Error</h1>
<p>The server encountered an internal error and was unable to complete your request. Either the server is overloaded or there is an error in the application.</p>
还有The browser (or proxy) sent a request that this server could not understand.
您对此问题有任何想法吗？代码如下。预先感谢
from flask import Flask, request, redirect, url_for, flash, jsonify
import numpy as np
import pickle as p
import json
from keras.models import load_model
app = Flask(__name__)
model=load_model('modelfordeploy.h5')
@app.route("/")
def hello():
return "machine learning model APIs!"
@app.route('/api/', methods=['GET','POST'])
def predict():
data = request.get_json(force=True)
print (data)
pred = np.array2string(model.predict(data))
output=[pred[0]]
print (output)
#take a list of dictionaries and convert them to json
return jsonify(output)
if __name__ == '__main__':
app.run(debug=True, port=8000)
和请求：
import requests
import json
url = 'http://127.0.0.1:8000/api'
[{"accx": [0.370115, 0.378549, 0.376621, 0.371801, 0.371962, 0.368428, 0.361681, 0.359030, 0.358548, 0.364010,
0.370115, 0.378549, 0.376621, 0.371801, 0.371962, 0.368428, 0.361681, 0.359030, 0.358548, 0.364010,
0.370115, 0.378549, 0.376621, 0.371801, 0.371962, 0.368428, 0.361681, 0.359030, 0.358548, 0.364010,
0.370115, 0.378549, 0.376621, 0.371801, 0.371962, 0.368428, 0.361681, 0.359030, 0.358548,
0.364010, 0.370115, 0.378549, 0.376621, 0.371801, 0.371962, 0.368428, 0.361681, 0.359030, 0.358548,
0.364010, 0.370115, 0.378549, 0.376621, 0.371801, 0.371962, 0.368428, 0.361681, 0.359030, 0.358548,
0.364010, 0.370115, 0.378549, 0.376621, 0.371801, 0.371962, 0.368428, 0.361681, 0.359030, 0.358548,
0.364010, 0.370115, 0.378549, 0.376621, 0.371801, 0.371962, 0.368428, 0.361681, 0.359030, 0.358548,
0.364010, 0.370115, 0.378549, 0.376621, 0.371801, 0.371962, 0.368428, 0.361681, 0.359030, 0.358548,
0.364010, 0.370115, 0.378549, 0.376621, 0.371801, 0.371962, 0.368428, 0.361681, 0.359030, 0.358548,
0.364010 ],
"accy": [-0.292964, -0.291493, -0.288891, -0.284932, -0.278032, -0.277240,-0.285271,-0.287986,
-0.291606, -0.290023, -0.292964, -0.291493, -0.288891, -0.284932, -0.278032, -0.277240,-0.285271,-0.287986,
-0.291606, -0.290023, -0.292964, -0.291493, -0.288891, -0.284932, -0.278032, -0.277240,-0.285271,-0.287986,
-0.291606, -0.290023, -0.292964, -0.291493, -0.288891, -0.284932, -0.278032, -0.277240,-0.285271,-0.287986,
-0.291606, -0.290023, -0.292964, -0.291493, -0.288891, -0.284932, -0.278032, -0.277240,-0.285271,-0.287986,
-0.291606, -0.290023, -0.292964, -0.291493, -0.288891, -0.284932, -0.278032, -0.277240,-0.285271,-0.287986,
-0.291606, -0.290023, -0.292964, -0.291493, -0.288891, -0.284932, -0.278032, -0.277240,-0.285271,-0.287986,
-0.291606, -0.290023, -0.292964, -0.291493, -0.288891, -0.284932, -0.278032, -0.277240,-0.285271,-0.287986,
-0.291606, -0.290023, -0.292964, -0.291493, -0.288891, -0.284932, -0.278032, -0.277240,-0.285271,-0.287986,
-0.291606, -0.290023, -0.292964, -0.291493, -0.288891, -0.284932, -0.278032, -0.277240,-0.285271,-0.287986,
-0.291606, -0.290023],
"accz": [2.085630, 2.089610, 2.088650, 2.092767, 2.101826, 2.098395, 2.103336, 2.105807, 2.101552,
2.095238,2.085630, 2.089610, 2.088650, 2.092767, 2.101826, 2.098395, 2.103336, 2.105807, 2.101552,
2.095238, 2.085630, 2.089610, 2.088650, 2.092767, 2.101826, 2.098395, 2.103336, 2.105807, 2.101552,
2.095238, 2.085630, 2.089610, 2.088650, 2.092767, 2.101826, 2.098395, 2.103336, 2.105807, 2.101552,
2.095238,2.085630, 2.089610, 2.088650, 2.092767, 2.101826, 2.098395, 2.103336, 2.105807, 2.101552,
2.095238,2.085630, 2.089610, 2.088650, 2.092767, 2.101826, 2.098395, 2.103336, 2.105807, 2.101552,
2.095238,2.085630, 2.089610, 2.088650, 2.092767, 2.101826, 2.098395, 2.103336, 2.105807, 2.101552,
2.095238,2.085630, 2.089610, 2.088650, 2.092767, 2.101826, 2.098395, 2.103336, 2.105807, 2.101552,
2.095238, 2.085630, 2.089610, 2.088650, 2.092767, 2.101826, 2.098395, 2.103336, 2.105807, 2.101552,
2.095238, 2.085630, 2.089610, 2.088650, 2.092767, 2.101826, 2.098395, 2.103336, 2.105807, 2.101552,
2.095238 ],
"gyrx": [0.009092, 0.004790, -0.003326, -0.007335, -0.005575, -0.005966, -0.011637, -0.012126,
-0.009388, -0.008508, 0.009092, 0.004790, -0.003326, -0.007335, -0.005575, -0.005966, -0.011637, -0.012126,
-0.009388, -0.008508, 0.009092, 0.004790, -0.003326, -0.007335, -0.005575, -0.005966, -0.011637, -0.012126,
-0.009388, -0.008508, 0.009092, 0.004790, -0.003326, -0.007335, -0.005575, -0.005966, -0.011637, -0.012126,
-0.009388, -0.008508, 0.009092, 0.004790, -0.003326, -0.007335, -0.005575, -0.005966, -0.011637, -0.012126,
-0.009388, -0.008508, 0.009092, 0.004790, -0.003326, -0.007335, -0.005575, -0.005966, -0.011637, -0.012126,
-0.009388, -0.008508, 0.009092, 0.004790, -0.003326, -0.007335, -0.005575, -0.005966, -0.011637, -0.012126,
-0.009388, -0.008508, 0.009092, 0.004790, -0.003326, -0.007335, -0.005575, -0.005966, -0.011637, -0.012126,
-0.009388, -0.008508, 0.009092, 0.004790, -0.003326, -0.007335, -0.005575, -0.005966, -0.011637, -0.012126,
-0.009388, -0.008508, 0.009092, 0.004790, -0.003326, -0.007335, -0.005575, -0.005966, -0.011637, -0.012126,
-0.009388, -0.008508 ],
"gyry": [0.032635, 0.036072, 0.041169, 0.041406, 0.040221, 0.051717, 0.060962, 0.041050,
0.022324, 0.012842,0.032635, 0.036072, 0.041169, 0.041406, 0.040221, 0.051717, 0.060962, 0.041050,
0.022324, 0.012842, 0.032635, 0.036072, 0.041169, 0.041406, 0.040221, 0.051717, 0.060962, 0.041050,
0.022324, 0.012842, 0.032635, 0.036072, 0.041169, 0.041406, 0.040221, 0.051717, 0.060962, 0.041050,
0.022324, 0.012842, 0.032635, 0.036072, 0.041169, 0.041406, 0.040221, 0.051717, 0.060962, 0.041050,
0.022324, 0.012842, 0.032635, 0.036072, 0.041169, 0.041406, 0.040221, 0.051717, 0.060962, 0.041050,
0.022324, 0.012842, 0.032635, 0.036072, 0.041169, 0.041406, 0.040221, 0.051717, 0.060962, 0.041050,
0.022324, 0.012842, 0.032635, 0.036072, 0.041169, 0.041406, 0.040221, 0.051717, 0.060962, 0.041050,
0.022324, 0.012842,0.032635, 0.036072, 0.041169, 0.041406, 0.040221, 0.051717, 0.060962, 0.041050,
0.022324, 0.012842,0.032635, 0.036072, 0.041169, 0.041406, 0.040221, 0.051717, 0.060962, 0.041050,
0.022324, 0.012842 ],
"gyrz": [0.032861, 0.036875, 0.033062, 0.024733, 0.014397, 0.009279, 0.009279, 0.006670, 0.003559
, -0.002161, 0.032861, 0.036875, 0.033062, 0.024733, 0.014397, 0.009279, 0.009279, 0.006670, 0.003559
, -0.002161, 0.032861, 0.036875, 0.033062, 0.024733, 0.014397, 0.009279, 0.009279, 0.006670, 0.003559
, -0.002161, 0.032861, 0.036875, 0.033062, 0.024733, 0.014397, 0.009279, 0.009279, 0.006670, 0.003559
, -0.002161, 0.032861, 0.036875, 0.033062, 0.024733, 0.014397, 0.009279, 0.009279, 0.006670, 0.003559
, -0.002161, 0.032861, 0.036875, 0.033062, 0.024733, 0.014397, 0.009279, 0.009279, 0.006670, 0.003559
, -0.002161, 0.032861, 0.036875, 0.033062, 0.024733, 0.014397, 0.009279, 0.009279, 0.006670, 0.003559
, -0.002161, 0.032861, 0.036875, 0.033062, 0.024733, 0.014397, 0.009279, 0.009279, 0.006670, 0.003559
, -0.002161, 0.032861, 0.036875, 0.033062, 0.024733, 0.014397, 0.009279, 0.009279, 0.006670, 0.003559
, -0.002161, 0.032861, 0.036875, 0.033062, 0.024733, 0.014397, 0.009279, 0.009279, 0.006670, 0.003559
, -0.002161]}]
j_data = json.dumps(data)
r = requests.post(url, data=j_data)
print(r, r.text)