消息推送系统

import redis

class MessageQueue:

def __init__(self):

self.redis_client = redis.StrictRedis(host='localhost', port=6379, decode_responses=True)

def send_message(self, message, channel):

self.redis_client.publish(channel, message)

def receive_messages(self, channel):

pubsub = self.redis_client.pubsub()

pubsub.subscribe(channel)

for message in pubsub.listen():

if message['type'] == 'message':

yield message['data']

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import tensorflow as tf

from sklearn.model_selection import train_test_split

import numpy as np

# 假设我们已经从消息队列中获取了训练数据

messages = ["Hello world", "How are you?", "I am fine", "Nice to meet you"]

实训管理平台

labels = [0, 1, 0, 1]

# 数据预处理

tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=1000)

tokenizer.fit_on_texts(messages)

sequences = tokenizer.texts_to_sequences(messages)

data = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=10)

# 划分训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)

# 构建模型

model = tf.keras.Sequential([

tf.keras.layers.Embedding(input_dim=1000, output_dim=16),

tf.keras.layers.GlobalAveragePooling1D(),

tf.keras.layers.Dense(1, activation='sigmoid')

])

model.compile(optimizer='adam',

loss='binary_crossentropy',

metrics=['accuracy'])

model.fit(X_train, y_train, epochs=10, batch_size=2)

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from flask import Flask, request, jsonify

app = Flask(__name__)

message_queue = MessageQueue()

@app.route('/send_message', methods=['POST'])

def send_message_route():

message = request.json['message']

channel = request.json['channel']

message_queue.send_message(message, channel)

return jsonify({"status": "success"})

@app.route('/predict', methods=['POST'])

def predict():

message = request.json['message']

sequence = tokenizer.texts_to_sequences([message])

padded_sequence = tf.keras.preprocessing.sequence.pad_sequences(sequence, maxlen=10)

prediction = model.predict(padded_sequence)[0][0]

return jsonify({"prediction": prediction})

if __name__ == '__main__':

app.run(debug=True)

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