统一消息系统

# models.py
from flask_sqlalchemy import SQLAlchemy
db = SQLAlchemy()
class Message(db.Model):
id = db.Column(db.Integer, primary_key=True)
title = db.Column(db.String(100), nullable=False)
content = db.Column(db.Text, nullable=False)
sender_id = db.Column(db.Integer, db.ForeignKey('user.id'))
receiver_id = db.Column(db.Integer, db.ForeignKey('user.id'))
timestamp = db.Column(db.DateTime, default=db.func.now())
def to_dict(self):
return {
'id': self.id,
'title': self.title,
'content': self.content,
'sender_id': self.sender_id,
'receiver_id': self.receiver_id,
'timestamp': self.timestamp.strftime('%Y-%m-%d %H:%M:%S')
}

# server.py
from flask import Flask, render_template
from flask_socketio import SocketIO, emit
app = Flask(__name__)
app.config['SECRET_KEY'] = 'secret!'
socketio = SocketIO(app)
@app.route('/')
def index():
return render_template('index.html')
@socketio.on('new_message')
def handle_new_message(data):
message = data['message']
emit('message_received', {'message': message}, broadcast=True)
if __name__ == '__main__':
socketio.run(app, debug=True)

# model.py
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
import torch
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForQuestionAnswering.from_pretrained("bert-base-uncased")
def answer_question(question, context):
inputs = tokenizer.encode_plus(question, context, return_tensors="pt")
outputs = model(**inputs)
answer_start = torch.argmax(outputs.start_logits)
answer_end = torch.argmax(outputs.end_logits) + 1
answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][answer_start:answer_end]))
return answer