106 lines
3.1 KiB
Python
106 lines
3.1 KiB
Python
from flask import Flask, render_template, request
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from flask_socketio import SocketIO, emit, join_room
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import eventlet
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import openai
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openai.api_key = "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"
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import markdown
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app = Flask(__name__)
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app.config['SECRET_KEY'] = 'your_secret_key'
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socketio = SocketIO(app, async_mode='eventlet')
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from flask_sqlalchemy import SQLAlchemy
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app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///chat.db'
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app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
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db = SQLAlchemy(app)
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class Message(db.Model):
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id = db.Column(db.Integer, primary_key=True)
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username = db.Column(db.String(128), nullable=False)
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content = db.Column(db.String(1024), nullable=False)
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room = db.Column(db.String(128), nullable=False)
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def __init__(self, username, content, room):
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self.username = username
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self.content = content
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self.room = room
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# Create the database and tables
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#with app.app_context():
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# db.create_all()
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@app.route('/')
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def index():
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return render_template('index.html')
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@app.route('/chat/<room>')
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def chat(room):
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return render_template('chat.html', room=room)
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@socketio.on('join')
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def on_join(data):
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room = data['room']
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join_room(room)
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# Fetch previous messages from the database
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previous_messages = Message.query.filter_by(room=room).all()
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for message in previous_messages:
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emit(
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'previous_messages',
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{'username': message.username, 'message': message.content},
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room=request.sid
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)
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emit('message', f"{data['username']} has joined the room.", room=room)
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@socketio.on('message')
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def handle_message(data):
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# Save the message to the database
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new_message = Message(username=data['username'], content=data['message'], room=data['room'])
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db.session.add(new_message)
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db.session.commit()
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emit('message', f"{data['username']}: {data['message']}", room=data['room'])
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# Emit a temporary message indicating that GPT is processing
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emit('message', f"<span id='processing'>Processing...</span>", room=data['room'])
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# Call the chat_gpt function without blocking using eventlet.spawn
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eventlet.spawn(chat_gpt, data['username'], data['room'], data['message'])
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def chat_gpt(username, room, message):
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# Send user's message to ChatGPT API
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": message}]
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)
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# Extract response from ChatGPT API
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response_text = response['choices'][0]['message']['content']
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# Convert response_text to Markdown
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response_md = markdown.markdown(response_text, extensions=['fenced_code'])
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# Save ChatGPT's response in the database
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with app.app_context():
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chatgpt_response_message = Message(username="GPT-3.5", content=response_md, room=room)
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db.session.add(chatgpt_response_message)
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db.session.commit()
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socketio.emit('delete_processing_message', "", room=room)
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# Emit the response to the room
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socketio.emit('message', f"{username} (GPT-3.5): {response_md}", room=room)
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if __name__ == '__main__':
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socketio.run(app, host='0.0.0.0', port=5001)
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