Russell Ballestrini
20053db1d9
new file: .gitignore new file: app.py new file: requirements.txt new file: templates/chat.html new file: templates/index.html
56 lines
1.6 KiB
Python
56 lines
1.6 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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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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@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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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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emit('message', f"{data['username']}: {data['message']}", room=data['room'])
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openai.api_key = "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"
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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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# 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=5000)
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