115 lines
3.1 KiB
Python
115 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 markdown
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import openai
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import os
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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(
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username=data["username"], content=data["message"], room=data["room"]
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)
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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", 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(
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username="GPT-3.5", content=response_md, room=room
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)
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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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