136 lines
3.6 KiB
Python
136 lines
3.6 KiB
Python
from flask import Flask, render_template, request
|
|
from flask_socketio import SocketIO, emit, join_room
|
|
|
|
import eventlet
|
|
|
|
import markdown
|
|
|
|
import openai
|
|
|
|
import os
|
|
|
|
import time
|
|
|
|
|
|
app = Flask(__name__)
|
|
app.config["SECRET_KEY"] = "your_secret_key"
|
|
socketio = SocketIO(app, async_mode="eventlet")
|
|
|
|
|
|
from flask_sqlalchemy import SQLAlchemy
|
|
|
|
app.config["SQLALCHEMY_DATABASE_URI"] = "sqlite:///chat.db"
|
|
app.config["SQLALCHEMY_TRACK_MODIFICATIONS"] = False
|
|
db = SQLAlchemy(app)
|
|
|
|
|
|
class Message(db.Model):
|
|
id = db.Column(db.Integer, primary_key=True)
|
|
username = db.Column(db.String(128), nullable=False)
|
|
content = db.Column(db.String(1024), nullable=False)
|
|
room = db.Column(db.String(128), nullable=False)
|
|
|
|
def __init__(self, username, content, room):
|
|
self.username = username
|
|
self.content = content
|
|
self.room = room
|
|
|
|
|
|
# Create the database and tables
|
|
with app.app_context():
|
|
db.create_all()
|
|
|
|
|
|
@app.route("/")
|
|
def index():
|
|
return render_template("index.html")
|
|
|
|
|
|
@app.route("/chat/<room>")
|
|
def chat(room):
|
|
return render_template("chat.html", room=room)
|
|
|
|
|
|
@socketio.on("join")
|
|
def on_join(data):
|
|
room = data["room"]
|
|
join_room(room)
|
|
|
|
# Fetch previous messages from the database
|
|
previous_messages = Message.query.filter_by(room=room).all()
|
|
|
|
for message in previous_messages:
|
|
emit(
|
|
"previous_messages",
|
|
{"username": message.username, "message": message.content},
|
|
room=request.sid,
|
|
)
|
|
|
|
emit("message", f"{data['username']} has joined the room.", room=room)
|
|
|
|
|
|
@socketio.on("message")
|
|
def handle_message(data):
|
|
# Save the message to the database
|
|
new_message = Message(
|
|
username=data["username"], content=data["message"], room=data["room"]
|
|
)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
emit("message", f"{data['username']}: {data['message']}", room=data["room"])
|
|
|
|
# Emit a temporary message indicating that GPT is processing
|
|
emit("message", f"<span id='processing'>Processing...</span>", room=data["room"])
|
|
|
|
# Call the chat_gpt function without blocking using eventlet.spawn
|
|
eventlet.spawn(chat_gpt, data["username"], data["room"], data["message"])
|
|
|
|
def chat_gpt(username, room, message):
|
|
|
|
with app.app_context():
|
|
last_messages = (
|
|
Message.query.filter_by(room=room)
|
|
.order_by(Message.id.desc())
|
|
.limit(10)
|
|
.all()
|
|
)
|
|
|
|
chat_history = [
|
|
{"role": "system" if msg.username == "GPT-3.5" else "user", "content": msg.content}
|
|
for msg in reversed(last_messages)
|
|
]
|
|
|
|
chat_history.append({"role": "user", "content": message})
|
|
|
|
buffer = "" # Content buffer for accumulating the chunks
|
|
|
|
first_chunk = True
|
|
for chunk in openai.ChatCompletion.create(
|
|
model="gpt-3.5-turbo",
|
|
messages=chat_history,
|
|
stream=True,
|
|
):
|
|
content = chunk["choices"][0].get("delta", {}).get("content")
|
|
|
|
if content:
|
|
buffer += content # Accumulate content
|
|
|
|
if first_chunk:
|
|
socketio.emit("message_chunk", f"{username} (GPT-3.5): {content}", room=room)
|
|
first_chunk = False
|
|
else:
|
|
socketio.emit("message_chunk", content, room=room)
|
|
socketio.sleep(0) # Force immediate handling
|
|
|
|
# Save the entire completion to the database
|
|
with app.app_context():
|
|
new_message = Message(username="GPT-3.5", content=buffer, room=room)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
socketio.emit("delete_processing_message", "", room=room)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
socketio.run(app, host="0.0.0.0", port=5001)
|