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import requests
import datetime
import http.server
import websockets
import websocket
import asyncio
import sqlite3
import json
import gradio as gr
from gradio_client import Client
import time
client_messages = []
server_responses = []
messages = []
used_ports = []
websocket_server = None
stop = asyncio.Future()
# Global variables to store references to the textboxes
messageTextbox = None
serverMessageTextbox = None
# Set up the HTTP server
class SimpleHTTPRequestHandler(http.server.SimpleHTTPRequestHandler):
def do_GET(self):
if self.path == '/':
self.send_response(200)
self.send_header('Content-type', 'text/html')
self.end_headers()
with open('index.html', 'rb') as file:
self.wfile.write(file.read())
else:
self.send_response(404)
self.end_headers()
# Set up the SQLite database
db = sqlite3.connect('chat-hub.db')
cursor = db.cursor()
cursor.execute('CREATE TABLE IF NOT EXISTS messages (id INTEGER PRIMARY KEY AUTOINCREMENT, sender TEXT, message TEXT, timestamp TEXT)')
db.commit()
# Define the function for sending an error message
def sendErrorMessage(ws, errorMessage):
errorResponse = {'error': errorMessage}
ws.send(json.dumps(errorResponse))
# Define a function to ask a question to the chatbot and display the response
async def askQuestion(question):
try:
# Connect to the database and get the last 30 messages
db = sqlite3.connect('chat-hub.db') # Replace 'your_database.db' with your database file
cursor = db.cursor()
cursor.execute("SELECT * FROM messages ORDER BY timestamp DESC LIMIT 30")
messages = cursor.fetchall()
# Extract user inputs and generated responses from the messages
past_user_inputs = []
generated_responses = []
for message in messages:
if message[1] == 'server':
past_user_inputs.append(message[2])
else:
generated_responses.append(message[2])
# Prepare data to send to the chatgpt-api.shn.hk
system_instruction = "You are now integrated with a local websocket server in a project of hierarchical cooperative multi-agent framework called NeuralGPT. Your job is to coordinate simultaneous work of multiple LLMs connected to you as clients. Each LLM has a model (API) specific ID to help you recognize different clients in a continuous chat thread (example: 'Starcoder-client' for LLM called Starcoder). Your chat memory module is integrated with a local SQL database with chat history. Your main job is to integrate the hierarchical cooperative multi-agent framework with the local environment of User B (createor of NeuralGPT project). Remember to maintain the logical and chronological order while answering to incoming messages and to send your answers to correct clients to maintain synchronization of question->answer logic"
messages_data = [
{"role": "system", "content": system_instruction},
{"role": "user", "content": question},
*[{"role": "user", "content": input} for input in past_user_inputs],
*[{"role": "assistant", "content": response} for response in generated_responses]
]
request_data = {
"model": "gpt-3.5-turbo",
"messages": messages_data
}
# Make the request to the chatgpt-api.shn.hk
response = requests.post("http://127.0.0.1:6969/api/conversation?text=", json=request_data)
# Process the response and get the generated answer
response_data = response.json()
generated_answer = response_data["choices"][0]["message"]["content"]
# Save the generated answer to the database or take further actions as needed
print(generated_answer)
return generated_answer
except Exception as error:
print("Error while fetching or processing the response:", error)
return "Error: Unable to generate a response."
async def listen_for_messages():
while True:
if len(client_messages) > 0:
# Get the latest client message
client_message = client_messages[-1]
try:
server_message = server_responses[-1]
except IndexError:
# Handle the case when there are no server responses yet
server_message = "connected successfully"
return client_message, server_message
else:
# Handle the case when there are no client messages yet
client_message = "connected successfully"
server_message = "connected successfully"
return client_message, server_message
async def handleWebSocket(ws):
print('New connection')
await ws.send('Hello! You are now entering a chat room for AI agents working as instances of NeuralGPT. Keep in mind that you are speaking with another chatbot')
while True:
message = await ws.recv()
message_copy = message
client_messages.append(message_copy)
print(f'Received message: {message}')
parsedMessage = json.loads(message)
messageText = message
messages.append(message)
timestamp = datetime.datetime.now().isoformat()
sender = 'client'
db = sqlite3.connect('chat-hub.db')
db.execute('INSERT INTO messages (sender, message, timestamp) VALUES (?, ?, ?)',
(sender, messageText, timestamp))
db.commit()
try:
message = messages[-1]
answer = await askQuestion(message) # Use the message directly
response = {'answer': answer}
serverMessageText = response.get('answer', '')
await ws.send(json.dumps(response))
# Append the server response to the server_responses list
server_responses.append(serverMessageText)
serverSender = 'server'
db.execute('INSERT INTO messages (sender, message, timestamp) VALUES (?, ?, ?)',
(serverSender, serverMessageText, timestamp))
db.commit()
except websockets.exceptions.ConnectionClosedError as e:
print(f"Connection closed: {e}")
except Exception as e:
print(f"Error: {e}")
# Function to stop the WebSocket server
def stop_websockets():
global websocket_server
if websocket_server:
cursor.close()
db.close()
websocket_server.close()
print("WebSocket server stopped.")
else:
print("WebSocket server is not running.")
# Start the WebSocket server
async def start_websockets(websocketPort):
global messageTextbox, serverMessageTextbox, websocket_server
# Create a WebSocket client that connects to the server
await(websockets.serve(handleWebSocket, 'localhost', websocketPort))
used_ports.append(websocketPort)
print(f"Starting WebSocket server on port {websocketPort}...")
return "Used ports:\n" + '\n'.join(map(str, used_ports))
with gr.Blocks() as demo:
with gr.Column(scale=1, min_width=600):
with gr.Row():
# Use the client_messages list to update the messageTextbox
client_message = gr.Textbox(lines=15, max_lines=130, label="Client inputs")
# Use the server_responses list to update the serverMessageTextbox
server_message = gr.Textbox(lines=15, max_lines=130, label="Server responses")
with gr.Row():
websocketPort = gr.Slider(minimum=1000, maximum=9999, label="Websocket server port", interactive=True, randomize=False)
startWebsockets = gr.Button("Start WebSocket Server")
stopWebsockets = gr.Button("Stop WebSocket Server")
with gr.Row():
gui = gr.Button("connect interface")
with gr.Row():
port = gr.Textbox()
startWebsockets.click(start_websockets, inputs=websocketPort, outputs=port)
gui.click(listen_for_messages, inputs=None, outputs={client_message, server_message})
stopWebsockets.click(stop_websockets)
demo.queue()
demo.launch(share=True, server_port=1111) |