Spaces:
Running
Running
File size: 4,105 Bytes
209745e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
import os
import asyncio
import websockets
import sqlite3
import datetime
import fireworks.client
import streamlit as st
import threading
import conteneiro
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.llms import HuggingFaceHub
from langchain.llms.fireworks import Fireworks
from langchain.chat_models.fireworks import ChatFireworks
client_ports = []
# Define the websocket client class
class AgentsGPT:
def __init__(self):
self.status = st.sidebar.status(label="AgentsGPT", state="complete", expanded=False)
async def get_response(self, question):
os.environ["GOOGLE_CSE_ID"] = GOOGLE_CSE_ID
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
os.environ["FIREWORKS_API_KEY"] = FIREWORKS_API_KEY
os.environ["HUGGINGFACEHUB_API_TOKEN"] = HUGGINGFACEHUB_API_TOKEN
llm = Fireworks(model="accounts/fireworks/models/llama-v2-70b-chat", model_kwargs={"temperature":0, "max_tokens":1500, "top_p":1.0}, streaming=True)
tools = load_tools(["google-search", "llm-math"], llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, return_intermediate_steps=True)
response = agent({"input": question})
output = response["output"]
steps = response["intermediate_steps"]
serverResponse = f"AgentsGPT: {output}"
responseSteps = f"intermediate steps: {steps}"
answer = f"Main output: {output}. Intermediate steps: {steps}"
print(serverResponse)
print(responseSteps)
output_Msg = st.chat_message("ai")
output_Msg.markdown(serverResponse)
output_steps = st.chat_message("assistant")
output_steps.markdown(responseSteps)
return answer
# Define a function that will run the client in a separate thread
def run(self):
# Create a thread object
self.thread = threading.Thread(target=self.run_client)
# Start the thread
self.thread.start()
# Define a function that will run the client using asyncio
def run_client(self):
# Get the asyncio event loop
loop = asyncio.new_event_loop()
# Set the event loop as the current one
asyncio.set_event_loop(loop)
# Run the client until it is stopped
loop.run_until_complete(self.client())
# Stop the WebSocket client
async def stop_client():
global ws
# Close the connection with the server
await ws.close()
client_ports.pop()
print("Stopping WebSocket client...")
# Define a coroutine that will connect to the server and exchange messages
async def startClient(self, clientPort):
self.uri = f'ws://localhost:{clientPort}'
self.name = f"Chaindesk client port: {clientPort}"
conteneiro.clients.append(self.name)
status = self.status
# Connect to the server
async with websockets.connect(self.uri) as websocket:
# Loop forever
while True:
status.update(label=self.name, state="running", expanded=True)
# Listen for messages from the server
input_message = await websocket.recv()
print(f"Server: {input_message}")
input_Msg = st.chat_message("assistant")
input_Msg.markdown(input_message)
try:
response = await self.get_response(input_message)
res1 = f"Client: {response}"
output_Msg = st.chat_message("ai")
output_Msg.markdown(res1)
await websocket.send(res1)
status.update(label=self.name, state="complete", expanded=True)
except websockets.ConnectionClosed:
print("client disconnected")
continue
except Exception as e:
print(f"Error: {e}")
continue |