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| 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): | |
| GOOGLE_CSE_ID = os.environ["GOOGLE_CSE_ID"] | |
| GOOGLE_API_KEY = os.environ["GOOGLE_API_KEY"] | |
| FIREWORKS_API_KEY = os.environ["FIREWORKS_API_KEY"] | |
| HUGGINGFACEHUB_API_TOKEN = os.environ["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 |