import os

from agentscope_runtime.engine import AgentApp
from agentscope_runtime.engine.agents.agentscope_agent import AgentScopeAgent

from agentscope.agent import ReActAgent
from agentscope.model import OpenAIChatModel


agent = AgentScopeAgent(
    name="Friday",
    model=OpenAIChatModel(
        "gpt-4",
        api_key=os.getenv("OPENAI_API_KEY"),
    ),
    agent_config={
        "sys_prompt": "You're a helpful assistant named Friday.",
    },
    agent_builder=ReActAgent,  # Or use your own agent builder
)
app = AgentApp(agent=agent, endpoint_path="/process")

app.run(host="0.0.0.0", port=8090)

from agentscope_runtime.sandbox import BaseSandbox

with BaseSandbox() as box:
    # By default, pulls `agentscope/runtime-sandbox-base:latest` from DockerHub
    print(box.list_tools()) # List all available tools
    print(box.run_ipython_cell(code="print('hi')"))  # Run Python code
    print(box.run_shell_command(command="echo hello"))  # Run shell command
    input("Press Enter to continue...")

from agentscope_runtime.sandbox import GuiSandbox

with GuiSandbox() as box:
    # By default, pulls `agentscope/runtime-sandbox-gui:latest` from DockerHub
    print(box.list_tools()) # List all available tools
    print(box.desktop_url)  # Web desktop access URL
    print(box.computer_use(action="get_cursor_position"))  # Get mouse cursor position
    print(box.computer_use(action="get_screenshot"))       # Capture screenshot
    input("Press Enter to continue...")

from agentscope_runtime.sandbox import BrowserSandbox

with BrowserSandbox() as box:
    # By default, pulls `agentscope/runtime-sandbox-browser:latest` from DockerHub
    print(box.list_tools()) # List all available tools
    print(box.desktop_url)  # Web desktop access URL
    box.browser_navigate("https://www.google.com/")  # Open a webpage
    input("Press Enter to continue...")

from agentscope_runtime.sandbox import FilesystemSandbox

with FilesystemSandbox() as box:
    # By default, pulls `agentscope/runtime-sandbox-filesystem:latest` from DockerHub
    print(box.list_tools()) # List all available tools
    print(box.desktop_url)  # Web desktop access URL
    box.create_directory("test")  # Create a directory
    input("Press Enter to continue...")

# pip install "agentscope-runtime[agno]"
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agentscope_runtime.engine.agents.agno_agent import AgnoAgent

agent = AgnoAgent(
    name="Friday",
    model=OpenAIChat(
        id="gpt-4",
    ),
    agent_config={
        "instructions": "You're a helpful assistant.",
    },
    agent_builder=Agent,
)

# pip install "agentscope-runtime[autogen]"
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from agentscope_runtime.engine.agents.autogen_agent import AutogenAgent

agent = AutogenAgent(
    name="Friday",
    model=OpenAIChatCompletionClient(
        model="gpt-4",
    ),
    agent_config={
        "system_message": "You're a helpful assistant",
    },
    agent_builder=AssistantAgent,
)

# pip install "agentscope-runtime[langgraph]"
from typing import TypedDict
from langgraph import graph, types
from agentscope_runtime.engine.agents.langgraph_agent import LangGraphAgent


# define the state
class State(TypedDict, total=False):
    id: str


# define the node functions
async def set_id(state: State):
    new_id = state.get("id")
    assert new_id is not None, "must set ID"
    return types.Command(update=State(id=new_id), goto="REVERSE_ID")


async def reverse_id(state: State):
    new_id = state.get("id")
    assert new_id is not None, "ID must be set before reversing"
    return types.Command(update=State(id=new_id[::-1]))


state_graph = graph.StateGraph(state_schema=State)
state_graph.add_node("SET_ID", set_id)
state_graph.add_node("REVERSE_ID", reverse_id)
state_graph.set_entry_point("SET_ID")
compiled_graph = state_graph.compile(name="ID Reversal")
agent = LangGraphAgent(graph=compiled_graph)

from agentscope_runtime.engine.deployers import LocalDeployManager

# Create deployment manager
deployer = LocalDeployManager(
    host="0.0.0.0",
    port=8090,
)

# Deploy the app as a streaming service
deploy_result = await app.deploy(deployer=deployer)

from openai import OpenAI

client = OpenAI(base_url="http://0.0.0.0:8090/compatible-mode/v1")

response = client.responses.create(
  model="any_name",
  input="What is the weather in Beijing?"
)

print(response)