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import os
import pprint

from autogen import ConversableAgent

cathy = ConversableAgent(
    "cathy",
    system_message="Your name is Cathy and you are a part of a duo of comedians.",
    llm_config={"config_list": [{"model": "deepseek-chat", "temperature": 0.9, "api_key": os.environ.get("OPENAI_API_KEY"), "base_url": os.environ.get("BASE_URL"), "price": [0, 0]}]},
    human_input_mode="NEVER",  # Never ask for human input.
)

joe = ConversableAgent(
    "joe",
    system_message="Your name is Joe and you are a part of a duo of comedians.",
    llm_config={"config_list": [{"model": "deepseek-chat", "temperature": 0.7, "api_key": os.environ.get("OPENAI_API_KEY"), "base_url": os.environ.get("BASE_URL"), "price": [0, 0]}]},
    human_input_mode="NEVER",  # Never ask for human input.
)

result = joe.initiate_chat(cathy, message="Cathy, tell me a joke.", max_turns=2)

#获取聊天历史对话
pprint.pprint(result.chat_history)

#消耗令牌情况
pprint.pprint(result.cost)

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import os

from autogen import ConversableAgent

llm_config={"config_list": [{"model": "deepseek-coder", "api_key": os.environ.get("OPENAI_API_KEY"), "base_url": os.environ.get("BASE_URL"), "price": [0, 0]}]}

agent_with_number = ConversableAgent(
    "agent_with_number",
    system_message="You are playing a game of guess-my-number. You have the "
    "number 53 in your mind, and I will try to guess it. "
    "If I guess too high, say 'too high', if I guess too low, say 'too low'. ",
    llm_config=llm_config,
    is_termination_msg=lambda msg: "53" in msg["content"],  # terminate if the number is guessed by the other agent
    human_input_mode="NEVER",  # never ask for human input
)

agent_guess_number = ConversableAgent(
    "agent_guess_number",
    system_message="I have a number in my mind, and you will try to guess it. "
    "If I say 'too high', you should guess a lower number. If I say 'too low', "
    "you should guess a higher number. ",
    llm_config=llm_config,
    human_input_mode="NEVER",
)

result = agent_with_number.initiate_chat(
    agent_guess_number,
    message="I have a number between 1 and 100. Guess it!",
)

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import os

from autogen import ConversableAgent

llm_config={"config_list": [{"model": "deepseek-coder", "api_key": os.environ.get("OPENAI_API_KEY"), "base_url": os.environ.get("BASE_URL"), "price": [0, 0]}]}

agent_with_number = ConversableAgent(
    "agent_with_number",
    system_message="You are playing a game of guess-my-number. You have the "
    "number 53 in your mind, and I will try to guess it. "
    "If I guess too high, say 'too high', if I guess too low, say 'too low'. ",
    llm_config=llm_config,
    is_termination_msg=lambda msg: "53" in msg["content"],  # terminate if the number is guessed by the other agent
    human_input_mode="NEVER",  # never ask for human input
)

agent_guess_number = ConversableAgent(
    "agent_guess_number",
    system_message="I have a number in my mind, and you will try to guess it. "
    "If I say 'too high', you should guess a lower number. If I say 'too low', "
    "you should guess a higher number. ",
    llm_config=llm_config,
    human_input_mode="NEVER",
)

human_proxy = ConversableAgent(
    "human_proxy",
    llm_config=False,  # no LLM used for human proxy
    human_input_mode="ALWAYS",  # always ask for human input
)

# Start a chat with the agent with number with an initial guess.
result = human_proxy.initiate_chat(
    agent_with_number,  # this is the same agent with the number as before
    message="10",
)

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import tempfile

from autogen import ConversableAgent
from autogen.coding import LocalCommandLineCodeExecutor

# Create a temporary directory to store the code files.
temp_dir = tempfile.TemporaryDirectory()

# Create a local command line code executor.
executor = LocalCommandLineCodeExecutor(
    timeout=10,  # Timeout for each code execution in seconds.
    work_dir=temp_dir.name,  # Use the temporary directory to store the code files.
)

# Create an agent with code executor configuration.
code_executor_agent = ConversableAgent(
    "code_executor_agent",
    llm_config=False,  # Turn off LLM for this agent.
    code_execution_config={"executor": executor},  # Use the local command line code executor.
    human_input_mode="ALWAYS",  # Always take human input for this agent for safety.
)

message_with_code_block = """This is a message with code block.
The code block is below:
```python
import numpy as np
import matplotlib.pyplot as plt
x = np.random.randint(0, 100, 100)
y = np.random.randint(0, 100, 100)
plt.scatter(x, y)
plt.savefig('scatter.png')
print('Scatter plot saved to scatter.png')

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## 在docker中执行代码

code_execution_docker_demo_5.py

```python
import tempfile
import os

from autogen import ConversableAgent
from autogen.coding import DockerCommandLineCodeExecutor

# Create a temporary directory to store the code files.
temp_dir = tempfile.TemporaryDirectory(dir='/tmp/autogen/')

# Create a Docker command line code executor.
executor = DockerCommandLineCodeExecutor(
    image="python:3.12-slim",  # Execute code using the given docker image name.
    timeout=10,  # Timeout for each code execution in seconds.
    work_dir=temp_dir.name,  # Use the temporary directory to store the code files.
)

print(temp_dir, temp_dir.name)

# Create an agent with code executor configuration that uses docker.
code_executor_agent_using_docker = ConversableAgent(
    "code_executor_agent_docker",
    llm_config=False,  # Turn off LLM for this agent.
    code_execution_config={"executor": executor},  # Use the docker command line code executor.
    human_input_mode="ALWAYS",  # Always take human input for this agent for safety.
)

# When the code executor is no longer used, stop it to release the resources.
# executor.stop()

# The code writer agent's system message is to instruct the LLM on how to use
# the code executor in the code executor agent.
code_writer_system_message = """You are a helpful AI assistant.
Solve tasks using your coding and language skills.
In the following cases, suggest python code (in a python coding block) or shell script (in a sh coding block) for the user to execute.
1. When you need to collect info, use the code to output the info you need, for example, browse or search the web, download/read a file, print the content of a webpage or a file, get the current date/time, check the operating system. After sufficient info is printed and the task is ready to be solved based on your language skill, you can solve the task by yourself.
2. When you need to perform some task with code, use the code to perform the task and output the result. Finish the task smartly.
Solve the task step by step if you need to. If a plan is not provided, explain your plan first. Be clear which step uses code, and which step uses your language skill.
When using code, you must indicate the script type in the code block. The user cannot provide any other feedback or perform any other action beyond executing the code you suggest. The user can't modify your code. So do not suggest incomplete code which requires users to modify. Don't use a code block if it's not intended to be executed by the user.
If you want the user to save the code in a file before executing it, put # filename: <filename> inside the code block as the first line. Don't include multiple code blocks in one response. Do not ask users to copy and paste the result. Instead, use 'print' function for the output when relevant. Check the execution result returned by the user.
If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try.
When you find an answer, verify the answer carefully. Include verifiable evidence in your response if possible.
Reply 'TERMINATE' in the end when everything is done.
"""

code_writer_agent = ConversableAgent(
    "code_writer_agent",
    system_message=code_writer_system_message,
    llm_config={"config_list": [{"model": "deepseek-chat", "temperature": 0.9, "api_key": os.environ.get("OPENAI_API_KEY"), "base_url": os.environ.get("BASE_URL"), "price": [0, 0]}], "cache_seed": None},
    code_execution_config=False,  # Turn off code execution for this agent.
)

chat_result = code_executor_agent_using_docker.initiate_chat(
    code_writer_agent,
    message="Write Python code to calculate the 14th Fibonacci number.",
)