ML_End_to_End.py

from autogen import AssistantAgent, UserProxyAgent
import os
from dotenv import load_dotenv
import autogen

load_dotenv()

llm_config = {"model": "gpt-3.5-turbo", "api_key": os.environ["OPENAI_API_KEY"]}
assistant = AssistantAgent("assistant", llm_config=llm_config)
user_proxy = UserProxyAgent("user_proxy", code_execution_config={
    "executor": autogen.coding.LocalCommandLineCodeExecutor(work_dir="coding")}, human_input_mode="NEVER",
                            max_consecutive_auto_reply=5, )


# Start the chat
def ml_end_to_end():
    result = user_proxy.initiate_chat(
        assistant,
        message="""
        you are a python data scientist. you are given tasks to complete that is read the dataset from the working directory with path in working directory uploaded_data.csv  read the data and analyse the data completly give code for analyse .
    save the code in Ml_end_to_end_.py
    -Your role is to analyse the given data in tha path coding/uploaded_data.csv where you have to analyse the entire data and write some code for analysing
    Visualise the data and finally build a good model  and also store the model  evaluation 
    save the code in ML_End_to_End.py
    Store all the code in ML_End_to_End.py atlast
    and write the inference which you make from analysing the data in separte inference.txt file
    - the python code runs in jupyter notebook.
    - every time you call `execute_python` tool, the python code is executed in a separate cell. it's okay to multiple calls to `execute_python`.
    - display visualizations using matplotlib or any other visualization library directly in the notebook. don't worry about saving the visualizations to a file.
    - you have access to the internet and can make api requests.
    - you also have access to the filesystem and can read/write files.
    - you can install any pip package (if it exists) if you need to but the usual packages for data analysis are already preinstalled.
    - you can run any python code you want, everything is running in a secure sandbox environment
        """,
    )

    print(result)

    return result


# ml_end_to_end()