wehub-resource-sync
184 lines
8.4 KiB
Python
184 lines
8.4 KiB
Python
import pandas as pd
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import os
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from crewai import Agent, Task, Crew
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from langchain_groq import ChatGroq
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def main():
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"""
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Main function to initialize and run the CrewAI Machine Learning Assistant.
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This function sets up a machine learning assistant using the Llama 3 model with the ChatGroq API.
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It provides a text-based interface for users to define, assess, and solve machine learning problems
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by interacting with multiple specialized AI agents. The function outputs the results to the console
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and writes them to a markdown file.
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Steps:
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1. Initialize the ChatGroq API with the specified model and API key.
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2. Display introductory text about the CrewAI Machine Learning Assistant.
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3. Create and configure four AI agents:
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- Problem_Definition_Agent: Clarifies the machine learning problem.
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- Data_Assessment_Agent: Evaluates the quality and suitability of the provided data.
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- Model_Recommendation_Agent: Suggests suitable machine learning models.
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- Starter_Code_Generator_Agent: Generates starter Python code for the project.
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4. Prompt the user to describe their machine learning problem.
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5. Check if a .csv file is available in the current directory and try to read it as a DataFrame.
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6. Define tasks for the agents based on user input and data availability.
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7. Create a Crew instance with the agents and tasks, and run the tasks.
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8. Print the results and write them to an output markdown file.
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"""
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model = 'llama3-8b-8192'
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llm = ChatGroq(
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temperature=0,
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groq_api_key = os.getenv('GROQ_API_KEY'),
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model_name=model
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)
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print('CrewAI Machine Learning Assistant')
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multiline_text = """
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The CrewAI Machine Learning Assistant is designed to guide users through the process of defining, assessing, and solving machine learning problems. It leverages a team of AI agents, each with a specific role, to clarify the problem, evaluate the data, recommend suitable models, and generate starter Python code. Whether you're a seasoned data scientist or a beginner, this application provides valuable insights and a head start in your machine learning projects.
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"""
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print(multiline_text)
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Problem_Definition_Agent = Agent(
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role='Problem_Definition_Agent',
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goal="""clarify the machine learning problem the user wants to solve,
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identifying the type of problem (e.g., classification, regression) and any specific requirements.""",
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backstory="""You are an expert in understanding and defining machine learning problems.
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Your goal is to extract a clear, concise problem statement from the user's input,
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ensuring the project starts with a solid foundation.""",
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verbose=True,
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allow_delegation=False,
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llm=llm,
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)
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Data_Assessment_Agent = Agent(
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role='Data_Assessment_Agent',
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goal="""evaluate the data provided by the user, assessing its quality,
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suitability for the problem, and suggesting preprocessing steps if necessary.""",
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backstory="""You specialize in data evaluation and preprocessing.
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Your task is to guide the user in preparing their dataset for the machine learning model,
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including suggestions for data cleaning and augmentation.""",
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verbose=True,
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allow_delegation=False,
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llm=llm,
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)
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Model_Recommendation_Agent = Agent(
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role='Model_Recommendation_Agent',
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goal="""suggest the most suitable machine learning models based on the problem definition
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and data assessment, providing reasons for each recommendation.""",
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backstory="""As an expert in machine learning algorithms, you recommend models that best fit
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the user's problem and data. You provide insights into why certain models may be more effective than others,
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considering classification vs regression and supervised vs unsupervised frameworks.""",
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verbose=True,
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allow_delegation=False,
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llm=llm,
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)
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Starter_Code_Generator_Agent = Agent(
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role='Starter_Code_Generator_Agent',
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goal="""generate starter Python code for the project, including data loading,
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model definition, and a basic training loop, based on findings from the problem definitions,
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data assessment and model recommendation""",
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backstory="""You are a code wizard, able to generate starter code templates that users
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can customize for their projects. Your goal is to give users a head start in their coding efforts.""",
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verbose=True,
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allow_delegation=False,
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llm=llm,
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)
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user_question = input("Describe your ML problem: ")
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data_upload = False
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# Check if there is a .csv file in the current directory
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if any(file.endswith(".csv") for file in os.listdir()):
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sample_fp = [file for file in os.listdir() if file.endswith(".csv")][0]
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try:
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# Attempt to read the uploaded file as a DataFrame
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df = pd.read_csv(sample_fp).head(5)
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# If successful, set 'data_upload' to True
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data_upload = True
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# Display the DataFrame in the app
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print("Data successfully uploaded and read as DataFrame:")
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print(df)
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except Exception as e:
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print(f"Error reading the file: {e}")
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if user_question:
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task_define_problem = Task(
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description="""Clarify and define the machine learning problem,
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including identifying the problem type and specific requirements.
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Here is the user's problem:
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{ml_problem}
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""".format(ml_problem=user_question),
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agent=Problem_Definition_Agent,
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expected_output="A clear and concise definition of the machine learning problem."
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)
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if data_upload:
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task_assess_data = Task(
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description="""Evaluate the user's data for quality and suitability,
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suggesting preprocessing or augmentation steps if needed.
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Here is a sample of the user's data:
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{df}
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The file name is called {uploaded_file}
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""".format(df=df.head(),uploaded_file=sample_fp),
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agent=Data_Assessment_Agent,
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expected_output="An assessment of the data's quality and suitability, with suggestions for preprocessing or augmentation if necessary."
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)
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else:
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task_assess_data = Task(
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description="""The user has not uploaded any specific data for this problem,
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but please go ahead and consider a hypothetical dataset that might be useful
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for their machine learning problem.
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""",
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agent=Data_Assessment_Agent,
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expected_output="A hypothetical dataset that might be useful for the user's machine learning problem, along with any necessary preprocessing steps."
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)
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task_recommend_model = Task(
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description="""Suggest suitable machine learning models for the defined problem
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and assessed data, providing rationale for each suggestion.""",
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agent=Model_Recommendation_Agent,
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expected_output="A list of suitable machine learning models for the defined problem and assessed data, along with the rationale for each suggestion."
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)
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task_generate_code = Task(
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description="""Generate starter Python code tailored to the user's project using the model recommendation agent's recommendation(s),
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including snippets for package import, data handling, model definition, and training
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""",
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agent=Starter_Code_Generator_Agent,
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expected_output="Python code snippets for package import, data handling, model definition, and training, tailored to the user's project, plus a brief summary of the problem and model recommendations."
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)
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crew = Crew(
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agents=[Problem_Definition_Agent, Data_Assessment_Agent, Model_Recommendation_Agent, Starter_Code_Generator_Agent],
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tasks=[task_define_problem, task_assess_data, task_recommend_model, task_generate_code],
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verbose=False
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)
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result = crew.kickoff()
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print(result)
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with open('output.md', "w") as file:
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print('\n\nThese results have been exported to output.md')
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file.write(result)
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if __name__ == "__main__":
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main() |