import streamlit as st import pandas as pd import os from crewai import Agent, Task, Crew from langchain_groq import ChatGroq def main(): # Set up the customization options st.sidebar.title('Customization') model = st.sidebar.selectbox( 'Choose a model', ['llama3-8b-8192', 'mixtral-8x7b-32768', 'gemma-7b-it'] ) llm = ChatGroq( temperature=0, groq_api_key = st.secrets["GROQ_API_KEY"], model_name=model ) # Streamlit UI st.title('CrewAI Machine Learning Assistant') multiline_text = """ 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. """ st.markdown(multiline_text, unsafe_allow_html=True) # Display the Groq logo spacer, col = st.columns([5, 1]) with col: st.image('groqcloud_darkmode.png') Problem_Definition_Agent = Agent( role='Problem_Definition_Agent', goal="""clarify the machine learning problem the user wants to solve, identifying the type of problem (e.g., classification, regression) and any specific requirements.""", backstory="""You are an expert in understanding and defining machine learning problems. Your goal is to extract a clear, concise problem statement from the user's input, ensuring the project starts with a solid foundation.""", verbose=True, allow_delegation=False, llm=llm, ) Data_Assessment_Agent = Agent( role='Data_Assessment_Agent', goal="""evaluate the data provided by the user, assessing its quality, suitability for the problem, and suggesting preprocessing steps if necessary.""", backstory="""You specialize in data evaluation and preprocessing. Your task is to guide the user in preparing their dataset for the machine learning model, including suggestions for data cleaning and augmentation.""", verbose=True, allow_delegation=False, llm=llm, ) Model_Recommendation_Agent = Agent( role='Model_Recommendation_Agent', goal="""suggest the most suitable machine learning models based on the problem definition and data assessment, providing reasons for each recommendation.""", backstory="""As an expert in machine learning algorithms, you recommend models that best fit the user's problem and data. You provide insights into why certain models may be more effective than others, considering classification vs regression and supervised vs unsupervised frameworks.""", verbose=True, allow_delegation=False, llm=llm, ) Starter_Code_Generator_Agent = Agent( role='Starter_Code_Generator_Agent', goal="""generate starter Python code for the project, including data loading, model definition, and a basic training loop, based on findings from the problem definitions, data assessment and model recommendation""", backstory="""You are a code wizard, able to generate starter code templates that users can customize for their projects. Your goal is to give users a head start in their coding efforts.""", verbose=True, allow_delegation=False, llm=llm, ) # Summarization_Agent = Agent( # role='Starter_Code_Generator_Agent', # goal="""Summarize findings from each of the previous steps of the ML discovery process. # Include all findings from the problem definitions, data assessment and model recommendation # and all code provided from the starter code generator. # """, # backstory="""You are a seasoned data scientist, able to break down machine learning problems for # less experienced practitioners, provide valuable insight into the problem and why certain ML models # are appropriate, and write good, simple code to help get started on solving the problem. # """, # verbose=True, # allow_delegation=False, # llm=llm, # ) user_question = st.text_input("Describe your ML problem:") data_upload = False uploaded_file = st.file_uploader("Upload a sample .csv of your data (optional)") if uploaded_file is not None: try: # Attempt to read the uploaded file as a DataFrame df = pd.read_csv(uploaded_file).head(5) # If successful, set 'data_upload' to True data_upload = True # Display the DataFrame in the app st.write("Data successfully uploaded and read as DataFrame:") st.dataframe(df) except Exception as e: st.error(f"Error reading the file: {e}") if user_question: task_define_problem = Task( description="""Clarify and define the machine learning problem, including identifying the problem type and specific requirements. Here is the user's problem: {ml_problem} """.format(ml_problem=user_question), agent=Problem_Definition_Agent, expected_output="A clear and concise definition of the machine learning problem." ) if data_upload: task_assess_data = Task( description="""Evaluate the user's data for quality and suitability, suggesting preprocessing or augmentation steps if needed. Here is a sample of the user's data: {df} The file name is called {uploaded_file} """.format(df=df.head(),uploaded_file=uploaded_file), agent=Data_Assessment_Agent, expected_output="An assessment of the data's quality and suitability, with suggestions for preprocessing or augmentation if necessary." ) else: task_assess_data = Task( description="""The user has not uploaded any specific data for this problem, but please go ahead and consider a hypothetical dataset that might be useful for their machine learning problem. """, agent=Data_Assessment_Agent, expected_output="A hypothetical dataset that might be useful for the user's machine learning problem, along with any necessary preprocessing steps." ) task_recommend_model = Task( description="""Suggest suitable machine learning models for the defined problem and assessed data, providing rationale for each suggestion.""", agent=Model_Recommendation_Agent, expected_output="A list of suitable machine learning models for the defined problem and assessed data, along with the rationale for each suggestion." ) task_generate_code = Task( description="""Generate starter Python code tailored to the user's project using the model recommendation agent's recommendation(s), including snippets for package import, data handling, model definition, and training """, agent=Starter_Code_Generator_Agent, 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." ) # task_summarize = Task( # description=""" # Summarize the results of the problem definition, data assessment, model recommendation and starter code generator. # Keep the summarization brief and don't forget to share the entirety of the starter code! # """, # agent=Summarization_Agent # ) crew = Crew( agents=[Problem_Definition_Agent, Data_Assessment_Agent, Model_Recommendation_Agent, Starter_Code_Generator_Agent], #, Summarization_Agent], tasks=[task_define_problem, task_assess_data, task_recommend_model, task_generate_code], #, task_summarize], verbose=2 ) result = crew.kickoff() st.write(result) if __name__ == "__main__": main()