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app.py

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+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()
+

requirements.txt

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+streamlit==1.31.0
+crewai==0.1.32
+pandas==1.5.1
+langchain-groq==0.0.1