LLM_Reason.py

import os
from groq import Groq

# Initialize the Groq client with the API key from the environment variable
client = Groq(
    api_key=os.environ.get("GROQ_API_KEY"),
)

def analyze_data(data: str, model: str = "llama3-8b-8192") -> str:
    """
    Function to send data to Groq and request a detailed analysis.

    Args:
    - data (str): The data to be analyzed (could be text, tables, or JSON).
    - model (str): The model to be used for the analysis (default is "llama3-8b-8192").

    Returns:
    - str: The detailed analysis from Groq.
    """
    
    # Construct the prompt to ask for a detailed analysis
    prompt = f"""
    Here is a dataset:

    {data}

    Please provide a detailed analysis in rich text format without the use of stars(*), of the data, including trends, patterns, and any insights you can derive from it.
    """

    # Send the prompt to Groq and request a completion for the analysis
    response = client.chat.completions.create(
        messages=[
            {"role": "user", "content": prompt},
        ],
        model=model,  # Using the provided model
    )

    # Return the detailed analysis from the response
    return response.choices[0].message.content


# Example usage
if __name__ == "__main__":
    # Sample data (could be any dataset or text)
    data = """
    | Name     | Age | Occupation      | Income  |
    |----------|-----|-----------------|---------|
    | Alice    | 30  | Software Engineer | 100000 |
    | Bob      | 25  | Data Scientist   | 95000  |
    | Charlie  | 35  | Manager         | 120000 |
    | David    | 28  | Designer        | 85000  |
    """

    # Call the function to get detailed analysis
    analysis = analyze_data(data)

    # Print the analysis
    print("Detailed Analysis:")
    print(analysis)