backend/modules/data_visualization.py

import logging
from pathlib import Path
import json

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def generate_visualization(file_path: str, request: str) -> dict:
    """
    Generate visualization code for Excel data using Hugging Face models.
    
    Args:
        file_path (str): Path to the Excel file
        request (str): Natural language visualization request
        
    Returns:
        dict: Generated code and visualization description
    """
    logger.info(f"Generating visualization for {file_path}: {request}")
    
    # Mock implementation - would use actual Hugging Face models in production
    # Would actually read Excel data and generate real matplotlib/seaborn code
    
    # Example mock visualization code
    mock_code = """
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Read the Excel file
df = pd.read_excel('data.xlsx')

# Create the visualization
plt.figure(figsize=(10, 6))
sns.barplot(data=df, x='Category', y='Values')
plt.title('Data Visualization')
plt.xlabel('Categories')
plt.ylabel('Values')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
"""
    
    return {
        "code": mock_code,
        "description": "This code generates a bar plot showing the relationship between categories and their corresponding values from your Excel data.",
        "note": "This is a mock response. In production, the code would be generated based on actual Excel data analysis and user requirements."
    }