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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."
} |