app.py

import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
import io
import google.generativeai as genai
from PIL import Image, ImageDraw, ImageFont
import ast
import re
import traceback

def process_file(api_key, file, instructions):
    # Function implementation goes here
    pass  # Add proper indentation for actual implementation

def sanitize_code(code_block, columns):
    # Function implementation goes here
    pass  # Add proper indentation for actual implementation

def execute_plot_code(code, df):
    # Function implementation goes here
    pass  # Add proper indentation for actual implementation

def generate_error_image(message):
    """Create error indication image with message"""
    try:
        img = Image.new('RGB', (1920, 1080), color=(255, 255, 255))
        draw = ImageDraw.Draw(img)
        
        # Use default font
        font = ImageFont.load_default()
        
        # Calculate text position
        text_width, text_height = draw.textsize(message, font=font)
        x = (1920 - text_width) / 2
        y = (1080 - text_height) / 2
        
        # Draw message
        draw.text((x, y), message, font=font, fill=(255, 0, 0))
        
        return img
    except Exception as e:
        # Fallback if text rendering fails
        return Image.new('RGB', (1920, 1080), color=(255, 255, 255))

# Gradio interface
with gr.Blocks(theme=gr.themes.Default(spacing_size="lg")) as demo:
    gr.Markdown("# Professional Data Visualizer")
    
    with gr.Row():
        api_key = gr.Textbox(label="Gemini API Key", type="password")
        file = gr.File(label="Upload Data File", file_types=[".csv", ".xlsx"])
    
    instructions = gr.Textbox(label="Visualization Instructions")
    submit = gr.Button("Generate Insights", variant="primary")
    
    with gr.Row():
        outputs = [gr.Image(label=f"Visualization {i+1}", width=600) for i in range(3)]

    submit.click(
        process_file,
        inputs=[api_key, file, instructions],
        outputs=outputs
    )

if __name__ == "__main__":
    demo.launch()