app.py

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

def process_file(file, instructions, api_key):
    try:
        # Initialize Gemini
        genai.configure(api_key=api_key)
        model = genai.GenerativeModel('gemini-pro')
        
        # Read uploaded file
        file_path = file.name
        df = pd.read_csv(file_path) if file_path.endswith('.csv') else pd.read_excel(file_path)
        
        # Generate visualization code using Gemini
        prompt = f"""
        Analyze the following dataset and instructions:
        
        Data columns: {list(df.columns)}
        Instructions: {instructions}
        
        Based on this, create 3 appropriate visualizations. For each visualization, provide:
        1. A title
        2. The most suitable plot type (choose from: bar, line, scatter, hist)
        3. The column to use for the x-axis
        4. The column to use for the y-axis (use None for histograms)

        Return your response as a Python list of tuples:
        [
            ("Title 1", "plot_type1", "x_column1", "y_column1"),
            ("Title 2", "plot_type2", "x_column2", "y_column2"),
            ("Title 3", "plot_type3", "x_column3", "y_column3")
        ]
        """
        
        response = model.generate_content(prompt)
        plots = eval(response.text)
        
        # Generate visualizations
        images = []
        for plot in plots:
            fig, ax = plt.subplots(figsize=(10, 6))
            title, plot_type, x, y = plot
            
            if plot_type == 'bar':
                df.plot(kind='bar', x=x, y=y, ax=ax)
            elif plot_type == 'line':
                df.plot(kind='line', x=x, y=y, ax=ax)
            elif plot_type == 'scatter':
                df.plot(kind='scatter', x=x, y=y, ax=ax)
            elif plot_type == 'hist':
                df[x].hist(ax=ax)
            
            ax.set_title(title)
            ax.set_xlabel(x)
            ax.set_ylabel(y if y else 'Frequency')
            plt.tight_layout()
            
            buf = io.BytesIO()
            plt.savefig(buf, format='png')
            buf.seek(0)
            img = Image.open(buf)
            images.append(img)
            plt.close(fig)

        return images if len(images) == 3 else images + [Image.new('RGB', (800, 600), (255,255,255))]*(3-len(images))

    except Exception as e:
        error_message = f"Error: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
        print(error_message)  # Print to console for debugging
        error_image = Image.new('RGB', (800, 400), (255, 255, 255))
        draw = ImageDraw.Draw(error_image)
        draw.text((10, 10), error_message, fill=(255, 0, 0))
        return [error_image] * 3

with gr.Blocks(theme=gr.themes.Default()) as demo:
    gr.Markdown("# Data Analysis Dashboard")
    
    with gr.Row():
        file = gr.File(label="Upload Dataset", file_types=[".csv", ".xlsx"])
        instructions = gr.Textbox(label="Analysis Instructions", placeholder="Describe the analysis you want...")
    
    api_key = gr.Textbox(label="Gemini API Key", type="password")
    submit = gr.Button("Generate Insights", variant="primary")
    
    output_images = [gr.Image(label=f"Visualization {i+1}") for i in range(3)]

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

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