import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
import io
import ast
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-2.5-pro-preview-03-25')
        
        # 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
        response = model.generate_content(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(s) to use for the y-axis (can be a list for multiple columns, or None for histograms)
            5. Any necessary data preprocessing steps (e.g., grouping, sorting, etc.)

            Return your response as a Python list of dictionaries:
            [
                {{"title": "...", "plot_type": "...", "x": "...", "y": "...", "preprocessing": "..."}},
                {{"title": "...", "plot_type": "...", "x": "...", "y": "...", "preprocessing": "..."}},
                {{"title": "...", "plot_type": "...", "x": "...", "y": "...", "preprocessing": "..."}}
            ]
        """)

        # Extract code block safely
        code_block = response.text
        if '```python' in code_block:
            code_block = code_block.split('```python')[1].split('```')[0].strip()
        elif '```' in code_block:
            code_block = code_block.split('```')[1].strip()
        
        print("Generated code block:")
        print(code_block)
        
        plots = ast.literal_eval(code_block)
        
        # Generate visualizations
        images = []
        for plot in plots[:3]:  # Ensure max 3 plots
            fig, ax = plt.subplots(figsize=(10, 6))
            
            # Apply preprocessing if any
            if plot['preprocessing']:
                exec(plot['preprocessing'])
            
            if plot['plot_type'] == 'bar':
                df.plot(kind='bar', x=plot['x'], y=plot['y'], ax=ax)
            elif plot['plot_type'] == 'line':
                df.plot(kind='line', x=plot['x'], y=plot['y'], ax=ax)
            elif plot['plot_type'] == 'scatter':
                df.plot(kind='scatter', x=plot['x'], y=plot['y'], ax=ax)
            elif plot['plot_type'] == 'hist':
                df[plot['x']].hist(ax=ax)
            
            ax.set_title(plot['title'])
            ax.set_xlabel(plot['x'])
            ax.set_ylabel(plot['y'] if plot['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()