app.py

import gradio as gr
import pandas as pd
import sweetviz as sv
import tempfile
import os

class DataAnalyzer:
    def __init__(self):
        self.temp_dir = tempfile.mkdtemp()
    
    def generate_sweetviz_report(self, df):
        # Create Sweetviz report
        report = sv.analyze(df)
        
        # Save to temporary file with specific name
        report_path = os.path.join(self.temp_dir, "sweetviz_report.html")
        report.show_html(report_path, open_browser=False)
        
        # Read the generated HTML
        with open(report_path, 'r', encoding='utf-8') as f:
            html_content = f.read()
            
        # Clean up the temporary file
        os.remove(report_path)
        
        return html_content

def create_interface():
    analyzer = DataAnalyzer()
    
    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        gr.Markdown("# Data Analysis Dashboard")
        
        with gr.Row():
            file_input = gr.File(label="Upload CSV")
            dataset_info = gr.JSON(label="Dataset Information")
        
        # Create a larger HTML viewer for the report
        report_html = gr.HTML(label="Analysis Report", height=800)
        
        def process_file(file):
            if file is None:
                return None, None
            
            try:
                df = pd.read_csv(file.name)
                
                # Convert 'value' column to numeric if possible
                df['value'] = pd.to_numeric(df['value'], errors='coerce')
                
                info = {
                    "Rows": len(df),
                    "Columns": len(df.columns),
                    "Memory Usage (MB)": round(df.memory_usage(deep=True).sum() / 1024**2, 2),
                    "Missing Values": df.isnull().sum().sum(),
                    "Column Types": df.dtypes.astype(str).to_dict()
                }
                
                # Generate Sweetviz report
                report = analyzer.generate_sweetviz_report(df)
                
                return info, report
                
            except Exception as e:
                return {"error": str(e)}, f"Error generating report: {str(e)}"
        
        file_input.change(
            fn=process_file,
            inputs=[file_input],
            outputs=[dataset_info, report_html]
        )
    
    return demo

if __name__ == "__main__":
    demo = create_interface()
    demo.launch(
        height=1000,  # Increased height for better report visibility
        show_error=True
    )