Create demo.py

demo.py

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+# Example script to run the demo without AI model dependencies for local testing
+# Saves this as demo.py
+
+import gradio as gr
+from app import read_file, analyze_data, generate_visualizations, display_analysis
+
+def simple_process_file(file):
+    """Simplified version without AI models for testing"""
+    # Read the file
+    df = read_file(file)
+    
+    if isinstance(df, str):  # If error message
+        return df, None, None, None
+    
+    # Analyze data
+    analysis = analyze_data(df)
+    
+    # Generate visualizations
+    visualizations = generate_visualizations(df)
+    
+    # Placeholder for AI recommendations
+    cleaning_recommendations = """
+    ## Data Cleaning Recommendations
+    
+    * Handle missing values by either removing rows or imputing with mean/median/mode
+    * Remove duplicate rows if present
+    * Convert date-like string columns to proper datetime format
+    * Standardize text data by removing extra spaces and converting to lowercase
+    * Check for and handle outliers in numerical columns
+    
+    Note: This is a demo recommendation (AI model not connected in demo mode)
+    """
+    
+    # Placeholder for AI insights
+    analysis_insights = """
+    ## Data Analysis Insights
+    
+    1. Examine the distribution of each numeric column
+    2. Analyze correlations between numeric features
+    3. Look for patterns in categorical data
+    4. Consider creating visualizations like histograms and scatter plots
+    5. Explore relationships between different variables
+    
+    Note: This is a demo insight (AI model not connected in demo mode)
+    """
+    
+    return analysis, visualizations, cleaning_recommendations, analysis_insights
+
+def demo_ui(file):
+    """Demo mode UI function"""
+    if file is None:
+        return "Please upload a file to begin analysis.", None, None, None
+    
+    # Process the file
+    analysis, visualizations, cleaning_recommendations, analysis_insights = simple_process_file(file)
+    
+    # Format analysis for display
+    analysis_html = display_analysis(analysis)
+    
+    # Prepare visualizations for display
+    viz_html = ""
+    if visualizations and not isinstance(visualizations, str):
+        for viz_name, fig in visualizations.items():
+            # Convert plotly figure to HTML
+            viz_html += f'<div style="margin-bottom: 30px;">{fig.to_html(full_html=False, include_plotlyjs="cdn")}</div>'
+    
+    # Combine analysis and visualizations
+    result_html = f"""
+    <div style="display: flex; flex-direction: column;">
+        <div>{analysis_html}</div>
+        <h2>Data Visualizations</h2>
+        <div>{viz_html}</div>
+    </div>
+    """
+    
+    return result_html, visualizations, cleaning_recommendations, analysis_insights
+
+# Create Gradio interface for demo mode
+with gr.Blocks(title="Data Visualization & Cleaning AI (Demo Mode)") as demo:
+    gr.Markdown("# Data Visualization & Cleaning AI")
+    gr.Markdown("**DEMO MODE** - Upload your data file (CSV, Excel, JSON, or TXT) and get automatic analysis and visualizations.")
+    
+    with gr.Row():
+        file_input = gr.File(label="Upload Data File")
+    
+    with gr.Tabs():
+        with gr.TabItem("Data Analysis"):
+            with gr.Row():
+                analyze_button = gr.Button("Analyze Data")
+            
+            with gr.Tabs():
+                with gr.TabItem("Analysis & Visualizations"):
+                    output = gr.HTML(label="Results")
+                with gr.TabItem("AI Cleaning Recommendations"):
+                    cleaning_recommendations_output = gr.Markdown(label="AI Recommendations")
+                with gr.TabItem("AI Analysis Insights"):
+                    analysis_insights_output = gr.Markdown(label="Analysis Insights")
+                with gr.TabItem("Raw Visualization Objects"):
+                    viz_output = gr.JSON(label="Visualization Objects")
+    
+    # Connect the button to function
+    analyze_button.click(
+        fn=demo_ui, 
+        inputs=[file_input], 
+        outputs=[output, viz_output, cleaning_recommendations_output, analysis_insights_output]
+    )
+
+# Launch the demo
+if __name__ == "__main__":
+    demo.launch()