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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() |