Spaces:
Running
Running
| import os | |
| import shutil | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| import plotly.express as px | |
| import gradio as gr | |
| css = ''' | |
| .gradio-container{max-width: 900px !important} | |
| h1{text-align:center} | |
| ''' | |
| def create_visualizations(data): | |
| plots = [] | |
| # Create figures directory | |
| figures_dir = "./figures" | |
| shutil.rmtree(figures_dir, ignore_errors=True) | |
| os.makedirs(figures_dir, exist_ok=True) | |
| # Histograms for numeric columns | |
| numeric_cols = data.select_dtypes(include=['number']).columns | |
| for col in numeric_cols: | |
| plt.figure() | |
| sns.histplot(data[col], kde=True) | |
| plt.title(f'Histogram of {col}') | |
| plt.xlabel(col) | |
| plt.ylabel('Frequency') | |
| hist_path = os.path.join(figures_dir, f'histogram_{col}.png') | |
| plt.savefig(hist_path) | |
| plt.close() | |
| plots.append(hist_path) | |
| # Box plots for numeric columns | |
| for col in numeric_cols: | |
| plt.figure() | |
| sns.boxplot(x=data[col]) | |
| plt.title(f'Box Plot of {col}') | |
| box_path = os.path.join(figures_dir, f'boxplot_{col}.png') | |
| plt.savefig(box_path) | |
| plt.close() | |
| plots.append(box_path) | |
| # Scatter plot matrix | |
| if len(numeric_cols) > 1: | |
| plt.figure() | |
| sns.pairplot(data[numeric_cols]) | |
| plt.title('Scatter Plot Matrix') | |
| scatter_matrix_path = os.path.join(figures_dir, 'scatter_matrix.png') | |
| plt.savefig(scatter_matrix_path) | |
| plt.close() | |
| plots.append(scatter_matrix_path) | |
| # Correlation heatmap | |
| if len(numeric_cols) > 1: | |
| plt.figure() | |
| corr = data[numeric_cols].corr() | |
| sns.heatmap(corr, annot=True, cmap='coolwarm') | |
| plt.title('Correlation Heatmap') | |
| heatmap_path = os.path.join(figures_dir, 'correlation_heatmap.png') | |
| plt.savefig(heatmap_path) | |
| plt.close() | |
| plots.append(heatmap_path) | |
| # Bar charts for categorical columns | |
| categorical_cols = data.select_dtypes(include=['object']).columns | |
| if not categorical_cols.empty: | |
| for col in categorical_cols: | |
| plt.figure() | |
| data[col].value_counts().plot(kind='bar') | |
| plt.title(f'Bar Chart of {col}') | |
| plt.xlabel(col) | |
| plt.ylabel('Count') | |
| bar_path = os.path.join(figures_dir, f'bar_chart_{col}.png') | |
| plt.savefig(bar_path) | |
| plt.close() | |
| plots.append(bar_path) | |
| # Line charts (if a 'date' column is present) | |
| if 'date' in data.columns: | |
| plt.figure() | |
| data['date'] = pd.to_datetime(data['date']) | |
| data.set_index('date').plot() | |
| plt.title('Line Chart of Date Series') | |
| line_chart_path = os.path.join(figures_dir, 'line_chart.png') | |
| plt.savefig(line_chart_path) | |
| plt.close() | |
| plots.append(line_chart_path) | |
| # Scatter plot using Plotly | |
| if len(numeric_cols) >= 2: | |
| fig = px.scatter(data, x=numeric_cols[0], y=numeric_cols[1], title='Scatter Plot') | |
| scatter_plot_path = os.path.join(figures_dir, 'scatter_plot.html') | |
| fig.write_html(scatter_plot_path) | |
| plots.append(scatter_plot_path) | |
| # Pie chart for categorical columns (only the first categorical column) | |
| if not categorical_cols.empty: | |
| fig = px.pie(data, names=categorical_cols[0], title='Pie Chart of ' + categorical_cols[0]) | |
| pie_chart_path = os.path.join(figures_dir, 'pie_chart.html') | |
| fig.write_html(pie_chart_path) | |
| plots.append(pie_chart_path) | |
| # Heatmaps (e.g., for a correlation matrix or cross-tabulation) | |
| if len(numeric_cols) > 1: | |
| heatmap_data = data[numeric_cols].corr() | |
| fig = px.imshow(heatmap_data, text_auto=True, title='Heatmap of Numeric Variables') | |
| heatmap_plot_path = os.path.join(figures_dir, 'heatmap_plot.html') | |
| fig.write_html(heatmap_plot_path) | |
| plots.append(heatmap_plot_path) | |
| # Violin plots for numeric columns | |
| for col in numeric_cols: | |
| plt.figure() | |
| sns.violinplot(x=data[col]) | |
| plt.title(f'Violin Plot of {col}') | |
| violin_path = os.path.join(figures_dir, f'violin_plot_{col}.png') | |
| plt.savefig(violin_path) | |
| plt.close() | |
| plots.append(violin_path) | |
| return plots | |
| def analyze_data(file_input): | |
| data = pd.read_csv(file_input.name) | |
| return create_visualizations(data) | |
| # Example file path | |
| example_file_path = "./example/🤗example.csv" | |
| with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo: | |
| gr.Markdown("# DATA BOARD📊\nUpload a `.csv` file to generate various visualizations and interactive plots.") | |
| file_input = gr.File(label="Upload your `.csv` file") | |
| submit = gr.Button("Generate Dashboards") | |
| # Display images and interactive plots in a gallery | |
| gallery = gr.Gallery(label="Visualizations") | |
| # Example block with cache_examples set to True | |
| examples = gr.Examples( | |
| examples=[[example_file_path]], | |
| inputs=file_input, | |
| outputs=gallery, | |
| fn=analyze_data, # Provide the processing function | |
| cache_examples=True # Enable caching | |
| ) | |
| submit.click(analyze_data, file_input, gallery) | |
| if __name__ == "__main__": | |
| demo.launch() |