Update app.py

app.py

@@ -1,152 +1,152 @@
-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()
+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(ssr_mode=False)