Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from scipy.stats import kstest, norm, expon, lognorm, chi2, beta, gamma, uniform, pareto, cauchy, t, \
+    weibull_min, laplace, logistic, burr, invgamma, invgauss, gompertz, triang, loglaplace, levy, gumbel_r, gumbel_l, \
+    rayleigh, powerlaw
+import warnings
+import tempfile
+
+# Suppress specific runtime warnings
+warnings.filterwarnings("ignore", category=RuntimeWarning)
+
+
+# Function to check distribution type
+def check_distribution(target_column):
+    data = target_column.dropna()
+
+    # Distribution dictionaries
+    distributions = {
+        'Normal': norm,
+        'Exponential': expon,
+        'Lognormal': lognorm,
+        'Chi-square': chi2,
+        'Beta': beta,
+        'Gamma': gamma,
+        'Uniform': uniform,
+        'Pareto': pareto,
+        'Cauchy': cauchy,
+        'Student\'s t': t,
+        'Weibull': weibull_min,
+        'Laplace': laplace,
+        'Logistic': logistic,
+        'Burr': burr,
+        'Inverse Gamma': invgamma,
+        'Inverse Gaussian': invgauss,
+        'Gompertz': gompertz,
+        'Triangular': triang,
+        'Log-Laplace': loglaplace,
+        'Levy': levy,
+        'Gumbel Right': gumbel_r,
+        'Gumbel Left': gumbel_l,
+        'Rayleigh': rayleigh,
+        'Powerlaw': powerlaw
+    }
+
+    # Colors for top 3 distributions and the normal distribution
+    distribution_colors = ['gold', 'silver', 'brown']
+    normal_color = 'red'
+    actual_data_color = 'black'
+
+    # List to store results
+    results = []
+
+    # Test each distribution with error handling
+    for name, distribution in distributions.items():
+        try:
+            params = distribution.fit(data)
+            D, p_value = kstest(data, distribution.cdf, args=params)
+            results.append((name, p_value, params))
+        except Exception as e:
+            print(f"Skipping {name} distribution due to an error: {e}")
+
+    # Sort by p-value and get top 3
+    results.sort(key=lambda x: x[1], reverse=True)
+    top_3_results = results[:3]
+
+    # Create a single plot for all distributions
+    plt.figure(figsize=(12, 8), dpi=400)
+
+    # Plot the original data distribution as a histogram
+    sns.histplot(data, kde=False, stat="density", bins=30, color=actual_data_color, label='Actual Data Distribution')
+
+    # Overlay the actual data KDE line
+    sns.kdeplot(data, color=actual_data_color, lw=2, label='Actual Data Distribution Line')
+
+    # Overlay the top 3 best fit distributions
+    for i, (name, p_value, params) in enumerate(top_3_results):
+        best_fit_data = np.linspace(min(data), max(data), 1000)
+        pdf = distributions[name].pdf(best_fit_data, *params)
+        p_value_text = "<0.0001" if p_value < 0.0001 else f"{p_value:.12f}"
+        plt.plot(best_fit_data, pdf, color=distribution_colors[i], lw=2, label=f'{name} Fit (p-value={p_value_text})')
+
+    plt.title("Top 3 Best Fit Distributions Overlaid")
+    plt.legend()
+
+    # Save plot to temporary file
+    temp_file_fit = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
+    plt.savefig(temp_file_fit.name)
+    plt.close()
+
+    best_fit_plot = temp_file_fit.name
+
+    # Prepare result text with top 3 distributions and p-values
+    result_text = "Top 3 best matched distributions:\n"
+    for i, (name, p_value, _) in enumerate(top_3_results):
+        p_value_text = "<0.0001" if p_value < 0.0001 else f"{p_value:.12f}"
+        result_text += f"Top {i + 1}: {name} with a p-value of {p_value_text}\n"
+
+    # Add disclaimer about p-value significance
+    result_text += "\nDisclaimer: A significant p-value (below 0.05) indicates that the distribution does not conform well to the actual data distribution."
+
+    # Normal distribution comparison
+    mean, std = norm.fit(data)
+    normal_best_fit_data = np.linspace(min(data), max(data), 1000)
+    normal_pdf = norm.pdf(normal_best_fit_data, mean, std)
+    ks_stat, normal_p_value = kstest(data, 'norm', args=(mean, std))
+
+    p_value_text = "<0.0001" if normal_p_value < 0.0001 else f"{normal_p_value:.12f}"
+
+    plt.figure(figsize=(12, 8), dpi=400)
+    sns.histplot(data, kde=False, stat="density", bins=30, color=actual_data_color, label='Actual Data Distribution')
+    sns.kdeplot(data, color=actual_data_color, lw=2, label='Actual Data Distribution Line')
+    plt.plot(normal_best_fit_data, normal_pdf, color=normal_color, lw=2, label=f'Normal Fit (p-value={p_value_text})')
+    plt.title("Comparison with Normal Distribution")
+    plt.legend()
+
+    # Save plot to temporary file
+    temp_file_normal = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
+    plt.savefig(temp_file_normal.name)
+    plt.close()
+
+    normal_comparison_plot = temp_file_normal.name
+
+    return result_text, best_fit_plot, normal_comparison_plot
+
+
+# Function to load the CSV file and extract numeric column names
+def load_file(file):
+    df = pd.read_csv(file.name)
+    numeric_columns = df.select_dtypes(include=[np.number]).columns.tolist()
+    return gr.update(choices=numeric_columns), df
+
+
+# Function to analyze the selected column
+def analyze_column(selected_column, df):
+    result_text, best_fit_plot, normal_comparison_plot = check_distribution(df[selected_column])
+    return result_text, best_fit_plot, normal_comparison_plot
+
+
+# Define the Gradio app layout
+with gr.Blocks() as demo:
+    gr.Markdown("# Distribution Fitting\n")
+
+    file_input = gr.File(label="Upload CSV File")
+    column_selector = gr.Dropdown(label="Select Target Column", choices=[])
+    analyze_button = gr.Button("Fit")
+    output_text = gr.Textbox(label="Results")
+    best_fit_plot_output = gr.Image(label="Best Fit Distributions")
+    normal_comparison_output = gr.Image(label="Comparison with Normal Distribution")
+
+    # State management
+    df_state = gr.State(None)
+
+    # Load the file and populate the dropdown
+    file_input.upload(load_file, inputs=file_input, outputs=[column_selector, df_state])
+
+    # Perform analysis on the selected column
+    analyze_button.click(analyze_column, inputs=[column_selector, df_state],
+                         outputs=[output_text, best_fit_plot_output, normal_comparison_output])
+
+demo.launch()