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import gradio as gr
import pandas as pd
import os
import shutil
from omnibin import generate_binary_classification_report, ColorScheme
# Define results directory
RESULTS_DIR = "/tmp/results"
# Map string color schemes to enum values
COLOR_SCHEME_MAP = {
"DEFAULT": ColorScheme.DEFAULT,
"MONOCHROME": ColorScheme.MONOCHROME,
"VIBRANT": ColorScheme.VIBRANT
}
def process_csv(csv_file, n_bootstrap=1000, dpi=72, color_scheme="DEFAULT"):
# Convert string color scheme to enum
color_scheme_enum = COLOR_SCHEME_MAP[color_scheme]
# Read the CSV file
df = pd.read_csv(csv_file.name)
# Check if required columns exist
required_columns = ['y_true', 'y_pred']
if not all(col in df.columns for col in required_columns):
raise ValueError("CSV file must contain 'y_true' and 'y_pred' columns")
# Clean up results directory if it exists
if os.path.exists(RESULTS_DIR):
shutil.rmtree(RESULTS_DIR)
# Create fresh results directory
os.makedirs(RESULTS_DIR, exist_ok=True)
# Generate the report
report_path = generate_binary_classification_report(
y_true=df['y_true'].values,
y_scores=df['y_pred'].values,
output_path=os.path.join(RESULTS_DIR, "classification_report.pdf"),
n_bootstrap=n_bootstrap,
random_seed=42,
dpi=dpi,
color_scheme=color_scheme_enum
)
# Get paths to individual plots
plots_dir = os.path.join(RESULTS_DIR, "plots")
plot_paths = {
"ROC and PR Curves": os.path.join(plots_dir, "roc_pr.png"),
"Metrics vs Threshold": os.path.join(plots_dir, "metrics_threshold.png"),
"Confusion Matrix": os.path.join(plots_dir, "confusion_matrix.png"),
"Calibration Plot": os.path.join(plots_dir, "calibration.png"),
"Prediction Distribution": os.path.join(plots_dir, "prediction_distribution.png"),
"Metrics Summary": os.path.join(plots_dir, "metrics_summary.png")
}
# Return both the PDF and the plot images
return report_path, *plot_paths.values()
# Create the Gradio interface
iface = gr.Interface(
fn=process_csv,
inputs=[
gr.File(label="Upload CSV file with 'y_true' and 'y_pred' columns"),
gr.Number(label="Number of Bootstrap Iterations", value=1000, minimum=100, maximum=10000),
gr.Number(label="DPI", value=72, minimum=50, maximum=300),
gr.Dropdown(label="Color Scheme", choices=["DEFAULT", "MONOCHROME", "VIBRANT"], value="DEFAULT")
],
outputs=[
gr.File(label="Classification Report PDF"),
gr.Image(label="ROC and PR Curves"),
gr.Image(label="Metrics vs Threshold"),
gr.Image(label="Confusion Matrix"),
gr.Image(label="Calibration Plot"),
gr.Image(label="Prediction Distribution"),
gr.Image(label="Metrics Summary")
],
title="Binary Classification Report Generator",
description="Upload a CSV file containing 'y_true' and 'y_pred' columns to generate a binary classification report.\n\n"
"'y_true': reference standard (0s or 1s).\n\n"
"'y_pred': model prediction (continuous value between 0 and 1).\n\n"
"This application takes approximately 35 seconds to generate the report.\n",
examples=[["scores.csv", 1000, 72, "DEFAULT"]],
cache_examples=False
)
if __name__ == "__main__":
iface.launch()
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