Update app.py

app.py

@@ -2,12 +2,12 @@ import gradio as gr
 import pandas as pd
 import os
 import shutil
-from omnibin import generate_binary_classification_report
+from omnibin import generate_binary_classification_report, ColorScheme
 
 # Define results directory
 RESULTS_DIR = "/tmp/results"
 
-def process_csv(csv_file, n_bootstrap=1000, dpi=72, color="blue"):
+def process_csv(csv_file, n_bootstrap=1000, dpi=72, color_scheme=ColorScheme.DEFAULT):
     # Read the CSV file
     df = pd.read_csv(csv_file.name)
     
@@ -31,7 +31,7 @@ def process_csv(csv_file, n_bootstrap=1000, dpi=72, color="blue"):
         n_bootstrap=n_bootstrap,
         random_seed=42,
         dpi=dpi,
-        color=color
+        color_scheme=color_scheme
     )
     
     # Get paths to individual plots
@@ -55,7 +55,7 @@ iface = gr.Interface(
         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", choices=["blue", "red", "green", "purple", "orange"], value="blue")
+        gr.Dropdown(label="Color Scheme", choices=["DEFAULT", "MONOCHROME", "VIBRANT"], value="DEFAULT")
     ],
     outputs=[
         gr.File(label="Classification Report PDF"),
@@ -70,7 +70,7 @@ iface = gr.Interface(
     description="Upload a CSV file containing 'y_true' and 'y_pred' columns to generate a binary classification report."
                 "'y_true': reference standard (0s or 1s)."
                 "'y_pred': model prediction (continuous value between 0 and 1)",
-    examples=["scores.csv"],
+    examples=["scores.csv", 1000, 72, "DEFAULT"],
     cache_examples=False
 )