Update app.py

app.py

@@ -1,8 +1,11 @@
 import gradio as gr
 import pandas as pd
 import os
+import shutil
 from omnibin import generate_binary_classification_report
-import tempfile
+
+# Define results directory
+RESULTS_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "results")
 
 def process_csv(csv_file):
     # Read the CSV file
@@ -13,20 +16,25 @@ def process_csv(csv_file):
     if not all(col in df.columns for col in required_columns):
         raise ValueError("CSV file must contain 'y_true' and 'y_pred' columns")
     
-    # Create a temporary directory for the output
-    with tempfile.TemporaryDirectory() as temp_dir:
-        # 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(temp_dir, "classification_report.pdf"),
-            n_bootstrap=1000,
-            random_seed=42,
-            dpi=72
-        )
-        
-        # Return the PDF file
-        return report_path
+    # 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=1000,
+        random_seed=42,
+        dpi=72
+    )
+    
+    # Return the PDF file
+    return report_path
 
 # Create the Gradio interface
 iface = gr.Interface(