Update app.py

app.py

@@ -5,7 +5,7 @@ import shutil
 from omnibin import generate_binary_classification_report
 
 # Define results directory
-RESULTS_DIR = "/tmp/results"
+RESULTS_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "results")
 
 def process_csv(csv_file):
     # Read the CSV file
@@ -33,14 +33,33 @@ def process_csv(csv_file):
         dpi=72
     )
     
-    # Return the PDF file
-    return report_path
+    # 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"),
-    outputs=gr.File(label="Classification Report PDF"),
+    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 comprehensive classification report with 6 figures.",
     examples=[],