Update app.py

app.py

@@ -159,6 +159,7 @@ def classify_images(image_dir, model_pipeline, model_idx):
     for folder_name, ground_truth_label in [('real', 1), ('ai', 0)]:
         folder_path = os.path.join(image_dir, folder_name)
         if not os.path.exists(folder_path):
+            print(f"Folder not found: {folder_path}")
             continue
         for img_name in os.listdir(folder_path):
             img_path = os.path.join(folder_path, img_name)
@@ -167,13 +168,15 @@ def classify_images(image_dir, model_pipeline, model_idx):
                 
                 # Ensure that each image is being processed by the correct model pipeline
                 pred = model_pipeline(img)
-                pred_label = np.argmax([x['score'] for x in pred])
+                pred_label = 0 if pred[0]['label'] == 'AI' else 1  # Assuming 'AI' is label 0 and 'Real' is label 1
 
                 preds.append(pred_label)
                 labels.append(ground_truth_label)
                 images.append(img_name)
             except Exception as e:
                 print(f"Error processing image {img_name} in model {model_idx}: {e}")
+    
+    print(f"Model {model_idx} processed {len(images)} images")
     return labels, preds, images
 
 # Function to generate evaluation metrics
@@ -210,13 +213,18 @@ def process_zip(zip_file):
     # Run classification for each model
     results = {}
     for idx in range(len(models)):
-        print(f"Processing with model {idx}")
+        print(f"Processing with model {models[idx]}")  # Debugging to show which model is being used
 
         # Create a new pipeline for each model within the loop
         pipe = pipeline("image-classification", f"{models[idx]}")  
+        print(f"Initialized pipeline for {models[idx]}")  # Confirm pipeline is initialized correctly
 
         # Classify images with the correct pipeline per model
         labels, preds, images = classify_images(extracted_dir, pipe, idx)
+        
+        # Debugging: Print the predictions to ensure they're different
+        print(f"Predictions for model {models[idx]}: {preds}")
+
         accuracy, roc_score, report, cm_fig, roc_fig = evaluate_model(labels, preds)
 
         # Store results for each model
@@ -228,7 +236,7 @@ def process_zip(zip_file):
 
     shutil.rmtree(extracted_dir)  # Clean up extracted files
 
-    # Return results for all three models
+    # Return results for all models
     return (results['Model_0_accuracy'], results['Model_0_roc_score'], results['Model_0_report'], 
             results['Model_0_cm_fig'], results['Model_0_roc_fig'],
            results['Model_1_accuracy'], results['Model_1_roc_score'], results['Model_1_report'], 
@@ -238,6 +246,7 @@ def process_zip(zip_file):
 
 
 
+
 # Single image section
 def load_url(url):
     try: