test: ela params

app.py

@@ -273,16 +273,17 @@ def predict_image_with_html(img, confidence_threshold, augment_methods, rotate_d
         img_pil = img
     img_pil, results = predict_image(img_pil, confidence_threshold)
     img_np = np.array(img_pil)  # Convert PIL Image to NumPy array
+    img_np_og = np.array(img)  # Convert PIL Image to NumPy array
 
     gradient_image = gradient_processing(img_np)  # Added gradient processing
     minmax_image = minmax_preprocess(img_np)  # Added MinMax processing
 
     # First pass - standard analysis
-    ela1 = ELA(img_np, quality=75, scale=50, contrast=20, linear=False, grayscale=True)
+    ela1 = ELA(img_np_og, quality=75, scale=50, contrast=20, linear=False, grayscale=True)
 
     # Second pass - enhanced visibility
-    ela2 = ELA(img_np, quality=75, scale=75, contrast=25, linear=False, grayscale=True)
-    ela3 = ELA(img_np, quality=75, scale=75, contrast=25, linear=False, grayscale=False)
+    ela2 = ELA(img_np_og, quality=75, scale=75, contrast=25, linear=False, grayscale=True)
+    ela3 = ELA(img_np_og, quality=75, scale=75, contrast=25, linear=False, grayscale=False)
     
     forensics_images = [img_pil, ela1, ela2, ela3, gradient_image, minmax_image]
     
@@ -320,7 +321,7 @@ with gr.Blocks(css="#post-gallery { overflow: hidden !important;} .grid-wrap{ ov
                     with gr.Column(scale=2):
                         # Custom HTML component to display results in 5 columns
                         results_html = gr.HTML(label="Model Predictions")
-                        forensics_gallery = gr.Gallery(label="Post Processed Images", visible=True, columns=[3], rows=[2], container=False, height="auto", object_fit="contain", elem_id="post-gallery")
+                        forensics_gallery = gr.Gallery(label="Post Processed Images", visible=True, columns=[4], rows=[2], container=False, height="auto", object_fit="contain", elem_id="post-gallery")
 
                         outputs = [image_output, forensics_gallery, results_html]