feat: enhance image prediction output and UI components

- Modified `predict_image_with_json` to return a structured table of model results, excluding model path.
- Updated Gradio interface to include a debug JSON output for raw model results.
- Adjusted Dataframe headers to reflect the new output structure, removing the model path for clarity.
- Improved UI descriptions and ensured consistent formatting for better readability.

app_mcp.py

@@ -260,17 +260,23 @@ def predict_image_with_json(img, confidence_threshold, augment_methods, rotate_d
     
     forensics_images = [img_pil, ela1, ela2, ela3, gradient_image, minmax_image]
     
-    return img_pil, forensics_images, results
+    # Prepare table rows for Dataframe (exclude model path)
+    table_rows = [[
+        r.get("Model", ""),
+        r.get("Contributor", ""),
+        r.get("AI Score", ""),
+        r.get("Real Score", ""),
+        r.get("Label", "")
+    ] for r in results]
+    return img_pil, forensics_images, table_rows, results
 
 with gr.Blocks(css="#post-gallery { overflow: hidden !important;} .grid-wrap{ overflow-y: hidden !important;} .ms-gr-ant-welcome-icon{ height:unset !important;} .tabs{margin-top:10px;}") as iface:
     with ms.Application() as app:
         with antd.ConfigProvider():
             antdx.Welcome(
-                icon=
-                "https://cdn-avatars.huggingface.co/v1/production/uploads/639daf827270667011153fbc/WpeSFhuB81DY-1TjNUmV_.png",
+                icon="https://cdn-avatars.huggingface.co/v1/production/uploads/639daf827270667011153fbc/WpeSFhuB81DY-1TjNUmV_.png",
                 title="Welcome to Project OpenSight",
-                description=
-                "The OpenSight aims to be an open-source SOTA generated image detection model. This HF Space is not only an introduction but a educational playground for the public to evaluate and challenge current open source models.  **Space will be upgraded shortly; inference on all 6 models should take about 1.2~ seconds.** "
+                description="The OpenSight aims to be an open-source SOTA generated image detection model. This HF Space is not only an introduction but a educational playground for the public to evaluate and challenge current open source models.  **Space will be upgraded shortly; inference on all 6 models should take about 1.2~ seconds.** "
             )
             with gr.Tab("👀 Detection Models Eval / Playground"):
                 gr.Markdown("# Open Source Detection Models Found on the Hub\n\n - **Space will be upgraded shortly;** inference on all 6 models should take about 1.2~ seconds once we're back on CUDA.\n - The **Community Forensics** mother of all detection models is now available for inference, head to the middle tab above this.\n - Lots of exciting things coming up, stay tuned!")
@@ -294,12 +300,16 @@ with gr.Blocks(css="#post-gallery { overflow: hidden !important;} .grid-wrap{ ov
                         # Use Gradio-native Dataframe to display results with headers
                         results_table = gr.Dataframe(
                             label="Model Predictions",
-                            headers=["Model", "Contributor", "HF Model Path", "AI Score", "Real Score", "Label"],
-                            datatype=["str", "str", "str", "number", "number", "str"]
+                            headers=["Model", "Contributor", "AI Score", "Real Score", "Label"],
+                            datatype=["str", "str", "number", "number", "str"]
                         )
                         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")
+                        debug_json = gr.JSON(label="Raw Model Results")
+                        debug_accordion = gr.Accordion("Debug Output (Raw JSON)", open=False)
+                        with debug_accordion:
+                            debug_json.render()
 
-                        outputs = [image_output, forensics_gallery, results_table]
+                        outputs = [image_output, forensics_gallery, results_table, debug_json]
                 
                 # Show/hide rotate slider based on selected augmentation method
                 augment_checkboxgroup.change(lambda methods: gr.update(visible="rotate" in methods), inputs=[augment_checkboxgroup], outputs=[rotate_slider])