update ALL

app.py

@@ -192,7 +192,15 @@ def predict(image_path, detector_name):
             ax.set_ylabel('Confidence')
             ax.set_title('Detector Confidence Scores')
             ax.axhline(y=0.6, color='gray', linestyle='--', alpha=0.5, label='Vote Threshold (0.6)')
-            ax.legend()
+            
+            # Add custom legend for colors
+            from matplotlib.patches import Patch
+            legend_elements = [
+                Patch(facecolor='green', label='Real'),
+                Patch(facecolor='red', label='Fake'),
+                ax.lines[0] # The threshold line
+            ]
+            ax.legend(handles=legend_elements)
             
             # Add value labels
             for bar in bars:
@@ -258,6 +266,12 @@ with demo:
     *   **Confidence**: The confidence with which the model determines if the image is real or fake.
     *   **Elapsed Time**: The time the model needed to make the prediction (excluding preprocessing or model building).
 
+    ### Understanding the Results produced by "ALL"
+    *   Runs all available detectors (R50_TF, R50_nodown, CLIP-D, P2G, NPR) sequentially on the input image.
+    *   Produces a **Majority Vote** verdict (Real/Fake) considering only confident predictions (> 0.6). Also generates a **Confidence Plot** visualizing each model's score and a textual **Explanation** of the consensus.
+    *   In the plot, **Green** bars indicate a **Real** prediction, while **Red** bars indicate a **Fake** prediction.
+
+
     ### Note
     ⚠️ Due to file size limitations, model weights need to be downloaded automatically on first use. This may take a few moments. <br>
     ⚠️ To provide a free service, all models run on CPU. The detection process may take a few seconds, depending on the image size and the selected detector.
@@ -285,6 +299,10 @@ with demo:
     
     with gr.Accordion("📚 Model Details", open=False):
         gr.Markdown("""
+        ### **ALL**
+        *   **Description**: Runs all available detectors (R50_TF, R50_nodown, CLIP-D, P2G, NPR) sequentially on the input image.
+        *   **Results**: Produces a **Majority Vote** verdict (Real/Fake) considering only confident predictions (> 0.6). Also generates a **Confidence Plot** visualizing each model's score and a textual **Explanation** of the consensus.
+
         ### **R50_TF**
         *   **Description**: A ResNet50 architecture modified to exclude downsampling at the first layer. It uses "learned prototypes" in the classification head for robust detection.
         *   **Paper**: [TrueFake: A Real World Case Dataset of Last Generation Fake Images also Shared on Social Networks](https://arxiv.org/pdf/2504.20658)