modify app.py

app.py

@@ -270,32 +270,33 @@ demo = gr.Blocks(title="Deepfake Detection Space", theme=gr.themes.Soft())
 with demo:
     gr.Markdown("""
     # 🔍 Deepfake Detection Space
-    This space provides an **educational, open-source demonstrator** collecting a series of baseline state-of-the-art methods for deepfake detection, allowing for free and unlimited use.
-    
-    ### ⚠️ Important Limitations
-    * **Not Proprietary Software:** This tool showcases open-source networks. It **does not** reflect the performance, robustness, or accuracy of Truebees' commercial detection methods.
-    * **"In-the-Wild" Performance Drop:** These models were trained on non-social datasets. You may experience a severe drop in accuracy when testing highly compressed images downloaded from social media platforms or messaging apps (e.g., WhatsApp).
-
-    ### Training & Performance
-    All methods have been trained using the **[DeepShield dataset](https://zenodo.org/records/15648378)**, on images generated with **Stable Diffusion XL** and **StyleGAN 2**. 
-    You can expect performance comparable to the results shown in [Dell'Anna et al. (2025)](https://arxiv.org/pdf/2504.20658).
-
-    ### Understanding the Results
-    * **Prediction**: Tells if an image is **Real** or **Fake**.
-    * **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).
+    This space provides an **educational, open-source demonstrator** collecting a series of baseline state-of-the-art methods for deepfake detection, allowing for free and unlimited use!
 
-    ### 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 **Weighted Majority Vote** verdict (Real/Fake). Each model's vote is weighted by a fixed importance score (summing to 1) based on user ranking **and its confidence score**. Only confident predictions (> 0.6) are counted.
-    * You can find the specific weights used for each model in the **"⚖️ Weight Details"** menu below.
-    * 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
+    If you want to train the detectors in this Space yourself, check out our [GitHub repository](https://github.com/truebees-ai/Image-Deepfake-Detectors-Public-Library).    
+    
+    ### ⚠️ Important
+    * This tool showcases open-source detectors. It **does not** reflect the performance, robustness, or accuracy of Truebees' commercial detection methods. Want to give it a try? check **[Truebees Website](https://www.truebees.eu)**
+    * These models were trained on image not shared on social networks from the **[DeepShield dataset](https://zenodo.org/records/15648378)**. You may experience a drop in accuracy when testing highly compressed images downloaded from social media platforms or messaging apps.
+    * You can expect performance comparable to the results shown in [Dell'Anna et al. (2025)](https://arxiv.org/pdf/2504.20658).
+    
     ⚠️ 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.
     """)
+    with gr.Accordion("📊 Understanding the Results", open=False):
+            gr.Markdown("""
+            ### Individual Detectors
+            * **Prediction**: Tells if an image is **Real** or **Fake**.
+            * **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).
+
+            ### The "ALL" Option
+            * Runs all available detectors (R50_TF, R50_nodown, CLIP-D, P2G, NPR) sequentially on the input image.
+            * Produces a **Weighted Majority Vote** verdict (Real/Fake). Each model's vote is weighted by a fixed importance score (summing to 1) based on user ranking **and its confidence score**. Only confident predictions (> 0.6) are counted.
+            * You can find the specific weights used for each model in the **"⚖️ Weight Details"** menu below.
+            * 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.
+            """)
+    
     
     with gr.Row():
         with gr.Column():
@@ -320,8 +321,7 @@ with demo:
     with gr.Accordion("⚖️ Weight Details", open=False):
         gr.Markdown(f"""
         ### **Detector Weights**
-        The weights are assigned based on the ranking (based on the results of [TrueFake: A Real World Case Dataset of Last Generation Fake Images also Shared on Social Networks](https://arxiv.org/pdf/2504.20658)): **CLIP-D > R50_TF > R50_nodown > P2G > NPR**, such that their sum equals 1.
-        
+        The weights are assigned according to the models' performance ranking on the **[DeepShield dataset](https://zenodo.org/records/15648378)** (**CLIP-D > R50_TF > R50_nodown > P2G > NPR**), ensuring their total sum equals 1.        
         | Detector | Weight |
         | :--- | :---: |
         | **CLIP-D** | {DETECTOR_WEIGHTS['CLIP-D']:.2f} |
@@ -334,7 +334,7 @@ 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.
+        * **Description**: Runs all available detectors (R50_TF, R50_nodown, CLIP-D, P2G, NPR) sequentially on the input image. The complete code for training and testing these models on your own datasets is available in our [GitHub repository](https://github.com/truebees-ai/Image-Deepfake-Detectors-Public-Library).
         * **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**