Update app.py

app.py

@@ -67,10 +67,9 @@ def segment(rgb: np.ndarray):
         "latency_ms": int((time.time()-t0)*1000),
     }
 
-# --- 3. GRADIO INTERFACE WITH EXAMPLES ---
+# --- 3. GRADIO INTERFACE ---
 
 # Define the paths to your example images
-# Ensure the folder "examples" exists and contains these specific files
 example_files = [
     ["examples/1.jpg"],
     ["examples/2.jpg"],
@@ -81,7 +80,7 @@ example_files = [
 
 with gr.Blocks(title="Panoramic Radiograph Segmentation") as demo:
     gr.Markdown("## Dental X-Ray Segmentation App")
-    gr.Markdown("Upload a panoramic radiograph (or click an example below) to detect teeth and structures.")
+    gr.Markdown("Upload a panoramic radiograph (or click an example below) to detect teeth.")
 
     with gr.Row():
         # --- Left Column: Input ---
@@ -105,5 +104,17 @@ with gr.Blocks(title="Panoramic Radiograph Segmentation") as demo:
     # Link the button to the function
     submit_btn.click(fn=segment, inputs=img_in, outputs=[img_out, json_out], api_name="/predict")
 
+    # --- CITATIONS SECTION ---
+    gr.Markdown("---") 
+    gr.Markdown(
+        """
+        ### Credits & Citations
+        Credits & Citations:
+        * **Methodology:** Brahmi, W., & Jdey, I. (2024). Automatic tooth instance segmentation and identification from panoramic X-Ray images using deep CNN. *Multimedia Tools and Applications, 83*(18), 55565–55585.
+        * **Literature Review:** Brahmi, W., Jdey, I., & Drira, F. (2024). Exploring the role of Convolutional Neural Networks (CNN) in dental radiography segmentation: A comprehensive Systematic Literature Review. *Engineering Applications of Artificial Intelligence, 133*, 108510.
+        * **Dataset:** [Panoramic Dental X-rays (Mendeley Data)](https://data.mendeley.com/datasets/73n3kz2k4k/3)
+        """
+    )
+
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)