Update app.py

app.py

@@ -1,3 +1,44 @@
+# import gradio as gr
+# import cv2
+# import numpy as np
+# from ultralytics import YOLO
+
+
+# model = YOLO(r"best.pt")
+
+# def process_image(image):
+#     image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)  
+#     results = model.predict(image, conf=0.15)
+    
+#     if len(results[0].boxes.cls) == 1:
+#         mask_tensor = results[0].masks.data[0].cpu().numpy()
+#         mask = (mask_tensor * 255).astype(np.uint8)
+        
+#         mask = cv2.resize(mask, (image.shape[1], image.shape[0]))
+#         kernel = np.ones((5, 5), np.uint8)
+#         mask = cv2.dilate(mask, kernel, iterations=2)
+#         mask = cv2.erode(mask, kernel, iterations=2)
+        
+#         rgba_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGBA)
+#         rgba_image[:, :, 3] = mask
+        
+#         return rgba_image
+#     else:
+#         return "Error: Uploaded image has more than one face. Please upload a different image."
+
+
+# demo = gr.Interface(
+#     fn=process_image,
+#     inputs=gr.Image(type="numpy"),
+#     outputs=gr.Image(type="numpy"),
+#     title="Face Segmentation",
+#     description="Upload an image"
+# )
+
+# if __name__ == "__main__":
+#     demo.launch()
+
+
 import gradio as gr
 import cv2
 import numpy as np
@@ -10,7 +51,9 @@ def process_image(image):
     image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)  
     results = model.predict(image, conf=0.15)
     
-    if len(results[0].boxes.cls) == 1:
+    detected_faces = len(results[0].boxes.cls) if results[0].boxes is not None else 0
+    
+    if detected_faces == 1:
         mask_tensor = results[0].masks.data[0].cpu().numpy()
         mask = (mask_tensor * 255).astype(np.uint8)
         
@@ -23,9 +66,10 @@ def process_image(image):
         rgba_image[:, :, 3] = mask
         
         return rgba_image
-    else:
+    elif detected_faces > 1:
         return "Error: Uploaded image has more than one face. Please upload a different image."
-
+    else:
+        return "Error: No face detected. Please upload a valid image."
 
 demo = gr.Interface(
     fn=process_image,