Update app.py

app.py

@@ -6,40 +6,44 @@ import gradio as gr
 from PIL import Image
 from io import BytesIO
 
+@spaces.GPU  
 @spaces.GPU  
 def crop_face(base64_image):
-    # Decode the base64 image
-    img_data = base64.b64decode(base64_image)
-    np_arr = np.frombuffer(img_data, np.uint8)
-    image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+    try:
+        # Decode the base64 image
+        img_data = base64.b64decode(base64_image)
+        np_arr = np.frombuffer(img_data, np.uint8)
+        image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+        
+        if image is None:
+            return "Could not decode the image or no data in buffer"
     
-    if image is None:
-        print("Could not decode the image")
-        return None
+        # Load the pre-trained face detector
+        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+        # Convert the image to grayscale
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
-    # Load the pre-trained face detector
-    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+        # Detect faces in the image
+        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
 
-    # Convert the image to grayscale
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        # If no faces are detected, return message
+        if len(faces) == 0:
+            return "No faces found"
 
-    # Detect faces in the image
-    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+        # Crop the first face found
+        x, y, w, h = faces[0]
+        face_crop = image[y:y+h, x:x+w]
 
-    # If no faces are detected, return None
-    if len(faces) == 0:
-        print("No faces found")
-        return None
+        # Encode the cropped face to base64
+        _, buffer = cv2.imencode('.jpg', face_crop)
+        face_base64 = base64.b64encode(buffer).decode('utf-8')
 
-    # Crop the first face found
-    x, y, w, h = faces[0]
-    face_crop = image[y:y+h, x:x+w]
+        return face_base64
 
-    # Encode the cropped face to base64
-    _, buffer = cv2.imencode('.jpg', face_crop)
-    face_base64 = base64.b64encode(buffer).decode('utf-8')
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
 
-    return face_base64
 
 def image_to_base64(image):
     # Convert PIL Image to Bytes
@@ -66,4 +70,4 @@ base64_converter_interface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    gr.TabbedInterface([face_crop_interface, base64_converter_interface], ["Crop Face", "Convert to Base64"]).launch(share=True)
+    gr.TabbedInterface([face_crop_interface, base64_converter_interface], ["Crop Face", "Convert to Base64"]).launch()