Update app.py

app.py

@@ -6,12 +6,18 @@ import gradio as gr
 from io import BytesIO
 
 @spaces.GPU
-def get_face_embedding(base64_image):
-    # Decode the base64 image
-    img_data = base64.b64decode(base64_image)
-    np_arr = np.frombuffer(img_data, np.uint8)
-    image = face_recognition.load_image_file(BytesIO(img_data))
-    
+def get_face_embedding(image_input):
+    # Check if the input is a URL
+    if isinstance(image_input, str) and (image_input.startswith("http://") or image_input.startswith("https://")):
+        response = requests.get(image_input)
+        image = face_recognition.load_image_file(BytesIO(response.content))
+    else:
+        # Assume input is a base64 encoded string
+        if ',' in image_input:
+            image_input = image_input.split(',')[1]  # Remove the prefix
+        img_data = base64.b64decode(image_input)
+        image = face_recognition.load_image_file(BytesIO(img_data))
+
     # Get the face encodings for all faces in the image
     face_encodings = face_recognition.face_encodings(image)
 
@@ -22,13 +28,14 @@ def get_face_embedding(base64_image):
     # Return the first face encoding as a list
     return face_encodings[0].tolist()
 
+
 # Define the Gradio interface
 interface = gr.Interface(
     fn=get_face_embedding,
     inputs="text",
     outputs="json",
     title="Face Embedding Extractor",
-    description="Input a base64 encoded image to get a 128-dimensional face embedding vector. If no face is detected, an empty list is returned."
+    description="Input a base64 encoded image or an image link to get a 128-dimensional face embedding vector. If no face is detected, an empty list is returned."
 )
 
 if __name__ == "__main__":