diff --git a/README.md b/README.md
index b8797b7..0f3387c 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,27 @@
-# anime-transfer-gradio
+# Operator: anime-transfer-gradio
+
+Author: shiyu22
+
+## Overview
+
+Use gradio to call [style-transfer-animegan](https://towhee.io/filip-halt/style-transfer-animegan).
+
+## Interface
+
+```python
+__init__(self)
+```
+
+None
+
+```python
+__call__(self, source: str = 'upload')
+```
+
+Args:
+
+- dource:
+  - image soure for input image, defauts to 'upload', you can also change to 'webcam'.
+  
+  - supported types: str
 
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/anime_transfer_gradio.py b/anime_transfer_gradio.py
new file mode 100644
index 0000000..5ddc40d
--- /dev/null
+++ b/anime_transfer_gradio.py
@@ -0,0 +1,51 @@
+import gradio
+import numpy
+from PIL import Image
+from pathlib import Path
+
+from towhee.operator import Operator
+from towhee import pipeline
+
+class AnimeTransferGradio(Operator):
+    """
+    AnimeTransferGradio operator
+    """
+    def __init__(self) -> None:
+        super().__init__()
+
+    def __call__(self, source: str = 'upload') -> None:
+        interface = gradio.Interface(self.trans_img, [gradio.inputs.Image(type="pil", source=source),
+                                                      gradio.inputs.Radio(["celeba", "facepaintv1","facepaintv2", "hayao", "paprika", 'shinkai'])],
+                                     gradio.outputs.Image(type="pil"), allow_flagging='never', allow_screenshot=False)
+        interface.launch(enable_queue=True)
+
+    @staticmethod
+    def trans_img(input, version):
+        trans_pipeline = 'filip-halt/style-transfer-animegan'
+
+        # Resizing the image while keeping aspect ratio.
+        size = (512, 512)
+        input.thumbnail(size, Image.ANTIALIAS)
+        # Saving image to file for input. Very low chance of concurrent file saves during the time
+        # between saving and taking first step of pipeline, so avoiding locks for now. In addition,
+        # current gradio is set to queue so there will never be parallel runs for this.
+        path = str(Path.cwd() / 'test.jpg')
+        input.save(path)
+
+        if version == 'celeba':
+            x = pipeline(trans_pipeline, tag='celeba')(path)
+        elif version == 'facepaintv1':
+            x = pipeline(trans_pipeline, tag='facepaintv1')(path)
+        elif version == 'facepaintv2':
+            x = pipeline(trans_pipeline, tag='facepaintv2')(path)
+        elif version == 'hayao':
+            x = pipeline(trans_pipeline, tag='hayao')(path)
+        elif version == 'paprika':
+            x = pipeline(trans_pipeline, tag='paprika')(path)
+        elif version == 'shinkai':
+            x = pipeline(trans_pipeline, tag='shinkai')(path)
+
+        # Converting from channel-first, [0,1] value RGB, numpy array to PIL image.
+        x = numpy.transpose(x[0][0], (1, 2, 0))
+        x = Image.fromarray((x * 255).astype(numpy.uint8))
+        return x