gradio demo

gradio/demo.py

@@ -0,0 +1,28 @@
+import cv2
+import paddlehub as hub
+import gradio as gr
+
+model = hub.Module(name='U2Net')
+
+def infer(img):
+  result = model.Segmentation(
+      images=[cv2.imread(img.name)],
+      paths=None,
+      batch_size=1,
+      input_size=320,
+      output_dir='output',
+      visualization=True)
+  return result[0]['front'][:,:,::-1], result[0]['mask']
+
+inputs = gr.inputs.Image(type='file', label="Original Image")
+outputs = [
+           gr.outputs.Image(type="numpy",label="Front"),
+           gr.outputs.Image(type="numpy",label="Mask")
+           ]
+
+title = "Artline"
+description = "demo for OpenAI's CLIP. To use it, simply upload your image, or click one of the examples to load them and optionally add a text label seperated by commas to help clip classify the image better. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://openai.com/blog/clip/'>CLIP: Connecting Text and Images</a> | <a href='https://github.com/openai/CLIP'>Github Repo</a></p>"
+
+
+gr.Interface(infer, inputs, outputs, title=title, description=description, article=article).launch(debug=True)

requirements.txt

@@ -3,3 +3,7 @@ scikit-image==0.14.0
 torch==0.4.1.post2
 torchvision==0.2.1
 pillow==7.2.0
+opencv
+paddlepaddle
+paddlehub
+gradio