import grc

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+import gradio.components as grc
 from wmdetection.models import get_watermarks_detection_model
 from wmdetection.pipelines.predictor import WatermarksPredictor
 import os, glob
@@ -22,6 +23,6 @@ def predict(image, threshold=0.5):
 examples = glob.glob(os.path.join('images', 'clean', '*'))
 examples.extend(glob.glob(os.path.join('images', 'watermark', '*')))
 examples = [[e, 0.5] for e in examples]
-iface = gr.Interface(fn=predict, inputs=[gr.inputs.Image(type="pil"), gr.inputs.Number(label="threshold", default=0.5), ], 
-             examples=examples, outputs=[gr.outputs.Textbox(label="class"), gr.outputs.Textbox(label="wm_confidence")])
+iface = gr.Interface(fn=predict, inputs=[grc.Image(type="pil"), grc.Number(label="threshold", default=0.5)], 
+             examples=examples, outputs=[grc.Textbox(label="class"), grc.Textbox(label="wm_confidence")])
 iface.launch()