Create app.py

app.py

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+import torch
+import gradio as gr
+model = torch.hub.load("PeterL1n/RobustVideoMatting", "mobilenetv3") # or "resnet50"
+
+convert_video = torch.hub.load("PeterL1n/RobustVideoMatting", "converter")
+
+def inference(video):
+  convert_video(
+      model,                           # The loaded model, can be on any device (cpu or cuda).
+      input_source=video,        # A video file or an image sequence directory.
+      input_resize=None,       # [Optional] Resize the input (also the output).
+      downsample_ratio=0.25,           # [Optional] If None, make downsampled max size be 512px.
+      output_type='video',             # Choose "video" or "png_sequence"
+      output_composition='com.mp4',    # File path if video; directory path if png sequence.
+      output_alpha="pha.mp4",          # [Optional] Output the raw alpha prediction.
+      output_foreground="fgr.mp4",     # [Optional] Output the raw foreground prediction.
+      output_video_mbps=4,             # Output video mbps. Not needed for png sequence.
+      seq_chunk=12,                    # Process n frames at once for better parallelism.
+      num_workers=1,                   # Only for image sequence input. Reader threads.
+      progress=True                    # Print conversion progress.
+  )
+  return 'com.mp4'
+  
+title = "Anime2Sketch"
+description = "demo for Anime2Sketch. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2104.05703'>Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis</a> | <a href='https://github.com/Mukosame/Anime2Sketch'>Github Repo</a></p>"
+
+gr.Interface(
+    inference, 
+    gr.inputs.Video(label="Input"), 
+    gr.outputs.Video(label="Output"),
+    title=title,
+    description=description,
+    article=article,
+    enable_queue=True
+    ).launch(debug=True)