Update app.py

app.py

@@ -1,30 +1,39 @@
 import os
+import sys
 import gradio as gr
 import subprocess
+from fastai.vision.all import *
+import torch
 
-title = "DaGAN Demo"
-description = "Reconstruction and Animation demos for DaGAN. The videos will be cropped to 5 seconds because of limitations of computing resources (inference is being run on CPU, unfortunately)."
+# set device to GPU
+device = torch.device('cpu')
 
-def inference(mode, img, video):
-    os.makedirs('tmp', exist_ok=True)
 
-    cmd = f"ffmpeg -y -ss 00:00:00 -i {video} -to 00:00:05 -c copy cut_vid.mp4"
-    subprocess.run(cmd.split())
+os.system('git clone https://github.com/iPERDance/iPERCore.git')
+os.system('curl -o ./iPERCore/assets/checkpoints.zip http://101.32.75.151:10086/checkpoints.zip')
+os.system('curl -o ./iPERCore/assets/samples.zip http://101.32.75.151:12345/iper_plus_plus_latest_samples.zip')
 
-    video = "cut_vid.mp4"
-    os.system(f"python run.py --source_image {img} --driving_video {video} --result_video tmp/result.mp4 --mode {mode}")
+title = "full-body-animation"
+description = "A demo for full-body-animation Official Repo"
+article = "Official Repo: https://github.com/iPERDance/iPERCore"
+
+def inference(img, video):
+    if not os.path.exists('temp'):
+        os.system('mkdir temp')
+    
+    os.system(f"python iPERCore.services.run_imitator --gpu_ids 0 --num_source 2 --src_path {img} --ref_path {video} --output_dir tmp/result.mp4")
     return "tmp/result.mp4"
 
+
 gr.Interface(
     inference,
     [
-        gr.Dropdown(["reconstruction", "animation"]),
         gr.inputs.Image(type="filepath", label="Source (only used if mode is animation)"),
         gr.inputs.Video(type="mp4", label="Driving Video")
     ],
     outputs=gr.outputs.Video(type="mp4", label="Output Video"),
     title=title,
     description=description,
+    article=article,
     theme="huggingface",
-    allow_flagging=False).launch(debug=False,enable_queue=True)
-
+    allow_flagging=False).launch(debug=True,enable_queue=True