push

gradio_app.py

@@ -42,7 +42,7 @@ download_models()
 def run_epic_inference(video_path, caption, motion_type):
     temp_input_path = "/app/temp_input.mp4"
     output_dir = f"/app/output_anchor"
-    video_output_path = f"{output_dir}/videos/output.mp4"
+    video_output_path = f"{output_dir}/masked_videos/output.mp4"
     traj_name = motion_type
     traj_txt = f"/app/inference/v2v_data/test/trajs/{traj_name}.txt"
     # Save uploaded video
@@ -65,7 +65,6 @@ def run_epic_inference(video_path, caption, motion_type):
 
     # Run inference command
     try:
-    
         result = subprocess.run(command, capture_output=True, text=True, check=True)
         print("Getting Anchor Videos run successfully.")
         logs = result.stdout
@@ -80,6 +79,7 @@ def run_epic_inference(video_path, caption, motion_type):
         return f"Inference succeeded but no output video found in {output_dir}", None
 
 def inference(video_path, caption, motion_type):
+    logs, video_masked = run_epic_inference(video_path, caption, motion_type)
 
     MODEL_PATH="/app/pretrained/CogVideoX-5b-I2V"
 
@@ -122,12 +122,25 @@ def inference(video_path, caption, motion_type):
     else:
         print(f"Error occurred during inference: {result.stderr}")
     
+    # Print output directory contents
+    print(f"\nContents of output directory: {out_dir}")
+    for root, dirs, files in os.walk(out_dir):
+        level = root.replace(out_dir, '').count(os.sep)
+        indent = ' ' * 4 * level
+        print(f"{indent}{os.path.basename(root)}/")
+        sub_indent = ' ' * 4 * (level + 1)
+        for f in files:
+            print(f"{sub_indent}{f}")
+    
+        logs = result.stdout
+
+    return logs, str(f"{out_dir}/output.mp4")
 
 # -----------------------------
 # Step 3: Create Gradio UI
 # -----------------------------
 demo = gr.Interface(
-    fn=run_epic_inference,
+    fn=inference,
     inputs=[
         gr.Video(label="Upload Video (MP4)"),
         gr.Textbox(label="Caption", placeholder="e.g., Amalfi coast with boats"),