Create app.py

app.py

@@ -0,0 +1,72 @@
+import os, subprocess
+import uuid, tempfile
+import gradio as gr
+from huggingface_hub import snapshot_download
+
+os.makedirs("pretrained", exist_ok=True)
+snapshot_download(
+    repo_id = "jiawei011/L4GM",
+    local_dir = "./pretrained"
+)
+
+# Folder containing example images
+examples_folder = "data_test"
+
+# Retrieve all file paths in the folder
+video_examples = [
+    os.path.join(examples_folder, file)
+    for file in os.listdir(examples_folder)
+    if os.path.isfile(os.path.join(examples_folder, file))
+]
+
+
+def generate(input_video):
+
+    #--test_path data_test/otter-on-surfboard_fg.mp4
+    workdir = "results"
+    pretrained_model = "pretrained/recon.safetensors"
+    num_frames = 1
+    test_path = input_video
+    
+    try:
+        # Run the inference command
+        subprocess.run(
+            [
+                "python", "infer_3d.py", "big",
+                f"workspace={workdir},
+                f"resume={pretrained_model}",
+                f"num_frames={num_frames}",
+                f"test_path={test_path}",
+            ],
+            check=True
+        )
+
+        
+        # Retrieve the file name without the extension
+        #removed_bg_file_name = os.path.splitext(os.path.basename(removed_bg_path))[0]
+        output_videos = glob(os.path.join(f"{workdir}", "*.mp4"))
+        return output_videos
+    except subprocess.CalledProcessError as e:
+        return f"Error during inference: {str(e)}"
+
+with gr.Blocks() as demo:
+    with gr.Column():
+        with gr.Row():
+            with gr.Column():
+                input_video = gr.Video(label="Input Video")
+                submit_btn = gr.Button("Submit")
+            with gr.Column():
+                output_result = gr.Video(label="Result")
+
+        gr.Examples(
+            examples = video_examples,
+            inputs = [input_video]
+        )
+
+    submit_btn.click(
+        fn = generate,
+        inputs = [input_video],
+        outputs = [output_result]
+    )
+
+demo.queue().launch(show_api=False, show_error=True)