Upload app.py with huggingface_hub

app.py

@@ -1,125 +1,40 @@
-import gradio as gr
-
-import spaces
-import torch
-from gradio_rerun import Rerun
-import rerun as rr
-import rerun.blueprint as rrb
+import subprocess
 from pathlib import Path
-import uuid
-
-from mini_dust3r.api import OptimizedResult, inferece_dust3r, log_optimized_result
-from mini_dust3r.model import AsymmetricCroCo3DStereo
 
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-model = AsymmetricCroCo3DStereo.from_pretrained(
-    "naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt"
-).to(DEVICE)
+PIXI_PATH = Path("/home/user/.pixi/bin/pixi")
+PIXI_VERSION = "0.32.1"
 
 
-def create_blueprint(image_name_list: list[str], log_path: Path) -> rrb.Blueprint:
-    # dont show 2d views if there are more than 4 images as to not clutter the view
-    if len(image_name_list) > 4:
-        blueprint = rrb.Blueprint(
-            rrb.Horizontal(
-                rrb.Spatial3DView(origin=f"{log_path}"),
-            ),
-            collapse_panels=True,
+def check_and_install_pixi() -> None:
+    try:
+        subprocess.check_call(f"{PIXI_PATH} --version", shell=True)
+    except subprocess.CalledProcessError:
+        print("pixi not found. Installing pixi...")
+        # Install pixi using the provided installation script
+        subprocess.check_call(
+            f"PIXI_VERSION=v{PIXI_VERSION} curl -fsSL https://pixi.sh/install.sh | bash",
+            shell=True,
         )
-    else:
-        blueprint = rrb.Blueprint(
-            rrb.Horizontal(
-                contents=[
-                    rrb.Spatial3DView(origin=f"{log_path}"),
-                    rrb.Vertical(
-                        contents=[
-                            rrb.Spatial2DView(
-                                origin=f"{log_path}/camera_{i}/pinhole/",
-                                contents=[
-                                    "+ $origin/**",
-                                ],
-                            )
-                            for i in range(len(image_name_list))
-                        ]
-                    ),
-                ],
-                column_shares=[3, 1],
-            ),
-            collapse_panels=True,
-        )
-    return blueprint
-
-
-@spaces.GPU
-def predict(image_name_list: list[str] | str):
-    # check if is list or string and if not raise error
-    if not isinstance(image_name_list, list) and not isinstance(image_name_list, str):
-        raise gr.Error(
-            f"Input must be a list of strings or a string, got: {type(image_name_list)}"
+        subprocess.check_call(
+            f"{PIXI_PATH} self-update --version {PIXI_VERSION}", shell=True
         )
-    uuid_str = str(uuid.uuid4())
-    filename = Path(f"/tmp/gradio/{uuid_str}.rrd")
-    rr.init(f"{uuid_str}")
-    log_path = Path("world")
-
-    if isinstance(image_name_list, str):
-        image_name_list = [image_name_list]
-
-    optimized_results: OptimizedResult = inferece_dust3r(
-        image_dir_or_list=image_name_list,
-        model=model,
-        device=DEVICE,
-        batch_size=1,
-    )
-
-    blueprint: rrb.Blueprint = create_blueprint(image_name_list, log_path)
-    rr.send_blueprint(blueprint)
-
-    rr.set_time_sequence("sequence", 0)
-    log_optimized_result(optimized_results, log_path)
-    rr.save(filename.as_posix())
-    return filename.as_posix()
-
-
-with gr.Blocks(
-    css=""".gradio-container {margin: 0 !important; min-width: 100%};""",
-    title="Mini-DUSt3R Demo",
-) as demo:
-    # scene state is save so that you can change conf_thr, cam_size... without rerunning the inference
-    gr.HTML('<h2 style="text-align: center;">Mini-DUSt3R Demo</h2>')
-    gr.HTML(
-        '<p style="text-align: center;">Unofficial DUSt3R demo using the mini-dust3r pip package</p>'
-    )
-    gr.HTML(
-        '<p style="text-align: center;">More info <a href="https://github.com/pablovela5620/mini-dust3r">here</a></p>'
-    )
-    with gr.Tab(label="Single Image"):
-        with gr.Column():
-            single_image = gr.Image(type="filepath", height=300)
-            run_btn_single = gr.Button("Run")
-            rerun_viewer_single = Rerun(height=900)
-            run_btn_single.click(
-                fn=predict, inputs=[single_image], outputs=[rerun_viewer_single]
-            )
-
-            example_single_dir = Path("examples/single_image")
-            example_single_files = sorted(example_single_dir.glob("*.png"))
-
-            examples_single = gr.Examples(
-                examples=example_single_files,
-                inputs=[single_image],
-                outputs=[rerun_viewer_single],
-                fn=predict,
-                cache_examples="lazy",
-            )
-    with gr.Tab(label="Multi Image"):
-        with gr.Column():
-            multi_files = gr.File(file_count="multiple")
-            run_btn_multi = gr.Button("Run")
-            rerun_viewer_multi = Rerun(height=900)
-            run_btn_multi.click(
-                fn=predict, inputs=[multi_files], outputs=[rerun_viewer_multi]
-            )
-
-
-demo.launch()
+        subprocess.check_call(f"{PIXI_PATH} --version", shell=True)
+
+
+def run_command(command: str) -> None:
+    try:
+        subprocess.check_call(command, shell=True)
+    except subprocess.CalledProcessError as e:
+        print(f"run command {command}. Error: {e}")
+
+
+if __name__ == "__main__":
+    check_and_install_pixi()
+    # install lsof
+    run_command(command=f"{PIXI_PATH} global install lsof")
+    # kill anything running on port 7860
+    run_command(command=f"{PIXI_PATH.parent}/lsof -t -i:7860 | xargs -r kill")
+    # clean current environment
+    run_command(command=f"{PIXI_PATH} clean")
+    # run spaces app
+    run_command(command=f"{PIXI_PATH} run -e spaces app")