Prepare job build context

app.py

@@ -536,58 +536,7 @@ with gr.Blocks(title="FoundationPose Inference", theme=gr.themes.Soft()) as demo
                         outputs=cad_init_output
                     )
 
-                # Sub-tab 1.2: Model-Free Init
-                with gr.Tab("Model-Free (Reference-Based)"):
-                    gr.Markdown("""
-                    **Model-Free Mode**: ⚠️ Coming Soon
-
-                    This mode would reconstruct a 3D model from reference images using photogrammetry,
-                    but is not yet implemented.
-
-                    **Alternative**: Use photogrammetry software to create a mesh first:
-                    - [Meshroom](https://alicevision.org/#meshroom) (free, open-source)
-                    - [COLMAP](https://colmap.github.io/) (free, open-source)
-                    - [RealityCapture](https://www.capturingreality.com/) (commercial)
-
-                    Then use the CAD-Based tab with your generated mesh.
-                    """)
-
-                    with gr.Row():
-                        with gr.Column():
-                            free_object_id = gr.Textbox(
-                                label="Object ID",
-                                placeholder="e.g., target_cube",
-                                value="target_cube"
-                            )
-
-                            free_ref_files = gr.File(
-                                label="Reference Images (16-24 recommended)",
-                                file_count="multiple",
-                                file_types=["image"]
-                            )
-
-                            gr.Markdown("### Camera Intrinsics")
-                            with gr.Row():
-                                free_fx = gr.Number(label="fx", value=500.0)
-                                free_fy = gr.Number(label="fy", value=500.0)
-                            with gr.Row():
-                                free_cx = gr.Number(label="cx", value=320.0)
-                                free_cy = gr.Number(label="cy", value=240.0)
-
-                            free_init_button = gr.Button("Initialize Model-Free", variant="primary")
-
-                        with gr.Column():
-                            free_init_output = gr.Textbox(
-                                label="Initialization Result",
-                                lines=5,
-                                interactive=False
-                            )
-
-                    free_init_button.click(
-                        fn=gradio_initialize_model_free,
-                        inputs=[free_object_id, free_ref_files, free_fx, free_fy, free_cx, free_cy],
-                        outputs=free_init_output
-                    )
+                # Sub-tab 1.2: Model-Free Init (disabled)
 
         # Tab 2: Estimate Pose
         with gr.Tab("Estimate Pose"):

deploy.sh

@@ -67,7 +67,7 @@ PY
 mkdir -p .deploy
 
 L1_INPUTS=(Dockerfile.base)
-L2_INPUTS=(Dockerfile.base)
+L2_INPUTS=(Dockerfile.base Dockerfile download_weights.py)
 
 L1_HASH=$(hash_files "${L1_INPUTS[@]}")
 L2_HASH=$(hash_files "${L2_INPUTS[@]}")
@@ -187,20 +187,16 @@ if [ -n "${JOB_ID}" ] && [ -x "${HF_BIN}" ]; then
     for i in $(seq 1 40); do
         JOB_STAGE=$("${HF_BIN}" jobs inspect "${JOB_ID}" | python3 -c "import sys, json; data=json.load(sys.stdin)[0]; print(data.get('status', {}).get('stage', 'UNKNOWN'))" 2>/dev/null || echo "UNKNOWN")
         echo "  Job stage: ${JOB_STAGE}"
-        case "${JOB_STAGE}" in
-            SUCCESS|SUCCEEDED|COMPLETED|DONE)
-                echo "✓ L1 image build job completed"
-                echo "${L1_HASH}" > "${LAST_L1_HASH_FILE}"
-                break
-                ;;
-            FAILED|ERROR|CANCELED|CANCELLED)
-                echo "✗ Image build job failed: ${JOB_STAGE}"
-                exit 1
-                ;;
-            *)
-                sleep 30
-                ;;
-        esac
+        if [[ "${JOB_STAGE}" =~ ^(SUCCESS|SUCCEEDED|COMPLETED|DONE)$ ]]; then
+            echo "✓ L1 image build job completed"
+            echo "${L1_HASH}" > "${LAST_L1_HASH_FILE}"
+            break
+        elif [[ "${JOB_STAGE}" =~ ^(FAILED|ERROR|CANCELED|CANCELLED)$ ]]; then
+            echo "✗ Image build job failed: ${JOB_STAGE}"
+            exit 1
+        else
+            sleep 30
+        fi
     done
 fi
 fi
@@ -272,20 +268,16 @@ if [ -n "${JOB_ID_L2}" ] && [ -x "${HF_BIN}" ]; then
     for i in $(seq 1 60); do
         JOB_STAGE=$("${HF_BIN}" jobs inspect "${JOB_ID_L2}" | python3 -c "import sys, json; data=json.load(sys.stdin)[0]; print(data.get('status', {}).get('stage', 'UNKNOWN'))" 2>/dev/null || echo "UNKNOWN")
         echo "  Job stage: ${JOB_STAGE}"
-        case "${JOB_STAGE}" in
-            SUCCESS|SUCCEEDED|COMPLETED|DONE)
-                echo "✓ L2 image build job completed"
-                echo "${L2_HASH}" > "${LAST_L2_HASH_FILE}"
-                break
-                ;;
-            FAILED|ERROR|CANCELED|CANCELLED)
-                echo "✗ L2 image build job failed: ${JOB_STAGE}"
-                exit 1
-                ;;
-            *)
-                sleep 30
-                ;;
-        esac
+        if [[ "${JOB_STAGE}" =~ ^(SUCCESS|SUCCEEDED|COMPLETED|DONE)$ ]]; then
+            echo "✓ L2 image build job completed"
+            echo "${L2_HASH}" > "${LAST_L2_HASH_FILE}"
+            break
+        elif [[ "${JOB_STAGE}" =~ ^(FAILED|ERROR|CANCELED|CANCELLED)$ ]]; then
+            echo "✗ L2 image build job failed: ${JOB_STAGE}"
+            exit 1
+        else
+            sleep 30
+        fi
     done
 fi
 fi

download_weights.py

@@ -7,15 +7,17 @@ from pathlib import Path
 # Always download weights from model repository
 repo = os.environ.get('FOUNDATIONPOSE_MODEL_REPO', 'gpue/foundationpose-weights')
 token = os.environ.get('HF_TOKEN')
+weights_dir = os.environ.get('FOUNDATIONPOSE_WEIGHTS_DIR', '/app/FoundationPose/weights')
 
 print(f'Downloading weights from {repo}...')
 
 try:
     from huggingface_hub import snapshot_download
 
+    Path(weights_dir).mkdir(parents=True, exist_ok=True)
     snapshot_download(
         repo_id=repo,
-        local_dir='weights',
+        local_dir=weights_dir,
         token=token,
         repo_type='model'
     )