Fix: Async startup to bypass platform init errors and upload missing config files

app.py

@@ -9,19 +9,38 @@ Tabs:
 import sys, os, json, urllib.request
 
 import subprocess
+import threading
 import torch
 import torch.nn.functional as F
 
-# ── HF Space Pre-run ──────────────────────────────────────────────────────────
-# Hugging Face doesn't run pre_run.sh automatically for Gradio SDK. We do it here.
-_MM_INIT = "/usr/local/lib/python3.10/site-packages/mmcv/__init__.py"
-if not os.path.exists(_MM_INIT):
-    print("🚀 Initializing HF Space environment (running pre_run.sh)...")
-    try:
-        # We use subprocess to ensure output is captured in the logs
-        subprocess.run(["sh", "pre_run.sh"], check=True)
-    except Exception as e:
-        print(f"⚠️ Pre-run setup failed: {e}")
+# ── Global Setup State ───────────────────────────────────────────────────────
+# Background thread to install MMCV/MMDET without blocking the Gradio UI
+MMDET_OK = False
+SETUP_LOG = "🚀 Initializing detection environment (this takes ~3 mins)..."
+
+def run_setup():
+    global MMDET_OK, SETUP_LOG
+    _MM_INIT = "/usr/local/lib/python3.10/site-packages/mmcv/__init__.py"
+    if not os.path.exists(_MM_INIT):
+        try:
+            # Explicitly stream to logs
+            process = subprocess.Popen(["sh", "pre_run.sh"], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
+            for line in process.stdout:
+                print(f"[SETUP] {line.strip()}")
+            process.wait()
+            if process.returncode == 0:
+                MMDET_OK = True
+                SETUP_LOG = "✅ Detection environment ready."
+            else:
+                SETUP_LOG = f"⚠️ Setup failed with exit code {process.returncode}."
+        except Exception as e:
+            SETUP_LOG = f"⚠️ Pre-run setup failed: {e}"
+    else:
+        MMDET_OK = True
+        SETUP_LOG = "✅ Detection environment ready (cached)."
+
+# Start setup immediately
+threading.Thread(target=run_setup, daemon=True).start()
 
 from torchvision import transforms
 from PIL import Image, ImageDraw
@@ -382,10 +401,10 @@ with gr.Blocks(title="GeoViG Demo", theme=_THEME, css="footer { display: none !i
                                          info="M: 40.7 Box AP · B: 42.5 Box AP")
                     det_thr   = gr.Slider(0.1, 0.9, value=0.7, step=0.05, label="Score threshold")
                     det_btn   = gr.Button("🔍 Detect & Segment", variant="primary")
-                    det_output_txt = gr.Textbox(label="Status", lines=2)
+                    det_output_txt = gr.Textbox(label="Status", lines=2, value=SETUP_LOG)
                 with gr.Column(scale=2):
                     with gr.Row():
-                        det_output_img = gr.Image(label="Detection (Boxes)", type="pil")
+                        det_output_img = gr.Image(label="Detection Results", type="pil")
                         seg_output_img = gr.Image(label="Instance Segmentation (Masks)", type="pil")
 
             det_btn.click(fn=detect, inputs=[det_image, det_size, det_thr],
@@ -426,7 +445,7 @@ with gr.Blocks(title="GeoViG Demo", theme=_THEME, css="footer { display: none !i
                     med_btn   = gr.Button("🔬 Segment", variant="primary")
                 with gr.Column(scale=1):
                     med_output_img = gr.Image(label="Segmentation Result", type="pil")
-                    med_output_txt = gr.Textbox(label="Status", lines=3)
+                    med_output_txt = gr.Textbox(label="Status", lines=3, value=SETUP_LOG)
 
             med_btn.click(fn=segment_medical, inputs=[med_image, med_dataset, med_thr],
                           outputs=[med_output_img, med_output_txt])

packages.txt

@@ -0,0 +1,13 @@
+cmake
+ffmpeg
+git
+git-lfs
+libgl1
+libglib2.0-0
+libsm6
+libxext6
+libxrender1
+rsync
+ninja-build
+build-essential
+libgl1-mesa-glx