Upload app.py with huggingface_hub

app.py

@@ -10,13 +10,20 @@ import cv2
 import gradio as gr
 import numpy as np
 import torch
-from PIL import Image
 
 # ── paths ──────────────────────────────────────────────────────────────────
 ROOT = Path(__file__).resolve().parent
-sys.path.insert(0, str(ROOT))
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+# inline constants so the app works even if src imports have issues
+DEFECT_THRESHOLD = float(os.getenv("PDD_DEFECT_THRESHOLD", 0.55))
+PANEL_DEFECT_RATIO = 0.005
+IMG_SIZE = 384
+PATCH_SIZE = 512
+PATCH_STRIDE = 256
+BACKBONE = os.getenv("PDD_BACKBONE", "tf_efficientnetv2_s.in21k_ft_in1k")
 
-from src import config as C
 from src.infer import load_model, predict_image, render_visualization
 
 # ── model (lazy) ───────────────────────────────────────────────────────────
@@ -106,7 +113,7 @@ with gr.Blocks(title="Paint Defect Detector", theme=gr.themes.Soft()) as demo:
             img_input = gr.Image(label="Car Body Panel Photo", type="numpy")
             vin_input = gr.Textbox(label="VIN (optional)", placeholder="e.g. XTA210930Y2837465")
             threshold = gr.Slider(
-                minimum=0.1, maximum=0.9, value=C.DEFECT_THRESHOLD, step=0.05,
+                minimum=0.1, maximum=0.9, value=DEFECT_THRESHOLD, step=0.05,
                 label="Defect Threshold",
                 info="Patch probability above this value is marked as defective."
             )