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app.py

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+"""Gradio Space — Paint Defect Detector."""
+from __future__ import annotations
+
+import os
+import sys
+import tempfile
+from pathlib import Path
+
+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))
+
+from src import config as C
+from src.infer import load_model, predict_image, render_visualization
+
+# ── model (lazy) ───────────────────────────────────────────────────────────
+_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+_model = None
+
+CHECKPOINT = ROOT / "checkpoints" / "best.pt"
+
+
+def _get_model():
+    global _model
+    if _model is None:
+        if not CHECKPOINT.exists():
+            raise gr.Error(
+                "No trained checkpoint found at checkpoints/best.pt. "
+                "Please train the model first and upload the checkpoint."
+            )
+        _model = load_model(CHECKPOINT, device=_device)
+    return _model
+
+
+# ── inference ──────────────────────────────────────────────────────────────
+
+def run_inference(image: np.ndarray, vin: str, threshold: float) -> tuple:
+    if image is None:
+        return None, "⚠️ Please upload an image.", ""
+
+    # Convert RGB (Gradio) → BGR (OpenCV)
+    bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+    try:
+        model = _get_model()
+    except gr.Error as e:
+        return None, str(e), ""
+
+    result = predict_image(bgr, model, _device, threshold=threshold)
+    vis_bgr = render_visualization(result)
+    vis_rgb = cv2.cvtColor(vis_bgr, cv2.COLOR_BGR2RGB)
+
+    # ── verdict text ──────────────────────────────────────────────────────
+    verdict = "🔴  DEFECT DETECTED" if result["is_defect"] else "🟢  NO DEFECT — OK"
+    vin_line = f"**VIN:** {vin.strip()}\n\n" if vin.strip() else ""
+    summary = (
+        f"{vin_line}"
+        f"**Verdict:** {verdict}\n\n"
+        f"**Defect ratio:** {result['defect_ratio']*100:.2f}%\n\n"
+        f"**Max patch probability:** {result['max_prob']:.3f}\n\n"
+        f"**Defect regions found:** {len(result['boxes'])}\n\n"
+        f"**Panel size:** {result['panel_size']['w']} × {result['panel_size']['h']} px"
+    )
+
+    # ── boxes table ───────────────────────────────────────────────────────
+    if result["boxes"]:
+        rows = "\n".join(
+            f"| {i+1} | {b['x']},{b['y']} | {b['w']}×{b['h']} | {b['confidence']:.3f} |"
+            for i, b in enumerate(result["boxes"])
+        )
+        table = (
+            "### Defect Regions\n"
+            "| # | Position (x,y) | Size (w×h) | Confidence |\n"
+            "|---|----------------|------------|------------|\n"
+            + rows
+        )
+    else:
+        table = ""
+
+    return vis_rgb, summary, table
+
+
+# ── UI ─────────────────────────────────────────────────────────────────────
+
+DESCRIPTION = """
+# 🚗 Paint Defect Detector
+
+Upload a photo of a car body panel to detect paint defects using a sliding-window
+**EfficientNetV2-S** classifier. The model returns a heatmap overlay with bounding
+boxes around defective regions.
+
+> **Note:** A trained checkpoint (`checkpoints/best.pt`) must be present.
+"""
+
+with gr.Blocks(title="Paint Defect Detector", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(DESCRIPTION)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            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,
+                label="Defect Threshold",
+                info="Patch probability above this value is marked as defective."
+            )
+            run_btn = gr.Button("🔍 Analyze", variant="primary")
+
+        with gr.Column(scale=1):
+            img_output = gr.Image(label="Visualization (Heatmap + Bounding Boxes)", type="numpy")
+            verdict_md = gr.Markdown(label="Result")
+            table_md = gr.Markdown(label="Defect Regions")
+
+    run_btn.click(
+        fn=run_inference,
+        inputs=[img_input, vin_input, threshold],
+        outputs=[img_output, verdict_md, table_md],
+    )
+
+    gr.Examples(
+        examples=[],
+        inputs=[img_input],
+        label="Examples"
+    )
+
+if __name__ == "__main__":
+    demo.launch()