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app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+from ultralytics import YOLO
+import time
+import os
+
+# Reduce CPU overhead
+os.environ["OMP_NUM_THREADS"] = "1"
+
+MODEL_PATH = "best (1).pt"
+
+# Load model once
+model = YOLO(MODEL_PATH)
+
+CLASS_COLORS = {
+    "good": "green",
+    "bad": "red"
+}
+
+def predict(image):
+    if image is None:
+        return "No image provided"
+
+    # Convert PIL → numpy
+    img = np.array(image)
+
+    start = time.time()
+    result = model(img, verbose=False)[0]
+    latency = (time.time() - start) * 1000
+
+    probs = result.probs.data.cpu().numpy()
+    class_id = int(np.argmax(probs))
+    confidence = float(probs[class_id]) * 100
+
+    label = result.names[class_id]
+
+    color = CLASS_COLORS.get(label, "black")
+
+    output = (
+        f"<h2 style='color:{color}; text-align:center;'>"
+        f"{label.upper()}</h2>"
+        f"<p style='text-align:center;'>"
+        f"Confidence: <b>{confidence:.2f}%</b><br>"
+        f"Inference Time: {latency:.1f} ms"
+        f"</p>"
+    )
+
+    return output
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🔍 AI Simple Defect Classifier")
+    gr.Markdown(
+        "Upload or capture an image to classify an industrial part as **GOOD** or **BAD**."
+    )
+
+    with gr.Row():
+        image_input = gr.Image(
+            type="pil",
+            label="Input Image",
+            sources=["upload", "webcam"]
+        )
+
+    output = gr.HTML()
+
+    classify_btn = gr.Button("Run Inspection")
+
+    classify_btn.click(
+        fn=predict,
+        inputs=image_input,
+        outputs=output
+    )
+
+demo.launch(theme=gr.themes.Soft(), share=True)

best (1).pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d649dc20daa800e4aed759f49b1f330f55659a57ce45f7f6ef606160f858b2d0
+size 2972232

req

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+ultralytics==8.3.241
+torch
+torchvision
+opencv-python-headless
+pillow
+numpy
+gradio