Update app.py

app.py

@@ -1,74 +1,79 @@
-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)
+import gradio as gr
+import numpy as np
+from PIL import Image
+import subprocess
+import sys
+
+subprocess.check_call([sys.executable, "-m", "pip", "install", "ultralytics"])
+
+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)