requirements.tx

app.py

@@ -0,0 +1,31 @@
+import torch
+from ultralytics import YOLO
+import cv2
+import numpy as np
+import gradio as gr
+
+# ✅ Load YOLOv8 model from Hugging Face
+model_path = "https://huggingface.co/Sakthi3214/pcb_detection/resolve/main/best.pt"
+model = YOLO(model_path)
+
+def detect_pcb_faults(image):
+    """Runs YOLOv8 on the input image and returns detected objects."""
+    results = model(image)  # Run inference
+    boxes = results[0].boxes.xyxy.cpu().numpy()  # Extract bounding boxes
+    confs = results[0].boxes.conf.cpu().numpy()  # Extract confidence scores
+
+    # Draw bounding boxes
+    for (x1, y1, x2, y2), conf in zip(boxes, confs):
+        cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
+        cv2.putText(image, f"{conf:.2f}", (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
+    return image
+
+# ✅ Gradio UI
+gr.Interface(
+    fn=detect_pcb_faults,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Image(type="numpy"),
+    title="PCB Fault Detection",
+    description="Upload a PCB image to detect defects using YOLOv8."
+).launch()