upload model and app

app.py

@@ -0,0 +1,42 @@
+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image, ImageDraw
+import numpy as np
+
+# Load your YOLOv8 model
+model = YOLO("my_yolo_model.onnx")  # or "yolov8n.pt"
+
+def predict(image):
+    # Convert Gradio's numpy array to PIL Image
+    pil_image = Image.fromarray(image)
+    
+    # Run YOLOv8 inference
+    results = model(pil_image)
+    
+    # Extract bounding boxes and labels
+    boxes = results[0].boxes.xyxy.cpu().numpy()  # Coordinates
+    classes = results[0].boxes.cls.cpu().numpy()  # Class IDs
+    confidences = results[0].boxes.conf.cpu().numpy()  # Confidence scores
+    
+    # Draw bounding boxes on the image (PIL)
+    draw = ImageDraw.Draw(pil_image)
+    for box, cls, conf in zip(boxes, classes, confidences):
+        x1, y1, x2, y2 = box
+        label = f"{model.names[int(cls)]} {conf:.2f}"
+        
+        # Draw rectangle and label
+        draw.rectangle([x1, y1, x2, y2], outline="blue", width=2)
+        draw.text((x1, y1), label, fill="red")
+    
+    return pil_image  # Return PIL Image (Gradio handles RGB)
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(label="Input Image"),
+    outputs=gr.Image(label="Detected Objects"),
+    title="Pathole Detection by Yunusa Jibrin ",
+    examples=["example1.jpg", "example2.jpg"],  # Optional
+)
+
+demo.launch()

my_yolo_model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2bd2230e8beb864929f3275c66f7c9f076b7b77f6d01f39ad18a781351227c1c
+size 12251059