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app.py

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+from ultralytics import YOLO  
+import torch
+import cv2
+import numpy as np
+import gradio as gr
+from PIL import Image
+
+# Load YOLOv8 model
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = YOLO("yolov8x.pt")  # Load a more powerful YOLOv8 model
+model.to(device)
+model.eval()
+
+# Load COCO class labels
+CLASS_NAMES = model.names  # YOLO's built-in class names
+
+def preprocess_image(image):
+    image = Image.fromarray(image)
+    image = image.convert("RGB")
+    return image
+
+def detect_objects(image):
+    image = preprocess_image(image)
+    results = model.predict(image)  # Run YOLO inference
+
+    # Convert results to bounding box format
+    image = np.array(image)
+    for result in results:
+        for box, cls, conf in zip(result.boxes.xyxy, result.boxes.cls, result.boxes.conf):
+            x1, y1, x2, y2 = map(int, box[:4])
+            class_name = CLASS_NAMES[int(cls)]  # Get class name
+            confidence = conf.item() * 100  # Convert confidence to percentage
+
+            # Draw a bolder bounding box
+            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 4)  # Increased thickness
+
+            # Larger text for class label
+            label = f"{class_name} ({confidence:.1f}%)"
+            cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 
+                        1, (0, 255, 0), 3, cv2.LINE_AA)  # Larger text
+
+    return image
+
+# Gradio UI with Submit button
+iface = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=gr.Image(type="numpy", label="Detected Objects"),
+    title="Object Detection",
+    description="Use webcam or Upload an image to detect objects.",
+    allow_flagging="never"  # Disables unwanted flags
+)
+
+iface.launch()

requirements.txt

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+ultralytics
+torch
+torchvision
+opencv-python
+pillow
+gradio