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app.py

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+import gradio as gr
+import cv2
+import torch
+from ultralytics import YOLO
+import numpy as np
+
+# Load the YOLOv8 model (replace 'best.pt' with the path to your model)
+model = YOLO('best.pt')
+
+# Function to perform object detection using YOLOv8
+def detect_objects(image):
+    # Convert the image to RGB (OpenCV loads images as BGR)
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    # Perform inference on the image
+    results = model.predict(image_rgb)
+
+    # Get bounding boxes and class labels from the results
+    annotated_image = results[0].plot()  # YOLOv8 has a plot method that returns an annotated image
+    
+    return annotated_image
+
+# Gradio Interface (take a photo using webcam or upload an image)
+def take_photo_and_detect():
+    # OpenCV function to take a photo using the webcam
+    def capture_image():
+        cap = cv2.VideoCapture(0)
+        ret, frame = cap.read()
+        cap.release()
+        return frame
+
+    # Set up the Gradio interface
+    demo = gr.Interface(
+        fn=detect_objects,                    # The function to perform object detection
+        inputs=gr.Image(source="webcam", tool="editor", label="Take a photo or upload an image"),
+        outputs=gr.Image(label="Detected Objects"),
+        title="YOLOv8 Object Detection",
+        description="Take a photo or upload an image to detect objects using the YOLOv8 model."
+    )
+
+    demo.launch()
+
+# Start the app
+if __name__ == "__main__":
+    take_photo_and_detect()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7834784fd9d0a1d69d5c698637a998f256023f8ae5fc4c7809f27cd0f0834334
+size 46786711

requirements.txt

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+ultralytics==8.3.11
+opencv-python
+gradio