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app.py

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+import gradio as gr
+from ultralytics import YOLO
+import cv2
+import numpy as np
+from PIL import Image
+
+# Load your YOLO model
+model = YOLO('best.pt')
+
+# Function for image prediction
+def predict_image(image):
+    # Convert PIL image to OpenCV format (numpy array)
+    image_cv = np.array(image)
+    image_cv = cv2.cvtColor(image_cv, cv2.COLOR_RGB2BGR)  # Convert to BGR format for YOLO
+
+    # Perform inference
+    results = model(image_cv)
+
+    # Get the annotated image with bounding boxes
+    annotated_image = results[0].plot()  # Use the first result and plot annotations
+
+    # Convert annotated image (BGR) back to RGB format for Gradio
+    annotated_image_rgb = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
+    return annotated_image_rgb
+
+# Function for webcam/live video
+def predict_webcam(frame):
+    # Perform inference
+    results = model(frame)
+
+    # Get the annotated frame
+    annotated_frame = results[0].plot()  # Use the first result and plot annotations
+    return annotated_frame
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=predict_image,  # Function for image upload
+    inputs=gr.Image(type="pil", label="Upload an Image"),  # Image input
+    outputs=gr.Image(type="numpy", label="Detected Image"),  # Annotated output
+    live=False,  # Disable live video for image upload interface
+)
+
+iface.launch()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6341592910ffc3642d7157a81ad1ee9f00e38cdc262a10137a896f4bd2a07f39
+size 19174739

requirements.txt

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+ultralytics
+gradio
+opencv-python-headless
+numpy
+pillow