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import gradio as gr
from ultralytics import YOLO
from PIL import Image
import numpy as np
# Load the YOLOv9 model
model = YOLO('best.pt') # Make sure best.pt is in the same folder
def detect(image):
results = model(image)
annotated_frame = results[0].plot() # Draw predictions on the image
# Get detection results
detections = []
for r in results:
boxes = r.boxes
for box in boxes:
x1, y1, x2, y2 = box.xyxy[0].tolist() # Get box coordinates
conf = float(box.conf[0]) # Get confidence
cls = int(box.cls[0]) # Get class
class_name = model.names[cls] # Get class name
detections.append({
'class': cls,
'class_name': class_name,
'confidence': conf,
'box': [x1, y1, x2, y2]
})
return Image.fromarray(annotated_frame), detections
# Launch the Gradio interface
gr.Interface(
fn=detect,
inputs=gr.Image(type="pil"),
outputs=[
gr.Image(type="pil", label="Detected Image"),
gr.JSON(label="Detection Results")
],
title="YOLOv9 (Ultralytics) Object Detection",
description="Upload an image to run object detection using your custom YOLOv9 model. The results will show both the annotated image and the detection details including class names.",
).launch()
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