app.py

from PIL import Image
import numpy as np
import gradio as gr
import pandas as pd
import torch
import json
from ultralytics import YOLO

print(f"Is CUDA available: {torch.cuda.is_available()}")
print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")

# Load the model
# detect_model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt', force_reload=True, _verbose=False)
detect_model = YOLO('yolov8m_2023-10-23_best.pt')

def detect(image):
  results = detect_model(image, conf=0.1)
  try:
    results = results[0].boxes.xyxy[0].cpu().numpy()
    top = int(results[1])
    left = int(results[0])
    width = int(results[2] - results[0])
    height = int(results[3] - results[1])
    return {
        "top": top,
        "left": left,
        "width": width,
        "height": height
    }
  except:
    return {
        "top": 0,
        "left": 0,
        "width": 0,
        "height": 0
    }
    
title = "🐢"

gr.Interface(
    fn=detect, 
    inputs=gr.Image(type="pil", label="Input Image"),
    outputs=[gr.JSON()],
  #  live=True,
    title=title,
).launch()