import torch
import cv2
import numpy as np
import gradio as gr
from PIL import Image


model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)


model.conf = 0.25  
model.iou = 0.45  
model.agnostic = False  
model.multi_label = False  
model.max_det = 1000


def detect(img):


    results = model(img, size=640)

    predictions = results.pred[0]
    boxes = predictions[:, :4] # x1, y1, x2, y2
    scores = predictions[:, 4]
    categories = predictions[:, 5]
    new_image = np.squeeze(results.render())
    
  
    # resize image
    new_image = cv2.resize(new_image, dim, interpolation = cv2.INTER_AREA) 

    return new_image

    


css = ".output-image, .input-image, .image-preview {height: 600px !important}"

iface = gr.Interface(fn=detect, 
                     inputs=gr.inputs.Image(type="numpy",), 
                     outputs=gr.outputs.Image(type="numpy",),
                     css=css,
                     enable_queue=True)
iface.launch(debug=True, cache_examples=True)