import gradio as gr
import torch
from PIL import Image

# Images
torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg', 'bus.jpg')

# Model
model_name ='model.pt'  # force_reload=True to update

if model_name:
    model = torch.hub.load('ultralytics/yolov5', 'custom', path=model_name, force_reload=True)
else:
    model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
    

def yolo(im, size=640):
    g = (size / max(im.size))  # gain
    im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  # resize

    results = model(im)  # inference
    results.render()  # updates results.imgs with boxes and labels
    return Image.fromarray(results.imgs[0])


inputs = gr.inputs.Image(type='pil', label="Original Image")
outputs = gr.outputs.Image(type="pil", label="Output Image")

title = "YOLOv5"
description = "YOLOv5 Gradio demo for object detection. Upload an image or click an example image to use."
article = "<p style='text-align: center'> THis Demo is meant to detect specific models of fire extinguishers , trained on an artificially generated dataset from IFC MODEL with Blender" \
          "The aim is to simulate real world fire extinguishers as much possible in order for the object detector to recognizeit" \
        
         

examples = [['23327.png'], ['download.jpg']]
gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch(
    debug=True)


# try again