app.py

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 = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # force_reload=True to update
model =  torch.hub.load('/yolov5', 'custom', path='/saved_model/s1000_best.pt', source='local')  # local model


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 = "S1000 Detection"
description = "YOLOv5 Gradio demo for object detection. Upload an image or click an example image to use."
article = "<p style='text-align: center'>YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes " \
          "simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, " \
          "and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov5'>Source code</a> |" \
          "<a href='https://apps.apple.com/app/id1452689527'>iOS App</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>"

path_folder = '/datasets/s1000/'
examples = [[path_folder+'s1000 (1).png'], [path_folder+'s1000 (2).png'],[path_folder+'s1000 (3).png'],[path_folder+'s1000 (4).png'],[path_folder+'s1000 (5).png']]
gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch(
    debug=True)