app.py

import gradio as gr
import os
import torch

print(f"Version Gradio: {gr.__version__}")

def update_value(val):
  return f'Value is set to {val}'

def yolov7_inference(
    image: gr.Image = None,
    conf_threshold: gr.Slider = 0.20,
):

    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    path = 'y7-prdef.pt'
    model = torch.hub.load("WongKinYiu/yolov7","custom",f"{path}")
    model.conf = conf_threshold
    results = model([image], size=640)
    return results.render()[0]

inputs = [
    gr.Image(label="input image"),
    gr.Slider(minimum=0, maximum=1, step=0.1, label='Value'),
]

outputs = [
   gr.Image(label="output image"),
]

gr.Interface(
    fn = yolov7_inference,
    inputs = inputs,
    outputs = outputs, 
    title = "- The detection of jar lid defects using Yolov7 -",
    description = "contact: rrighart@googlemail.com", 
    examples = [["example1.JPG"], ["example2.JPG"], ["example3.JPG"]],

).launch(debug=True)