import gradio as gr
import os
import torch

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]
        
demo = gr.Blocks()

with demo:

  dd = gr.Interface(
    yolov7_inference,
    gr.Image(type="pil"),
    "image",
    title="The detection of jar lid defects using Yolov7",

    examples=[
        os.path.join(os.path.dirname(__file__), "example1.JPG"),
        os.path.join(os.path.dirname(__file__), "example2.JPG"),
        os.path.join(os.path.dirname(__file__), "example3.JPG"),
    ],
)

  md = gr.Markdown("Confidence Threshold")
  conf_threshold = gr.Slider(minimum=0, maximum=1, step=0.1, label='Value')
  #inp = gr.Slider(minimum=0.0, maximum=1.0, value=0.2, step=0.05, label="Value"),
  #inp.change(fn=yolov7_inference, inputs=inp, outputs=md)
  conf_threshold.change(fn=update_value, inputs=conf_threshold, outputs=md)

demo.launch()