app.py

import torch
import gradio as gr
from huggingface_hub import hf_hub_download
from PIL import Image

REPO_ID = "hiraltalsaniya/YOLOv7_face_mask"
FILENAME = "best.pt"


yolov7_custom_weights = hf_hub_download(repo_id=REPO_ID, filename=FILENAME,repo_type='space')

model = torch.hub.load('WongKinYiu/yolov7:main',model='custom', path_or_model=yolov7_custom_weights, force_reload=True)
def object_detection(im, size=416):
    results = model(im) 
    results.render()  
    return Image.fromarray(results.imgs[0])

title = "Yolov7 Custom"

image = gr.inputs.Image(shape=(416, 416), image_mode="RGB", source="upload", label="Upload Image", optional=False)
outputs = gr.outputs.Image(type="pil", label="Output Image")

Custom_description="<center>Custom Training Performed on Colab  </center><br> <br> <b>1st</b> class is for Person With mask<br><b>2nd</b> class is for Person Without mask"

Footer = ("Created for testing purpose")

examples1=[["Image1.jpeg"],["Image2.jpeg"],["Image3.jpeg"],["Image4.jpeg"],["Image5.jpeg"],["Image6.jpeg"],["horses.jpeg"],["horses.jpeg"]]

Top_Title="<center>Yolov7  Trained on custome data set Face with mask and without mask People Detection"
css = ".output-image, .input-image {height: 50rem !important; width: 100% !important;}"
css = ".image-preview {height: auto !important;}"

gr.Interface(
    fn=object_detection,
    inputs=image,
    outputs=outputs,
    title=Top_Title,
    description=Custom_description,
    article=Footer,
    examples=[["mask-person-2.jpg"], ["mask-person-3.jpg"]]).launch()