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import torch | |
import gradio as gr | |
from huggingface_hub import hf_hub_download | |
from PIL import Image | |
REPO_ID = "michaelwja/burn-detection" | |
FILENAME = "skin_burn.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 Skin Burn Detection" | |
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="Custom Training Performed on colab style='text-decoration: underline' target='_blank'>Link</a> </center><br> <center>Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors </center> <br> <b>1st</b> class is for Person Detected<br><b>2nd</b> class is for Car Detected" | |
Footer = ( | |
"Model train on our custom dataset") | |
Top_Title="<center>Yolov7 🚀 Custom Trained style='text-decoration: underline' target='_blank'></center></a>Face with mask and face without mask 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,).launch() | |