import torch import gradio as gr from huggingface_hub import hf_hub_download from PIL import Image REPO_ID = "owaiskha9654/Yolov7_Custom_Object_Detection" FILENAME = "best.pt" yolov7_custom_weights = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) model = torch.hub.load('Owaiskhan9654/yolov7-1:main',model='custom', path_or_model=yolov7_custom_weights, force_reload=True) # My Github repository https://github.com/Owaiskhan9654 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="
Custom Training Performed on Kaggle Link

Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

1st class is for Person Detected
2nd class is for Car Detected" Footer = ( "
Model Trained by: Owais Ahmad Data Scientist at Thoucentric Visit Profile
" "
Model Trained Kaggle Kernel Link
" "
Kaggle Profile Link
" "
HuggingFace🤗 Model Deployed Repository Link
" ) examples1=[["Image1.jpeg"],["Image2.jpeg"],["Image3.jpeg"],["Image4.jpeg"],["Image5.jpeg"],["Image6.jpeg"],["horses.jpeg"],["horses.jpeg"]] Top_Title="
Yolov7 🚀 Custom Trained by Owais Ahmad
🚗Car and 👦Person 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=[["car-person-2.jpg"], ["car-person-2.jpg"]]).launch()