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app.py

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+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="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 custome dataset")
+
+examples1=[["Image1.jpeg"],["Image2.jpeg"],["Image3.jpeg"],["Image4.jpeg"],["Image5.jpeg"],["Image6.jpeg"],["horses.jpeg"],["horses.jpeg"]]
+
+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,
+    examples=[["mask-person-2.jpg"], ["mask-person-2.jpg"]]).launch()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9ddf943735392f1b54a13c2774267e8f55205aecba367540dce87df21635a7c1
+size 12217893

requirements.txt

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+#Yolov7 WongKinYiu Requirements
+
+# Usage: pip install -r requirements.txt
+
+# Base ----------------------------------------
+matplotlib>=3.2.2
+numpy>=1.18.5
+opencv-python>=4.1.1
+Pillow>=7.1.2
+PyYAML>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1
+torch>=1.7.0,!=1.12.0
+torchvision>=0.8.1,!=0.13.0
+tqdm>=4.41.0
+protobuf<4.21.3
+
+# Logging -------------------------------------
+tensorboard>=2.4.1
+# wandb
+
+# Plotting ------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+
+# Export --------------------------------------
+# coremltools>=4.1  # CoreML export
+# onnx>=1.9.0  # ONNX export
+# onnx-simplifier>=0.3.6  # ONNX simplifier
+# scikit-learn==0.19.2  # CoreML quantization
+# tensorflow>=2.4.1  # TFLite export
+# tensorflowjs>=3.9.0  # TF.js export
+# openvino-dev  # OpenVINO export
+
+# Extras --------------------------------------
+ipython  # interactive notebook
+psutil  # system utilization
+thop  # FLOPs computation