Create app.py

app.py

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+import gradio as gr
+import os
+
+import argparse
+import time
+from pathlib import Path
+
+import cv2
+import torch
+import torch.backends.cudnn as cudnn
+from numpy import random
+
+from models.experimental import attempt_load
+from utils.datasets import LoadStreams, LoadImages
+from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
+    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
+from utils.plots import plot_one_box
+from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel
+from PIL import Image
+ 
+from huggingface_hub import hf_hub_download
+
+def load_model(model_name):
+    model_path = hf_hub_download(repo_id=f"Yolov7/{model_name}", filename=f"{model_name}.pt")
+
+    return model_path
+
+
+model_names = [
+    "yolov7",
+    "yolov7-e6e",
+    "yolov7-e6",
+]
+
+models = {model_name: load_model(model_name) for model_name in model_names}
+
+