inference and integration using gradio app

app.py

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+import gradio as gr
+import os
+
+os.system("wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt")
+os.system("wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s6.pt")
+
+from models.experimental import attempt_load
+from utils.augmentations import letterbox
+from utils.plots import Annotator
+from utils.general import non_max_suppression, scale_coords
+from utils.torch_utils import *
+import sys
+import numpy as np
+import random
+
+
+def detect(img, weights):
+    gpu_id="cuda:0"
+    device = select_device(device=gpu_id)
+    model = attempt_load(weights+'.pt', device=device)
+    torch.no_grad()
+    model.to(device).eval()
+    half = False  # half precision only supported on CUDA
+    if half:
+        model.half()
+
+    img_size = 640
+
+    # Get names and colors
+    names = model.names if hasattr(model, 'names') else model.modules.names
+    colors = [[random.randint(0, 255) for _ in range(3)]
+              for _ in range(len(names))]
+    if img is None:
+        sys.exit(0)
+
+    # Run inference
+    t0 = time_sync()
+
+    im0 = img.copy()
+    img = letterbox(img, img_size, stride=int(model.stride.max()), auto=False and True)[0]
+    img = np.stack(img, 0)
+
+    img = img.transpose((2, 0, 1))[::-1]  # BGR to RGB, to 3x416x416
+
+    img = np.ascontiguousarray(img)
+
+    img = torch.from_numpy(img).to(device)
+
+    if half:
+        img = img.half()
+    else:
+        img = img.float()  # if half else img.float()  # uint8 to fp16/32
+    img /= 255.0  # 0 - 255 to 0.0 - 1.0
+    if len(img.shape) == 3:
+        img = img[None]  # expand for batch dim
+    # Inference
+    t1 = time_sync()
+    pred = model(img, augment=False, profile=False)[0]
+
+    # to float
+    if half:
+        pred = pred.float()
+
+    # Apply NMS
+    pred = non_max_suppression(
+        pred, 0.1, 0.5, classes=None, agnostic=False)
+    t2 = time_sync()
+    annotator = Annotator(im0, line_width=3, example=str(names))
+    # Process detections
+    for i, det in enumerate(pred):  # detections per image
+        s = ''
+        s += '%gx%g ' % img.shape[2:]  # print string
+        if det is not None and len(det):
+            # Rescale boxes from img_size to im0 size
+            det[:, :4] = scale_coords(
+                img.shape[2:], det[:, :4], im0.shape).round()
+
+            # Print results
+            for c in det[:, -1].unique():
+                n = (det[:, -1] == c).sum()  # detections per class
+                s += '%g %ss, ' % (n, names[int(c)])  # add to string
+
+            # show results
+            for *xyxy, conf, cls in det:
+                label = '%s %.2f' % (names[int(cls)], conf)
+                annotator.box_label(xyxy, label, color=colors[int(cls)])
+        im0 = annotator.result()
+        # Print time (inference + NMS)
+        infer_time = t2 - t1
+        
+        print('%sDone.  %s' %
+                (s, infer_time))
+
+    print('Done. (%.3fs)' % (time.time() - t0))
+    
+    return im0
+ 
+
+if __name__ == '__main__':
+    gr.Interface(detect,[gr.Image(type="numpy"),gr.Dropdown(choices=["yolov5s","yolov5s6"])], 
+    gr.Image(type="numpy"),title="Yolov5",examples=[["data/images/bus.jpg", "yolov5s"]],
+    description="Gradio based demo for <a href='https://github.com/ultralytics/yolov5' style='text-decoration: underline' target='_blank'>ultralytics/yolov5</a>, new state-of-the-art for real-time object detection").launch()