new file: app.py

new file: requirements.txt
new file: yolov5s.pt

app.py

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+import torch
+import cv2
+import numpy as np
+import gradio as gr
+
+# load model
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
+
+# set model parameters
+model.conf = 0.25  # NMS confidence threshold
+model.iou = 0.45  # NMS IoU threshold
+model.agnostic = False  # NMS class-agnostic
+model.multi_label = False  # NMS multiple labels per box
+model.max_det = 1000  # maximum number of detections per image
+
+# set image
+img = '/content/apple_img.jpg'
+
+def detect(img):
+
+# perform inference
+    results = model(img, size=640)
+
+# inference with test time augmentation
+    results = model(img, augment=True)
+# parse results
+    predictions = results.pred[0]
+    boxes = predictions[:, :4] # x1, y1, x2, y2
+    scores = predictions[:, 4]
+    categories = predictions[:, 5]
+    return results
+# show detection bounding boxes on image
+
+image = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label()
+examples = ['dog.jpg']
+
+intf = gr.Interface(fn=detect, inputs=image, outputs=image, examples=examples)
+intf.launch(inline=False)

requirements.txt

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+opencv_python
+torch
+ultralytics==8.0.4
+ultralyticsplus==0.0.3

yolov5s.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8b3b748c1e592ddd8868022e8732fde20025197328490623cc16c6f24d0782ee
+size 14808437