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app.py

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+import gradio as gr
+from ultralytics import YOLO
+import cv2
+
+examples=[["photo/a.jpg","Image1"],["photo/b.jpg","Image2"],
+          ["photo/c.jpg","Image3"],["photo/d.jpg","Image4"],
+          ["photo/e.jpg","Image5"],["photo/f.jpg","Image6"],
+          ["photo/g.jpg","Image7"],["photo/h.jpg","Image8"]]
+
+
+def detect_objects_on_image(image_path):
+    image = cv2.imread(image_path)
+    model = YOLO("best.pt")
+    results = model.predict(image_path)
+    result = results[0]
+    output = []
+    for box in result.boxes:
+        x1, y1, x2, y2 = [
+            round(x) for x in box.xyxy[0].tolist()
+            
+        ]
+        class_id = box.cls[0].item()
+        prob = round(box.conf[0].item(), 2)
+        output.append([
+            x1, y1, x2, y2, result.names[class_id], prob
+        ])
+        cv2.rectangle(
+            image,
+            (x1, y1),
+            (x2, y2),
+            color=(0, 0, 255),
+            thickness=2,
+            lineType=cv2.LINE_AA
+        )
+        
+        cv2.putText(image,result.names[class_id],  (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36,255,12), 2)
+    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+
+inputs_image = [
+    gr.components.Image(type="filepath", label="Input Image"),
+]
+outputs_image = [
+    gr.components.Image(type="numpy", label="Output Image"),
+]
+demo = gr.Interface(
+    fn=detect_objects_on_image,
+    inputs=inputs_image,
+    outputs=outputs_image,
+    title="Yolov8 Custom Object Detection",
+    examples=examples,
+    cache_examples=False,
+)
+
+if __name__ == "__main__":
+    demo.launch()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c3f2d3fdeac6306a116b2eca70c7824ddaf952d4d6e94c66bbd0d051909cb630
+size 22511726

requirements.txt

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+# Ultralytics requirements
+# Example: pip install -r requirements.txt
+
+# Base ----------------------------------------
+matplotlib>=3.3.0
+numpy>=1.22.2 # pinned by Snyk to avoid a vulnerability
+opencv-python>=4.6.0
+pillow>=7.1.2
+pyyaml>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1
+torch>=1.8.0
+torchvision>=0.9.0
+tqdm>=4.64.0
+ultralytics
+# Logging -------------------------------------
+# tensorboard>=2.13.0
+# dvclive>=2.12.0
+# clearml
+# comet
+
+# Plotting ------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+
+# Export --------------------------------------
+# coremltools>=7.0.b1  # CoreML export
+# onnx>=1.12.0  # ONNX export
+# onnxsim>=0.4.1  # ONNX simplifier
+# nvidia-pyindex  # TensorRT export
+# nvidia-tensorrt  # TensorRT export
+# scikit-learn==0.19.2  # CoreML quantization
+# tensorflow>=2.4.1  # TF exports (-cpu, -aarch64, -macos)
+# tflite-support
+# tensorflowjs>=3.9.0  # TF.js export
+# openvino-dev>=2023.0  # OpenVINO export
+
+# Extras --------------------------------------
+psutil  # system utilization
+py-cpuinfo  # display CPU info
+# thop>=0.1.1  # FLOPs computation
+# ipython  # interactive notebook
+# albumentations>=1.0.3  # training augmentations
+# pycocotools>=2.0.6  # COCO mAP
+# roboflow