App

.gitignore

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+flagged/
+*.pt

app.py

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 import gradio as gr
+from gradio.outputs import Label
+import cv2
 
-def greet(name):
-    return "Hello " + name + "!!"
+from ultralytics import YOLO
 
-iface = gr.Interface(fn=greet, inputs="image", outputs="image")
-iface.launch()
+model = YOLO('best.pt')
+path  = [['pothole_screenshot.png']]
+
+def show_preds(image_path):
+    image = cv2.imread(image_path)
+    outputs = model.predict(source=image_path, return_outputs=True)
+    for image_id, result in enumerate(outputs):
+        print(result['det'])
+        for i, det in enumerate(result['det']):
+            print(det)
+            cv2.rectangle(
+                image,
+                (int(det[0]), int(det[1])),
+                (int(det[2]), int(det[3])),
+                color=(0, 0, 255),
+                thickness=2,
+                lineType=cv2.LINE_AA
+            )
+    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+gr_interface = gr.Interface(
+    fn=show_preds,
+    inputs=gr.inputs.Image(type="filepath", label="Input Image"),
+    outputs=gr.outputs.Image(type="numpy", label="Output Image"),
+    title="Pothole detector",
+    examples=path,
+)
+
+gr_interface.launch(inline=False, share=False, debug=True)

requirements.txt

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+# Ultralytics requirements
+# Usage: pip install -r requirements.txt
+
+# Base ----------------------------------------
+hydra-core>=1.2.0
+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
+torchvision>=0.8.1
+tqdm>=4.64.0
+ultralytics
+
+# Logging -------------------------------------
+tensorboard>=2.4.1
+# clearml
+# comet
+
+# Plotting ------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+
+# Export --------------------------------------
+# coremltools>=6.0  # CoreML export
+# onnx>=1.12.0  # ONNX export
+# onnx-simplifier>=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)
+# tensorflowjs>=3.9.0  # TF.js export
+# openvino-dev  # OpenVINO export
+
+# Extras --------------------------------------
+ipython  # interactive notebook
+psutil  # system utilization
+thop>=0.1.1  # FLOPs computation
+# albumentations>=1.0.3
+# pycocotools>=2.0.6  # COCO mAP
+# roboflow
+
+# HUB -----------------------------------------
+GitPython>=3.1.24