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app.py

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+import gradio as gr
+import cv2
+import requests
+import os
+
+from ultralytics import YOLO
+
+model = YOLO('best_gd.pt')
+image_paths = ['pothole_example.jpg', 'pothole_screenshot.png']
+
+def show_preds_image(image):
+    # Save the uploaded image temporarily
+    image_path = "uploaded_image.jpg"
+    cv2.imwrite(image_path, image[:, :, ::-1])  # Convert BGR to RGB and save the image
+    
+    image = cv2.imread(image_path)
+    outputs = model.predict(source=image_path)
+    #print("output>>>>>>>>>>>>>>>>>>>>>>>>", outputs)
+    results = outputs[0].boxes.xyxy.cpu().numpy()
+    for det in results:
+        x1, y1, x2, y2 = det[:4]
+        label = "Pothole"
+        cv2.rectangle(
+            image,
+            (int(x1), int(y1)),
+            (int(x2), int(y2)),
+            color=(0, 0, 255),
+            thickness=2,
+            lineType=cv2.LINE_AA,
+        )
+        cv2.putText(
+            image,
+            label,
+            (int(x1), int(y1) - 10),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.9,
+            (0, 0, 255),
+            2,
+            cv2.LINE_AA,
+        )
+    os.remove(image_path)  # Remove the temporary image file
+    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+inputs_image = [
+    gr.inputs.Image(label="Upload Image"),
+]
+outputs_image = [
+    gr.outputs.Image(type="numpy"),
+]
+interface_image = gr.Interface(
+    fn=show_preds_image,
+    inputs=inputs_image,
+    outputs=outputs_image,
+    title="Pothole detector",
+    examples=[],
+    cache_examples=False,
+)
+
+interface_image.launch()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b5840aec4869c6891ac3d9262fc03e930d87af04f32ec077c1c50874b21cba65
+size 52033238

requirements.txt

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+opencv-python
+omegaconf
+ultralytics