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app.py

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+import cv2
+import numpy as np
+import gradio as gr
+from ultralyticsplus import YOLO
+
+# Load YOLO model
+model = YOLO("keremberke/yolov8n-pothole-segmentation")
+model.overrides['conf'] = 0.25
+
+# Constants
+SCALE = 0.005  # m² per pixel²
+COST_PER_M2 = 1000  # ₹ per m²
+
+def estimate_cost_from_image(image):
+    img_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    h, w = img_bgr.shape[:2]
+
+    # Save temporary image
+    temp_path = "temp.jpg"
+    cv2.imwrite(temp_path, img_bgr)
+
+    result = model.predict(source=temp_path, verbose=False)[0]
+    mask = result.masks.data.cpu().numpy()
+    total_area_px = mask.sum()
+    area_m2 = total_area_px * SCALE
+    estimated_cost = area_m2 * COST_PER_M2
+
+    # Overlay visualization
+    overlay = img_bgr.copy()
+    combined = np.max(mask, axis=0).astype(bool)
+    overlay[combined] = [0, 0, 255]
+    final = cv2.addWeighted(img_bgr, 0.7, overlay, 0.3, 0)
+    final_rgb = cv2.cvtColor(final, cv2.COLOR_BGR2RGB)
+
+    return final_rgb, f"{area_m2:.2f} m²", f"₹{estimated_cost:.0f}"
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=estimate_cost_from_image,
+    inputs=gr.Image(type="numpy", label="Upload Pothole Image"),
+    outputs=[
+        gr.Image(type="numpy", label="Overlay"),
+        gr.Text(label="Estimated Area"),
+        gr.Text(label="Repair Cost"),
+    ],
+    title="Pothole Repair Estimator",
+    description="Upload a pothole image to estimate repair cost using YOLOv8 segmentation model."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+ultralyticsplus
+ultralytics
+opencv-python
+gradio
+numpy