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9aa20d6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 | from __future__ import annotations
from collections import Counter
from pathlib import Path
import gradio as gr
from ultralytics import YOLO
ROOT = Path(__file__).resolve().parent
MODEL_CANDIDATES = [
ROOT / "runs" / "detect" / "yolov8_road-2" / "weights" / "best.pt",
ROOT / "runs" / "detect" / "yolov8_road" / "weights" / "best.pt",
ROOT / "best.pt",
ROOT / "yolov8n.pt",
]
MODEL_PATH = next((path for path in MODEL_CANDIDATES if path.exists()), None)
if MODEL_PATH is None:
raise FileNotFoundError(
"No model weights file was found. Please place 'best.pt' inside the "
"'weights' folder or in the project root."
)
model = YOLO(str(MODEL_PATH))
def detect_damage(image):
if image is None:
raise gr.Error("Please upload an image before starting detection.")
result = model(image, conf=0.25, imgsz=640, stream=False)[0]
annotated_image = result.plot()
boxes = result.boxes
if boxes is None or len(boxes) == 0:
summary = "✅ No damage was detected in this image."
return annotated_image, summary
detected_names = []
confidences = []
for box in boxes:
class_id = int(box.cls.item())
class_name = model.names[class_id]
confidence = round(float(box.conf.item()), 2)
detected_names.append(class_name)
confidences.append(confidence)
counts = Counter(detected_names)
lines = []
lines.append(f"Total objects detected: {len(detected_names)}")
lines.append("")
lines.append("Breakdown by type:")
for name, count in counts.items():
lines.append(f" • {name}: {count}")
lines.append("")
lines.append("Confidence scores:")
for name, confidence in zip(detected_names, confidences):
lines.append(f" • {name}: {confidence:.2f}")
summary = "\n".join(lines)
return annotated_image, summary
CUSTOM_CSS = """
@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@400;500;600;700;800&family=Inter:wght@400;500;600&display=swap');
* {
font-family: 'Inter', 'Poppins', sans-serif !important;
}
.gradio-container {
background: radial-gradient(circle at 10% 0%, #1e293b 0%, #0f172a 45%, #020617 100%) !important;
}
.hero {
background: linear-gradient(120deg, #0ea5e9 0%, #2563eb 45%, #7c3aed 100%);
padding: 42px 40px;
border-radius: 24px;
color: #ffffff;
box-shadow: 0 20px 45px rgba(37, 99, 235, 0.35);
position: relative;
overflow: hidden;
margin-bottom: 24px;
border: 1px solid rgba(255,255,255,0.15);
}
.hero::after {
content: "";
position: absolute;
top: -60px;
right: -60px;
width: 220px;
height: 220px;
background: rgba(255,255,255,0.08);
border-radius: 50%;
}
.hero-eyebrow {
display: inline-block;
font-size: 12px;
letter-spacing: 2px;
text-transform: uppercase;
font-weight: 600;
background: rgba(255,255,255,0.15);
padding: 6px 14px;
border-radius: 999px;
margin-bottom: 14px;
backdrop-filter: blur(4px);
}
.hero-title {
font-family: 'Poppins', sans-serif !important;
font-size: 34px;
font-weight: 800;
margin: 0 0 10px 0;
letter-spacing: -0.5px;
}
.hero-subtitle {
font-size: 15.5px;
color: rgba(255,255,255,0.9);
max-width: 640px;
line-height: 1.6;
font-weight: 400;
}
.panel {
border: 1px solid rgba(148, 163, 184, 0.18) !important;
border-radius: 20px !important;
padding: 22px !important;
background: rgba(15, 23, 42, 0.6) !important;
backdrop-filter: blur(10px);
box-shadow: 0 10px 30px rgba(0,0,0,0.25);
}
.panel-title {
font-family: 'Poppins', sans-serif !important;
font-size: 17px;
font-weight: 700;
color: #e2e8f0 !important;
margin-bottom: 4px;
display: flex;
align-items: center;
gap: 8px;
}
.panel-caption {
font-size: 13px;
color: #94a3b8 !important;
margin-bottom: 14px;
}
.primary-btn {
background: linear-gradient(90deg, #2563eb, #7c3aed) !important;
border: none !important;
color: white !important;
font-weight: 600 !important;
border-radius: 12px !important;
box-shadow: 0 8px 20px rgba(124, 58, 237, 0.35) !important;
transition: transform 0.15s ease, box-shadow 0.15s ease !important;
}
.primary-btn:hover {
transform: translateY(-1px);
box-shadow: 0 10px 26px rgba(124, 58, 237, 0.45) !important;
}
footer {
display: none !important;
}
"""
with gr.Blocks(
theme=gr.themes.Soft(
primary_hue="blue",
secondary_hue="violet",
neutral_hue="slate",
),
css=CUSTOM_CSS,
title="Road Damage Detection Studio",
) as demo:
gr.HTML(
"""
<div class="hero">
<span class="hero-eyebrow">AI Vision · Infrastructure Inspection</span>
<div class="hero-title">🛣️ Road Damage Detection Studio</div>
<div class="hero-subtitle">
Upload a photo of a road surface and let the detection engine
automatically locate, classify, and score every type of damage
— cracks, potholes, and more — in seconds.
</div>
</div>
"""
)
with gr.Row(equal_height=True):
with gr.Column(scale=1):
with gr.Group(elem_classes=["panel"]):
gr.HTML('<div class="panel-title">📤 Upload Image</div>')
gr.HTML('<div class="panel-caption">Choose a clear photo of the road surface to analyze.</div>')
image_input = gr.Image(
label="",
type="pil",
height=420,
sources=["upload"],
)
run_btn = gr.Button("✨ Run Detection", variant="primary", elem_classes=["primary-btn"])
with gr.Column(scale=1):
with gr.Group(elem_classes=["panel"]):
gr.HTML('<div class="panel-title">🔎 Detection Result</div>')
gr.HTML('<div class="panel-caption">Annotated image and detailed summary will appear here.</div>')
output_image = gr.Image(label="", height=420)
output_text = gr.Textbox(
label="Summary",
lines=12,
max_lines=20,
)
run_btn.click(
fn=detect_damage,
inputs=[image_input],
outputs=[output_image, output_text],
api_name="detect_damage",
)
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
debug=False,
) |