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b5a0c9c a1a77e0 b5a0c9c 1c5dc18 b5a0c9c 1c5dc18 b5a0c9c 46b306d b5a0c9c | 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 | import gradio as gr
from ultralytics import YOLO
import cv2
from PIL import Image
# Load the YOLO model - YOLOv11m for pothole, road damage, and garbage detection
try:
model = YOLO("model.pt")
except Exception as e:
print(f"Error loading model: {e}")
model = None
def predict(image, conf_threshold):
try:
if image is None or model is None:
return None, "Model not loaded or invalid image."
# Run inference
results = model(image, imgsz=768, conf=conf_threshold)
result = results[0]
# Plotting the detections on the image returns a BGR numpy array
annotated_image = result.plot()
annotated_image_rgb = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
# Detection overview text
boxes = result.boxes
class_names = result.names
if len(boxes) == 0:
detection_summary = "No civic issues detected in this image."
else:
# Count detections safely
detection_counts = {}
for box in boxes:
# box.cls is usually a tensor. Safe conversion to integer:
cls_id = int(box.cls.item() if hasattr(box.cls, "item") else box.cls[0])
cls_name = class_names.get(cls_id, f"Class {cls_id}")
detection_counts[cls_name] = detection_counts.get(cls_name, 0) + 1
summary_lines = ["**Detections:**"]
for cls_name, count in detection_counts.items():
summary_lines.append(f"- {count} {cls_name}(s)")
detection_summary = "\n".join(summary_lines)
return Image.fromarray(annotated_image_rgb), detection_summary
except Exception as e:
import traceback
error_msg = f"ERROR during prediction: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
return None, error_msg
# Gradio Interface
with gr.Blocks(title="PotholeNet-YOLO11m-v1 🛑") as interface:
gr.Markdown("# 🛑 PotholeNet-YOLO11m-v1")
gr.Markdown("**Aamchi City AI Civic System** — Real-time pothole, road damage, and garbage detection for Indian urban roads.")
gr.Markdown("Upload an image of a road to detect infrastructure issues. The model was trained on 23,000+ street-level images.")
with gr.Row():
with gr.Column():
input_image = gr.Image(type="pil", label="Upload Street Image")
conf_slider = gr.Slider(minimum=0.01, maximum=1.0, value=0.25, step=0.01, label="Confidence Threshold")
submit_btn = gr.Button("Detect Civic Issues", variant="primary")
with gr.Column():
output_image = gr.Image(type="pil", label="Detection Results")
detection_text = gr.Textbox(label="Detection Summary", interactive=False, lines=4)
submit_btn.click(
fn=predict,
inputs=[input_image, conf_slider],
outputs=[output_image, detection_text]
)
gr.Markdown("### Intended Use")
gr.Markdown("Real-time pothole detection, Automated civic issue reporting, Infrastructure health monitoring.")
gr.Markdown("**Developer:** Vansh Momaya")
if __name__ == "__main__":
interface.launch(server_name="0.0.0.0", server_port=7860)
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