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README.md

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----
-title: Road Damage Detection
-emoji: 🚀
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 6.2.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import torch
+from ultralytics import YOLO
+import cv2
+import numpy as np
+from PIL import Image
+import json
+
+# Load model
+model = YOLO('models/best.pt')
+
+# Class mapping sesuai dengan mobile app
+CLASS_NAMES = {
+    0: 'amblas',
+    1: 'bergelombang', 
+    2: 'berlubang',
+    3: 'retak_buaya'
+}
+
+def detect_road_damage(image, confidence_threshold=0.5):
+    """
+    Deteksi kerusakan jalan dari gambar
+    """
+    try:
+        # Convert PIL to numpy array
+        if isinstance(image, Image.Image):
+            image = np.array(image)
+        
+        # Run inference
+        results = model(image, conf=confidence_threshold)
+        
+        detections = []
+        annotated_image = image.copy()
+        
+        for result in results:
+            boxes = result.boxes
+            if boxes is not None:
+                for box in boxes:
+                    # Extract detection info
+                    x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
+                    confidence = float(box.conf[0])
+                    class_id = int(box.cls[0])
+                    class_name = CLASS_NAMES.get(class_id, 'unknown')
+                    
+                    # Calculate dimensions (estimasi)
+                    width_pixels = x2 - x1
+                    height_pixels = y2 - y1
+                    
+                    # Estimasi ukuran dalam cm (asumsi 1 pixel = 0.1 cm)
+                    width_cm = width_pixels * 0.1
+                    depth_cm = height_pixels * 0.1
+                    
+                    detection = {
+                        'class': class_name,
+                        'confidence': confidence,
+                        'bbox': [float(x1), float(y1), float(x2), float(y2)],
+                        'width_cm': width_cm,
+                        'depth_cm': depth_cm
+                    }
+                    detections.append(detection)
+                    
+                    # Draw bounding box
+                    cv2.rectangle(annotated_image, 
+                                (int(x1), int(y1)), (int(x2), int(y2)), 
+                                (0, 255, 0), 2)
+                    
+                    # Add label
+                    label = f"{class_name}: {confidence:.2f}"
+                    cv2.putText(annotated_image, label,
+                              (int(x1), int(y1-10)),
+                              cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+        
+        # Prepare response
+        response = {
+            'detections': detections,
+            'count': len(detections),
+            'status': 'success'
+        }
+        
+        return annotated_image, json.dumps(response, indent=2)
+        
+    except Exception as e:
+        error_response = {
+            'error': str(e),
+            'status': 'error'
+        }
+        return image, json.dumps(error_response, indent=2)
+
+# Gradio interface
+with gr.Blocks(title="VGTec Road Damage Detector") as demo:
+    gr.Markdown("# 🛣️ VGTec Road Damage Detection API")
+    gr.Markdown("Upload gambar jalan untuk mendeteksi kerusakan (amblas, bergelombang, berlubang, retak_buaya)")
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(type="pil", label="Upload Gambar Jalan")
+            confidence_slider = gr.Slider(
+                minimum=0.1, 
+                maximum=1.0, 
+                value=0.5, 
+                step=0.1,
+                label="Confidence Threshold"
+            )
+            detect_btn = gr.Button("🔍 Deteksi Kerusakan", variant="primary")
+        
+        with gr.Column():
+            output_image = gr.Image(label="Hasil Deteksi")
+            output_json = gr.Code(label="JSON Response", language="json")
+    
+    # Event handler
+    detect_btn.click(
+        fn=detect_road_damage,
+        inputs=[input_image, confidence_slider],
+        outputs=[output_image, output_json]
+    )
+    
+    # Examples
+    gr.Examples(
+        examples=[
+            ["examples/berlubang.jpg", 0.5],
+            ["examples/retak_buaya.jpg", 0.6],
+        ],
+        inputs=[input_image, confidence_slider],
+        outputs=[output_image, output_json],
+        fn=detect_road_damage,
+        cache_examples=True
+    )
+
+if __name__ == "__main__":
+    demo.launch()

models/best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:854825ce8d6a0c0179e51f863c4627761f6d95a729c6cf5b646236bad96236e2
+size 6254762

requirements.txt

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+ultralytics==8.0.196
+gradio==4.44.0
+torch==2.1.0
+torchvision==0.16.0
+opencv-python==4.8.1.78
+Pillow==10.0.1
+numpy==1.24.3