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app.py

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+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)

model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f380cd373f61f2bc71f7fcc1b0ec072194dc2cd933fd05bc1ae5ad136a333b78
+size 40540780

requirements.txt

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+ultralytics
+gradio==4.44.1
+pillow
+opencv-python-headless