Initial deployment

app.py

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+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image
+import numpy as np
+
+# Load model
+model = YOLO("best.pt")
+
+def detect(image):
+    # Run inference
+    results = model(image, conf=0.5, iou=0.6)
+    
+    # Get annotated image
+    annotated = results[0].plot()
+    
+    # Convert BGR to RGB
+    annotated_rgb = annotated[..., ::-1]
+    
+    # Print detections
+    detections = []
+    for box in results[0].boxes:
+        class_name = model.names[int(box.cls)]
+        confidence = float(box.conf)
+        detections.append(f"{class_name}: {confidence:.2f}")
+    
+    detection_text = "\n".join(detections) if detections else "No objects detected"
+    
+    return Image.fromarray(annotated_rgb), detection_text
+
+# Gradio UI
+demo = gr.Interface(
+    fn=detect,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=[
+        gr.Image(label="Detection Result"),
+        gr.Textbox(label="Detections")
+    ],
+    title="243263S - Traffic Cone & Cardboard Box Detector",
+    description="Upload an image to detect traffic cones and cardboard boxes!",
+    examples=[],
+)
+
+demo.launch()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:409a1c01b2d4a3b020e7cdb9b42be928140371bcb7227acca6e38d88dc961845
+size 19178586

requirements.txt

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+ultralytics
+wandb
+gradio
+pillow