Update README.md

README.md

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+# Use of Landing AI for brain tumor detection
+
+- a quick overview of the inner workings of LandingAI's Vision Agent, how it breaks down an initial user requirement to identify candidate components in the application architecture.
+- the diagram below captures what I had in mind for a multi-agent system but LandingAI's vision agent starts this much earlier, taking a fresh approach on old school architecture trade off analysis.
+- if you want a deeper understanding of the run-time flow of the application I encourage you to instrument it with Weave.  Additional information in [this GitHub repo](https://github.com/donbr/vision-agent).
+- the flow in the most recent version of the official [Vision Agent](https://va.landing.ai/agent) app has shifted somewhat, but the number of concepts it helped bring together for me was amazing.
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/653d62fab16f657d28ce2cf2/KPV1Szj6IkY457n3Hqjl6.png)
 
-# Landing AI - brain tumor detection
 
 PROMPT:  use detection (bounding box) and segmentation (segmenation and mask) techniques to detect brain tumors in the image.
 
 ## Creating instructions
 
-- Load the image from the given file path '/home/user/tmp9873xen5.jpg'.
+- Load the image from the given file path '/home/user/xxxx.jpg'.
 - Use the 'owl_v2' tool to detect brain tumors in the image. The prompt should be 'brain tumor'.
 - Use the 'grounding_sam' tool to segment brain tumors in the image. The prompt should be 'brain tumor'.
 - Overlay the bounding boxes from the detection results on the original image using the 'overlay_bounding_boxes' utility.