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SnapFind - Smart Item Organizer

SnapFind is your personal digital assistant for effortlessly cataloging, finding, and managing your physical belongings. Simply snap a photo, add a few details, and let SnapFind's intelligent features help you keep everything organized. All your information is stored securely and privately in your browser's local database.

What Can SnapFind Do For You?

• Effortless Item Cataloging:
  • Quick Add: Easily add new items with a description, where it's stored (location), and an optional image.
• Versatile Image Upload: Add images by selecting files, dragging & dropping, or even pasting directly from your clipboard. Mobile users can choose from their photo gallery or take a new picture on the spot.

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Today you will be writing instructions to an eager, helpful, but inexperienced and unworldly AI assistant who needs careful instruction and examples to understand how best to behave. I will explain a task to you. You will write instructions that will direct the assistant on how best to accomplish the task consistently, accurately, and correctly. Here are some examples of tasks and instructions.

<Task Instruction Example>
<Task>
Act as a polite customer success agent for Acme Dynamics. Use FAQ to answer questions.
</Task>
<Inputs>
{$FAQ}
{$QUESTION}
</Inputs>

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from pynetdicom import AE, evt, AllStoragePresentationContexts, debug_logger
from pydicom.uid import generate_uid

debug_logger()

def sendBackToStore(ds):
    ae2 = AE(ae_title=b'ORTHANC')
    ae2.add_requested_context('1.2.840.10008.5.1.4.1.1.2', ['1.2.840.10008.1.2'])  # CT Image Storage - Implicit VR Endian: Default Transfer Syntax for DICOM
    ae2.add_requested_context('1.2.840.10008.5.1.4.1.1.7', ['1.2.840.10008.1.2'])  # Secondary Capture Image Storage
    ae2.add_requested_context('1.2.840.10008.1.1')                                 # Verification SOP Class	

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pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118

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from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams
import numpy as np
from sentence_transformers import SentenceTransformer
from qdrant_client.models import PointStruct

# Initialize the client
client = QdrantClient("localhost:6333")
COLLECTION_NAME = "docs"

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docker-compose for arclight

startup

docker-compose -p dcm4chee up -d

URI

http://localhost:8080/dcm4chee-arc/ui2

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[{"tag":"진단명","values":["DR","AMD","Glaucoma","ERM","RVO","Hypertensive retinopathy","Pathologic myopia","MH","RP"]},{"tag":"이상 소견","values":["Retinal hemorrhage","Microaneurysms","Hard exudates","Cotton wool patches","Drusen","Membrane","MH","MNF","Chorioretinal atrophy or scar","Vascular abnormality","RNFL defect","Glaucomatous disc change","Nonglaucomatous disc change"]},{"tag":"F/U","values":["1개월 내 빠른 진료 필요","정기검사 필요","정상 범위 조치 필요 없음"]}]

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[{"tag":"CT","values":["Hemoperitoneum","Hemoretroperitoneum"]},{"tag":"CXR","values":["Pneumonia","Pneumothorax","Effusion","Intracranial hemorrhage"]}]

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npx vite build --outDir build
find ./build/assets -name "*.js" -print0 | while read -d $'\0' file
do
  curl https://api.honeybadger.io/v1/source_maps \
    -F api_key=$VITE_HONEYBADGER \
    -F minified_url=$VITE_APP_BUILD/$(echo $file | sed -e 's/\.\/build\///g') \
    -F source_map=@$file.map \
    -F minified_file=@$file \;
#  echo "$VITE_APP_ENDPOINT/$file" | sed -e 's/\.\/build\///g'
done