Labrak Yanis qanastek

## dialog_demo.json
{
  "version": "0.0.2+2bb4785ab2dd71d245c71e853d8f2fa504fe2127",
  "timestamp": "2025-07-18T17:58:50Z",
  "model": "client=<botocore.client.BedrockRuntime object at 0x7fc319ec91c0> model_id='anthropic.claude-3-5-sonnet-20240620-v1:0' region_name='us-east-1' provider='anthropic' supports_tool_choice_values=('auto', 'any', 'tool')",
  "seed": 226296126,
  "id": 1752861530588,
  "parentId": null,
  "complete": false,
  "personas": {
    "DOCTOR": {

## create_conda_env_huggingface.sh
conda create --name huggingface python=3.9 -y
conda activate HuggingFace
pip install transformers
pip install datasets
pip install seqeval

## docker_ps_results.txt
CONTAINER ID   IMAGE                                     COMMAND                  CREATED          STATUS          PORTS                  NAMES
bfb3458d5282   qanastek/hugsvision-api-cpu-only:latest   "uvicorn main:app --…"   13 seconds ago   Up 10 seconds   0.0.0.0:8888->80/tcp   stoic_matsumoto

## run.sh
docker run -p 80:8888 hugsvision:latest

## build.sh
docker build --tag hugsvision:latest .

## table.csv

          
            Model
            Accuracy
            Size

            
              VGG16
              38.27%
              512.0 MB

            
              DeiT
              71.60%
              327.0 MB

            
              DenseNet121
              77.78%
              27.1 MB

            
              MobileNetV2
              75.31%
              8.77 MB

            
              ShuffleNetV2
              76.54%
              4.99 MB

## multiple_images_client.py
import requests
images = ["images/akiec.jpg", "images/bcc.jpg"]
requests.post(
    f"http://127.0.0.1:8000/predicts/ShuffleNetV2",
    files = [('files', open(path, 'rb')) for path in images]
).json()

## client_single_image.py
import requests
requests.post(
    f"http://127.0.0.1:8888/predict/ShuffleNetV2",
    files = {
        'file': open("images/akiec.jpg", 'rb')
    }
).json()

## client_models.py
import requests
requests.get("http://127.0.0.1:8888/models").json()

## docker_ps.txt
docker ps
	{
	"version": "0.0.2+2bb4785ab2dd71d245c71e853d8f2fa504fe2127",
	"timestamp": "2025-07-18T17:58:50Z",
	"model": "client=<botocore.client.BedrockRuntime object at 0x7fc319ec91c0> model_id='anthropic.claude-3-5-sonnet-20240620-v1:0' region_name='us-east-1' provider='anthropic' supports_tool_choice_values=('auto', 'any', 'tool')",
	"seed": 226296126,
	"id": 1752861530588,
	"parentId": null,
	"complete": false,
	"personas": {
	"DOCTOR": {
	conda create --name huggingface python=3.9 -y
	conda activate HuggingFace
	pip install transformers
	pip install datasets
	pip install seqeval
	CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
	bfb3458d5282 qanastek/hugsvision-api-cpu-only:latest "uvicorn main:app --…" 13 seconds ago Up 10 seconds 0.0.0.0:8888->80/tcp stoic_matsumoto
Model	Accuracy	Size
VGG16	38.27%	512.0 MB
DeiT	71.60%	327.0 MB
DenseNet121	77.78%	27.1 MB
MobileNetV2	75.31%	8.77 MB
ShuffleNetV2	76.54%	4.99 MB
	import requests
	images = ["images/akiec.jpg", "images/bcc.jpg"]
	requests.post(
	f"http://127.0.0.1:8000/predicts/ShuffleNetV2",
	files = [('files', open(path, 'rb')) for path in images]
	).json()
	import requests
	requests.post(
	f"http://127.0.0.1:8888/predict/ShuffleNetV2",
	files = {
	'file': open("images/akiec.jpg", 'rb')
	}
	).json()
	import requests
	requests.get("http://127.0.0.1:8888/models").json()