curl --request GET \
  --url http://studio.premai.io/api/v1/public/recommendations/{snapshotId} \
  --header 'Authorization: Bearer <token>'

import requests

url = "http://studio.premai.io/api/v1/public/recommendations/{snapshotId}"

headers = {"Authorization": "Bearer <token>"}

response = requests.get(url, headers=headers)

print(response.text)

const options = {method: 'GET', headers: {Authorization: 'Bearer <token>'}};

fetch('http://studio.premai.io/api/v1/public/recommendations/{snapshotId}', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

{
  "snapshot_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "status": "processing",
  "recommended_models": [
    {
      "base_model_id": "<string>",
      "recommended": true,
      "full_hyperparameters": {
        "n_epochs": 123,
        "batch_size": 123,
        "learning_rate_multiplier": 123
      },
      "lora_hyperparameters": {
        "n_epochs": 123,
        "batch_size": 123,
        "learning_rate_multiplier": 123
      },
      "reason_for_recommendation": "<string>"
    }
  ],
  "recommended_experiments": [
    {
      "base_model_id": "<string>",
      "batch_size": 123,
      "learning_rate_multiplier": 123,
      "n_epochs": 123,
      "training_type": "full",
      "recommended": true,
      "reason_for_recommendation": "<string>"
    }
  ]
}

{
  "error": "<string>"
}

{
  "error": "<string>"
}