Source code for dymaxionlabs.models

from dymaxionlabs import files
from dymaxionlabs.utils import get_api_url, get_api_key, get_project_id

import json
import requests

DYM_PREDICT = '/estimators/{estimatorId}/predict/'
DYM_PREDICTED = '/estimators/{estimatorId}/predicted/'
DYM_PREDICTION_DETAIL = '/predictionjob/{predictionId}'
DYM_PROJECT_DETAIL = '/projects/{projectId}'
DYM_PROJECT_FILES = '/files/?limit=1000&project_uuid={projectId}'

[docs]class PredictionJob: """ Class that represents a PredictionJob in DymaxionLabs API A PredictionJob is a background job that performs the prediction using a previously trained Estimator and your uploaded images. """ def __init__(self, id, estimator, finished, image_files, result_files): """Constructor Args: id: PredictionJob id estimator: related estimator instance finished: PredictionJob's state image_files: array of strings that contains the names of image to predict results_files: array of strings that contains the names of results """ = id self.estimator = estimator self.finished = finished self.image_files = image_files self.results_files = result_files
[docs] def status(self): """Get status of a PredictionJob Returns: Returns a boolean whether the job finished or not """ if self.finished: return self.finished else: headers = { 'Authorization': 'Api-Key {}'.format(get_api_key()), 'Accept-Language': 'es' } url = '{url}{path}'.format( url=get_api_url(), path=DYM_PREDICTION_DETAIL.format( r = requests.get(url, headers=headers) data = json.loads(r.text) if data['finished']: self.finished = data['finished'] self.results_files = data['result_files'] return data['finished']
[docs] def download_results(self, output_dir="."): """Download results from a finished PredictionJob Args: output_dir: path for storing results """ if self.status(): for f in self.results_files:, output_dir)
[docs]class Estimator: """ Class that represents an Estimator in DymaxionLabs API """ def __init__(self, uuid): """Constructor Args: uuid: Estiamtor uuid prediction_job: related PredictionJob """ self.uuid = uuid self.prediction_job = None
[docs] def predict_files(self, remote_files=[], local_files=[]): """Predict files This function will start a prediction job over the specified files. You can predict over already upload images by providing a list of +remote_files+, or over images in your disk by providing a list of +local_files+. Local files will be uploaded before prediction. Args: remote_files: array of string with the names of already uploaded files local_files: array of string with the names of local files Returns: Returns a dict with info about the new PredictionJob """ for local_file in local_files: f = file.upload(local_file) remote_files.append(f['name']) data = {'files': remote_files} headers = { 'Authorization': 'Api-Key {}'.format(get_api_key()), 'Accept-Language': 'es' } url = '{url}{path}'.format( url=get_api_url(), path=DYM_PREDICT.format(estimatorId=self.uuid)) r =, json=data, headers=headers) data = json.loads(r.text)['detail'] self.prediction_job = PredictionJob(id=data['id'], estimator=data['estimator'], finished=data['finished'], image_files=data['image_files'], result_files=data['result_files']) return self.prediction_job
[docs] @classmethod def all(cls): """Obtain all UUIDs of estimators from your project Returns: Returns an array of UUIDs """ headers = { 'Authorization': 'Api-Key {}'.format(get_api_key()), 'Accept-Language': 'es' } url = '{url}{path}'.format( url=get_api_url(), path=DYM_PROJECT_DETAIL.format(projectId=get_project_id())) r = requests.get(url, headers=headers) return json.loads(r.text)['estimators']
[docs]class Project: def __init__(self): """Constructor Uses the environment variable to create the object """ self.uuid = get_project_id()
[docs] def files(self): """Obtain all info about the uploaded files from your project Returns: Returns a array of File objects """ headers = { 'Authorization': 'Api-Key {}'.format(get_api_key()), 'Accept-Language': 'es' } url = '{url}{path}'.format( url=get_api_url(), path=DYM_PROJECT_FILES.format(projectId=self.uuid)) r = requests.get(url, headers=headers) files = [] for v in json.loads(r.text)['results']: files.append(files.File(self, v['name'], v['metadata'])) return files