new roboflow python package

.gitignore

@@ -1,5 +1,3 @@
-test.py
-
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

README.md

@@ -1,79 +1 @@
-# Roboflow Python Library
-
-This is a helper library to load your [Roboflow](https://roboflow.ai) datasets
-into your python scripts and Jupyter notebooks.
-
-## Requirements
-
-This package requires python >=3.6 and a (free)
-[Roboflow](https://roboflow.ai) account.
-
-## Installing
-
-#### With PIP
-```sh
-pip install roboflow
-```
-
-#### With Anaconda
-```sh
-conda install roboflow
-```
-
-## Setup
-
-The `roboflow` package works in conjunction with your
-[Roboflow](https://roboflow.ai) account.
-
-From your `Account` page, click `Roboflow Keys` to get your API key.
-You can then use the `roboflow` python package to manage downloading
-your datasets in various formats.
-
-```python
-import roboflow
-roboflow.auth("<<YOUR API KEY>>")
-info = roboflow.load("chess-sample", 1, "tfrecord")
-
-# dataset is now downloaded and unzipped in your current directory
-# and info contains the paths you need to load it into your favorite
-# machine learning libraries
-```
-
-By default the folder is named
-```
-${dataset-name}.${version-number}-${version-name}.${format}
-```
-(For example, `Chess Sample.v1-small-gray.coco`).
-
-The file hierarchy is three folders containing the `train`, `valid`, and `test`
-data you selected in the Roboflow upload flow (and the format you specified
-in `roboflow.load` above). There is also a `README.roboflow.txt` describing the preprocessing and augmentation steps and, optionally, a `README.dataset.txt`
-provided by the person who shared the dataset.
-
-![Example file layout](img/file-layout.png)
-
-## Doing Inference
-It's important to pre-process your images for inference the same way you
-pre-processed your training images. For this, get a pre-processor via the
-`roboflow.infer` method which will return a function you can use to pre-process
-your images.
-
-```python
-import roboflow
-roboflow.auth("<<YOUR API KEY>>")
-process = roboflow.preprocessor("chess-sample", 1)
-images = process.fromFile("example.jpg") # returns a numpy array (of 1 image, unless you used tiling)
-```
-
-## Benefits
-
-This package currently provides two main benefits over downloading and loading
-your datasets manually.
-
-1. If you have previously loaded your dataset, it will automatically use the local copy rather than re-downloading.
-2. You can dynamically choose the export format at runtime rather than export-time.
-
-## Roadmap
-
-We plan to include more features in the future to allow you (for example,
-to let you easily do inference on your trained models).
+# Roboflow Python Library

meta.yaml

@@ -11,4 +11,4 @@ requirements:
 about:
   license: Apache
   license_file: LICENSE
-  summary: Loader for Roboflow datasets.
+  summary: The official Roboflow Python Package

pyproject.toml

@@ -0,0 +1,6 @@
+[build-system]
+requires = [
+    "setuptools>=57",
+    "wheel"
+]
+build-backend = "setuptools.build_meta"

requirements.txt

@@ -0,0 +1,15 @@
+certifi==2021.5.30
+chardet==4.0.0
+cycler==0.10.0
+idna==2.10
+kiwisolver==1.3.1
+matplotlib==3.4.2
+numpy==1.21.0
+opencv-python==4.5.3.56
+Pillow==8.3.0
+pyparsing==2.4.7
+python-dateutil==2.8.1
+python-dotenv==0.18.0
+requests==2.25.1
+six==1.16.0
+urllib3==1.26.6

roboflow/__init__.py

@@ -1,77 +1,53 @@
+import json
 import time
+
 import requests
 
-API_URL = "https://api.roboflow.ai"
-_token = None
-_token_expires = 0
+from roboflow.core.project import Project
 
-def token():
-    global _token
-    return _token
+TOKEN = None
+API_URL = "http://localhost:5000"
+TOKEN_EXPIRES = None
+USER_API_KEY = ""
 
-def auth(api_key):
-    global _token
 
+def auth(api_key):
+    global TOKEN, TOKEN_EXPIRES
+    global USER_API_KEY
+    USER_API_KEY = api_key
     response = requests.post(API_URL + "/token", data=({
         "api_key": api_key
     }))
 
-    r = response.json();
-    if "error" in r:
-        raise RuntimeError(response.text)
-
-    _token = r["token"]
-    _token_expires = time.time() + r["expires_in"]
-
-    return r
-
-def dataset(name):
-    global _token
-
-    if not _token:
-        raise Exception("You must first auth with your API key to call this method.")
-
-    response = requests.get(API_URL + "/dataset/" + name, params=({
-        "access_token": _token
-    }))
-
-    r = response.json();
-    if "error" in r:
-        raise RuntimeError(response.text)
-
-    return r
-
-def version(dataset_name, version_id):
-    global _token
-
-    if not _token:
-        raise Exception("You must first auth with your API key to call this method.")
-
-    response = requests.get(API_URL + "/dataset/" + dataset_name + '/' + str(version_id), params=({
-        "access_token": _token
-    }))
-
-    r = response.json();
-    if "error" in r:
+    r = response.json()
+    if "error" in r or response.status_code != 200:
         raise RuntimeError(response.text)
 
-    return r
+    TOKEN = r['token']
+    TOKEN_EXPIRES = r['expires_in']
+    return Roboflow(api_key, TOKEN, TOKEN_EXPIRES)
 
-def export(dataset_name, version_id, format):
-    global _token
 
-    if not _token:
-        raise Exception("You must first auth with your API key to call this method.")
+class Roboflow():
+    def __init__(self, api_key, access_token, token_expires):
+        self.api_key = api_key
+        self.access_token = access_token
+        self.token_expires = token_expires
 
-    response = requests.get(API_URL + "/dataset/" + dataset_name + '/' + str(version_id) + '/' + format, params=({
-        "access_token": _token
-    }))
+    def list_workspaces(self):
+        workspaces = requests.get(API_URL + '/workspaces?access_token=' + self.access_token).json()
+        print(json.dumps(workspaces, indent=2))
+        return workspaces
 
-    r = response.json();
-    if "error" in r:
-        raise RuntimeError(response.text)
+    def load_workspace(self):
+        pass
 
-    return r
+    def load(self, dataset_slug):
+        # TODO: Change endpoint once written
+        LOAD_ENDPOINT = "ENDPOINT_TO_GET_DATSET_INFO" + dataset_slug
+        response = requests.get(LOAD_ENDPOINT).json()
+        return Project(self.api_key, response['dataset_slug'], response['type'], response['exports'])
 
-def load(dataset, *args):
-    print(f"loading {dataset} {args}")
+    def __str__(self):
+        json_value = {'api_key': self.api_key, 'auth_token': self.access_token, 'token_expires': self.token_expires}
+        return json.dumps(json_value, indent=2)