Merge branch 'main' of github.com:roboflow-ai/roboflow-python into feature/sequenced_uploads_support

Author: bigbitbus

.DS_Store

@@ -0,0 +1,3 @@
+ROBOFLOW_API_KEY=your-roboflow-private-api-key
+PROJECT_NAME=your-project-name
+PROJECT_VERSION=1

.env-example

@@ -0,0 +1,32 @@
+name: Publish WorkFlow
+
+on:
+  release:
+    types: [created]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.8]
+    steps:
+      - name: 🛎️ Checkout
+        uses: actions/checkout@v3
+        with:
+          ref: ${{ github.head_ref }}
+      - name: 🐍 Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: 🦾 Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install ".[dev]"
+      - name: 🚀 Publish to PyPi
+        env: 
+          PYPI_USERNAME: ${{ secrets.PYPI_USERNAME }}
+          PYPI_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
+          PYPI_TEST_PASSWORD: ${{ secrets.PYPI_TEST_PASSWORD }}
+        run: |
+          make publish -e PYPI_USERNAME=$PYPI_USERNAME -e PYPI_PASSWORD=$PYPI_PASSWORD -e PYPI_TEST_PASSWORD=$PYPI_TEST_PASSWORD

.github/workflows/publish.yml

@@ -1,8 +1,10 @@
 name: Test WorkFlow
 
 on:
+  push:
+    branches: [main]
   pull_request:
-    branches: [master]
+    branches: [main]
 
 jobs:
   build:
@@ -15,7 +17,8 @@ jobs:
       - name: 🛎️ Checkout
         uses: actions/checkout@v3
         with:
-          ref: ${{ github.head_ref }}
+          ref: ${{ github.event.pull_request.head.ref }}
+          repository: ${{ github.event.pull_request.head.repo.full_name }}
       - name: 🐍 Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v2
         with:

.github/workflows/test.yml

@@ -150,4 +150,5 @@ train/
 valid/
 data.yaml
 README.roboflow.txt
-*.zip
+*.zip
+.DS_Store

.gitignore

@@ -0,0 +1,7 @@
+ARG PYTHON_VERSION=3.8
+FROM python:$PYTHON_VERSION-slim
+RUN apt-get update && apt-get install make && rm -rf /var/lib/apt/lists/*
+WORKDIR /workspace
+COPY . .
+RUN pip install -e ".[dev]"
+ENTRYPOINT [ "/bin/bash" ]

Dockerfile.dev

@@ -14,4 +14,9 @@ check_code_quality:
 	flake8 $(check_dirs) --count --select=E9,F63,F7,F82 --show-source --statistics
 	# exit-zero treats all errors as warnings. E203 for black, E501 for docstring, W503 for line breaks before logical operators 
 	flake8 $(check_dirs) --count --max-line-length=88 --exit-zero  --ignore=D --extend-ignore=E203,E501,W503  --statistics
-	
+	
+publish:
+	python setup.py sdist bdist_wheel
+	twine upload -r testpypi dist/* -u ${PYPI_USERNAME} -p ${PYPI_TEST_PASSWORD} --verbose 
+	twine check dist/*
+	twine upload dist/* -u ${PYPI_USERNAME} -p ${PYPI_PASSWORD} --verbose 

Makefile

@@ -11,32 +11,32 @@
 the official Roboflow python package that interfaces with the [Roboflow API](https://docs.roboflow.com). Key features of
 Roboflow:
 
-- Import and Export image datasets into any supported [formats](https://roboflow.com/formats)
+- Import and Export image datasets into any supported [format](https://roboflow.com/formats)
 - [Preprocess](https://docs.roboflow.com/image-transformations/image-preprocessing)
   and [augment](https://docs.roboflow.com/image-transformations/image-augmentation) data using Roboflow's dataset
   management tools
 - Train computer vision models using [Roboflow Train](https://docs.roboflow.com/train) and deploy
   to [production](https://docs.roboflow.com/inference)
 - Use [community curated projects](https://universe.roboflow.com/) to start building your own vision-powered products
 
-## Installation:
+## Installation
 
 To install this package, please use `Python 3.6` or higher. We provide three different ways to install the Roboflow
 package to use within your own projects.
 
 Install from PyPi (Recommended):
 
-```
+```bash
 pip install roboflow
 ```
 
 Install from Source:
 
-```
+```bash
 git clone https://github.com/roboflow-ai/roboflow-python.git
 cd roboflow-python
 python3 -m venv env
-source env/bin/activate 
+source env/bin/activate
 pip3 install -r requirements.txt
 ```
 
@@ -51,7 +51,7 @@ rf = roboflow.Roboflow(api_key=YOUR_API_KEY_HERE)
 # List all projects for your workspace
 workspace = rf.workspace()
 
-# Load a certain project, workspace url is optional
+# Load a certain project (workspace url is optional)
 project = rf.project("PROJECT_ID")
 
 # List all versions of a specific project
@@ -61,7 +61,7 @@ project.versions()
 project.upload("UPLOAD_IMAGE.jpg")
 
 # Retrieve the model of a specific project
-project.version("1").model
+model = project.version("1").model
 
 # predict on a local image
 prediction = model.predict("YOUR_IMAGE.jpg")
@@ -79,49 +79,109 @@ prediction.json()
 prediction.save(output_path='predictions.jpg')
 ```
 
-## Using this package for a specifc project
+## Using this package for a specific project
 
-If you have a specific project from your workspace you'd like to run in a notebook follow along on this tutorial [Downloading Datasets from Roboflow for Training (Python)](https://www.youtube.com/watch?v=76E6esnez8E)
+If you have a specific project from your workspace you'd like to run in a notebook, follow along with this tutorial: [Downloading Datasets from Roboflow for Training (Python)](https://www.youtube.com/watch?v=76E6esnez8E).
 
 Selecting the format you'd like your project to be exported as while choosing the `show download code` option will display code snippets you can use in either Jupyter or your terminal. These code snippets will include your `api_key`, project, and workspace names.
 
 ![Alt Text](https://media.giphy.com/media/I5g06mUnVzdX7iT2Gf/giphy.gif)
 
-## Deving locally
-1. clone repo
-2. create a virtual env: `virtualenv local_dev`
-3. activate virtual env: `source local_dev/bin/activate`
-4. install packages from repo: `pip3 install -e ".[dev]"`
-5. create script on top level of `ROBOFLOW-PYTHON` and reference local `roboflow` directory: `from roboflow import Roboflow`
-6. when done, uptick the pip package minor version number in `setup.py`
-7. manually add any new dependencies to the `requirements.txt` and list of dependencies in `setup.py` (careful not to overwrite any packages that might screw up backwards dependencies for object detection, etc.) 
+## Developing locally
 
-### Code Quality
+### Using Docker
 
-We provide a `Makefile` to format and ensure code quality. **Be sure to run them before creating a PR**.
+To set the Docker container up for the first time:
 
+```bash
+# Clone this repo
+git clone [email protected]:roboflow-ai/roboflow-python.git && cd roboflow-python
+
+# Copy the environment variables template
+# Be sure to update the values with your account's information
+
+# Build our development image
+docker build -t roboflow-python -f Dockerfile.dev .
+
+# Run container and map current folder in it
+docker run --rm -it \
+  -v $(pwd)/:/workspace/ \
+  --env-file .env \
+  roboflow-python
+
+# Run tests
+python -m unittest
 ```
-# format your code with `black` and `isort` run
-make style
-# check code with flake8
-make check_code_quality
+
+#### Change Python version
+
+You can pass the build arg `PYTHON_VERSION` to dynamically change python version at build time
+
+```bash
+docker build  -t roboflow-python --build-arg PYTHON_VERSION=3.9 -f Dockerfile.dev .
+```
+
+Will use `python:3.9-slim`
+
+**Note** If you are using [VSCode](https://code.visualstudio.com/) we recommend you read the ["Developing inside a Container"](https://code.visualstudio.com/docs/remote/containers) tutorial.
+
+### Using Virtualenv
+
+```bash
+# Clone this repo
+git clone [email protected]:roboflow-ai/roboflow-python.git && cd roboflow-python
+
+# create virtual env
+virtualenv local_dev
+
+# activate virtual env
+source local_dev/bin/activate
+
+# install dependencies
+pip3 install -e ".[dev]"
 ```
 
-**Note** This tests will be run automatically when you commit thanks to git hooks.
+### Testing
 
-### Tests
+Make sure you have your `virtualenv` spun up before running tests. Execute the `unittest` command at the `/root` level directory.
 
-You need to have the following `env` variables, 
+Run tests:
 
+```bash
+python -m unittest
 ```
-ROBOFLOW_API_KEY="<YOUR_ROBOFLOW_API>"
-PROJECT_NAME="<YOUR_PROJECT_NAME>"
-PROJECT_VERSION="" # by default "1"
+
+### Contributing
+
+1. Increment the pip package minor version number in `setup.py`
+1. Manually add any new dependencies to `requirements.txt` with a version such as `chardet==4.0.0` and list of dependencies in `setup.py` (Be careful not to overwrite any packages that might screw up backwards dependencies for object detection, etc.)
+
+### Code Quality
+
+When creating new functions, please follow the [Google style Python docstrings](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html). See example below:
+
+```python
+def example_function(param1: int, param2: str) -> bool:
+    """Example function that does something.
+
+    Args:
+        param1: The first parameter.
+        param2: The second parameter.
+
+    Returns:
+        The return value. True for success, False otherwise.
+
+    """
 ```
 
-Then, assuming you have installed the `dev` packages (`pip install -e ".[dev]`), you can run the tests
+We provide a `Makefile` to format and ensure code quality. **Be sure to run them before creating a PR**.
 
 ```bash
+# format code with `black` and `isort`
+make style
 
-python -m unittest
+# check code with flake8
+make check_code_quality
 ```
+
+**Note** These tests will be run automatically when you commit thanks to git hooks.

README.md

@@ -1,8 +1,8 @@
-certifi==2021.5.30
+certifi==2022.12.7
 chardet==4.0.0
 cycler==0.10.0
 idna==2.10
-kiwisolver==1.3.1
+kiwisolver>=1.3.1
 matplotlib
 numpy>=1.18.5
 opencv-python>=4.1.2
@@ -12,9 +12,9 @@ python-dateutil
 python-dotenv
 requests
 six
-urllib3==1.26.6
+urllib3>=1.26.6
 wget
 tqdm>=4.41.0
 PyYAML>=5.3.1
 wget
-requests_toolbelt
+requests_toolbelt

requirements.txt

@@ -1,14 +1,17 @@
 import json
 import sys
+import time
 
 import requests
 
 from roboflow.config import API_URL, APP_URL, DEMO_KEYS
 from roboflow.core.project import Project
 from roboflow.core.workspace import Workspace
 
+__version__ = "0.2.29"
 
-def check_key(api_key, model, notebook):
+
+def check_key(api_key, model, notebook, num_retries=0):
     if type(api_key) is not str:
         raise RuntimeError(
             "API Key is of Incorrect Type \n Expected Type: "
@@ -26,11 +29,23 @@ def check_key(api_key, model, notebook):
         else:
             # validate key normally
             response = requests.post(API_URL + "/?api_key=" + api_key)
-            r = response.json()
 
-            if "error" in r or response.status_code != 200:
+            if response.status_code == 401:
                 raise RuntimeError(response.text)
+
+            if response.status_code != 200:
+                # retry 5 times
+                if num_retries < 5:
+                    print("retrying...")
+                    time.sleep(1)
+                    num_retries += 1
+                    return check_key(api_key, model, notebook, num_retries)
+                else:
+                    raise RuntimeError(
+                        "There was an error validating the api key with Roboflow server."
+                    )
             else:
+                r = response.json()
                 return r
     else:  # then you're using a dummy key
         sys.stdout.write(