Cleaning up various housekeeping issues (#31)

* Move samples images to new 'samples' folder

* new COS and max-base

* Use prod bucket

* Add license headers

* Test pip install from reqs file

* Fix flake8 issues

* Ignore qa

* Change 'assets' to 'samples' in README

* Update README.md

* Add tutorial button

* Add license header to Dockerfile

.travis.yml

@@ -10,8 +10,9 @@ install:
   - docker run -it -d -p 5000:5000 max-scene-classifier
 
 before_script:
-  - pip install pytest requests
+  - pip install -r test-requirements.txt
   - sleep 30
 
 script:
+  - flake8 . --max-line-length=127
   - pytest tests/test.py

Dockerfile

@@ -1,6 +1,22 @@
-FROM codait/max-base:v1.1.1
-
-ARG model_bucket=http://max-assets.s3.us.cloud-object-storage.appdomain.cloud/scene-classifier/1.0
+#
+# Copyright 2018-2019 IBM Corp. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+FROM codait/max-base:v1.1.3
+
+ARG model_bucket=https://s3.us-south.cloud-object-storage.appdomain.cloud/max-assets-prod/max-scene-classifier/1.0.1
 ARG model_file=assets.tar.gz
 
 WORKDIR /workspace

README.md

@@ -1,10 +1,12 @@
 [![Build Status](https://travis-ci.org/IBM/MAX-Scene-Classifier.svg?branch=master)](https://travis-ci.org/IBM/MAX-Scene-Classifier) [![Website Status](https://img.shields.io/website/http/max-scene-classifier.max.us-south.containers.appdomain.cloud/swagger.json.svg?label=api+demo)](http://max-scene-classifier.max.us-south.containers.appdomain.cloud/)
 
+[<img src="docs/deploy-max-to-ibm-cloud-with-kubernetes-button.png" width="400px">](http://ibm.biz/max-to-ibm-cloud-tutorial)
+
 # IBM Code Model Asset Exchange: Scene Classifier
 
 This repository contains code to instantiate and deploy an image classification model. This model recognizes the 365 different classes of scene/location in the [Places365-Standard subset of the Places2 Dataset](http://places2.csail.mit.edu/). The model is based on the [Places365-CNN Model](https://github.com/CSAILVision/places365) and consists of a pre-trained deep convolutional net using the ResNet architecture, trained on the [ImageNet-2012](http://www.image-net.org/challenges/LSVRC/2012/) data set. The pre-trained model is then fine-tuned on the Places365-Standard dataset. The input to the model is a 224x224 image, and the output is a list of estimated class probabilities.
 
-The specific model variant used in this repository is the [PyTorch Places365 ResNet18 Model](https://github.com/CSAILVision/places365#pre-trained-cnn-models-on-places365-standard). The model files are hosted on [IBM Cloud Object Storage](http://max-assets.s3.us.cloud-object-storage.appdomain.cloud/scene-classifier/1.0/assets.tar.gz). The code in this repository deploys the model as a web service in a Docker container. This repository was developed as part of the [IBM Code Model Asset Exchange](https://developer.ibm.com/code/exchanges/models/).
+The specific model variant used in this repository is the [PyTorch Places365 ResNet18 Model](https://github.com/CSAILVision/places365#pre-trained-cnn-models-on-places365-standard). The model files are hosted on [IBM Cloud Object Storage](https://s3.us-south.cloud-object-storage.appdomain.cloud/max-assets-prod/max-scene-classifier/1.0.1/assets.tar.gz). The code in this repository deploys the model as a web service in a Docker container. This repository was developed as part of the [IBM Code Model Asset Exchange](https://developer.ibm.com/code/exchanges/models/) and the public API is powered by [IBM Cloud](https://ibm.biz/Bdz2XM).
 
 ## Model Metadata
 | Domain | Application | Industry  | Framework | Training Data | Input Data Format |
@@ -27,7 +29,7 @@ using Places Database"](http://places.csail.mit.edu/places_NIPS14.pdf), Advances
 | This repository | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) | [LICENSE](LICENSE) |
 | Model Weights | [CC BY License](https://creativecommons.org/licenses/by/4.0/) | [Places365-CNN](https://github.com/CSAILVision/places365)|
 | Model Code (3rd party) | [MIT](https://opensource.org/licenses/MIT) | [Places365-CNN](https://github.com/CSAILVision/places365)|
-| Test assets | Various | [Asset README](assets/README.md) |
+| Test assets | Various | [Asset README](samples/README.md) |
 
 ## Pre-requisites:
 
@@ -63,6 +65,8 @@ $ kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Scene-Classifier/ma
 
 The model will be available internally at port `5000`, but can also be accessed externally through the `NodePort`.
 
+A more elaborate tutorial on how to deploy this MAX model to production on [IBM Cloud](https://ibm.biz/Bdz2XM) can be found [here](http://ibm.biz/max-to-ibm-cloud-tutorial).
+
 ## Run Locally
 
 1. [Build the Model](#1-build-the-model)
@@ -105,14 +109,14 @@ $ docker run -it -p 5000:5000 max-scene-classifier
 
 The API server automatically generates an interactive Swagger documentation page. Go to `http://localhost:5000` to load it. From there you can explore the API and also create test requests.
 
-Use the `model/predict` endpoint to load a test image (you can use one of the test images from the `assets` folder) and get predicted labels for the image from the API.
+Use the `model/predict` endpoint to load a test image (you can use one of the test images from the `samples` folder) and get predicted labels for the image from the API.
 
 ![Swagger Doc Screenshot](docs/swagger-screenshot.png)
 
 You can also test it on the command line, for example:
 
 ```
-$ curl -F "image=@assets/aquarium.jpg" -XPOST http://localhost:5000/model/predict
+$ curl -F "image=@samples/aquarium.jpg" -XPOST http://localhost:5000/model/predict
 ```
 
 You should see a JSON response like that below:

api/__init__.py

@@ -1,2 +1,17 @@
-from .metadata import ModelMetadataAPI
-from .predict import ModelPredictAPI, ModelLabelsAPI
+#
+# Copyright 2018-2019 IBM Corp. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from .metadata import ModelMetadataAPI  # noqa
+from .predict import ModelPredictAPI, ModelLabelsAPI  # noqa

api/metadata.py

@@ -1,10 +1,27 @@
+#
+# Copyright 2018-2019 IBM Corp. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
 from core.model import ModelWrapper
 
 from maxfw.core import MAX_API, MetadataAPI, METADATA_SCHEMA
 
+
 class ModelMetadataAPI(MetadataAPI):
 
     @MAX_API.marshal_with(METADATA_SCHEMA)
     def get(self):
         """Return the metadata associated with the model"""
-        return ModelWrapper.MODEL_META_DATA
+        return ModelWrapper.MODEL_META_DATA

api/predict.py

@@ -1,8 +1,24 @@
+#
+# Copyright 2018-2019 IBM Corp. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
 from core.model import ModelWrapper, read_image
 
 from maxfw.core import MAX_API, PredictAPI, MetadataAPI
 
-from flask_restplus import Namespace, Resource, fields
+from flask_restplus import fields
 from werkzeug.datastructures import FileStorage
 
 
@@ -20,6 +36,7 @@
     'labels': fields.List(fields.Nested(model_label), description='Class labels that can be predicted by the model')
 })
 
+
 class ModelLabelsAPI(MetadataAPI):
     '''API for getting information about available class labels'''
 
@@ -35,7 +52,7 @@ def get(self):
         return result
 
 
-# === Predict API 
+# === Predict API
 
 label_prediction = MAX_API.model('LabelPrediction', {
     'label_id': fields.String(required=False, description='Label identifier'),
@@ -50,8 +67,9 @@ def get(self):
 
 # set up parser for image input data
 image_parser = MAX_API.parser()
-image_parser.add_argument('image', type=FileStorage, location='files', required=True, 
-                            help='An image file (encoded as PNG or JPG/JPEG)')
+image_parser.add_argument('image', type=FileStorage, location='files', required=True,
+                          help='An image file (encoded as PNG or JPG/JPEG)')
+
 
 class ModelPredictAPI(PredictAPI):
 
@@ -66,9 +84,9 @@ def post(self):
         image_data = args['image'].read()
         image = read_image(image_data)
         preds = model_wrapper.predict(image)
-        
+
         label_preds = [{'label_id': p[0], 'label': p[1], 'probability': p[2]} for p in [x for x in preds]]
         result['predictions'] = label_preds
         result['status'] = 'ok'
 
-        return result   
+        return result

app.py

@@ -1,3 +1,19 @@
+#
+# Copyright 2018-2019 IBM Corp. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
 from maxfw.core import MAXApp
 from api import ModelMetadataAPI, ModelPredictAPI, ModelLabelsAPI
 from config import API_TITLE, API_DESC, API_VERSION
@@ -6,4 +22,4 @@
 max_app.add_api(ModelMetadataAPI, '/metadata')
 max_app.add_api(ModelLabelsAPI, '/labels')
 max_app.add_api(ModelPredictAPI, '/predict')
-max_app.run()
+max_app.run()