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Hackathon/Codelab vision sample (GoogleCloudPlatform#788)
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‎codelabs/flex_and_vision/README.md‎

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# Python Google Cloud Vision sample for Google App Engine Flexible Environment
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This sample demonstrates how to use the [Google Cloud Vision API](https://cloud.google.com/vision/), [Google Cloud Storage](https://cloud.google.com/storage/), and [Google Cloud Datastore](https://cloud.google.com/datastore/) on [Google App Engine Flexible Environment](https://cloud.google.com/appengine).
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## Setup
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Create a new project with the [Google Cloud Platform console](https://console.cloud.google.com/).
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Make a note of your project ID, which may be different than your project name.
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Make sure to [Enable Billing](https://pantheon.corp.google.com/billing?debugUI=DEVELOPERS)
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for your project.
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Download the [Google Cloud SDK](https://cloud.google.com/sdk/docs/) to your
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local machine. Alternatively, you could use the [Cloud Shell](https://cloud.google.com/shell/docs/quickstart), which comes with the Google Cloud SDK pre-installed.
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Initialize the Google Cloud SDK (skip if using Cloud Shell):
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gcloud init
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Create your App Engine application:
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gcloud app create
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Set an environment variable for your project ID, replacing `[YOUR_PROJECT_ID]`
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with your project ID:
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export PROJECT_ID=[YOUR_PROJECT_ID]
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## Getting the sample code
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Run the following command to clone the Github repository:
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git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git
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Change directory to the sample code location:
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cd python-docs-samples/codelabs/flex_and_vision
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## Authentication
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Enable the APIs:
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gcloud service-management enable vision.googleapis.com
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gcloud service-management enable storage-component.googleapis.com
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gcloud service-management enable datastore.googleapis.com
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Create a Service Account to access the Google Cloud APIs when testing locally:
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gcloud iam service-accounts create hackathon \
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--display-name "My Hackathon Service Account"
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Give your newly created Service Account appropriate permissions:
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gcloud projects add-iam-policy-binding ${PROJECT_ID} \
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--member serviceAccount:hackathon@${PROJECT_ID}.iam.gserviceaccount.com \
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--role roles/owner
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After creating your Service Account, create a Service Account key:
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gcloud iam service-accounts keys create ~/key.json --iam-account \
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hackathon@${PROJECT_ID}.iam.gserviceaccount.com
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Set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to point to where
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you just put your Service Account key:
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export GOOGLE_APPLICATION_CREDENTIALS="/home/${USER}/key.json"
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## Running locally
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Create a virtual environment and install dependencies:
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virtualenv -p python3 env
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source env/bin/activate
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pip install -r requirements.txt
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Create a Cloud Storage bucket. It is recommended that you name it the same as
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your project ID:
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gsutil mb gs://${PROJECT_ID}
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Set the environment variable `CLOUD_STORAGE_BUCKET`:
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export CLOUD_STORAGE_BUCKET=${PROJECT_ID}
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Start your application locally:
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python main.py
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Visit `localhost:8080` to view your application running locally. Press `Control-C`
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on your command line when you are finished.
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When you are ready to leave your virtual environment:
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deactivate
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## Deploying to App Engine
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Open `app.yaml` and replace <your-cloud-storage-bucket> with the name of your
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Cloud Storage bucket.
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Deploy your application to App Engine using `gcloud`. Please note that this may
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take several minutes.
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gcloud app deploy
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Visit `https://[YOUR_PROJECT_ID].appspot.com` to view your deployed application.

‎codelabs/flex_and_vision/app.yaml‎

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runtime: python
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env: flex
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entrypoint: gunicorn -b :$PORT main:app
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runtime_config:
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python_version: 3
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env_variables:
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CLOUD_STORAGE_BUCKET: <your-cloud-storage-bucket>

‎codelabs/flex_and_vision/main.py‎

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# Copyright 2017 Google Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# [START app]
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from datetime import datetime
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import logging
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import os
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from flask import Flask, redirect, render_template, request
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from google.cloud import datastore
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from google.cloud import storage
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from google.cloud import vision
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CLOUD_STORAGE_BUCKET = os.environ.get('CLOUD_STORAGE_BUCKET')
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app = Flask(__name__)
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@app.route('/')
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def homepage():
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# Create a Cloud Datastore client.
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datastore_client = datastore.Client()
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# Use the Cloud Datastore client to fetch information from Datastore about
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# each photo.
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query = datastore_client.query(kind='Faces')
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image_entities = list(query.fetch())
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# Return a Jinja2 HTML template and pass in image_entities as a parameter.
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return render_template('homepage.html', image_entities=image_entities)
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@app.route('/upload_photo', methods=['GET', 'POST'])
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def upload_photo():
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photo = request.files['file']
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# Create a Cloud Storage client.
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storage_client = storage.Client()
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# Get the bucket that the file will be uploaded to.
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bucket = storage_client.get_bucket(CLOUD_STORAGE_BUCKET)
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# Create a new blob and upload the file's content.
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blob = bucket.blob(photo.filename)
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blob.upload_from_string(
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photo.read(), content_type=photo.content_type)
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# Make the blob publicly viewable.
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blob.make_public()
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# Create a Cloud Vision client.
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vision_client = vision.Client()
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# Use the Cloud Vision client to detect a face for our image.
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source_uri = 'gs://{}/{}'.format(CLOUD_STORAGE_BUCKET, blob.name)
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image = vision_client.image(source_uri=source_uri)
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faces = image.detect_faces(limit=1)
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# If a face is detected, save to Datastore the likelihood that the face
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# displays 'joy,' as determined by Google's Machine Learning algorithm.
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if len(faces) > 0:
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face = faces[0]
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# Convert the face.emotions.joy enum type to a string, which will be
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# something like 'Likelihood.VERY_LIKELY'. Parse that string by the
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# period to extract only the 'VERY_LIKELY' portion.
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face_joy = str(face.emotions.joy).split('.')[1]
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else:
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face_joy = 'Unknown'
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# Create a Cloud Datastore client.
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datastore_client = datastore.Client()
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# Fetch the current date / time.
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current_datetime = datetime.now()
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# The kind for the new entity.
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kind = 'Faces'
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# The name/ID for the new entity.
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name = blob.name
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# Create the Cloud Datastore key for the new entity.
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key = datastore_client.key(kind, name)
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# Construct the new entity using the key. Set dictionary values for entity
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# keys blob_name, storage_public_url, timestamp, and joy.
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entity = datastore.Entity(key)
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entity['blob_name'] = blob.name
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entity['image_public_url'] = blob.public_url
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entity['timestamp'] = current_datetime
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entity['joy'] = face_joy
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# Save the new entity to Datastore.
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datastore_client.put(entity)
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# Redirect to the home page.
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return redirect('/')
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@app.errorhandler(500)
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def server_error(e):
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logging.exception('An error occurred during a request.')
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return """
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An internal error occurred: <pre>{}</pre>
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See logs for full stacktrace.
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""".format(e), 500
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if __name__ == '__main__':
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# This is used when running locally. Gunicorn is used to run the
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# application on Google App Engine. See entrypoint in app.yaml.
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app.run(host='127.0.0.1', port=8080, debug=True)
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# [END app]

‎codelabs/flex_and_vision/requirements.txt‎

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Flask==0.12
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gunicorn==19.6.0
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google-cloud-vision==0.22.0
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google-cloud-storage==0.22.0
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google-cloud-datastore==0.22.0

‎codelabs/flex_and_vision/templates/homepage.html‎

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<h1>Google Cloud Platform - Face Detection Sample</h1>
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<p>This Python Flask application demonstrates App Engine Flexible, Google Cloud
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Storage, Datastore, and the Cloud Vision API.</p>
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<br>
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<html>
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<body>
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<form action="upload_photo" method="POST" enctype="multipart/form-data">
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Upload File: <input type="file" name="file"><br>
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<input type="submit" name="submit" value="Submit">
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</form>
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{% for image_entity in image_entities %}
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<img src="{{image_entity['image_public_url']}}" width=200 height=200>
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<p>{{image_entity['blob_name']}} was uploaded {{image_entity['timestamp']}}.</p>
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<p>Joy Likelihood for Face: {{image_entity['joy']}}</p>
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{% endfor %}
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</body>
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</html>

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