This notebook was prepared by Algorithmia. Source and license info is on GitHub.
Algorithmia¶
Reference: Algorithmia Documentation
Table of Contents:
- Installation
- Authentication
- Face Detection
- Content Summarizer
- Latent Dirichlet Allocation
- Optical Character Recognition
1. Installation¶
You need to have the algorithmia package (version 0.9.3) installed for this notebook.
You can install the package using the pip package manager:
In [ ]:
pip install algorithmia==0.9.3
In [1]:
import Algorithmia
import pprint
pp = pprint.PrettyPrinter(indent=2)
2. Authentication¶
You only need your Algorithmia API Key to run the following commands.
In [2]:
API_KEY = 'YOUR_API_KEY'
# Create a client instance
client = Algorithmia.client(API_KEY)
In [3]:
from IPython.display import Image
face_url = 'https://s3.amazonaws.com/algorithmia-assets/data-science-ipython-notebooks/face.jpg'
# Sample Face Image
Image(url=face_url)
Out[3]:
In [4]:
Algorithmia.apiKey = 'Simple ' + API_KEY
input = [face_url, "data://.algo/temp/face_result.jpg"]
algo = client.algo('opencv/FaceDetection/0.1.8')
algo.pipe(input)
# Result Image is in under another algorithm name because FaceDetection calls ObjectDetectionWithModels
result_image_data_api_path = '.algo/opencv/ObjectDetectionWithModels/temp/face_result.jpg'
# Result Image with coordinates for the detected face region
result_coord_data_api_path = '.algo/opencv/ObjectDetectionWithModels/temp/face_result.jpgrects.txt'
result_file = Algorithmia.file(result_image_data_api_path).getBytes()
result_coord = Algorithmia.file(result_coord_data_api_path).getString()
# Show Result Image
Image(data=result_file)
Out[4]:
In [5]:
# Show detected face region coordinates
print 'Detected face region coordinates: ' + result_coord
Detected face region coordinates: 103 88 280 280
4. Content Summarizer¶
SummarAI is an advanced content summarizer with the option of generating context-controlled summaries. It is based on award-winning patented methods related to artificial intelligence and vector space developed at Lawrence Berkeley National Laboratory.
In [6]:
# Get a Wikipedia article as content
wiki_article_name = 'Technological Singularity'
client = Algorithmia.client(API_KEY)
algo = client.algo('web/WikipediaParser/0.1.0')
wiki_page_content = algo.pipe(wiki_article_name)['content']
print 'Wikipedia article length: ' + str(len(wiki_page_content))
Wikipedia article length: 39683
In [7]:
# Summarize the Wikipedia article
client = Algorithmia.client(API_KEY)
algo = client.algo('SummarAI/Summarizer/0.1.2')
summary = algo.pipe(wiki_page_content.encode('utf-8'))
print 'Wikipedia generated summary length: ' + str(len(summary['summarized_data']))
print summary['summarized_data']
Wikipedia generated summary length: 406 The term was popularized by mathematician, computer scientist and science fiction author Vernor Vinge, who argues that artificial intelligence, human biological enhancement, or brain computer interfaces could be possible causes of the singularity...The technological singularity is a hypothetical event related to the advent of genuine artificial general intelligence also known as quote_tokenstrong AI"...
In [8]:
# Get up to 20 random Wikipedia articles
client = Algorithmia.client(API_KEY)
algo = client.algo('web/WikipediaParser/0.1.0')
random_wiki_article_names = algo.pipe({"random":20})
random_wiki_articles = []
for article_name in random_wiki_article_names:
try:
article_content = algo.pipe(article_name)['content']
random_wiki_articles.append(article_content)
except:
pass
print 'Number of Wikipedia articles scraped: ' + str(len(random_wiki_articles))
Number of Wikipedia articles scraped: 17
In [9]:
# Find topics from 20 random Wikipedia articles
algo = client.algo('nlp/LDA/0.1.0')
input = {"docsList": random_wiki_articles, "mode": "quality"}
topics = algo.pipe(input)
pp.pprint(topics)
[ { u'album': 9,
u'bomfunk': 10,
u'kurepa': 13,
u'mathematics': 9,
u"mc's": 9,
u'music': 9,
u'university': 21,
u'zagreb': 9},
{ u'british': 23,
u'hindu': 115,
u'india': 47,
u'indian': 33,
u'movement': 24,
u'national': 22,
u'political': 28,
u'rss': 29},
{ u'berlin': 9,
u'film': 26,
u'gotcha': 12,
u'jonathan': 33,
u'sasha': 23,
u'states': 8,
u'township': 15,
u'united': 8},
{ u'belfast': 8,
u'building': 6,
u'church': 12,
u'history': 7,
u'incumbent': 7,
u'james': 5,
u"rev'd": 7,
u'worship': 5}]
In [10]:
from IPython.display import Image
businesscard_url = 'https://s3.amazonaws.com/algorithmia-assets/data-science-ipython-notebooks/businesscard.jpg'
# Sample Image
Image(url=businesscard_url)
Out[10]:
In [11]:
input = {"src": businesscard_url,
"hocr":{
"tessedit_create_hocr":1,
"tessedit_pageseg_mode":1,
"tessedit_char_whitelist":"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-@/.,:()"}}
algo = client.algo('tesseractocr/OCR/0.1.0')
pp.pprint(algo.pipe(input))
{ u'compound': { u'': 95,
u'206.552.9054': 84,
u'@doppenhe': 85,
u'AALGORITHMIA': 53,
u'CEO': 88,
u'DIEGO': 88,
u'OPPENHEIMER': 88,
u'Q': 55,
u'[email protected]': 83,
u'doppenheimer': 79,
u'o': 77},
u'result': u' \n \n \n \nAALGORITHMIA \nDIEGO \nOPPENHEIMER \nCEO \[email protected] \no \n@doppenhe \n206.552.9054 \nQ \ndoppenheimer \n \n \n'}