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:
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pipinstallalgorithmia==0.9.3In [1]:
importAlgorithmiaimportpprintpp=pprint.PrettyPrinter(indent=2)2. Authentication¶
You only need your Algorithmia API Key to run the following commands.
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API_KEY='YOUR_API_KEY'# Create a client instanceclient=Algorithmia.client(API_KEY)In [3]:
fromIPython.displayimportImageface_url='https://s3.amazonaws.com/algorithmia-assets/data-science-ipython-notebooks/face.jpg'# Sample Face ImageImage(url=face_url)Out[3]:
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Algorithmia.apiKey='Simple '+API_KEYinput=[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 ObjectDetectionWithModelsresult_image_data_api_path='.algo/opencv/ObjectDetectionWithModels/temp/face_result.jpg'# Result Image with coordinates for the detected face regionresult_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 ImageImage(data=result_file)Out[4]:
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# Show detected face region coordinatesprint'Detected face region coordinates: '+result_coordDetected 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.
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# Get a Wikipedia article as contentwiki_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
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# Summarize the Wikipedia articleclient=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']))printsummary['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"...
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# Get up to 20 random Wikipedia articlesclient=Algorithmia.client(API_KEY)algo=client.algo('web/WikipediaParser/0.1.0')random_wiki_article_names=algo.pipe({"random":20})random_wiki_articles=[]forarticle_nameinrandom_wiki_article_names:try:article_content=algo.pipe(article_name)['content']random_wiki_articles.append(article_content)except:passprint'Number of Wikipedia articles scraped: '+str(len(random_wiki_articles))Number of Wikipedia articles scraped: 17
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# Find topics from 20 random Wikipedia articlesalgo=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]:
fromIPython.displayimportImagebusinesscard_url='https://s3.amazonaws.com/algorithmia-assets/data-science-ipython-notebooks/businesscard.jpg'# Sample ImageImage(url=businesscard_url)Out[10]:
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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'diego@algorithmia.com': 83, u'doppenheimer': 79, u'o': 77}, u'result': u' \n \n \n \nAALGORITHMIA \nDIEGO \nOPPENHEIMER \nCEO \ndiego@algorithmia.com \no \n@doppenhe \n206.552.9054 \nQ \ndoppenheimer \n \n \n'} 