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data2Insights-1.0
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تبلیغات ما
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مشاهده بیشتر
تبلیغات ما
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تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Data2Insights package
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | data2Insights-1.0 |
| نام | data2Insights |
| نسخه کتابخانه | 1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Deepika |
| ایمیل نویسنده | <mekalabhagyadeepika@gmail.com> |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/data2Insights/ |
| مجوز | - |
Python Package
Data2Insights of python packages can be used to integrate the data2insights services with your applications to enhance services with the data2insighs services. You can use this package easily in the popular framworks and libraries(EX:Python,Flask). Form the below installation command you can download the package.
Library Installation
> pip3 install data2insights
Python Text Analytics Models
The Text analysis models can take a text as the input and gives the appropriate predicted results for that model.The input text should contain only alphabets (or) combination of both alphabets and special characters (or) combination of both alphabets and digits,then user gets the predicted result for that model .
Emotion Model
The Emotion model of data2insights will take text as the input and returns the type of emotion of the given text as the response object.
Method Name :
classifiers.emotion(data='provide input text'); # for single text
batchclassifiers.emotion(column='provide column number',files={'file': open('provide full path of file', 'rb')}); # for batch process
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.emotion(data='provide input text')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Emotion",
"classifications": {
"Prediction_accuracy": "36.04",
"emotion": "Happy",
"given_text": "hi"
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "829",
"query_limit_request": 1,
"status_code": 200
}
Example for batch text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.batchclassifiers.emotion(column='provide column number',files={'file': open('provide full path of file', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Processed_file location": "./outputfiles/3/emotion/data371.csv",
"msg": "file_processing_completed",
"status_code": 200
}
Sentiment Model
The Sentiment model of data2insights will take text as the input and return the type of sentiment of the given text as the response object.
Method Name :
classifiers.sentiment(data='provide input text'); # for single text
batchclassifiers.sentiment(column='provide column number',files={'file': open('provide full path of file', 'rb')}); # for batch process
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.sentiment(data='provide input text')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Sentiment",
"classifications": {
"Prediction_accuracy": "99.59",
"given_text": "hi",
"sentiment": "Neutral"
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "830",
"query_limit_request": 1,
"status_code": 200
}
Example for batch text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.batchclassifiers.sentiment(column='provide column number',files={'file': open('provide full path of file', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Processed_file location": "./outputfiles/3/sentiment/data372.csv",
"msg": "file_processing_completed",
"status_code": 200
}
Topic Model
The Topic model of data2insights will take text as the input and return the type of topic of the given text as the response object.
Method Name :
classifiers.topic(data='provide input text'); # for single text
batchclassifiers.topic(column='provide column number',files={'file': open('provide full path of file', 'rb')}); # for batch process
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.topic(data='provide input text')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Topic",
"classifications": {
"Prediction_accuracy": "38.10",
"given_text": "hi",
"topic": "beauty_style"
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "815",
"query_limit_request": 1,
"status_code": 200
}
Example for batch text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.batchclassifiers.topic(column='provide column number',files={'file': open('provide full path of file', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Processed_file location": "./outputfiles/3/topic/data385.csv",
"msg": "file_processing_completed",
"status_code": 200
}
Spam Model
The Spam model of data2insights will take text as the input and return whether the text is spam or not.
Method Name :
classifiers.spam(data='provide input text'); # for single text
batchclassifiers.spam(column='provide column number',files={'file': open('provide full path of file', 'rb')}); # for batch process
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.spam(data='provide input text')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Spam",
"classifications": {
"Prediction_accuracy": "80.28",
"given_text": "hi",
"spam": "NotSpam"
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "820",
"query_limit_request": 1,
"status_code": 200
}
Example for batch text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.batchclassifiers.spam(column='provide column number',files={'file': open('provide full path of file', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Processed_file location": "./outputfiles/3/spam/data386.csv",
"msg": "file_processing_completed",
"status_code": 200
}
Age Model
The Age model of data2insights will predict the age of, who wrote the given input text.
Method Name :
classifiers.age(data='provide input text'); # for single text
batchclassifiers.age(column='provide column number',files={'file': open('provide full path of file', 'rb')}); # for batch process
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.age(data='provide input text')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Age",
"classifications": {
"Prediction_accuracy": "99.08",
"age": "18_24",
"given_text": "hi"
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "828",
"query_limit_request": 1,
"status_code": 200
}
Example for batch text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.batchclassifiers.age(column='provide column number',files={'file': open('provide full path of file', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Processed_file location": "./outputfiles/3/age/data388.csv",
"msg": "file_processing_completed",
"status_code": 200
}
Gender Model
The Gender model of data2insights will predict the gender of the, who wrote the given input text either Male or Female.
Method Name :
classifiers.gender(data='provide input text'); # for single text
batchclassifiers.gender(column='provide column number',files={'file': open('provide full path of file', 'rb')}); # for batch process
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.gender(data='provide input text')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Gender",
"classifications": {
"Prediction_accuracy": "99.87",
"gender": "Female",
"given_text": "hi"
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "817",
"query_limit_request": 1,
"status_code": 200
}
Example for batch text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.batchclassifiers.gender(column='provide column number',files={'file': open('provide full path of file', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Processed_file location": "./outputfiles/3/gender/data387.csv",
"msg": "file_processing_completed",
"status_code": 200
}
TweetSentiment Model
Twitter sentiment analysis identifies the opinion of tweet text which has only 280 characters of text as Positive,Neutral,Negative.
Method Name :
classifiers.tweetsentiment(data='provide input text'); # for single text
batchclassifiers.tweetsentiment(column='provide column number',files={'file': open('provide full path of file', 'rb')}); # for batch process
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.tweetsentiment(data='provide input text')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Tweet_sentiment",
"classifications": {
"Prediction_accuracy": "55.24",
"given_text": "hi",
"tweetsentiment": "Positive"
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "819",
"query_limit_request": 1,
"status_code": 200
}
Example for batch text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.batchclassifiers.tweetsentiment(column='provide column number',files={'file': open('provide full path of file', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Processed_file location": "./outputfiles/3/tweetsentiment/data389.csv",
"msg": "file_processing_completed",
"status_code": 200
}
Entity Model
The Entity model of data2insights will predict the entities in the given input text(Location,Person, Location, Organisation etc..).
Method Name :
classifiers.entity(data='provide input text'); # for single text
batchclassifiers.entity(column='provide column number',files={'file': open('provide full path of file', 'rb')}); # for batch process
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.entity(data='provide input text')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Extraction_id": "Entity",
"content_type": "application-json",
"extractions": {
"entity": [],
"given_text": "hi"
},
"query_limit": "1000",
"query_limit_remaining": "827",
"query_limit_request": 1,
"status_code": 200
}
Example for batch text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.batchclassifiers.entity(column='provide column number',files={'file': open('provide full path of file', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Processed_file location": "./outputfiles/3/entity/data396.csv",
"msg": "file_processing_completed",
"status_code": 200
}
TweetEntity Model
The Twitter Entity Extraction service allows us to identify the entions,Hashtags,Symbols ,Url’s and User_profile_url’s from given twitter tweets.
Method Name :
classifiers.tweetentity(data='provide input text'); # for single text
batchclassifiers.tweetentity(column='provide column number',files={'file': open('provide full path of file', 'rb')}); # for batch process
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.tweetentity(data='provide input text')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Extraction_id": "Tweet_entity",
"content_type": "application-json",
"extractions": {
"given_text": "hi",
"tweetentity": {
"Entities": [],
"Hashtags": [],
"symbols": [],
"urls": [],
"user_profiles_url": [],
"users": []
}
},
"query_limit": "1000",
"query_limit_remaining": "818",
"query_limit_request": 1,
"status_code": 200
}
Example for batch text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.batchclassifiers.tweetentity(column='provide column number',files={'file': open('provide full path of file', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Processed_file location": "./outputfiles/3/tweetentity/data420.csv",
"msg": "file_processing_completed",
"status_code": 200
}
Keyword Model
The Keyword model of data2insights will take text as the input and return keywords in the given text.
Method Name :
classifiers.keyword(data='provide input text'); # for single text
batchclassifiers.keyword(column='provide column number',files={'file': open('provide full path of file', 'rb')}); # for batch process
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.keyword(data='provide input text')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Extraction_id": "Keyword",
"content_type": "application-json",
"extractions": {
"given_text": "hi",
"keywords": []
},
"query_limit": "1000",
"query_limit_remaining": "826",
"query_limit_request": 1,
"status_code": 200
}
Example for batch text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.batchclassifiers.keyword(column='provide column number',files={'file': open('provide full path of file', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Processed_file location": "./outputfiles/3/keyword/data397.csv",
"msg": "file_processing_completed",
"status_code": 200
}
UrlExtraction Model
The Url Extraction service allows us to identity the full content,title,html page,top image,authors,keyword,summary and publication date from the given url.
Method Name :
text->classifiers.urlextraction(data='provide input text'); # for single text
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.urlextraction(data='provide input text')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Extraction_id": "Url_extraction",
"content_type": "application-json",
"extractions": {
"Response": {
"ALL_IMAGES_URLS": [
"https://i.stack.imgur.com/ziXaw.jpg?s=32&g=1"
],
"AUTHORS": [],
"FULL CONTENT": "I am using requests to get the image from remote URL. Since the images will always be 16x16, I want to convert them to base64 , so that I can embed them later to use in HTML img tag.
import requests import base64 response = requests.get(url).content print(response) b = base64.b64encode(response) src = "data:image/png;base64," + b
The output for response is:
response = b'GIF89a\x80\x00\x80\x00\xc4\x1f\x00\xff\xff\xff\x00\x00\x00\xff\x00\x00\xff\x88\x88"""\xffff\...
The HTML part is:
<img src="{{src}}"/>
But the image is not displayed.",
"HTML_PAGE": "<div><p>I am using <code>requests</code> to get the image from remote URL. Since the images will always be 16x16, I want to convert them to <code>base64</code>, so that I can embed them later to use in HTML <code>img</code> tag.</p>
<pre><code>import requests
import base64
response = requests.get(url).content
print(response)
b = base64.b64encode(response)
src = "data:image/png;base64," + b
</code></pre>
<p>The output for <code>response</code> is:</p>
<pre><code>response = b'GIF89a\x80\x00\x80\x00\xc4\x1f\x00\xff\xff\xff\x00\x00\x00\xff\x00\x00\xff\x88\x88"""\xffff\...
</code></pre>
<p>The HTML part is:</p>
<pre><code><img src="{{src}}"/>
</code></pre>
<p>But the image is not displayed.</p>
<p>How can I properly base-64 encode the <code>response</code>?</p>
</div>",
"KEYWORDS": [
"url",
"base64",
"image",
"tagimport",
"src",
"requests",
"using",
"response",
"python",
"html",
"srcsrcbut"
],
"PUBLICATION DATE": null,
"SUMMARY": "I am using requests to get the image from remote URL.
Since the images will always be 16x16, I want to convert them to base64 , so that I can embed them later to use in HTML img tag.
import requests import base64 response = requests.get(url).content print(response) b = base64.b64encode(response) src = "data:image/png;base64," + bThe output for response is:response = b'GIF89a\x80\x00\x80\x00\xc4\x1f\x00\xff\xff\xff\x00\x00\x00\xff\x00\x00\xff\x88\x88"""\xffff\...
The HTML part is:<img src="{{src}}"/>But the image is not displayed.",
"TITLE": "Python requests base64 image",
"TOP_IMAGE": "https://cdn.sstatic.net/Sites/stackoverflow/Img/apple-touch-icon@2.png?v=73d79a89bded"
},
"given_url": "https://stackoverflow.com/questions/30280495/python-requests-base64-image"
},
"query_limit": "1000",
"query_limit_remaining": "825",
"query_limit_request": 1,
"status_code": 200
}
Personalitytraits Model
The Personality Traits model of data2insights will take text as the input and return the Personality Traits of the given text as the response object.
Method Name :
classifiers.personalitytraits(data='provide input text'); # for single text
batchclassifiers.personalitytraits(column='provide column number',files={'file': open('provide full path of file', 'rb')}); # for batch process
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.personalitytraits(data='provide input text')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
Classifier_id: 'Topic',
classifications: {
Prediction_accuracy: '85.09',
given_text: 'Welcome to d2i',
topic: 'recreation'
},
content_type: 'application-json',
query_limit: '5000',
query_limit_remaining: '4935',
query_limit_request: 1,
status_code: 200
}
Example for batch text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.batchclassifiers.personalitytraits(column='provide column number',files={'file': open('provide full path of file', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Processed_file location": "./outputfiles/102/personalityTraits/Sample-Spreadsheet-10-rows221.xls",
"msg": "file_processing_completed",
"status_code": 200
}
Readability Model
The Readability model of data2insights will take text as the input and return the type of some metrics of the given text as the response object.
Method Name :
classifiers.readability(data='provide input text'); # for single text
batchclassifiers.readability(column='provide column number',files={'file': open('provide full path of file', 'rb')}); # for batch process
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.readability(data='provide input text')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Metrics": {
"document_readability": {
"Automated_Readability_Index": -11.6,
"Coleman_liau_index": -33.81,
"Dalechal": 0.05,
"Flesch_Kindrade_Grade": -3.5,
"Flesch_Reading_Score": 121.22,
"Gunning_Fog": 0.4,
"Linsear_Write": -0.5,
"Smog_index": 0.0
},
"given_text": "hi"
},
"Metrix_id": "Document_readability",
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "824",
"query_limit_request": 1,
"status_code": 200
}
Example for batch text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.batchclassifiers.readability(column='provide column number',files={'file': open('provide full path of file', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Processed_file location": "./outputfiles/3/readability/data421.csv",
"msg": "file_processing_completed",
"status_code": 200
}
Similarity Model
The Similarity model of data2insights will find the similarity between two texts.
Method Name :
text->classifiers.similarity(data1='provide text',data2='provide text'); # for single text
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.similarity(data1='provide text',data2='provide text')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Metrics": {
"Cosine_similarity": 0.0,
"Jaccard_similarity": 0.0,
"given_text1": "hi",
"given_text2": "hello"
},
"Metrix_id": "Document_similarity",
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "823",
"query_limit_request": 1,
"status_code": 200
}
QandA Model
The Q&A model of data2insights will find the answer for the given question from the given paragraph.
Method Name :
classifiers.QA(question='provide question', paragraph='provide paragraph'); # for single text
batchclassifiers.QA(column1='provide question column number',column2='provide paragraph column number',files={'file': open('provide full path of file', 'rb')}); # for batch process
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.QA(question='provide question', paragraph='provide paragraph')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
content_type: 'application-json',
'q&a': {
Answer: 'welcome to data2insights',
given_paragraph: 'welcome to data2insights',
given_question: 'welcome to d2i?'
},
query_limit: '5000',
query_limit_remaining: '4923',
query_limit_request: 1,
status_code: 200
}
Example for batch text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.batchclassifiers.QA(column1='provide question column number',column2='provide paragraph column number',files={'file': open('provide full path of file', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Processed_file location": "./outputfiles/102/QandA/Sample-Spreadsheet-10-rows221.xls",
"msg": "file_processing_completed",
"status_code": 200
}
HempTopic Model
The HempTopic model of data2insights will helps you in identifying topics.
Method Name :
classifiers.hemptopic(data='provide input text'); # for single text
batchclassifiers.hemptopic(column='provide column number',files={'file': open('provide full path of file', 'rb')}); # for batch process
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.hemptopic(data='provide input text')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classification_id": "Hemptopic",
"classifications": {
"Prediction_accuracy": "83.65",
"given_text": "hi",
"hemptopic": "Beauty"
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "814",
"query_limit_request": 1,
"status_code": 200
}
Example for batch text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.batchclassifiers.hemptopic(column='provide column number',files={'file': open('provide full path of file', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Processed_file location": "./outputfiles/3/hemptopic/data502.csv",
"msg": "file_processing_completed",
"status_code": 200
}
BertEntity Model
The BertEntity model of data2insights will helps you to identify the entities.
Method Name :
classifiers.bertentity(data='provide input text'); # for single text
batchclassifiers.bertentity((column='provide column number',files={'file': open('provide full path of file', 'rb')}); # for batch process
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.bertentity(data='provide input text')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Extraction_id": "Bertentity",
"content_type": "application-json",
"extractions": {
"Bertentity": [
{
"bertentity": "O",
"text": "hi"
}
],
"given_text": "hi"
},
"query_limit": "1000",
"query_limit_remaining": "813",
"query_limit_request": 1,
"status_code": 200
}
Example for batch text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.batchclassifiers.bertentity((column='provide column number',files={'file': open('provide full path of file', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Processed_file location": "./outputfiles/3/bertentity/data501.csv",
"msg": "file_processing_completed",
"status_code": 200
}
Summarization Model
The Summarization model of data2insights will helps you summarize the given text.
Method Name :
text->classifiers.summarization(data='provide input text'); # for single text
Example for single text :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.classifiers.summarization(data='provide input text')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
Python Vision Analytics Models
The Vision analysis models can take a image as the input and gives the appropriate predicted results for that model.The input image should contain .png (or) .jpeg formats only and it should be either local file or remote file url.
Logo Model
The Logo model of data2insights will predict the 27 types of logos from the given input image.
Method Name :
visionclassifiers.logo(data='provide image url');
visionclassifiers.logoupload(files={'file': open('provide image full path', 'rb')});
Example for image URL :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.logo(data='provide image url')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Logo",
"classifications": {
"Logos": []
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "812",
"query_limit_request": 1,
"status_code": 200
}
Example for upload local image :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.logoupload(files={'file': open('provide image full path', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Logo",
"classifications": {
"Logos": []
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "774",
"query_limit_request": 1,
"status_code": 200
}
Bird Model
The Bird model of data2insights will predict the 27 types of birds from the given input image.
Method Name :
visionclassifiers.bird(data='provide image url');
visionclassifiers.birdupload(files={'file': open('provide image full path', 'rb')});
Example for image URL :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.bird(data='provide image url')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{"Classifier_id": "Bird",
"classifications": {
"Birds": [
{
"Bird": "toucan_bird",
"Coordinates": {
"Bottom": 771.510648727417,
"Left": 53.76867055892944,
"Right": 1191.6043281555176,
"Top": 14.702987670898438
},
"Prediction_accuracy": 99,
"bird": 1
}
]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "794",
"query_limit_request": 1,
"status_code": 200
}
Example for upload local image :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.birdupload(files={'file': open('provide image full path', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Bird",
"classifications": {
"Birds": [
{
"Bird": "parrot_bird",
"Coordinates": {
"Bottom": 523.5589575767517,
"Left": 60.50408989191055,
"Right": 711.0763740539551,
"Top": 58.13420504331589
},
"Prediction_accuracy": 92,
"bird": 1
}
]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "772",
"query_limit_request": 1,
"status_code": 200
}
Transport Model
The Transport model of data2insights will predict the vehicle types from the given input image.
Method Name :
visionclassifiers.transport(data='provide image url');
visionclassifiers.transportupload(files={'file': open('provide image full path', 'rb')});
Example for image URL :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.transport(data='provide image url')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Transport",
"classifications": {
"Transports": [
{
"Coordinates": {
"Bottom": 800.0,
"Left": 46.82081937789917,
"Right": 1174.506425857544,
"Top": 8.72507095336914
},
"Prediction_accuracy": 76,
"Transport": "auto",
"transport": 1
}
]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "785",
"query_limit_request": 1,
"status_code": 200
}
Example for upload local image :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.transportupload(files={'file': open('provide image full path', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Transport",
"classifications": {
"Transports": [{
"Coordinates": {
"Bottom": 502.32422828674316,
"Left": 41.04209750890732,
"Right": 651.0549342632294,
"Top": 30.10187178850174
},
"Prediction_accuracy": 31,
"Transport": "aeroplane",
"transport": 1
}]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "771",
"query_limit_request": 1,
"status_code": 200
}
Plant Model
The Plant model of data2insights will predict the plant types from the given input image.
Method Name :
visionclassifiers.plant(data='provide image url');
visionclassifiers.plantupload(files={'file': open('provide image full path', 'rb')});
Example for image URL :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.plant(data='provide image url')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{"Classifier_id": "Plant",
"classifications": {
"Plants": [
{
"Coordinates": {
"Bottom": 754.0493965148926,
"Left": 9.73820686340332,
"Right": 1200.0,
"Top": 27.520751953125
},
"Plant": "plants_indoor",
"Prediction_accuracy": 58,
"plant": 1
},
{
"Coordinates": {
"Bottom": 800.0,
"Left": 54.79602813720703,
"Right": 1131.5191984176636,
"Top": 10.229825973510742
},
"Plant": "plants_outdoor",
"Prediction_accuracy": 53,
"plant": 2
}
]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "803",
"query_limit_request": 1,
"status_code": 200
}
Example for upload local image :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.plantupload(files={'file': open('provide image full path', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Plant",
"classifications": {
"Plants": [
{
"Coordinates": {
"Bottom": 490.9296751022339,
"Left": 0.0,
"Right": 730.0,
"Top": 24.103980362415314
},
"Plant": "plants_indoor",
"Prediction_accuracy": 54,
"plant": 1
}
]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "769",
"query_limit_request": 1,
"status_code": 200
}
Age Model
The Age model of data2insights will predict the age of the persons from the given input image.
Method Name :
visionclassifiers.age(data='provide image url');
visionclassifiers.ageupload(files={'file': open('provide image full path', 'rb')});
Example for image URL :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.age(data='provide image url')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Age",
"classifications": {
"Faces": [
{
"Age": "31-55",
"Coordinates": {
"Bottom": 287.21981048583984,
"Left": 505.4616093635559,
"Right": 690.896487236023,
"Top": 18.590307235717773
},
"Face": 1,
"Predicted_accuracy": 51
}
]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "798",
"query_limit_request": 1,
"status_code": 200
}
Example for upload local image :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.ageupload(files={'file': open('provide image full path', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Age",
"classifications": {
"Faces": []
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "761",
"query_limit_request": 1,
"status_code": 200
}
Gender Model
The Gender model of data2insights will predict the gender of the persons from the given input image.
Method Name :
visionclassifiers.gender(data='provide image url');
visionclassifiers.genderupload(files={'file': open('provide image full path', 'rb')});
Example for image URL :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.gender(data='provide image url')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Gender",
"classifications": {
"Faces": [
{
"Coordinates": {
"Bottom": 295.8600044250488,
"Left": 507.1301221847534,
"Right": 694.7623014450073,
"Top": 10.44924259185791
},
"Face": 1,
"Gender": "Male",
"Predicted_accuracy": 99
}
]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "777",
"query_limit_request": 1,
"status_code": 200
}
Example for upload local image :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.genderupload(files={'file': open('provide image full path', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Gender",
"classifications": {
"Faces": []
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "763",
"query_limit_request": 1,
"status_code": 200
}
Emotion Model
The Emotion model of data2insights will predict the emotion of the person from the given input image.
Method Name :
visionclassifiers.emotion(data='provide image url');
visionclassifiers.emotionupload(files={'file': open('provide image full path', 'rb')});
Example for image URL :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.emotion(data='provide image url')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{"Classifier_id": "Emotion",
"classifications": {
"Faces": [
{
"Coordinates": {
"Bottom": 283.68217945098877,
"Left": 519.590413570404,
"Right": 677.3368835449219,
"Top": 43.988871574401855
},
"Emotion": "Neutral",
"Face": 1,
"Predicted_accuracy": 54
},
{
"Coordinates": {
"Bottom": 282.6207637786865,
"Left": 510.5981111526489,
"Right": 689.1172885894775,
"Top": 29.67963218688965
},
"Emotion": "Surprise",
"Face": 2,
"Predicted_accuracy": 37
}
]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "792",
"query_limit_request": 1,
"status_code": 200
}
Example for upload local image :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.emotionupload(files={'file': open('provide image full path', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Emotion",
"classifications": {
"Faces": []
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "762",
"query_limit_request": 1,
"status_code": 200
}
Color Model
The Color model of data2insights will predict the different color from the given input image.
Method Name :
visionclassifiers.color(data='provide image url');
visionclassifiers.colorupload(files={'file': open('provide image full path', 'rb')});
Example for image URL :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.color(data='provide image url')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Color",
"classifications": {
"Faces": [
{
"Color": "white_people",
"Coordinates": {
"Bottom": 791.8387413024902,
"Left": 224.97146129608154,
"Right": 987.045407295227,
"Top": 47.0322847366333
},
"Face": 1,
"Predicted_accuracy": 99
}
]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "799",
"query_limit_request": 1,
"status_code": 200
}
Example for upload local image :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.colorupload(files={'file': open('provide image full path', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Color",
"classifications": {
"Faces": [
{
"Color": "white_people",
"Coordinates": {
"Bottom": 513.8462805747986,
"Left": 38.191078901290894,
"Right": 642.4347186088562,
"Top": 38.06664854288101
},
"Face": 1,
"Predicted_accuracy": 96
}
]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "760",
"query_limit_request": 1,
"status_code": 200
}
Scene Model
The Scene model of data2insights will predict the different scenes from the given input image.
Method Name :
visionclassifiers.scene(data='provide image url');
visionclassifiers.sceneupload(files={'file': open('provide image full path', 'rb')});
Example for image URL :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.scene(data='provide image url')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{"Classifier_id": "Scene",
"classifications": {
"Scenes": [
{
"Coordinates": {
"Bottom": 795.6767559051514,
"Left": 211.76365613937378,
"Right": 954.124116897583,
"Top": 36.2668514251709
},
"Prediction_accuracy": 48,
"Scene": "Office_building",
"scene": 1
}
]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "790",
"query_limit_request": 1,
"status_code": 200
}
Example for upload local image :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.sceneupload(files={'file': open('provide image full path', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Scene",
"classifications": {
"Scenes": [
{
"Coordinates": {
"Bottom": 795.6767559051514,
"Left": 211.76365613937378,
"Right": 954.124116897583,
"Top": 36.2668514251709
},
"Prediction_accuracy": 48,
"Scene": "Office_building",
"scene": 1
}
]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "790",
"query_limit_request": 1,
"status_code": 200
}
Weather Model
The Weather model of data2insights will predict the weather in the given input image.
Method Name :
visionclassifiers.weather(data='provide image url');
visionclassifiers.weatherupload(files={'file': open('provide image full path', 'rb')});
Example for image URL :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.weather(data='provide image url')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Weather",
"classifications": {
"Scenes": [
{
"Coordinates": {
"Bottom": 786.3454818725586,
"Left": 22.405779361724854,
"Right": 1196.6458797454834,
"Top": 62.87107467651367
},
"Prediction_accuracy": 90,
"Weather": "weather_cloudy",
"scene": 1
}
]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "789",
"query_limit_request": 1,
"status_code": 200
}
Example for upload local image :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.weatherupload(files={'file': open('provide image full path', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Weather",
"classifications": {
"Scenes": [{
"Coordinates": {
"Bottom": 521.9473385810852,
"Left": 9.529821276664734,
"Right": 723.5642737150192,
"Top": 29.81929510831833
},
"Prediction_accuracy": 99,
"Weather": "weather_snowy",
"scene": 1
}]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "764",
"query_limit_request": 1,
"status_code": 200
}
Violence Model
The Violence model of data2insights will predict the violence from the given input image.
Method Name :
visionclassifiers.violence(data='provide image url');
visionclassifiers.violenceupload(files={'file': open('provide image full path', 'rb')});
Example for image URL :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.violence(data='provide image url')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{"Classifier_id": "Violence",
"classifications": {
"Scenes": [
{
"Coordinates": {
"Bottom": 797.3269462585449,
"Left": 111.29786968231201,
"Right": 1200.0,
"Top": 0.0
},
"Prediction_accuracy": 99,
"Violence": "war",
"scene": 1
}
]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "787",
"query_limit_request": 1,
"status_code": 200
}
Example for upload local image :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.violenceupload(files={'file': open('provide image full path', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Violence",
"classifications": {
"Scenes": [{
"Coordinates": {
"Bottom": 527.7309834957123,
"Left": 62.09136366844177,
"Right": 730.0,
"Top": 0.0
},
"Prediction_accuracy": 99,
"Violence": "war",
"scene": 1
}, {
"Coordinates": {
"Bottom": 527.7526861429214,
"Left": 13.07406336069107,
"Right": 720.9626841545105,
"Top": 16.71631395816803
},
"Prediction_accuracy": 60,
"Violence": "violence",
"scene": 2
}]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "766",
"query_limit_request": 1,
"status_code": 200
}
TLO Model
The Tlo model of data2insights will predict the trees,laks, and oceans from the given input image.
Method Name :
visionclassifiers.tlo(data='provide image url');
visionclassifiers.tloupload(files={'file': open('provide image full path', 'rb')});
Example for image URL :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.tlo(data='provide image url')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{"Classifier_id": "Tlo",
"classifications": {
"Scenes": [
{
"Coordinates": {
"Bottom": 784.9786281585693,
"Left": 15.252542495727539,
"Right": 1200.0,
"Top": 68.20473670959473
},
"Prediction_accuracy": 99,
"Tlo": "lake",
"scene": 1
}
]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "788",
"query_limit_request": 1,
"status_code": 200
}
Example for upload local image :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.tloupload(files={'file': open('provide image full path', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "Tlo",
"classifications": {
"Scenes": [{
"Coordinates": {
"Bottom": 510.66392064094543,
"Left": 18.766419291496277,
"Right": 719.1625744104385,
"Top": 101.74509018659592
},
"Prediction_accuracy": 99,
"Tlo": "lake",
"scene": 1
}]
},
"content_type": "application-json",
"query_limit": "1000",
"query_limit_remaining": "765",
"query_limit_request": 1,
"status_code": 200
}
General Objects Model
The Object model of data2insights will predict the different types of general objects from the given input image.It predicts 545 types of objects.
Method Name :
visionclassifiers.generalobject(data='provide image url');
visionclassifiers.generalobjectupload(files={'file': open('provide image full path', 'rb')});
Example for image URL :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.generalobject(data='provide image url')
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "General_Object",
"classifications": {
"GeneralObjects": [
{
"Class": "Clothing",
"Coordinates": {
"Bottom": 781.9650173187256,
"Left": 326.60393714904785,
"Right": 913.2122039794922,
"Top": 258.1010580062866
},
"Object": 1,
"Score": 73
},
{
"Class": "Face",
"Coordinates": {
"Bottom": 274.97315406799316,
"Left": 508.36490392684937,
"Right": 689.9345397949219,
"Top": 28.40385138988495
},
"Object": 2,
"Score": 54
},
{
"Class": "Person",
"Coordinates": {
"Bottom": 795.4741477966309,
"Left": 309.1881036758423,
"Right": 943.9865827560425,
"Top": 30.157431960105896
},
"Object": 3,
"Score": 42
},
{
"Class": "Man",
"Coordinates": {
"Bottom": 794.6691036224365,
"Left": 319.6103096008301,
"Right": 909.6429347991943,
"Top": 29.600465297698975
},
"Object": 4,
"Score": 40
}
]
},
"content_type": "application-json",
"status_code": 200,
"query_limit": "10000",
"query_limit_remaining": "9287",
"query_limit_request": 1
}
Example for upload local image :
from data2Insights import data2insights
# Use the API key,account Id and email Id from your account,
credentials = data2insights('provide accountid','provide apikey','provide userid')
# Pass the input as parameter to the method
credentials.visionclassifiers.generalobjectupload(files={'file': open('provide image full path', 'rb')})
Output:
#This is exactly the parsed JSON that the Data2Insights API returns!
{
"Classifier_id": "General_Object",
"classifications": {
"GeneralObjects": [
{
"Class": "Clothing",
"Coordinates": {
"Bottom": 781.9650173187256,
"Left": 326.60393714904785,
"Right": 913.2122039794922,
"Top": 258.1010580062866
},
"Object": 1,
"Score": 73
},
{
"Class": "Face",
"Coordinates": {
"Bottom": 274.97315406799316,
"Left": 508.36490392684937,
"Right": 689.9345397949219,
"Top": 28.40385138988495
},
"Object": 2,
"Score": 54
},
{
"Class": "Person",
"Coordinates": {
"Bottom": 795.4741477966309,
"Left": 309.1881036758423,
"Right": 943.9865827560425,
"Top": 30.157431960105896
},
"Object": 3,
"Score": 42
},
{
"Class": "Man",
"Coordinates": {
"Bottom": 794.6691036224365,
"Left": 319.6103096008301,
"Right": 909.6429347991943,
"Top": 29.600465297698975
},
"Object": 4,
"Score": 40
}
]
},
"content_type": "application-json",
"status_code": 200,
"query_limit": "10000",
"query_limit_remaining": "9287",
"query_limit_request": 1
}
نحوه نصب
نصب پکیج whl data2Insights-1.0:
pip install data2Insights-1.0.whl
نصب پکیج tar.gz data2Insights-1.0:
pip install data2Insights-1.0.tar.gz