Sentiment Analysis Last updated: 2022-07-24

The Sentiment Analysis is an interface that allows other computer programs to send texts to our servers and receive high-quality emotional evaluation of natural language. This interface opens of opportunities for software developers to create emotional computing algorithms based on excellent artificial intelligence.

A few examples:

Communication with clients: Companies can integrate our machine learning technologies and Natural Language Processing, NLP aimed at learning natural language, which will allow them to instantly receive the emotional attitude of customers.
Customer Satisfaction: Sales teams can manage sales and after-sales service policies.
Tracking of digital community: Digital marketing departments can instantly recognize the mood of the company's customers and respond in a timely manner to the emotional attitude expressed in the blog, Instagram, Facebook, and other communities.
Qualitative analysis of political opinions: Social research agencies can analyze the emotional attitude of the electorate to certain political and social issues.
Social climate: HR departments can assess the psychosocial risks in the company and prevent emotional stress and burnout of employees.

CategorizationLast updated: 2022-07-24

The Categorization is an interface that allows other computer programs to send nomenclature to our servers and get them classified into categories set in advance. This interface opens of opportunities for software developers to structure and generalize certain products into groups for navigation or tagging purposes based on excellent artificial intelligence.

A few examples:

Searching for products and services: Online stores can integrate our machine learning technologies to instantly search for and select products and services, allowing them to instantly get their names in categories.
Personnel selection: Personnel departments can select employees based on their requirements for qualifications, competencies, and skills.
Catalog navigation: Allows customers to quickly find the necessary products, navigate for navigation or search through a given product catalog.

Text Classification Last updated: 2022-07-24

The Text Classification is an interface that allows other computer programs classify texts on various topics (politics, technology, medicine, sports, etc.). This interface opens of opportunities for software developers to create criteria based on training data, solved by the method of computational linguistics based on excellent artificial intelligence.

A few examples:

Defining topics: Online publications, political and news portals can instantly classify news and publications by topic and place them in the desired thematic sections.
Mailing and filtering: Determine the subject of texts and redirect them to people interested in receiving them quickly.
Document management: Classify correspondence and documents for further processing and storage.

Text Preprocessing Last updated: 2022-07-24

The Text Preprocessing is an interface that allows other computer programs to pre-process text information for further processing by digital methods. This interface opens of opportunities for software developers to further process text information using machine learning, deep learning, and transfer learning methods based on excellent artificial intelligence.

A few examples:

Qualitative assessment: Companies interested in evaluating text information based on qualitative characteristics (mood, emotions, etc.) can instantly transform text information for subsequent digital processing.
Thematic assessment: Extract useful content from text information and explore it digitally.
Preprocessing: Manage only the important and useful content of text documents.

Fuzzy Search Last updated: 2022-07-24

TheFuzzy Search is an interface that allows you to search through text data without using a hard text comparison but based on the similarity of the data. This interface opens of possibilities for software developers to instantly search for any words and expressions with automatic correction of spelling errors based on excellent artificial intelligence.

A few examples:

Smart Search: Companies interested in performing high-quality search for any text queries for customers by classifiers or lists.
Search with limited data: Perform an instant selection of the necessary information on the client's request, even with limited input data.
Search for typos: Instantly provide customers with the necessary information for queries with errors, typos, and word forms.

Image Classification Last updated: 2022-07-24

The Image Classification is an interface that allows you to classify images by certain attributes. This interface opens of possibilities for software developers to instantly make decisions based on the content of the image based on excellent artificial intelligence.

A few examples:

Emotional state: Companies interested in assessing the psychological or emotional state of a photo.
Situation analysis: Perform an instant analysis of the situation from its photo image and make the right decision.
Defect Analysis: Instantly recognize defects from an image from the sales floor or shop floor.
Important surfaces: Assess the condition in critical conditions, such as the road surface for cracks and holes, or find dirty and faulty solar panels from photos taken from drones or cameras.

Object detection Last updated: 2022-07-24

The Object detection is an interface that allows you to recognize and detect certain objects in photos. This interface opens of possibilities for software developers to search for the presence or absence of objects in photos based on excellent artificial intelligence.

A few examples:

Product on the shelves: Companies interested in controlling the display of goods on the shelves in the trading floor.
Explore the area: Study the objects present in the area from the photo.
Image Content: Use photos to find out who or what is in the frame.
Counting: Find out how many fish are in the shoal, cars on the road, customers in the trading floor.

Video Object detection Last updated: 2022-07-24

The Video Object detection is an interface that allows you to recognize and detect certain objects in video. This interface opens up a number of possibilities for software developers to search for the presence or absence of objects in video based on excellent artificial intelligence.

A few examples:

Product on the shelves: Companies interested in monitoring compliance with service standards in the sales floor.
Search for deviations: Study the events that happened on the video and process only the important ones.
Video Content: Use the video to respond immediately.
Search for content: Work with the content of a video image by instantly highlighting its content and all the events associated with it.

Stream Video Recognition Last updated: 2022-07-24

The Stream Video Recognition is an interface that allows you to recognize and detect certain objects in the video stream in real time. This interface opens up a number of possibilities for software developers to search for the presence or absence of real-time objects based on excellent artificial intelligence.

A few examples:

Visual marketing: Companies that conduct an effective advertising campaign and explore the interest and attractiveness for customers
Road traffic: Know the traffic congestion on the video stream from the web cameras installed on the streets
Point of Sale: Evaluate traffic near an existing or future point of sale to choose the best location
Operational control: detect objects located in the perimeter, such as a car or house
Defect detection: Continuous visual assessment of the quality of parts and products on the conveyor or at the workplace from web cameras
Illegal content: Instantly check the video stream for illegal or prohibited content, such as on streaming sites

IntegrationsLast updated: 2022-07-24

Authorization

To work with our api, you will need to recieve a token. To do this, go to my.cloud-ai.tech.

If you already have an account, enter your credentials.
If you haven't created an account yet, click “Create New Account”. Then fill in all the fields. After registration, to confirm your account, you will need to click on the link that will be sent to your email address that you specified during registration. After confirming your account, go back to my.cloud-ai.tech and enter your credentials.

Warning

Without a token, you will not be able to use our api and all requests will return http status code 401.

After all, the browser will direct you to a page where you can get a token

Headers

To pass the token and other parameters, use the request headers. Our api uses the following headers:

Bearer	To pass the token.
Content-Type	Description of the data contained in the request body.

Request method

Use one of the methods depending on request:

POST
PUT
DELETE
GET

Response codes from the server

200	Successful request.
204	Successful request, but no response was provided.
400	Executing request error.
401	Invalid token.
403	Not enough rights to execute the request.
404	The request was not found.
415	Invalid Content-Type header value.
500	Internal server error.

APIs

Authorization

LogIn

Request

URI	https://my.cloud-ai.tech/api/HomeController/LogIn
Request method	POST
URI parametrs	-
Header parameters	Bearer = Your token
Body paramert	{ "email": "your e-mail", "password": "your password" }

Example fetch request on js:

										
		fetch("https://my.cloud-ai.tech/api/HomeController/LogIn", {
			body : {
				"email": "user@gmail.com",
				"password": "Qwe123"
			},
			headers: {
				"Bearer": yourTokenHere,
				"Content-Type": "application/json"
			},
			method: "POST"
		})

Responce

OK

Code Status - 200

{ "authToken": "erVNigiLCJqdGkiOiMzM2LCJpc3iJJc3N1ZXIiLCJhdWQiOiJBdWRpZW5jZSJ9.1bQY1iY5MUsV9O2_CoCwSKKrYf7fni8B0JKWJE1rbnc", "expiresIn": 10000 }

Check Authorization

Request

URI	https://my.cloud-ai.tech/api/Authorization/CheckAuthorization
Request method	GET
URI parametrs	-
Header parameters	Bearer = Your token
Body paramert	-

Example fetch request on js:

									
		fetch("https://my.cloud-ai.tech/api/Authorization/CheckAuthorization", {
			headers: {
				"Bearer": yourTokenHere,
			},
			method: "GET"
		})

Responce

OK

Code Status - 204

Recognition Analysis

Smile Recognition

Request

Example fetch request on js:

										
		const body = new FormData();
		body.append("image", "@cat.jpg;type=image/jpeg");
		
		fetch("https://recognition.cloud-ai.tech/api/v1.0/RecognitionAnalysis/SmileRecognition", {
			body,
			headers: {
				"Bearer" : yourTokenHere,
				"Content-Type": "multipart/form-data"
			},
			method: "POST"
		})

URI	https://recognition.cloud-ai.tech/api/v1.0/RecognitionAnalysis/SmileRecognition
Request method	POST
URI parametrs	-
Header parameters	Bearer = Your token Content-Type" = "multipart/form-data"
Image format	".jpg", ".jpeg", ".png"
Maximum file size	12MB
Body paramert	FormData with array of bytes

Responce

OK

Code Status - 200

Body - { "userId": "string", "smileValue": 0, "noSmileValue": 0 }

Smile Object Detection

Request

URI	https://recognition.cloud-ai.tech/api/v1.0/RecognitionAnalysis/SmileObjectDetection
Request method	POST
URI parametrs	-
Header parameters	Bearer = Your token Content-Type" = "multipart/form-data"
Image format	".jpg", ".jpeg", ".png"
Maximum file size	12MB
Body paramert	FormData with array of bytes

Example fetch request on js:

										
		const body = new FormData();
		body.append("image", "@cat.jpg;type=image/jpeg");
		
		fetch("https://recognition.cloud-ai.tech/api/v1.0/RecognitionAnalysis/SmileObjectDetection", {
			body,
			headers: {
				"Bearer" : yourTokenHere,
				"Content-Type": "multipart/form-data"
			},
			method: "POST"
		})

Responce

OK

Code Status - 200

Body - { "userId": "string", "resultImageBytes": "string" }

Video Smile Detection

Request

URI	https://recognition.cloud-ai.tech/api/v1.0/RecognitionAnalysis/VideoSmileDetection
Request method	POST
URI parametrs	-
Header parameters	Bearer = Your token Content-Type" = "multipart/form-data"
Video format	".mp4", ".avi", ".mov", ".mpg", ".wmv"
Maximum file size	100MB
Body paramert	FormData with array of bytes

Example fetch request on js:

										
		const body = new FormData();
		body.append("uploadedFile", "videoFile.mp4");
		
		fetch("https://recognition.cloud-ai.tech/api/v1.0/RecognitionAnalysis/VideoSmileDetection", {
			body,
			headers: {
				"Bearer" : yourTokenHere,
				"Content-Type": "multipart/form-data"
			},
			method: "POST"
		})

Responce

OK

Code Status - 200

Body - { "userId": "string", "faceVideoFrames": 0, "smileVideoFrames": 0, "totalVideoFrames": 0, "userVideoInfo": { "avgFrameRate": 0, "codecName": "string", "frameSize": { "isEmpty": true, "width": 0, "height": 0 }, "duration": 0 } }

Swagger documentation

Link to Swagger documentation