Susi Server

Read more about the article Displaying Private Skills and Drafts on SUSI.AI

Displaying Private Skills and Drafts on SUSI.AI

The ListPrivateSkillService and ListPrivateDraftSkillService endpoint was implemented on SUSI.AI Server for SUSI.AI Admins to view the bots and drafts created by users respectively. This allows admins to monitor the bots and drafts created by users, and delete the ones which violate the guidelines. Also admins can see the sites where the bot is being used. The endpoint of both ListPrivateSkillService and ListPrivateDraftSkillService is of GET type. Both of them have a compulsory access_token parameter but ListPrivateSkillService has an extra optional search parameter. access_token(necessary): It is the access_token of the logged in user. It means this endpoint cannot be accessed in anonymous mode. search: It fetches a bot with the searched name. The minimum user role is set to OPERATOR. API Development ListPrivateSkillService For creating a list, we need to access each property of botDetailsObject, in the following manner: Key → Group  → Language → Bot Name  → BotList The below code iterates over the uuid of all the users having a bot, then over different groupNames,languageNames, and finally over the botNames. If search parameter is passed then it searches for the bot_name in the language object. Each botDetails object consists of bot name, language, group and key i.e uuid of the user which is then added to the botList array. JsonTray chatbot = DAO.chatbot; JSONObject botDetailsObject = chatbot.toJSON(); JSONObject keysObject = new JSONObject(); JSONObject groupObject = new JSONObject(); JSONObject languageObject = new JSONObject(); List botList = new ArrayList(); JSONObject result = new JSONObject(); Iterator Key = botDetailsObject.keys(); List keysList = new ArrayList(); while (Key.hasNext()) { String key = (String) Key.next(); keysList.add(key); } for (String key_name : keysList) { keysObject = botDetailsObject.getJSONObject(key_name); Iterator groupNames = keysObject.keys(); List groupnameKeysList = new ArrayList(); while (groupNames.hasNext()) { String key = (String) groupNames.next(); groupnameKeysList.add(key); } for (String group_name : groupnameKeysList) { groupObject = keysObject.getJSONObject(group_name); Iterator languageNames = groupObject.keys(); List languagenamesKeysList = new ArrayList(); while (languageNames.hasNext()) { String key = (String) languageNames.next(); languagenamesKeysList.add(key); } for (String language_name : languagenamesKeysList) { languageObject = groupObject.getJSONObject(language_name); If search parameter is passed, then search for a bot with the given name and add the bot to the botList if it exists. It will return all bots which have bot name as the searched name. if (call.get("search", null) != null) { String bot_name = call.get("search", null); if(languageObject.has(bot_name)){ JSONObject botDetails = languageObject.getJSONObject(bot_name); botDetails.put("name", bot_name); botDetails.put("language", language_name); botDetails.put("group", group_name); botDetails.put("key", key_name); botList.add(botDetails); } } If search parameter is not passed, then it will return all the bots created by the users. else { Iterator botNames = languageObject.keys(); List botnamesKeysList = new ArrayList(); while (botNames.hasNext()) { String key = (String) botNames.next(); botnamesKeysList.add(key); } for (String bot_name : botnamesKeysList) { JSONObject botDetails = languageObject.getJSONObject(bot_name); botDetails.put("name", bot_name); botDetails.put("language", language_name); botDetails.put("group", group_name); botDetails.put("key", key_name); botList.add(botDetails); } } } } } List of all bots, botList is return as server response. ListPrivateDraftSkillService For creating a list we need to iterate over each user and check whether the user has a draft bot. We get all the authorized clients from DAO.getAuthorizedClients(). We then iterate over each client and get their…

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Read more about the article Implementing Volume Action in SUSI Smart Speaker

Implementing Volume Action in SUSI Smart Speaker

We all know that a Smart Speaker to excel above its competitors has to excel in first being a good “Speaker” and a speaker has a basic and essential feature which is “volume control”. But things get better if you can control your volume with your voice. So, we have implemented a feature that allows the user to control the volume of the audio with his/her voice. Below are the steps we had to follow to implement this feature   Step 1: Creating the Skills The skills required to implement the ‘volume-action’ is implemented in the SUSI Server repo itself. The skill is located in susi_server/conf/system_skills/general/en/en_0001_foundation.txt   And below are the skills required   set audio volume to *|set audio volume to * percent|set audio volume to * points|set volume to *|set volume to * percent|set volume to * points !console:Audio volume is now $1$ percent. {"actions":[ {"type":"audio_volume", "volume":$1$} ]} eol   We get the following response from the server   "actions": [      {        "volume": "80",        "type": "audio_volume"      },      {        "type": "answer",        "expression": "Audio volume is now 80 percent."      }   Step 2: Finding Volume Action in the server response Now that our Server responds to our queries regarding the voice change action , we must implement it in our Smart Speaker Client. We first create a custom class in our in the SUSI API Wrapper repo which has only one member   class VolumeAction(BaseAction):    def __init__(self , volume):        super().__init__()        self.volume = volume   We check through the actions in the server’s response   elif isinstance(action, VolumeAction):            result['volume'] = action.volume   Step 3: Implementing it in the client Now to implement the action in our client we use a library called ‘alsaaudio’ to control the master volume of our RaspberryPi                 m = alsaaudio.Mixer()                m.setvolume(int(reply['volume']))                os.system('play {0} &'.format(self.components.config['detection_bell_sound']))  # nosec #pylint-disable type: ignore                m = alsaaudio.Mixer()                m.setvolume(int(reply['volume']))                os.system('play {0} &'.format(self.components.config['detection_bell_sound']))  # nosec #pylint-disable type: ignore   Now the user can easily change the speaker using the voice commands References https://pypi.org/project/pyalsaaudio/ https://github.com/fossasia/susi_server https://github.com/fossasia/susi_api_wrapper https://github.com/fossasia/susi_linux   Tags GSoC, GSoC’18, SUSI.AI, SUSI Linux, Smart Speaker , SUSI API Wrapper, SUSI Server, FOSSASIA, Volume Action

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Read more about the article Make an API to check if an email address has been registered for SUSI.AI

Make an API to check if an email address has been registered for SUSI.AI

This blog post talks about the implementation of the checkRegistration.json API on the SUSI.AI server, which is a part of the AAA system. The API endpoint to check if an email address has been registered for SUSI is https://api.susi.ai/aaa/checkRegistration.json It accepts one compulsory url parameter - check_email - It is the parameter that contains the string type email address which the user enters in the email address field of the login screen. The minimalUserRole is set to ANONYMOUS for this API, as initially the registration status of the email address is unknown. API Development The parameter is first extracted via the post object that is passed to the serviceImpl function. The  parameter is then stored in a variable. If the parameter is absent, then it is set to the default value null. There is a check if the email is null. If null, an exception is thrown. This code snippet discusses the above two points - @Override public ServiceResponse serviceImpl(Query post, HttpServletResponse response, Authorization auth, final JsonObjectWithDefault permissions) throws APIException { String checkEmail = post.get("check_email", null); JSONObject result = new JSONObject(); if (checkEmail == null) { throw new APIException(422, "Email not provided."); } . . .   Set the credential variable of type ClientCredential by passing the parameters passwd_login and checkEmail to the ClientCredential constructor. Finally pass this credential variable to the getAuthentication method defined in the DAO to return the authentication object. The authentication object then invokes the authentication.getIdentity() method. If the result is null, it means the email address has not been registered yet and vice-versa. Internally, the entire checking procedure is done from the authentication.json file that is stored in data/settings/ directory of the server. The response object is then sent with three key values mainly, apart from the session object. They are - accepted -  true - It tells that the API call has been successful. exists - It tells that the email address has already been registered. check_email -  It is the same email address that was sent as a query parameter. Here are the important code snippets - Continuation of the first code snippet - . . . // check if id exists already ClientCredential credential = new ClientCredential(ClientCredential.Type.passwd_login, checkEmail); Authentication authentication =DAO.getAuthentication(credential); if (authentication.getIdentity() != null) { result.put("exists", true); } else { result.put("exists", false); } result.put("accepted", true); result.put("check_email", checkEmail); return new ServiceResponse(result); }   Sample response of checkRegistration.json API endpoint - { "check_email": "abc@email.com", "session": {"identity": { "type": "host", "name": "127.0.0.1_356778ca", "anonymous": true }}, "exists": true, "accepted": true }   The API development was done in the above explained way. This API will be used in improving the authentication flow in the Android client, where, if an email address has already been registered, then the user would be taken to the ‘Enter Password Screen’ otherwise he/she would be directed to the Signup screen. Resources Learn about Application Programming Interface (API) https://en.wikipedia.org/wiki/Application_programming_interface A blog on API development - An Introductory Guide https://dzone.com/articles/api-development-an-introductory-guide Check out some good login/signup UI flows https://medium.muz.li/login-sign-up-inspiration-for-mobile-apps-aeff34090bbd

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Read more about the article Fetch Five Star Skill Rating from getSkillList API in SUSI.AI Android

Fetch Five Star Skill Rating from getSkillList API in SUSI.AI Android

SUSI.AI had a thumbs up/down rating system till now, which has now been replaced by a five star skill rating system. Now, the user is allowed to rate the skill based on a five star rating system. The UI components include a rating bar and below the rating bar is a section that displays the skill rating statistics - total number of ratings, average rating and a graph showing the percentage of users who rated the skill with five stars, four stars and so on. SUSI.AI Skills are rules that are defined in SUSI Skill Data repo which are basically the processed responses that SUSI returns to the user queries. When a user queries something from the SUSI Android app, a query to SUSI Server is made which in turn fetches data from SUSI Skill Data and returns a JSON response to the app. Similarly, to get skill ratings, a call to the ‘/cms/getSkillList.json’ API is made. In this API, the server checks the SUSI Skill Data repo for the skills and returns a JSON response consisting of all the required information like skill name, author name, description, ratings, etc. to the app. Then, this JSON response is parsed to extract individual fields to display the appropriate information in the skill details screen of the app. API Information The endpoint to fetch skills is ‘/cms/getSkillList.json’ The endpoints takes three parameters as input - model - It tells the model to which the skill belongs. The default value is set to general. group - It tells the group(category) to which the skill belongs. The default value is set to All. language - It tells the language to which the skill belongs. The default value is set to en. Since all skills have to be fetched, this API is called for every group individually. For instance, call “https://api.susi.ai/cms/getSkillList.json?group=Knowledge” to get all skills in group “Knowledge”. Similarly, call for other groups. Here is a sample response of a skill named ‘Capital’ from the group Knowledge : "capital": { "model": "general", "group": "Knowledge", "language": "en", "developer_privacy_policy": null, "descriptions": "A skill to tell user about capital of any country.", "image": "images/capital.png", "author": "chashmeet singh", "author_url": "https://github.com/chashmeetsingh", "skill_name": "Capital", "terms_of_use": null, "dynamic_content": true, "examples": ["What is the capital of India?"], "skill_rating": { "negative": "0", "positive": "4", "feedback_count" : 0, "stars": { "one_star": 0, "four_star": 1, "five_star": 0, "total_star": 1, "three_star": 0, "avg_star": 4, "two_star": 0 } }, "creationTime": "2018-03-17T17:11:59Z", "lastAccessTime": "2018-06-06T00:46:22Z", "lastModifiedTime": "2018-03-17T17:11:59Z" }, It consists of all details about the skill called ‘Capital’: Model (model) Group (group) Language (language) Developer Privacy Policy (developer_privacy_policy) Description (descriptions) Image (image) Author (author) Author URL (author_url) Skill name (skill_name) Terms of Use (terms_of_use) Content Type (dynamic_content) Examples (examples) Skill Rating (skill_rating) Creation Time (creationTime) Last Access Time (lastAccessTime) Last Modified Time (lastModifiedTime) From among all this information, the information of interest for this blog is Skill Rating. This blog mainly deals with showing how to parse the JSON response to get the skill rating star values, so as to…

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Read more about the article Upload Avatar for a User in SUSI.AI Server

Upload Avatar for a User in SUSI.AI Server

In this blog post, we are going to discuss on how the feature to upload the avatar for a user was implemented on the SUSI.AI Server. The API endpoint by which a user can upload his/her avatar image is https://api.susi.ai/aaa/uploadAvatar.json. The endpoint is of POST type. It accepts two request parameters - image - It contains the entire image file sent from the client access_token - It is the access_token for the user The minimalUserRole is set to USER for this API, as only logged-in users can use this API. Going through the API development The image and access_token parameters are first extracted via the req object, that is passed to the main function. The  parameters are then stored in variables. There is a check if the access_token and image exists. It it doesn’t, an error is thrown. This code snippet discusses the above two points - protected void doPost(HttpServletRequest req, HttpServletResponse resp) throws ServletException, IOException { Part imagePart = req.getPart("image"); if (req.getParameter("access_token") != null) { if (imagePart == null) { result.put("accepted", false); result.put("message", "Image file not received"); } else { …. } else{ result.put("message","Access token are not given"); result.put("accepted",false); resp.setContentType("application/json"); resp.setCharacterEncoding("UTF-8"); resp.getWriter().write(result.toString()); } }   Then the input stream is extracted from the imagePart and stored. And post that the identity is checked if it is valid. The input stream is converted into the Image type using the ImageIO.read method. The image is eventually converted into a BufferedImage using a function, described below. public static BufferedImage toBufferedImage(Image img) { if (img instanceof BufferedImage) return (BufferedImage) img; // Create a buffered image with transparency BufferedImage bimage = new BufferedImage(img.getWidth(null), img.getHeight(null), BufferedImage.TYPE_INT_ARGB); // Draw the image on to the buffered image Graphics2D bGr = bimage.createGraphics(); bGr.drawImage(img, 0, 0, null); bGr.dispose(); // Return the buffered image return bimage; }   After that, the file path and name is set. The avatar for each user is stored in the /data/avatar_uploads/<uuid of the user>.jpg. The avatar is written to the path using the ImageIO.write function. Once, the file is stored on the server, the success response is sent and the client side receives it. Resources Source of the API - https://github.com/fossasia/susi_server/blob/development/src/ai/susi/server/api/aaa/UploadAvatarService.java

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Read more about the article Overriding the Basic File Attributes while Skill Creation/Editing on Server

Overriding the Basic File Attributes while Skill Creation/Editing on Server

In this blog post, we are going to understand the method for overriding basic file attributes while Skill creation/editing on SUSI Server. The need for this arose, when the creationTime for the Skill file that is stored on the server gets changed when the skill was edited. Need for the implementation As briefly explained above, the creationTime for the Skill file that is stored on the server gets changed when the skill is edited. Also, the need to override the lastModifiedTime was done, so that the Skills based on metrics gives correct results. Currently, we have two metrics for the SUSI Skills - Recently updated skills and Newest Skills. The former is determined by the lastModifiedTime and the later is determined by the creationTime. Due, to inconsistencies of these attributes, the skills that were shown were out of order. The lastModifiedTime was overridden to save the epoch date during Skill creation, so that the newly created skills don’t show up on the Recently Updated Skills section, whereas the creationTime was overridden to maintain the correct the time. Going through the implementation Let us first have a look on how the creationTime was overridden in the ModifySkillService.java file. …. BasicFileAttributes attr = null; Path p = Paths.get(skill.getPath()); try { attr = Files.readAttributes(p, BasicFileAttributes.class); } catch (IOException e) { e.printStackTrace(); } FileTime skillCreationTime = null; if( attr != null ) { skillCreationTime = attr.creationTime(); } if (model_name.equals(modified_model_name) && group_name.equals(modified_group_name) && language_name.equals(modified_language_name) && skill_name.equals(modified_skill_name)) { // Writing to File try (FileWriter file = new FileWriter(skill)) { file.write(content); json.put("message", "Skill updated"); json.put("accepted", true); } catch (IOException e) { e.printStackTrace(); json.put("message", "error: " + e.getMessage()); } // Keep the creation time same as previous if(attr!=null) { try { Files.setAttribute(p, "creationTime", skillCreationTime); } catch (IOException e) { System.err.println("Cannot persist the creation time. " + e); } } …. } . . .   Firstly, we get the BasicFileAttributes of the Skill file and store it in the attr variable. Next, we initialise the variable skillCreationTime of type FileTime to null and set the value to the existing creationTime. The new Skill file is saved on the path using the FileWriter instance, which changes the creationTime, lastModifiedTime to the time of editing of the skill. The above behaviour is not desired and hence, we want to override the creationTIme with the FileTime saved in skillCreationTIme. This ensures that the creation time of the skill is persisted, even after editing the skill. Now we are going to see how the lastModifiedTime was overridden in the CreateSkillService.java file. …. Path newPath = Paths.get(path); // Override modified date to an older date so that the recently updated metrics works fine // Set is to the epoch time try { Files.setAttribute(newPath, "lastModifiedTime", FileTime.fromMillis(0)); } catch (IOException e) { System.err.println("Cannot override the modified time. " + e); } . . .   For this, we get the newPath of the Skill file and then the lastModifiedTime for the Skill File is explicitly set to a particular time. We set it to FileTime.fromMillis(0)…

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Read more about the article Adding Filters for Lists of Skills on the SUSI.AI Server

Adding Filters for Lists of Skills on the SUSI.AI Server

In this blog post, we will learn how to add filters to the API responsible for fetching the list of skills i.e. the endpoint - https://api.susi.ai/cms/getSkillList.json. The purpose of adding filters is to return a list of skills based on some parameters associated with the skill, that would be required to allow the user to get the desired response that s/he may be using to display it on the UI. Overview of the API API to fetch the list of skills - URL -  https://api.susi.ai/cms/getSkillList.json It takes 5 optional parameters - model - It is the name of the model that user is requesting group - It is the name of the group that user is requesting language - It is the name of the language that user is requesting skill - It is the name of the skill that user is requesting applyFilter - It has true/false values, depending whether filtering is required If the request URL contains the parameter applyFilter as true, in that case the other 2 compulsory parameters are - filter_name - ascending/descending, depending upon the type of sorting the user wants filter_type - lexicographical, rating, etc based on what basis the filtering is going to happen So, we will now look into adding a new filter_type to the API. Detailed explanation of the implementation We can add filters based on the key values of the Metadata object of individual skills. The Metadata object for each skill is similar to the following object - { "model": "general", "group": "Knowledge", "language": "en", "developer_privacy_policy": null, "descriptions": "A skill that returns the anagrams for a word", "image": "images/anagrams.jpg", "author": "vivek iyer", "author_url": "https://github.com/Remorax", "author_email": null, "skill_name": "Anagrams", "protected": false, "terms_of_use": null, "dynamic_content": true, "examples": ["Anagram for best"], "skill_rating": { "negative": "0", "positive": "0", "stars": { "one_star": 0, "four_star": 0, "five_star": 0, "total_star": 0, "three_star": 0, "avg_star": 0, "two_star": 0 }, "feedback_count": 0 }, "creationTime": "2017-12-17T14:32:15Z", "lastAccessTime": "2018-06-19T17:50:01Z", "lastModifiedTime": "2017-12-17T14:32:15Z" }   We will now add provision for URL parameter, filter_type=feedback in the API, which will filter the results based on the feedback_count key, which tells the number of feedback/comments a skill has received. In the serviceImpl method of the ListSkillService class, we can see a code snippet that handles the filtering part, It checks the filter_type parameter received in the URL on if-else block. The code snippet looks like this - if (filter_type.equals("date")) { . . } else if (filter_type.equals("lexicographical")) { . . } else if (filter_type.equals("rating")) { . . }   Similarly, we will need to add an else if condition with feedback_type=feedback and write the code block inside it. Here is the code for it, which is explained in detail. . . else if (filter_type.equals("feedback")) { if (filter_name.equals("ascending")) { Collections.sort(jsonValues, new Comparator<JSONObject>() { @Override public int compare(JSONObject a, JSONObject b) { Integer valA; Integer valB; int result=0; try { valA = a.getJSONObject("skill_rating").getInt("feedback_count"); valB = b.getJSONObject("skill_rating").getInt("feedback_count"); result = Integer.compare(valA, valB); } catch (JSONException e) { e.printStackTrace(); } return result; } }); } else { Collections.sort(jsonValues, new Comparator<JSONObject>() {…

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Read more about the article Server Side Implement of Usage Analytics of a Skill in SUSI.AI

Server Side Implement of Usage Analytics of a Skill in SUSI.AI

Which skills are being used most frequently in SUSI.AI? And which are rarely used? Such statistics are needed to give proper responses from the most used skills. So when the user sends a query to SUSI, it searches for the best-suited skill to respond to that query. The usage count of that skill is incremented accordingly with the date. Storage of Skill Usage Data Create a skillUsage.json file to store the stats and make a JSONTray object for that in DAO.java file. Modify the SusiArguement.java file to check which skill is being currently used for the response and write the skill usage stats to skillUsage.json.The function updateUsageData takes the skillPath in parameter and splits it from slash character. Thus the model name, group name, language name and skill name can be obtained. Then it searches for the date in the JSON file. If it exists then its count value is increased by 1, else a new object is inserted in the JSON file that contains the date and the corresponding usage count. public void updateUsageData(String skillPath) { Boolean dateExists = false; for (int i = 0; i<usageData.length(); i++) { JSONObject dayUsage = usageData.getJSONObject(i); if (dayUsage.get("date").equals(today)){ dayUsage.put("count", dayUsage.getInt("count")+1+""); usageData.put(i,dayUsage); dateExists = true; break; } } } API to access the Skill Usage Data Create GetSkillUsageService.java file to return the usage stats stored in skillUsage.json. It runs at the endponit - /cms/getSkillUage.json and mining role required to access this API is anonymous. It simply reads the data stored in skillUsage.json file and returns it. JsonTray skillRating = DAO.skillUsage; JSONObject languageName = groupName.getJSONObject(language_name); if (languageName.has(skill_name)) { JSONArray skillUsage = languageName.getJSONArray(skill_name); result.put("skill_name", skill_name); result.put("skill_usage", skillUsage); result.put("accepted", true); result.put("message", "Skill usage fetched"); return new ServiceResponse(result); } Add the API file to skillUsage.json services = new Class[]{ ... //Skill usage data GetSkillUsageService.class ... } So now we know which skills are being used at what rate and make the required changes like server scaling, adding more accuracy to the feature etc. We can expand these stats to country wise or device wise usage distribution. Resources Javatpoint (how to get current date and time)  - https://www.javatpoint.com/java-get-current-date  

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Read more about the article Device wise Usage Statistics of a Skill in SUSI.AI

Device wise Usage Statistics of a Skill in SUSI.AI

The device wise usage distribution in SUSI.AI helps in understanding what kind of skills are used more on which type of devices, so that the skill creator can harness the core features of that device to enhance the skills or make the user experience smoother. For example, music playing skill may be used mostly on Smart Speakers whereas Android devices may have higher usage of alarm setting skill. Sending the device type (ex, web client) Send the device type parameter as “Web Client” along with the query while fetching reply from SUSI server in chat.json API. The parameter is device_type. // Add the type of device in the query { url += '&device_type=Web Client'; } Storage of Device Wise Skill Usage Data on SUSI Server Create a deviceWiseSkillUsage.json file to store the device wise skill usage stats and make a JSONTray object for that in src/ai/susi/DAO.java file. The JSON file contains the device type and the usage count on that type of device (like Android, iOS, Web Client, Smart Speaker and others). Modify the src/ai/susi/server/api/susi/SusiService.java file to fetch device_type from the query parameters and pass them SusiCognition constructor. public ServiceResponse serviceImpl(Query post, HttpServletResponse response, Authorization user, final JsonObjectWithDefault permissions) throws APIException { ... String deviceType = post.get("device_type", "Others"); ... SusiCognition cognition = new SusiCognition(q, timezoneOffset, latitude, longitude, countryCode, countryName, language, deviceType, count, user.getIdentity(), minds.toArray(new SusiMind[minds.size()])); ... } Modify the src/ai/susi/mind/SusiCognition.java file to accept the deviceType in the constructor parameters. Check which skill is being currently used for the response and update the skill’s usage stats for the current device in deviceWiseSkillUsage.json. Call the function updateDeviceWiseUsageData() to update the skill usage data. List<String> skills = dispute.get(0).getSkills(); for (String skill : skills) { updateDeviceWiseUsageData(skill, deviceType); } The updateDeviceWiseUsageData() function accepts the skill path and type of device. It parses the skill path to get the skill metadata like its model name, group name, language etc. The function then checks if the device already exists in the JSON file or not. If it exists then it increments the usage count by 1 else it creates an entry for the device in the JSON file and initializes it with the usage count 1. for (int i = 0; i < deviceWiseUsageData.length(); i++) { deviceUsage = deviceWiseUsageData.getJSONObject(i); if (deviceUsage.get("device_type").equals(deviceType)) { deviceUsage.put("count", deviceUsage.getInt("count") + 1); deviceWiseUsageData.put(i,deviceUsage); } } API to access the Device Wise Skill Usage Data Create GetDeviceWiseSkillUsageService.java file to return the usage stats stored in deviceWiseSkillUsage.json public ServiceResponse serviceImpl(Query call, HttpServletResponse response, Authorization rights, final JsonObjectWithDefault permissions) { ... // Fetch the query parameters JSONArray deviceWiseSkillUsage = languageName.getJSONArray(skill_name); result.put("skill_name", skill_name); result.put("skill_usage", deviceWiseSkillUsage); result.put("accepted", true); result.put("message", "Device wise skill usage fetched"); return new ServiceResponse(result); } Add the API file to src/ai/susi/server/api/susi/SusiServer.java services = new Class[]{ ... //Skill usage data GetDeviceWiseSkillUsageService.class ... }   Endpoint : /cms/getDeviceWiseSkillUsage.json Parameters model group language Skill Sample query: /cms/getDeviceWiseSkillUsage.json?model=general&group=Knowledge&language=en&skill=aboutsusi Sample response: { "skill_usage":[ { "device_type": "Web Client", "count": 1 }, { "device_type": "Android", "count": 4 }, { "device_type": "iOS", "count": 2 }, { "device_type": "Smart Speaker", "count": 1 }, {…

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Fetching Bots from SUSI.AI Server

Public skills of SUSI.AI are stored in susi_skill_cms repository. A private skill is different from a public skill. It can be viewed, edited and deleted only by the user of who created it. The purpose of private skill is that this acts as a chatbot. All the information of the bot like design, configuration etc is stored within the private skill itself. In order to show the lists of chatbots a user has created to him/her, we need to fetch these lists from the server. The API for fetching private skills is in ListSkillService.java servlet. This servlet is used for both the purposes for fetching public skills and private skills. How are bots fetched? All the private skills are stored in data/chatbot/chatbot.json location in the SUSI server. Hence, we can fetch these skills from here. The API call to fetch private skills is: https://api.susi.ai/cms/getSkillList.json?private=1&access_token=accessTokenHere The API call has two parameters: private = 1: This parameter tells the servlet that we’re asking for private skills because same API is used for fetching public skills as well. access_token : We have to pass the access-token in order to authenticate the user and access the chatbots of the specific user because the chatbots are stored according to user id of the users in chatbot.json. To get the user id, we need access-token. For fetching the chatbots from chatbot.json, we firstly authenticate the user. Then we check if the user has any chatbots or not. If the user has chatbots then we loop through chatbot.json to find the chatbots of user by using user id. After getting chatbots details, we simply present it in json structure. You can easily understand this through the following code snippets. For authenticating: String userId = null; String privateSkill = call.get("private", null); if (call.get("access_token", null) != null) {     ClientCredential credential = new ClientCredential(ClientCredential.Type.access_token, call.get("access_token", null));     Authentication authentication = DAO.getAuthentication(credential);     // check if access_token is valid     if (authentication.getIdentity() != null) {         ClientIdentity identity = authentication.getIdentity();         userId = identity.getUuid();     } } You can see that the parameter private is stored in privateSkill. We check if privateSkill is not null followed by checking if the user id provided is valid or not. When both the checks are successful, we create a JSON object in which we store all the bots. To do this, we loop through the chatbot.json file to find the user id provided in API call. If the user id doesn’t exist in chatbot.json file then a message “User has no chatbots.” is displayed. If the user id is found then we again loop through all the chatbots and put their name as the value of key “name”,  language as the value of key “language” and group as the value of key “group”. All these key value pairs for the chatbots are pushed in an array and finally that array is added to the JSON object. This JSON is then received as response of the API call. The following code will demonstrate this:…

Continue ReadingFetching Bots from SUSI.AI Server