Table of Contents
Looks
PictoBlox Block

say()

  • PictoBlox Extension: Looks
  • Mode: Stage Mode
  • Type: Stack Block

Description

The block gives its sprite a speech bubble with the specified text — the speech bubble stays until another speech or thought block is activated, or the stop sign is pressed.

Example

Sprite Direction

The example demonstrates the sprite direction in PictoBlox.
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Script

Output

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Language Translator with ChatGPT

The Language Translator with ChatGPT is a powerful system that enables real-time translation and conversation support, facilitating multilingual communication.
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Introduction

The Language Translator with ChatGPT and Speech Recognition is a system that helps people communicate across languages by providing real-time translation and conversation support. It combines language translation, chatbot capabilities, and speech recognition to facilitate multilingual communication.

Language Translator Using ChatGPT is a project that trains the ChatGPT language model with multilingual data to enable it to understand and translate text between different languages. It utilizes ChatGPT’s natural language processing abilities to generate human-like responses, making it ideal for building a language translation system. The training data includes sentence pairs in different languages and their corresponding translations.

Logic

Initially, two characters engage in a conversation. One character asks a question, and the other character converts it into a different language before answering it and providing a response.

  1. Open PictoBlox and create a new file.
  2. Select the environment as appropriate Block Coding Environment.
  3. To add the ChatGPT extension, click on the extension button located as shown in the image. This will enable the ChatGPT extension, allowing you to incorporate its capabilities into your project.
  4. To begin, select two sprites Hazel and John from the sprite options in the bottom left corner, as shown in the image.
  5. To upload a backdrop, use the “Choose Backdrop” option, which allows you to select and set a background image or scene for your activity. Backdrops are used as the type of background in our activity.
  6. To create a script, select a different sprite and then add various block scripts to customize its behavior.
  7. Let’s use the sprites Hazel and Joan for our script.
  8. For Hazel, navigate to the costumes section and enable the “Flip Horizontal” option to add a mirror effect. Set the positions of both sprites as if they are talking to each other.
  9. Click on John’s Sprite first. We will now begin writing a script as shown in the image.
  10. First, we will prompt the user to input a sentence. and using the say() method, the sprite will verbally repeat the same answer provided by the user.
  11. Next, by using the broadcast() block, we can send the translated answer to the second sprite to ensure that both sprites have the same response.
  12. select the second Hazel’s sprite and begin with the when I receive a () block. This block will initiate the action when the second sprite receives a message from the first sprite.
  13. Drag and drop the “translate() into()” function into the block. This block will translate any language of your choice. In this example, we are writing in Hindi language.
  14. We utilize the get AI Response block to obtain a response from ChatGPT. Then, using the say() method, Hazel, the sprite, will deliver the answer in translated sentences.
  15. To begin the script, simply click on the green flag button.

Output

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Noun Detector

Learn about noun detectors, tools or algorithms designed to identify and extract nouns from text or speech inputs.
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Introduction

A noun detector is a tool or algorithm designed to identify and extract nouns from a given text or speech input. Nouns are a type of word that typically represent people, places, things, or ideas. In the context of chat-based applications, a noun detector can be useful for extracting key information or identifying specific entities mentioned in a conversation. It can help in tasks such as named entity recognition, information retrieval, sentiment analysis, and many more.
A noun detector serves as a valuable component in language processing systems, helping to extract and utilize meaningful information from text or speech inputs in chat-based interactions.

Logic

First, ChatGPT generates random sentences, and we save this response in a variable. Then, it asks users to identify a noun from the given sentence. If the user’s answer matches the response generated by ChatGPT, it will say “Correct.” Otherwise, it will say “Incorrect answer.”

  1. Open PictoBlox and create a new file.
  2. Select the environment as appropriate Block Coding Environment.
  3. To add the ChatGPT extension, click on the extension button located as shown in the image. This will enable the ChatGPT extension, allowing you to incorporate its capabilities into your project.
  4. We drag and drop the “Ask (AI)” block from the ChatExtension, and we use it to ask for any random sentence as input from chatGPT.
  5. We create a new variable called sentence and assign the value of a random sentence generated by ChatGPT to it.
  6. Use the say() method to provide instructions for finding nouns in the given sentence.
  7. Drag and drop the get() from the () block from the ChatGPT extension to obtain information from the sentence.
  8. If we use an if-else loop, we prompt the user to identify a noun from a given sentence. If the user’s answer matches the response generated by ChatGPT, it will say Correct answer for 2 minutes.
  9. Otherwise, if the user’s answer does not match the response from ChatGPT, it will return Answer is not a noun for 2 seconds.
  10. To begin the script, simply click on the green flag button.

Code

Output

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Definition with ChatGPT

Discover an interactive way to get word definitions using ChatGPT and text-to-speech. Prompt users to choose a definition, generate it with ChatGPT, and have the sprite speak it out using the text-to-speech extension.
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Logic

We ask the user which definition they want, and based on their input, ChatGPT generates the definition of the particular word. The sprite then uses the text-to-speech extension to speak out the definition.

Follow the steps below:

  1. Open PictoBlox and create a new file.
  2. Choose a suitable coding environment for block-based coding.
  3. Add the text-to-speech and ChatGPT extensions to your project from the extension palette located at the bottom right corner of PictoBlox.
  4. We use the Set Maximum Length to () block to specify the maximum length in ChatGPT, which represents the maximum number of words allowed in a single message.
  5. We prompt users to indicate which definition they would like to receive.
  6. We pass this input to the define() block, which sends a request to ChatGPT to define the user answer.
  7. We utilize the getAI block to retrieve the most recent response from ChatGPT. Then, we use the say() method to have the sprite speak out this response.
  8. Finally, we utilize the speak() block from the text-to-speech extension to have the given text spoken aloud.

Code

Output

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Synonym and Antonym Conveter

Discover a unique experience in a Synonym/antonyms World, where the combined powers of Speech Recognition and ChatGPT Extension.
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Introduction

Hey! Welcome to the fascinating realm of “Synonym/Antonym World,” where the powers of Speech Recognition and ChatGPT converge. Immerse yourself in an innovative platform that not only recognizes your speech but also provides an extensive collection of synonyms and antonyms for any given word. With this powerful combination, you can effortlessly expand your vocabulary, explore alternative expressions, and delve into the nuances of language. Unleash the potential of speech recognition and ChatGPT as you navigate through a world where words find their perfect counterparts. Get ready to unlock new dimensions of linguistic exploration in the captivating Synonym/Antonym World!

Code

Logic

  1. Open PictoBlox and create a new file.
  2. Choose a suitable coding environment for Block-based coding.
  3. We create an instance of the Speech recognition.This class allows us to convert spoken audio into text.
  4. Next, we create an instance of the ChatGPT model called gpt. ChatGPT is a language model that can generate human-like text responses based on the input it receives. 
  5. Recognize speech for 5 seconds using recognize speech for ()s in the () block.
  6. Save the recognized result in the “input” variable.
  7. Use the “get(synonyms) of ()” function to obtain synonyms of the recognized speech result.
  8. ChatGPT will provide the answers for 10 response synonyms in the “Synonym World” based on the given input.
  9. Use the “get(antonyms) of ()” function to obtain antonyms of the recognized speech result.
  10. The output of the anonymous word of the given input will be displayed by the sprite.
  11. Click on the green flag to run the code.

Output

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Personalized Chat Experience :Interactive Chatbox

Experience an interactive chatbox where you can specify the tone of AI responses. Ask questions or share thoughts on various topics, and receive personalized answers.
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Introduction

In this interactive chatbox experience, the user has the freedom to specify the tone in which they would like the AI to respond. The tone can be anything they prefer: Normal, sarcastic, Friend that suits their preference.

Once the user has selected a particular tone, they can provide their input or ask a question. Based on their input, the AI will generate a response that aligns with the chosen tone. This allows for the creation of a conversational atmosphere similar to real-life interactions.

Users are encouraged to ask any question or share their thoughts on various topics. They can also engage in discussions or seek assistance with the information they need. The AI is there to facilitate a meaningful conversation and provide helpful responses based on the tone chosen by the user.

So, the user is requested to let the AI know the specific tone they would like it to adopt, and then they are free to ask any question they have in mind. The AI is here to provide a personalized and engaging chat experience!

Code

Logic

  1. Open PictoBlox and create a new file.
  2. Choose a suitable coding environment for Block-based coding.
  3. We create an instance of the Speech recognition.This class allows us to convert spoken audio into text.
  4. Next, we create an instance of the ChatGPT model called gpt. ChatGPT is a language model that can generate human-like text responses based on the input it receives. 
  5. We create an instance of the Text to Speech.This class allows us to speak the output.
  6. Use the “recognize speech” block to capture the user’s speech for a duration of 5 seconds.
  7. First, ask the user which format they prefer for the answer: normal, sarcastic, or friendly
  8. If the user requests a normal response, the ChatGPT extension will generate feedback in normal mode.
  9. If the user requests a sarcastic response, ChatGPT will generate a sarcastic answer.
  10. If the user requests a friendly response, ChatGPT will generate a friendly answer. Additionally, Sprite will speak the answer.
  11. The “speak” block, which utilizes the text-to-speech recognition extension, will vocalize the answer to the asked question.

In summary, we can describe it as a chatbox that provides answers in three different ways based on the user’s mood and entertainment preferences. When a question is asked, it will respond accordingly.

Output

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Transform Words into Colorful Emojis

Convert any word or phrase into a delightful sequence of emojis with our Emoji Converter.
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Introduction

Are you looking to add some fun and expressiveness to your conversations? Look no further! I’m here to help you convert any word or phrase into a colorful array of emojis. Whether you want to spice up your messages, or social media posts, or simply bring a smile to someone’s face, I’ve got you covered.

Just type in the word or phrase you want to transform, and I’ll generate a delightful sequence of emojis that capture the essence of your text. Emojis are a universal language that transcends words from happy faces to animals, objects, and everything in between.

So, let’s get started and infuse your text with a touch of emoji magic! 🎉🔥

Logic

This code allows the user to interact with the sprite and provide emojis, which are then transformed into a response using the ChatGPT model. The sprite then speaks the generated response using the provided emojis.

  1. Open PictoBlox and create a new file.
  2. Choose a suitable coding environment for Block-based coding.
  3. Define a sprite , Tobi.
  4. Then, we create an instance of the ChatGPT model using the ChatGPT class.
  5. The sprite will ask you which word you want to convert into emojis.
  6. ChatGPT will respond based on its using getAIresponce block.
  7. The sprite will display the chosen word in emojis.
  8. Press Green Flag to Run the code.

Code

Output

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Yoga Pose Timer Bot using Pose Classifier in ML Environment

The examples show how to use pose recognition in PictoBlox to maintain a yoga pose for a particular time interval.
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Script

The idea is simple, we’ll add one image of  each class in the “costume” column by making one new sprite which will we display on the stage according to input from user. we’ll also change name of the image according to pose.

  1. Add testing images to the backdrop and delete the default backdrop.
  2. Now, come back to the coding tab and select the Tobi sprite.
  3. We’ll start by adding a when flag clicked block from the Events palette.
  4. We made the new variable “count” by choosing the “Make a Variable” option from the Variables palette.
  5. Add the “hide variable () block from the Variables palette. Select count.
  6. Add the “turn () video on stage with () transparency” block from the Machine Learning palette. Select the off option at the first empty place, and for the second, write a 0 value.
  7. Add an “ask () and wait” block from the Sensing palette. Write an appropriate statement in an empty place.
  8. Add the “if () then” block from the control palette for checking the user’s input.
  9. In the empty place of the “if () then” block, add a condition checking block from the operators palette block. At the first empty place, put the answer block from the sensing palette, and at the second place, write an appropriate statement.
  10. Inside the “if () then” block, add a “broadcast ()” block from the Events palette block. Select the “New message” option and write an appropriate statement for broadcasting a message to another sprite.
  11. Add the “turn () video on stage with () transparency” block from the Machine Learning palette. Select the option at the first empty place, and for the second, write a 0 value.
  12. Add the “() key points” block from the Machine Learning palette. Select the show option.
  13. Add the “Set () to ()” block from the Variables palette. Select the count option at the first empty place, and for the second, write a 30 value.
  14. Add the Show variable () block from the Variables palette. Select count.
  15. Add “forever” from the Control palette.
  16. Inside the “forever” block, add an “analysis image from ()” block from the Machine Learning palette. Select the Web camera option.
  17. Inside the “forever” block, add an “if () then” block from the Control palette.
  18. In the empty place of the “if () then” block, add an “is identified class ()” block from the Machine Learning palette. Select the appropriate class from the options.
  19. Inside the “if () then” block, add an “say ()” block from the Looks palette block. Write an appropriate statement in an empty place.
  20. Add “change () by ()” from the Variables palette. Select the count option in the first empty place, and for the second, write a -1 value.

  21. Add the “if () then” block from the control palette for checking the user’s input.
  22. In the empty place of the “if () then” block, add a condition checking block from the operators palette block. In the first empty place, put the “count” block from the sensing palette, and in the second place, write 0.
  23. Add the “Set () to ()” block from the Variables palette. Select the count option at the first empty place, and for the second, write a 30 value.
  24. Add the “turn () video on stage with () transparency” block from the Machine Learning palette. Select the off option at the first empty place, and for the second, write a 0 value.
  25. Inside the “if () then” block, add an “say ()” block from the Looks palette block. Write an appropriate statement in an empty place.
  26. Add the “() key points” block from the Machine Learning palette. Select the hide option
  27. Add the “stop ()” block to the control pallet. Select all options.
  28. Repeat “if () then” block code for other classes, make appropriate changes in copying block code according to other classes, and add code just below it.
  29. The final block code looks like
  30. Now click on another sprite and write code.
  31. We’ll start writing code for this sprite by adding a when flag is clicked block from the Events palette.
  32. Add the “hide” block from the Looks pallet.
  33. Write a new code in the same sprite according to class and add the “when I receive ()” block from the Events palette. Select the appropriate class from the options.
  34. Add the “show” block from the Looks pallet.
  35. Add the “switch costume to ()” block from the Looks palette. Select the appropriate class from the options.
  36. Repeat the same code for other classes and make changes according to the class.

    Final Result

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Iris Classifier Bot in Block Coding using Number Classifier

The example shows how to use a number classifier in PictoBlox to make the Iris Classifier Bot.
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Script

The idea is simple, we’ll add one image of  each class in the “costume” column by making one new sprite which will we display on the stage according to input from user. we’ll also change name of the image according to iris type.

  1. Add iris image as another sprite and upload one image of all iris classes on costume.
  2. Now, come back to the coding tab and select the Tobi sprite.
  3. We’ll start by adding a when flag clicked block from the Events palette.
  4. Add an “ask () and wait” block from the Sensing palette. Write an appropriate statement in an empty place.
  5. Add the “set () as ()” block from the Machine Learning palette. Select the SepalLengthCm option at the first empty place, and for the second select an “answer” block from the Sensing palette.
  6. Add an “ask () and wait” block from the Sensing palette. Write an appropriate statement in an empty place.
  7. Add the “set () as ()” block from the Machine Learning palette. Select the SepalWidthCm option at the first empty place, and for the second select an “answer” block from the Sensing palette.
  8. Add an “ask () and wait” block from the Sensing palette. Write an appropriate statement in an empty place.
  9. Add the “set () as ()” block from the Machine Learning palette. Select the PetalLengthCm option at the first empty place, and for the second select an “answer” block from the Sensing palette.
  10. Add an “ask () and wait” block from the Sensing palette. Write an appropriate statement in an empty place.
  11. Add the “set () as ()” block from the Machine Learning palette. Select the PetalWidthCm option at the first empty place, and for the second select an “answer” block from the Sensing palette.
  12. Add an “analyse numbers” block from the Machine Learning palette.
  13. Add the “if () then” block from the control palette for checking the user’s input.
  14. In the empty place of the “if () then” block, add an “is identified class ()” block from the Machine Learning palette. Select the appropriate class from the options.
  15. Inside the “if () then” block, add an “say ()” block from the Looks palette block. Write an appropriate statement in an empty place.
  16. Inside the “if () then” block, add a “broadcast ()” block from the Events palette block. Select the “New message” option and write an appropriate statement for broadcasting a message to another sprite.
  17. Repeat “if () then” block code for other iris classes, make appropriate changes in copying block code according to other classes, and add code just below it.
  18. Final code of “Tobi” sprite is
  19. Now click on another sprite and write code.
  20. We’ll start writing code for this sprite by adding a when flag is clicked block from the Events palette.
  21. Add the “hide” block from the Looks pallet.
  22. Write a new code in the same sprite according to class and add the “when I receive ()” block from the Events palette. Select the appropriate class from the options.
  23. Add the “show” block from the Looks pallet.
  24. Add the “switch costume to ()” block from the Looks palette. Select the appropriate class from the options.
  25. Repeat the same code for other classes and make changes according to the class.

    Final Result

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Hand Sign Classifier Bot in Block Coding

The example shows how to use a hand pose classifier in PictoBlox to make the Sign Classifier Bot.
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Introduction

In this example project, we are going to create a machine learning model that can classify different sign messages from the camera feed or image.

Hand Gesture Classifier in Machine Learning Environment

The Hand Gesture Classifier is the extension of the ML Environment used for classifying different hand poses into different classes.

 

Hand Gesture Classifier Workflow

  1. Open PictoBlox and create a new file.
  2. You can click on “Machine Learning Environment” to open it.
  3. Click on “Create New Project“.
  4. A window will open. Type in a project name of your choice and select the Hand Gesture Classifier” extension. Click the “Create Project” button to open the Pose Classifier window.
  5. You shall see the Gesture Pose Classifier workflow with two classes already made for you. Your environment is all set. Now it’s time to upload the data.

Class in Pose Classifier

Class is the category in which the Machine Learning model classifies the poses. Similar posts are put in one class.

Class in Hand Pose Classifier

Class is the category in which the Machine Learning model classifies the hand poses. Similar hand poses are put in one class.

Adding Data to Class

You can perform the following operations to manipulate the data into a class.

  1. Naming the Class: You can rename the class by clicking on the edit button.
  2. Adding Data to the Class: You can add the data using the Webcam or by Uploading the files from the local folder.
    1. Webcam:

      Note: You can edit the capture setting in the camera with the following. Hold to Record allows you to capture images with pose till the time button is pressed. Whereas when it is off you can set the start delay and duration of the sample collection.

      If you want to change your camera feed, you can do it from the webcam selector in the top right corner.

Training the Model

After data is added, it’s fit to be used in model training. In order to do this, we have to train the model. By training the model, we extract meaningful information from the hand pose, and that in turn updates the weights. Once these weights are saved, we can use our model to make predictions on data previously unseen.

However, before training the model, there are a few hyperparameters that you should be aware of. Click on the “Advanced” tab to view them.

Note: These hyperparameters can affect the accuracy of your model to a great extent. Experiment with them to find what works best for your data.

Note: Hover your mouse over the question mark next to the hyperparameters to see their description.

It’s a good idea to train a numeric classification model for a high number of epochs. The model can be trained in both JavaScript and Python. In order to choose between the two, click on the switch on top of the Training panel.

Alert: Dependencies must be downloaded to train the model in Python, JavaScript will be chosen by default.

The accuracy of the model should increase over time. The x-axis of the graph shows the epochs, and the y-axis represents the accuracy at the corresponding epoch. Remember, the higher the reading in the accuracy graph, the better the model. The x-axis of the graph shows the epochs, and the y-axis represents the corresponding accuracy. The range of the accuracy is 0 to 1.

Testing the Model

To test the model, simply enter the input values in the “Testing” panel and click on the “Predict” button.

The model will return the probability of the input belonging to the classes.

Export in Block Coding

Click on the “Export Model” button on the top right of the Testing box, and PictoBlox will load your model into the Block Coding Environment if you have opened the ML Environment in the Block Coding.

Script

The idea is simple, we’ll add one image of  each class in the “costume” column by making one new sprite which will we display on the stage according to input from user. we’ll also change name of the image according to sign class type.

  1. Add one sign image as another sprite and upload at-least one image of all sign classes on costume.
  2. Now, come back to the coding tab and select the Tobi sprite.
  3. We’ll start by adding a when flag clicked block from the Events palette.
  4. Add the “open recognition window” block from the Machine Learning palette.
  5. Add an “forever” block from the Control palette.
  6. Add the “if () then” block from the control palette for checking the user’s input.
  7. In the empty place of the “if () then” block, add an “is identified class ()” block from the Machine Learning palette. Select the appropriate class from the options.
  8. Inside the “if () then” block, add an “say ()” block from the Looks palette block. Write an appropriate statement in an empty place.
  9. Inside the “if () then” block, add a “broadcast ()” block from the Events palette block. Select the “New message” option and write an appropriate statement for broadcasting a message to another sprite.
  10. Repeat “if () then” block code for other classes, make appropriate changes in copying block code according to other classes, and add code just below it.
  11. Final code of “Tobi” sprite is
  12. Now click on another sprite and write code.
  13. We’ll start writing code for this sprite by adding a when flag is clicked block from the Events palette.
  14. Add the “hide” block from the Looks pallet.
  15. Write a new code in the same sprite according to class and add the “when I receive ()” block from the Events palette. Select the appropriate class from the options.
  16. Add the “show” block from the Looks pallet.
  17. Add the “switch costume to ()” block from the Looks palette. Select the appropriate class from the options.
  18. Repeat the same code for other classes and make changes according to the class.

Final Output

 

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Customer Spending Money Classifier Bot in Block Coding

The example shows how to use a number classifier in PictoBlox to make the customer spending money classifier bot.
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Introduction

In this example project, we are going to create a Machine Learning Model that can predict the amount of money a customer will spend based on the input details added by the user.

Numbers(C/R) in Machine Learning Environment

Datasets on the internet are hardly ever fit to directly train on. Programmers often have to take care of unnecessary columns, text data, target columns, correlations, etc. Thankfully, PictoBlox’s ML Environment is packed with features to help us pre-process the data as per our liking.

Let’s create the ML model.

Opening Image Classifier Workflow

Alert: The Machine Learning Environment for model creation is available in the only desktop version of PictoBlox for Windows, macOS, or Linux. It is not available in Web, Android, and iOS versions.

Follow the steps below:

  1. Open PictoBlox and create a new file.
  2. Select the coding environment as Block Coding Environment.
  3. Select the “Open ML Environment” option under the “Files” tab to access the ML Environment.
  4. You’ll be greeted with the following screen.
    Click on “Create New Project“.
  5. You shall see the Numbers C/R workflow with an option to either “Upload Dataset” or “Create Dataset”.

    Uploading/Creating Dataset

    Datasets can either be uploaded or created on the ML Environment. Lets see how it is done.

    Uploading a dataset
    1. To upload a dataset, click on the Upload Dataset button and the Choose CSV from your files button.
      Note: An uploaded dataset must be a “.csv” file.
    2. Once uploaded the first 50 rows of the uploaded CSV document will show up in the window.
    Creating a Dataset
    1. To create a dataset, click on the Create Dataset button.
    2. Select the number of rows and columns that are to be added and click on the Create button. More rows and columns can be added as and when needed.

    Notes:

    1. Each column represents a feature. These are the values used by the model to train itself.
    2. The “Output” column contains the target values. These are the values that we expect the model to return when features are passed.
    3. The window only shows the first 50 rows of the dataset.
    4. Un-check the “Select All” checkbox to un-select all the columns.

    Training the Model

    After data is pre-processed and optimized, it’s fit to be used in model training. To train the model, simply click the “Train Model” button found in the “Training” panel.

    By training the model, meaningful information is extracted from the numbers, and that in turn updates the weights. Once these weights are saved, the model can be used to make predictions on data previously unseen.

    The model’s function is to use the input data and predict the output. The target column must always contain numbers.

    However, before training the model, there are a few hyperparameters that need to be understood. Click on the “Advanced” tab to view them.

    There are three hyperparameters that can be altered in the Numbers(C/R) Extension:

    1. Epochs– The total number of times the data will be fed through the training model. Therefore, in 10 epochs, the dataset will be fed through the training model 10 times. Increasing the number of epochs can often lead to better performance.
    2. Batch Size– The size of the set of samples that will be used in one step. For example, if there are 160 data samples in the dataset, and the batch size is set to 16, each epoch will be completed in 160/16=10 steps. This hyperparameter rarely needs any altering.
    3. Learning Rate– It dictates the speed at which the model updates the weights after iterating through a step. Even small changes in this parameter can have a huge impact on the model performance. The usual range lies between 0.001 and 0.0001.
    Note: Hover the mouse pointer over the question mark next to the hyperparameters to see their description.

    It’s a good idea to train a numeric classification model for a high number of epochs. The model can be trained in both JavaScript and Python. In order to choose between the two, click on the switch on top of the Training panel.

    Alert: Dependencies must be downloaded to train the model in Python, JavaScript will be chosen by default.

    The accuracy of the model should increase over time. The x-axis of the graph shows the epochs, and the y-axis represents the accuracy at the corresponding epoch.

    A window will open. Type in a project name of your choice and select the “Numbers(C/R)” extension. Click the “Create Project” button to open the Numbers(C/R) window.

    Testing the Model

    To test the model, simply enter the input values in the “Testing” panel and click on the “Predict” button.

    The model will return the probability of the input belonging to the classes.

Export in Block Coding

Click on the “Export Model” button on the top right of the Testing box, and PictoBlox will load your model into the Block Coding Environment if you have opened the ML Environment in the Block Coding.

Script

  1. Select the “Tobi” sprite.
  2. We’ll start by adding a “when flag clicked” block from the Events palette.
  3. Add an “ask () and wait” block from the Sensing palette. Write an appropriate statement in an empty place.
  4. Add the “if () then” block from the control palette for checking the user’s input.
  5. In the empty place of the “if () then” block, add an “()=()” block from the Operator palette. At the first empty place select an “answer” block from the Sensing palette and for the second place write an appropriate statement in an empty place.
  6. Add the “set gender as ()” block from the Machine Learning palette. Select the Male option at the empty place.
  7. Repeat “if () then” block code for other variables, make appropriate changes in copying block code according to other variables, and add code just below it.
  8. Add an “ask () and wait” block from the Sensing palette. Write an appropriate statement in an empty place.
  9. Add the “if () then” block from the control palette for checking the user’s input.
  10. In the empty place of the “if () then” block, add an “()=()” block from the Operator palette. At the first empty place select an “answer” block from the Sensing palette and for the second place write an appropriate statement in an empty place.
  11. Add the “set education as ()” block from the Machine Learning palette. Select the High School option at the empty place.
  12. Repeat “if () then” block code for other variables, make appropriate changes in copying block code according to other variables, and add code
  13. Add an “ask () and wait” block from the Sensing palette. Write an appropriate statement in an empty place.
  14. Add the “set () as ()” block from the Machine Learning palette. Select the age option at the first empty place, and for the second select an “answer” block from the Sensing palette.
  15. Repeat “ask () and wait” block code for other variables, make appropriate changes in copying block code according to other variables, and add code.
  16. Repeat “ask () and wait” block code for other variables, make appropriate changes in copying block code according to other variables, and add code.
  17. Add the “say ()” block from the Looks palette.
  18. Add an “join () ()” block from the Operator palette. Write an appropriate statement in an first empty place and at second empty place add “analyse numbers” block from the Machine Learning palette. 

    Final Result

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PictoBlox Extensions
  1. Scratch Based Default Extensions
    1. Motion
    2. Looks
    3. Sound
    4. Control
    5. Events
    6. Sensing
    7. Operators
    8. My Blocks
    9. Variables
  2. Artificial Intelligence
    1. Face Detection
    2. Object Detection
    3. Human Body Detection
    4. Computer Vision
    5. Text Recognition
    6. Speech Recognition
    7. ChatGPT
    8. Natural Language Processing
    9. Recognition Cards
    10. Text to Speech
    11. Translate
    12. Open CV
  3. Innovative Extensions
    1. QR Code Scanner
    2. Weather Data
    3. Physics Engine
    4. IFTTT Webhooks
    5. Internet of Things (IoT)
    6. Data Logger
    7. Music
    8. Video Sensing
    9. Pen
  4. Machine Learning Environment
    1. Image Classifier (ML)
    2. Object Detection (ML)
    3. Pose Classifier (ML)
    4. Hand Pose Classifier (ML)
    5. Audio Classifier (ML)
    6. Number Classifier and Regression (ML)
    7. Text Classifier (ML)
  5. Quarky
    1. Quarky (Main)
    2. Display (Quarky)
    3. Robot (Quarky)
    4. Sensors (Quarky)
    5. Speaker (Quarky)
    6. Quarky Ultimate Robots
    7. Quarky Expansion Board
    8. Mars Rover
    9. Humanoid (Quarky)
    10. Quadruped (Quarky)
    11. IoT House (Quarky)
    12. Quarky Mecanum
    13. Quarky Robotic Arm
    14. Dabble

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