ZoltarController.groovy

import java.io.FileInputStream;
import java.io.BufferedInputStream;
import java.io.IOException;
import java.io.InputStream;
import javax.sound.sampled.AudioFormat
import javax.sound.sampled.AudioSystem;
import javax.sound.sampled.DataLine
import javax.sound.sampled.SourceDataLine
import javax.sound.sampled.TargetDataLine
import javax.sound.sampled.UnsupportedAudioFileException;

import org.vosk.LogLevel;
import org.vosk.Recognizer;

import com.neuronrobotics.bowlerstudio.scripting.ScriptingEngine

import java.net.URI;
import java.net.URISyntaxException;
import java.nio.charset.StandardCharsets

import java.nio.file.Files
import java.nio.file.Paths
import java.util.logging.Level
import java.util.logging.Logger

import javax.sound.sampled.AudioInputStream
import javax.sound.sampled.LineEvent
import javax.sound.sampled.LineListener

import com.neuronrobotics.bowlerstudio.AudioPlayer
import com.neuronrobotics.bowlerstudio.AudioStatus
import com.neuronrobotics.bowlerstudio.ISpeakingProgress
import com.neuronrobotics.bowlerstudio.creature.MobileBaseCadManager
import com.neuronrobotics.bowlerstudio.creature.MobileBaseLoader
import com.neuronrobotics.bowlerstudio.BowlerKernel
import com.neuronrobotics.bowlerstudio.BowlerStudio
import com.neuronrobotics.bowlerstudio.BowlerStudioController
import com.neuronrobotics.bowlerstudio.IAudioProcessingLambda
import com.neuronrobotics.sdk.addons.kinematics.AbstractLink
import com.neuronrobotics.sdk.addons.kinematics.DHParameterKinematics
import com.neuronrobotics.sdk.addons.kinematics.MobileBase
import com.neuronrobotics.sdk.addons.kinematics.math.RotationNR
import com.neuronrobotics.sdk.addons.kinematics.math.TransformNR
import com.neuronrobotics.sdk.common.DeviceManager
import com.neuronrobotics.sdk.util.ThreadUtil
import com.squareup.okhttp.MediaType
import com.squareup.okhttp.OkHttpClient
import com.squareup.okhttp.Request
import com.squareup.okhttp.RequestBody
import com.squareup.okhttp.Response

import ai.djl.inference.Predictor
import ai.djl.modality.cv.Image
import ai.djl.modality.cv.ImageFactory
import ai.djl.modality.cv.output.BoundingBox
import ai.djl.modality.cv.output.DetectedObjects
import ai.djl.modality.cv.output.DetectedObjects.DetectedObject
import ai.djl.pytorch.jni.JniUtils
import ai.djl.repository.zoo.ZooModel
import groovy.ui.SystemOutputInterceptor
import javafx.application.Platform
import javafx.scene.control.Alert
import javafx.scene.control.Alert.AlertType
import javafx.scene.control.TextInputDialog
import marytts.LocalMaryInterface
import marytts.MaryInterface
import marytts.datatypes.MaryData
import marytts.exceptions.SynthesisException
import net.lingala.zip4j.ZipFile
import net.lingala.zip4j.exception.ZipException

import com.google.gson.Gson;
import com.google.gson.GsonBuilder;
import com.google.gson.reflect.TypeToken;
import java.lang.reflect.Type;

import org.opencv.core.CvType;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.objdetect.CascadeClassifier;
import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
import java.awt.image.WritableRaster;

import java.io.FileNotFoundException;
import javafx.application.Application;
import javafx.embed.swing.SwingFXUtils;
import javafx.scene.Group;
import javafx.scene.Scene;
import javafx.scene.control.Tab
import javafx.scene.image.ImageView;
import javafx.scene.image.WritableImage;
import javafx.scene.layout.HBox
import javafx.scene.layout.VBox

import org.opencv.videoio.VideoCapture;

import org.opencv.core.Size;
import org.opencv.objdetect.Objdetect;
import com.neuronrobotics.bowlerkernel.djl.ImagePredictorType;
import com.neuronrobotics.bowlerkernel.djl.PredictorFactory;
import com.neuronrobotics.bowlerkernel.djl.UniquePerson
import com.neuronrobotics.bowlerkernel.djl.UniquePersonFactory
import com.neuronrobotics.bowlerstudio.lipsync.RhubarbManager;
import com.neuronrobotics.bowlerstudio.lipsync.VoskLipSync
import com.neuronrobotics.bowlerstudio.opencv.OpenCVManager


boolean regen=false;
MobileBase base=DeviceManager.getSpecificDevice( "zoltar",{
	//If the device does not exist, prompt for the connection

	MobileBase m = MobileBaseLoader.fromGit(
			"https://github.com/Halloween2020TheChild/Zoltar.git",
			"zoltar.xml"
			)
	if(m==null)
		throw new RuntimeException("Arm failed to assemble itself")
	println "Connecting new device robot arm "+m
	regen=true;
	return m
})

// remove this and the script faults out BowlerStudio... wtf...
ScriptingEngine.gitScriptRun("https://github.com/Halloween2020TheChild/Zoltar.git", "ResnetWithCamera.groovy")

//if(regen) {
//	MobileBaseCadManager get = MobileBaseCadManager.get( base)
//	get.setConfigurationViewerMode(false)
//	get.generateCad()
//	Thread.sleep(100);
//	while(get.getProcesIndictor().get()<1){
//		println "Waiting for cad to get to 1:"+get.getProcesIndictor().get()
//		ThreadUtil.wait(1000)
//	}
//}
DHParameterKinematics arm = base.getAllDHChains().get(0);
//arm.homeAllLinks()
MobileBase head = arm.getSlaveMobileBase(5)
AbstractLink mouth =head.getAllDHChains().get(0).getAbstractLink(0)
AbstractLink eye =head.getAllDHChains().get(1).getAbstractLink(0)
eye.setTargetEngineeringUnits(-42.0);
eye.flush(0)

AudioPlayer.setLambda(com.neuronrobotics.bowlerstudio.lipsync.VoskLipSync.get());
double globalAmp=0;
double globalCurrentRoll=0;
double globalCurrentDeriv=0;
double globalCurrentCalculated=0;
boolean update=false;

class RollingAverage {
	private int depth;
	private double [] samples
	boolean stare=true;
	double rollingSum =0
	int index=0
	double max=-1;
	public RollingAverage(int depth) {
		this.depth = depth;
		samples=new double[depth];
	}
	double get(double current) {
		if(max>0) {
			if(current>max)
				current=max;
			if(current<-max)
				current=-max;
		}
		if(stare) {
			stare=false;
			rollingSum=0;
			for(int i=0;i<depth;i++) {
				samples[i]=current;
				rollingSum+=current;
			}
		}
		rollingSum-=samples[index];
		samples[index]=current;
		rollingSum+=current;
		index++;
		if(index==depth)
			index=0;
		return rollingSum/((double)depth)
	}

}
Predictor<BufferedImage, DetectedObjects> predictorDummy  = PredictorFactory.imageContentsFactory(ImagePredictorType.ultranet);

public class GPTInterface {
	private int width
	private int height
	private double tiltAngle
	Alert a;
	public final String AI_MODEL_NAME = "gpt-3.5-turbo";
	Tab t=new Tab()
	double percent=0;
	private String API_KEY;
	private static final String CHATGPT_API_URL = "https://api.openai.com/v1/chat/completions"



	static final String modelName = "vosk-model-en-us-daanzu-20200905";
	Type TT_mapStringString = new TypeToken<HashMap<String, Object>>() {}.getType();
	Gson gson = new GsonBuilder().disableHtmlEscaping().setPrettyPrinting().create();
	int maxSize = 240
	AudioStatus status;
	AudioStatus laststatus



	AudioFormat format = new AudioFormat(AudioFormat.Encoding.PCM_SIGNED, 60000, 16, 2, 4, 44100, false);
	DataLine.Info info = new DataLine.Info(TargetDataLine.class, format);
	TargetDataLine microphone;
	SourceDataLine speakers;
	UniquePersonFactory   upf= UniquePersonFactory.get();;
	VideoCapture capture = OpenCVManager.get(2).getCapture();

	int absoluteFaceSize=0;
	Mat matrix;
	WritableImage img = null;

	Predictor<BufferedImage, DetectedObjects> predictor  = PredictorFactory.imageContentsFactory(ImagePredictorType.ultranet);
	ImageFactory 		factory=ImageFactory.getInstance();
	int frames=0;
	org.opencv.core.Point noseCenterOfFace = null
	VBox workingMemory=new VBox()
	UniquePerson personToPayAttentionTo;
	long timePersonLastSeen=0;
	long attentionTImeout=10000;
	boolean startup = true;
	public GPTInterface(String APIKey) {
		this.API_KEY = APIKey;
		matrix =new Mat();
		upf.setWorkingMemory(workingMemory);
		for(int i=0;i<5;i++)
		if( capture.isOpened()) {
			if (capture.read(matrix)) {
				println "Camera flush "+i
			}
		}
		getFaces()
		startup=false
	}

	public String request(String phrase) throws IOException {
		return request(phrase, 0.7f,5);
	}

	public String promptFromMicrophone() {
		String result = VoskLipSync.promptFromMicrophone();

		if(result==null)
			result="What is my fortune?"
		return result;
	}
	public double lookVector() {
		if(noseCenterOfFace==null)
			return 0.0
		try {
			return noseCenterOfFace.x/((double)width)/2.0-0.5
		}catch(Exception ex) {
			return 0;
		}
	}
	public double nodVector() {
		if(noseCenterOfFace==null)
			return 0.0
		try {
			return noseCenterOfFace.y/((double)height)/2.0-0.5
		}catch(Exception ex) {
			return 0;
		}
	}
	public Rect[] getFaces() {
		if( capture.isOpened()) {
			//println "Camera Open"
			// If there is next video frame
			if (capture.read(matrix)) {
				frames++;
				//println "Frames "+frames
				MatOfRect faces = new MatOfRect();
				Mat grayFrame = new Mat();
				// face cascade classifier

				// convert the frame in gray scale
				Imgproc.cvtColor(matrix, grayFrame, Imgproc.COLOR_BGR2GRAY);
				// equalize the frame histogram to improve the result
				Imgproc.equalizeHist(grayFrame, grayFrame);

				// compute minimum face size (20% of the frame height, in our case)
				if (absoluteFaceSize == 0)
				{
					height = grayFrame.rows();
					width =grayFrame.cols();
					if (Math.round(height * 0.2f) > 0)
					{
						absoluteFaceSize = Math.round(height * 0.2f);
					}
				}
				// each rectangle in faces is a face: draw them!

				//println "Capture success"
				// Creating BuffredImage from the matrix
				BufferedImage image = new BufferedImage(matrix.width(),
						matrix.height(), BufferedImage.TYPE_3BYTE_BGR);

				WritableRaster raster = image.getRaster();
				DataBufferByte dataBuffer = (DataBufferByte) raster.getDataBuffer();
				byte[] data = dataBuffer.getData();
				matrix.get(0, 0, data);
				ai.djl.modality.cv.Image imgtmp =factory.fromImage(image)
				if(startup)println "Img from factory "+matrix.width()+" by "+matrix.height()+" bytes "+data.length
				DetectedObjects detection = predictor.predict(imgtmp);
				List<DetectedObject> items = detection.items();
				if(startup)println "Detections  "+items
				Rect[] facesArray = new Rect[items.size()];
				for (int detectionIndex = 0; detectionIndex < items.size(); detectionIndex++) {
					DetectedObject c = items.get(detectionIndex);
					BoundingBox cGetBoundingBox = c.getBoundingBox();
					def topLeft = cGetBoundingBox.getPoint();
					def rect = cGetBoundingBox.getBounds();
					Iterator<ai.djl.modality.cv.output.Point> path = cGetBoundingBox.getPath().iterator();
					ArrayList<ai.djl.modality.cv.output.Point> list = new ArrayList<>();
					// sort into an ordered list
					for(ai.djl.modality.cv.output.Point p:path) {
						boolean added=false;
						for(int j=0;j<list.size();j++) {
							if(p.getY()<list.get(j).getY()) {
								list.add(j, p);
								added=true;
								break;
							}
						}
						if(!added)
							list.add(p)
					}
					if(list.size()>=5) {

						def left = list.get(0)
						def right=list.get(1)

						if(left.getY()!=right.getY()) {
							double y=left.getY()-right.getY()
							double x=left.getX()-right.getX()
							tiltAngle=Math.toDegrees(Math.atan2(y, x))
							if(tiltAngle<-90) {
								tiltAngle+=180
							}
							//println "Tilt angle = "+tiltAngle
						}else {
							// angle is 0, they are the same
							tiltAngle = 0
						}
						//noseCenterOfFace = list.get(2)
					}
					//lm.get
					Rect crop=new Rect(topLeft.getX()*matrix.width(),topLeft.getY()*matrix.height(),rect.getWidth()*matrix.width() ,rect.getHeight()*matrix.height())
					//System.out.println(c);
					facesArray[detectionIndex]=crop;
					//System.out.println("Name: "+c.getClassName() +" probability "+c.getProbability()+" center x "+topLeft.getX()+" center y "+topLeft.getY()+" rect h"+rect.getHeight()+" rect w"+rect.getWidth() );
					Imgproc.rectangle(matrix, crop.tl(), crop.br(), new Scalar(0, 255, 0), 3);
					Imgproc.putText(matrix, c.getClassName(), new Point(topLeft.getX()*matrix.width(),topLeft.getY()*matrix.height()-5), 3,1,  new Scalar(0, 255, 0));
					if(list.size()>3) {
						for(int j=0;j<2;j++) {
							ai.djl.modality.cv.output.Point p= list.get(j)
							Imgproc.circle(matrix, new Point(p.getX(),p.getY()), 3, new Scalar(255, 0, 0))
						}
						ai.djl.modality.cv.output.Point n= list.get(2)
						Imgproc.circle(matrix, new Point(n.getX(),n.getY()), 5, new Scalar(0, 0, 255))
						for(int j=list.size()-2;j<list.size();j++) {
							ai.djl.modality.cv.output.Point p= list.get(j)
							Imgproc.circle(matrix, new Point(p.getX(),p.getY()), 3, new Scalar(255, 0, 255))
						}
					}
					upf.addFace(matrix,crop,list.get(2));

				}
				if( facesArray.length>0) {
					upf.setProcessFlag()
					Thread.sleep(1);
				}
				HashMap<UniquePerson,org.opencv.core.Point> lhm =  upf.getCurrentPersons()
				if(lhm!=null) {
					if((System.currentTimeMillis()-timePersonLastSeen)>attentionTImeout) {
						personToPayAttentionTo=null;
					}
					for(UniquePerson currentPerson:lhm.keySet()) {
						def p = lhm.get(currentPerson)
						if(personToPayAttentionTo==null) {
							personToPayAttentionTo=currentPerson;
							println "Found "+currentPerson.name
						}
						if(currentPerson.UUID==personToPayAttentionTo.UUID) {
							noseCenterOfFace=p;
							timePersonLastSeen = System.currentTimeMillis();
							Imgproc.putText(matrix, currentPerson.name,p , 3,1,  new Scalar(0, 255, 0));
							//
						}
					}
					lhm.clear()
					lhm=null
				}
				matrix.get(0, 0, data);
				//println detection

				// Creating the Writable Image
				if(img==null) {
					img = SwingFXUtils.toFXImage(image, null);
					t=new Tab("Imace capture ");
					HBox content = new HBox()
					content.getChildren().add(new ImageView(img))
					content.getChildren().add(workingMemory)
					t.setContent(content);
					BowlerStudioController.addObject(t, null);
				}else{
					SwingFXUtils.toFXImage(image, img);
				}
				return facesArray
			}
		}
		return [] as Rect[]
	}

	/*
	 * '{
	 "model": "gpt-3.5-turbo",
	 "messages": [{"role": "user", "content": "Say this is a test!"}],
	 "temperature": 0.7
	 }'
	 */

	public String request(String phrase,String name) throws IOException {

		if(Math.random()>0.99)
			phrase="Pretend you are a  fortune teller that only gives bad fortunes. Keep your response less than "+(maxSize*0.5)+" characters. The persons name is "+name+". make sure it is pg 13. if it is dark, make sure its dark humor. Respond to: "+phrase
		else
			phrase="Pretend you are a  Fortune teller that gives good fortunies. Keep your response less than "+(maxSize*0.5)+" charecters. The persons name is "+name+". Respond to: "+phrase
		return gptRaw( phrase, 0.9, 5);
	}

	public String cleanup(String phrase) throws IOException {

		//phrase="If the phrase is refering to onesself, change the following phrase into third person. Use they/them/thier pronouns only. Otherwise, if the phrase is directed towads the recipiant, rephrase it in the first person with I/me/mine. Respond only with teh corrected phrase. The phrase is: "+phrase
		return phrase;//gptRaw( phrase, 0.9, 5);
	}
	public String whatName(String phrase) throws IOException {

		phrase="Please extract the name the person wished to be called. Respond with the name only. If the name can not be found respond with only the single word 'Friend'. Do not use punctuation in the response. The phrase is: "+phrase
		return gptRaw( phrase,   0.9, 5);
	}
	public String gptRaw(String phrase, float randomness,int retrys) {
		if(retrys==0)
			return;
		try {

			String requestBody = String.format("{\"model\":\"%s\",\"messages\":\"%s\",\"temperature\":%f}", AI_MODEL_NAME, phrase, randomness);
			HashMap<String,Object> message = new HashMap();
			HashMap<String,String> messages = new HashMap();
			messages.put("role", "user")
			messages.put("content", phrase)
			message.put("model", AI_MODEL_NAME)
			message.put("temperature", randomness)
			message.put("messages", Arrays.asList(messages))
			message.put("max_tokens", 4096)

			requestBody = gson.toJson(message, TT_mapStringString);


			println requestBody
			OkHttpClient client = new OkHttpClient()

			MediaType mediaType = MediaType.parse("application/json");
			RequestBody body = RequestBody.create(mediaType, requestBody);

			Request request = new Request.Builder()
					.url(CHATGPT_API_URL)
					.method("POST", body)
					.addHeader("Content-Type", "application/json")
					.addHeader("Authorization", "Bearer " + API_KEY)
					.build();

			Response response = client.newCall(request).execute();
			String jsonString = response.body().string();
			println jsonString
			HashMap<String, Object> database = gson.fromJson(jsonString, TT_mapStringString);
			ArrayList<Object> choices = database.get("choices")
			HashMap<String, Object> firstChoice = choices.get(0)
			HashMap<String, Object> messageContent = firstChoice.get("message")
			String ret = messageContent.get("content").toString()
			println ret
			if(ret==null)
				throw new RuntimeException("No response!");
			return ret

		}catch(Throwable t) {
			BowlerStudio.printStackTrace(t)
			return request(phrase, randomness, retrys-1)
		}
	}

	public void close() {
		//capture.release();
		BowlerStudioController.removeObject(t)
		println "Clean Exit from robot controller"
	}

}

String keyLocation = ScriptingEngine.getWorkspace().getAbsolutePath()+"/gpt-key.txt"
if(!new File(keyLocation).exists()) {
	BowlerStudio.runLater({
		TextInputDialog dialog = new TextInputDialog("your OpenAI API Key here");
		dialog.setTitle("Enter your OpenAI Key");
		dialog.setHeaderText("Create key here - https://platform.openai.com/account/api-keys");
		dialog.setContentText("Please enter your key:");

		// Traditional way to get the response value.
		Optional<String> result = dialog.showAndWait();
		if (result.isPresent()){
			String resultGet = result.get()
			System.out.println("Your key: " + resultGet);
			new Thread({
				try {
					File myObj = new File(keyLocation);
					if (myObj.createNewFile()) {
						System.out.println("File created: " + myObj.getName());
					} else {
						System.out.println("File already exists.");
					}
					FileWriter myWriter = new FileWriter(keyLocation);
					myWriter.write(resultGet);
					myWriter.close();
					System.out.println("Successfully wrote key to your local file.");
				} catch (IOException e) {
					System.out.println("An error occurred.");
					e.printStackTrace();
				}


			}).start()
		}

	})
	return;
}
def fixVector(double[] jointSpaceVect,DHParameterKinematics arm ) {
	for (int i = 0; i < 6; i++) {
		AbstractLink link = arm.factory.getLink(arm.getLinkConfiguration(i));
		double val = jointSpaceVect[i];
		Double double1 = new Double(val);
		if(double1.isNaN() ||double1.isInfinite() ) {
			jointSpaceVect[i]=0;
		}
		if (val > link.getMaxEngineeringUnits()) {
			jointSpaceVect[i]=link.getMaxEngineeringUnits()-Double.MIN_VALUE;
			//println "Link "+i+" u-limit "+jointSpaceVect[i]
		}
		if (val < link.getMinEngineeringUnits()) {
			jointSpaceVect[i]=link.getMinEngineeringUnits()+Double.MIN_VALUE;
			//println "Link "+i+" l-limit "+jointSpaceVect[i]
		}
	}
}
enum AnimationMode{
	spiritWorld,
	facetrack,
	waitForSpeak
}
GPTInterface gpt
public double mouthOpenVector(AudioStatus s) {
	switch(s) {
		case AudioStatus.B_KST_SOUNDS:
			return 0.3;
		case AudioStatus.C_EH_AE_SOUNDS:
			return 0.6;
		case AudioStatus.D_AA_SOUNDS:
			return 1;
		case AudioStatus.E_AO_ER_SOUNDS:
			return 0.6;
		case AudioStatus.F_UW_OW_W_SOUNDS:
			return 0.2;
		case AudioStatus.G_F_V_SOUNDS:
			return 0.1;
		case AudioStatus.H_L_SOUNDS:
			return 0.9;
		case AudioStatus.A_PBM_SOUNDS:
			return 0.05
		case AudioStatus.X_NO_SOUND:
		default:
			break;
	}
	return 0;
}

class TabManagerDevice{
	String myName;
	boolean connected=false;
	ImageView imageView = new ImageView();
	Tab t = new Tab()
	public TabManagerDevice(String name) {
		myName=name;
		
	}
	
	String getName() {
		return myName
	}
	
	boolean connect() {
		connected=true;
		t.setContent(imageView)
		t.setText(myName)
		t.setOnCloseRequest({event ->
			disconnect()
		});
		BowlerStudioController.addObject(t, null)
		return connected
	}
	void disconnect() {
		if(connected) {
			BowlerStudioController.removeObject(t)
		}
		
	}
}
try {
	println "Loading API key from "+keyLocation
	String content = new String(Files.readAllBytes(Paths.get(keyLocation)));
	println "API key: "+content
	gpt = new GPTInterface(content)

	running =true
	response=null
	msLoop=16
	indexAnimationLoop=0
	numStepsPerLoop=2000/msLoop



	AnimationMode mode =AnimationMode.facetrack;
	new Thread({
		JniUtils.setGraphExecutorOptimize(false);
		boolean open=true;
		long timeOfLastBlink=0;
		long closeTime=80
		double tiltTarget = 0
		double tiltIncrement = 3
		long durationBetweenBlinks = (Math.random()*3000)+3000
		RollingAverage lookAvg = new RollingAverage(5)
		RollingAverage tiltAvg = new RollingAverage(10)
		RollingAverage cosAvg = new RollingAverage(10)
		RollingAverage nod = new RollingAverage(5)
		tiltAvg.setMax(20)
		while(running) {

			Thread.sleep(msLoop)
			//mouth.flush(0);
			double unitVextorOfNow=0;//=((double) indexAnimationLoop)/((double) numStepsPerLoop)
			double sinVal=0;// = lookAvg.get(Math.sin(unitVextorOfNow*Math.PI*2))
			double cosVal=0;// = cosAvg.get(Math.cos(unitVextorOfNow*Math.PI*2))
			double tiltangle=0;//=tiltAvg.get(0)
			double nodAngle =0;//=nod.get(0)

			Rect[] faces= gpt.getFaces()
			if(mode ==AnimationMode.facetrack) {
				if(open) {
					if(System.currentTimeMillis()-timeOfLastBlink>durationBetweenBlinks) {
						timeOfLastBlink=System.currentTimeMillis()
						durationBetweenBlinks = (Math.random()*3000)+3000
						open=false
					}
				}else {
					if(System.currentTimeMillis()-timeOfLastBlink>closeTime) {
						open=true
					}
				}

				eye.setTargetEngineeringUnits(open?10.0:-42.0);
				eye.flush(0);

				double look = lookAvg.get(gpt.lookVector())
				//println "Look "+look
				tiltTarget = tiltAvg.get(-gpt.tiltAngle*0.9)+10
				sinVal=-look*4-1.0;
				cosVal=cosAvg.get(0)
				nodAngle=nod.get(-gpt.nodVector())
			}else {
				sinVal = lookAvg.get(Math.sin(unitVextorOfNow*Math.PI*2))
				cosVal = cosAvg.get(Math.cos(unitVextorOfNow*Math.PI*2))
				tiltangle=tiltAvg.get(0)
				nodAngle =nod.get(0)

				eye.setTargetEngineeringUnits(-42);
			}

			TransformNR changed=new TransformNR()
			changed.setX(146)


			def headRnage=45
			def analogy = 0
			def analogz = 35
			changed.setZ(250+analogz*cosVal)
			changed.setY(analogy)
			def analogup = sinVal*headRnage
			def rot = 179.96+analogup
			//println "Tilt target "+tiltTarget
			changed.setRotation(new RotationNR(0,rot,-55+(headRnage*nodAngle)))
			TransformNR tilted= new TransformNR(0,0,0, RotationNR.getRotationZ(-90 ))
			changed=changed.times(tilted).times(new TransformNR(0,0,0, new RotationNR(0,-tiltTarget,tiltTarget)))

			double[] jointSpaceVect = arm.inverseKinematics(arm.inverseOffset(changed));

			fixVector(jointSpaceVect,arm)
			double bestsecs = arm.getBestTime(jointSpaceVect);
			double normalsecs = ((double)msLoop)/1000.0
			def vect;
			if(bestsecs>normalsecs) {
				double percentpossible = normalsecs/bestsecs*2

				TransformNR starttr=arm.getCurrentTaskSpaceTransform()
				TransformNR delta = starttr.inverse().times(changed);
				TransformNR scaled = delta.scale(percentpossible)
				TransformNR newTR= starttr.times(scaled)
				vect = arm.inverseKinematics(arm.inverseOffset(newTR));
				fixVector(vect,arm)
				TransformNR finaltr= arm.forwardOffset( arm.forwardKinematics(vect))
				if(!arm.checkTaskSpaceTransform(finaltr)) {
					println "\n\npercentage "+percentpossible
					println "Speed capped\t"+jointSpaceVect
					println "to\t\t\t"+vect
					println "changed"+changed
					println "starttr"+starttr
					println "delta"+delta
					//println "scaled"+scaledtrig
					println "newTR"+newTR
					println "ERROR, cant get to "+newTR
					//continue;
				}
			}else
				vect = jointSpaceVect
			msActual=normalsecs*1000
			try {
				//vect[6]=trig;
			}catch(Throwable tf) {
				//BowlerStudio.printStackTrace(t)
			}
			arm.setDesiredJointSpaceVector(vect, normalsecs);
			//println vect
			if(gpt.status!=gpt.laststatus) {
				gpt.laststatus=gpt.status

			}
			indexAnimationLoop+=1;
			if(indexAnimationLoop>=numStepsPerLoop) {
				indexAnimationLoop=0;
			}
		}
		println "Zoltar animation thread exit clean"
		eye.setTargetEngineeringUnits(-42.0);
		eye.flush(0)
	}).start()
	def tabHolder = DeviceManager.getSpecificDevice("TabHolder", {
		TabManagerDevice dev = new TabManagerDevice("TabHolder")
		dev.connect()
		return dev
	})
	ImageView imageView = tabHolder.imageView
	AudioStatus laststatus=null
	
	HashMap<AudioStatus,javafx.scene.image.Image> images = new HashMap<>()
	String url = "https://github.com/madhephaestus/TextToSpeechASDRTest.git"
	for(AudioStatus s:EnumSet.allOf(AudioStatus.class)) {
		try{
			File f = new File(ScriptingEngine.getRepositoryCloneDirectory(url).getAbsolutePath()+ "/img/lisa-"+s.parsed+".png")
			println "Loading "+f.getAbsolutePath()
			javafx.scene.image.Image image = new javafx.scene.image.Image(new FileInputStream(f.getAbsolutePath()));
			images.put(s, image)
		}catch(Throwable t){
			//BowlerStudio.printStackTrace(t);
		}
	}
	
	ISpeakingProgress sp ={double percent,AudioStatus status->
		
		gpt.status=status;
		gpt.percent=percent
		if(status!=laststatus) {
			//println percent+" " +status
			laststatus=status;
			BowlerStudio.runLater({
				imageView.setImage(images.get(status))
			})
		}
		if(mode==AnimationMode.waitForSpeak)
			mode=AnimationMode.facetrack
	}
	double voice =805
	// 805 mayb64
	// 857 laid back scottish?
	// 864 impatient scottish??
	double echo = 0.85
	mode =AnimationMode.facetrack
	while(gpt.personToPayAttentionTo==null) {
		Thread.sleep(100)
	}
	String name="Friend"
	boolean repeat=true;
	if(gpt.personToPayAttentionTo.name.startsWith("Person")) {
		BowlerKernel.speak("What shall i call you?", 100, 0, voice, 1, 1.0,sp)
		String nameTMp=gpt.promptFromMicrophone()
		name = gpt.whatName(nameTMp)
		if(name.toLowerCase().contains("friend")) {
			BowlerKernel.speak("I'm sorry, i missed that, can you say your name again please?", 100, 0, voice, 1, 1.0,sp)
			nameTMp=gpt.promptFromMicrophone()
			name = gpt.whatName(nameTMp)
		}
		gpt.personToPayAttentionTo.name=name;
		gpt.upf.save();
		repeat=false;
	}else {
		name  = gpt.personToPayAttentionTo.name;
	}
	String prompt;
	if(repeat)
		prompt = "Welcome back "+name+", what do you wish to ask Zol-Tar this time?"
	else
		prompt = "What do you wish to ask the mighty Zol-tar, "+name+"?"
	BowlerKernel.speak(prompt, 100, 0, voice, 1, 1.0,sp)

	//while(!Thread.interrupted() && gpt.capture.isOpened()) {Thread.sleep(100)}

	if(gpt.capture.isOpened())
		prompt = gpt.promptFromMicrophone();
	//mode =AnimationMode.facetrack
	Thread initialPrompt=new Thread({
		String p=gpt.cleanup(prompt)
		BowlerKernel.speak("Spirit World! Answer Me! "+(name.toLowerCase().contains("friend")?"my friend":name)+" is asking you: "+p, 400, 0, voice, echo, 1.0,sp)
	})
	if(gpt.capture.isOpened())initialPrompt.start()
	response  = gpt.request(prompt,name)
	if(gpt.capture.isOpened())println "\n\nResponse\n"+response
	if(gpt.capture.isOpened())initialPrompt.join()
	//if(gpt.capture.isOpened())mode =AnimationMode.facetrack
	if(gpt.capture.isOpened())BowlerKernel.speak(response, 100, 0, voice, 1, 1.0,sp)
}catch(Throwable tr) {
	BowlerStudio.printStackTrace(tr)
}
running=false
Thread.sleep(100)
mouth.setTargetEngineeringUnits(0);
eye.setTargetEngineeringUnits(-42.0);
gpt.close()
Thread.sleep(100)

//Platform.runLater( {gpt.a.close();})