import os
from NeuralNetwork_keras.create_neuralnetwork_keras import *
import numpy as np

class ArtificialIntelligence:
	def __init__(self):
		self.initialized = False
		
	def load_model(self):
		dir = os.getcwd()
		dir = os.path.join(dir, "model")
		print(dir)
		files = os.listdir(dir)
		files = [f for f in files if f.endswith((".h5"))]
		if not files:
			print("no model available !")
			return False
		else:
			self.initialized = True
			#at least one model available
			for k, f in enumerate(files):
				print(k, " :", f)
			i = int(input("Choose the file : "))
			if not i in range(len(files)):
				i = 0
			file_name = os.path.join("model", files[i])
			print("model found :", file_name)
			
			nb_input = 6
			model = create_neural_network(nb_input)
			model.load_weights(file_name)
			self.model = model
	
	def predict(self, input):
		"return a tuple for control keys : (acc, left, right)"
		if not self.initialized:
			print("No model loaded !")
			return False
		else:
			prediction = self.model.predict(np.array([input]))[0]
			threshold = 0.4
			right = prediction[1] > threshold #prediction[2]
			left = prediction[2] > threshold #prediction[1]
			if right and left:
				right = prediction[1] > prediction[2]
				left = prediction[2] > prediction[1]
			return prediction[0], int(right), int(left)
			# return prediction
	
	

	
	
	
6571413834a88bcbda85705d4113df91f4487838
<mask token>


class ArtificialIntelligence:

    def __init__(self):
        self.initialized = False
    <mask token>
    <mask token>

<mask token>


class ArtificialIntelligence:

    def __init__(self):
        self.initialized = False

    def load_model(self):
        dir = os.getcwd()
        dir = os.path.join(dir, 'model')
        print(dir)
        files = os.listdir(dir)
        files = [f for f in files if f.endswith('.h5')]
        if not files:
            print('no model available !')
            return False
        else:
            self.initialized = True
            for k, f in enumerate(files):
                print(k, ' :', f)
            i = int(input('Choose the file : '))
            if not i in range(len(files)):
                i = 0
            file_name = os.path.join('model', files[i])
            print('model found :', file_name)
            nb_input = 6
            model = create_neural_network(nb_input)
            model.load_weights(file_name)
            self.model = model
    <mask token>

<mask token>


class ArtificialIntelligence:

    def __init__(self):
        self.initialized = False

    def load_model(self):
        dir = os.getcwd()
        dir = os.path.join(dir, 'model')
        print(dir)
        files = os.listdir(dir)
        files = [f for f in files if f.endswith('.h5')]
        if not files:
            print('no model available !')
            return False
        else:
            self.initialized = True
            for k, f in enumerate(files):
                print(k, ' :', f)
            i = int(input('Choose the file : '))
            if not i in range(len(files)):
                i = 0
            file_name = os.path.join('model', files[i])
            print('model found :', file_name)
            nb_input = 6
            model = create_neural_network(nb_input)
            model.load_weights(file_name)
            self.model = model

    def predict(self, input):
        """return a tuple for control keys : (acc, left, right)"""
        if not self.initialized:
            print('No model loaded !')
            return False
        else:
            prediction = self.model.predict(np.array([input]))[0]
            threshold = 0.4
            right = prediction[1] > threshold
            left = prediction[2] > threshold
            if right and left:
                right = prediction[1] > prediction[2]
                left = prediction[2] > prediction[1]
            return prediction[0], int(right), int(left)

import os
from NeuralNetwork_keras.create_neuralnetwork_keras import *
import numpy as np


class ArtificialIntelligence:

    def __init__(self):
        self.initialized = False

    def load_model(self):
        dir = os.getcwd()
        dir = os.path.join(dir, 'model')
        print(dir)
        files = os.listdir(dir)
        files = [f for f in files if f.endswith('.h5')]
        if not files:
            print('no model available !')
            return False
        else:
            self.initialized = True
            for k, f in enumerate(files):
                print(k, ' :', f)
            i = int(input('Choose the file : '))
            if not i in range(len(files)):
                i = 0
            file_name = os.path.join('model', files[i])
            print('model found :', file_name)
            nb_input = 6
            model = create_neural_network(nb_input)
            model.load_weights(file_name)
            self.model = model

    def predict(self, input):
        """return a tuple for control keys : (acc, left, right)"""
        if not self.initialized:
            print('No model loaded !')
            return False
        else:
            prediction = self.model.predict(np.array([input]))[0]
            threshold = 0.4
            right = prediction[1] > threshold
            left = prediction[2] > threshold
            if right and left:
                right = prediction[1] > prediction[2]
                left = prediction[2] > prediction[1]
            return prediction[0], int(right), int(left)

import os
from NeuralNetwork_keras.create_neuralnetwork_keras import *
import numpy as np

class ArtificialIntelligence:
	def __init__(self):
		self.initialized = False
		
	def load_model(self):
		dir = os.getcwd()
		dir = os.path.join(dir, "model")
		print(dir)
		files = os.listdir(dir)
		files = [f for f in files if f.endswith((".h5"))]
		if not files:
			print("no model available !")
			return False
		else:
			self.initialized = True
			#at least one model available
			for k, f in enumerate(files):
				print(k, " :", f)
			i = int(input("Choose the file : "))
			if not i in range(len(files)):
				i = 0
			file_name = os.path.join("model", files[i])
			print("model found :", file_name)
			
			nb_input = 6
			model = create_neural_network(nb_input)
			model.load_weights(file_name)
			self.model = model
	
	def predict(self, input):
		"return a tuple for control keys : (acc, left, right)"
		if not self.initialized:
			print("No model loaded !")
			return False
		else:
			prediction = self.model.predict(np.array([input]))[0]
			threshold = 0.4
			right = prediction[1] > threshold #prediction[2]
			left = prediction[2] > threshold #prediction[1]
			if right and left:
				right = prediction[1] > prediction[2]
				left = prediction[2] > prediction[1]
			return prediction[0], int(right), int(left)
			# return prediction
	
	

	
	
	