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from openai import OpenAI
import os
import ipdb
import base64

api_key = os.getenv('OPENAI_API_KEY')
client = OpenAI()
client.api_key = api_key

# Function to encode the image

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	def infer(self, img):
		self.inputs[0]['host'] = np.ravel(img)
		
		# transfer data to the gpu
		for inp in self.inputs:
			# print(type(inp['device']))
			cuda.memcpy_htod_async(inp['device'], inp['host'], self.stream)
		# run inference
		self.context.execute_async(batch_size=1,
				bindings=self.bindings,

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import psutil
import time
import logging
import os
import numpy as np

try:
##    os.remove("./Data/Pi_ML")
##    os.remove("./Data/Pi_CV")
	pass

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# return heading angle from machine learning model
angle = np.radians(max(model.predict(image)))

'''
speed_slider: lowest speed of each motor
angle: heading angle corresponding to current frame
angle_last: heading angle corresponding to previous frame
kP, kD, kI: P - I - D coefficient defined by users
steering_bias_slider: bias coefficient between two motors 

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# pre-processing input images and pedict with model
def predict_from_model(image,model,labels):
    image = cv2.resize(image,(80,80))
    image = np.stack((image,)*3, axis=-1)
    prediction = labels.inverse_transform([np.argmax(model.predict(image[np.newaxis,:]))])
    return prediction
    
fig = plt.figure(figsize=(15,3))
cols = len(crop_characters)
grid = gridspec.GridSpec(ncols=cols,nrows=1,figure=fig)

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# Load model architecture, weight and labels
json_file = open('MobileNets_character_recognition.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
model.load_weights("License_character_recognition_weight.h5")
print("[INFO] Model loaded successfully...")

labels = LabelEncoder()
labels.classes_ = np.load('license_character_classes.npy')

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# save model architectur as json file
model_json = model.to_json()
with open("MobileNets_character_recognition.json", "w") as json_file:
  json_file.write(model_json)

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BATCH_SIZE = 64

my_checkpointer = [
                EarlyStopping(monitor='val_loss', patience=5, verbose=0),
                ModelCheckpoint(filepath="License_character_recognition.h5", verbose=1, save_weights_only=True)
                ]

result = model.fit(image_gen.flow(trainX, trainY, batch_size=BATCH_SIZE), 
                   steps_per_epoch=len(trainX) // BATCH_SIZE, 
                   validation_data=(testX, testY), 

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# Create our model with pre-trained MobileNetV2 architecture from imagenet
def create_model(lr=1e-4,decay=1e-4/25, training=False,output_shape=y.shape[1]):
    baseModel = MobileNetV2(weights="imagenet", 
                            include_top=False,
                            input_tensor=Input(shape=(80, 80, 3)))

    headModel = baseModel.output
    headModel = AveragePooling2D(pool_size=(3, 3))(headModel)
    headModel = Flatten(name="flatten")(headModel)
    headModel = Dense(128, activation="relu")(headModel)