V1.0.0
(it's still a bit buggy)
a simple way to create chatbots Ai, image classification Ai, image generation Ai, image supper resolution Ai and more!!
you can install the package from pypi (pip)
pip install CustomIntents
A package build on top of keras for creating and training deep learning chatbots (text classification), image classification, image generation Ai, image super resolution, style transforming and linear regression models in just three lines of code
the package is inspired by NeuralNine, neuralintents package a packege that let you build chatbot models with 3 lines of code
- SuperRes class
- ImageGenerator class
- ChatBot class
- JsonIntents
- ImageClassificator class
- StyleTransformer class
- BinaryImageClassificator
- PLinearRegression class
- scanner moudule
def __init__(self, input_size: tuple = (300, 300),
upscale_factor: int = 3,
cpu_only: bool = False):input_size : this is only required when training or finetuning
upscale_factor : the upscale factor
cpu_only : whether to use only CPU or not
def upscale_image(self, img,
save_name: str = None,
save_image: bool = True):img : the image you want to upscale
save_image : whether to save the image
save_name : the name of the saved file
def upscale_image_from_path(self, img_path,
save_name: str = None,
save_image: bool = True)its like upscale_image method except that it requires a path to the image
def load_training_data(self, dataset_path: str,
validation_split: float = 0.2,
batch_size: int = 8)dataset_path : path to the training and validation data directory
validation_split : a float between 0 and 1 where the validation split is specified
batch_size : batch size
def train(self, lr: float = 0.001,
optimizer: str = "Adam",
epochs: int = 50,
ESPCNCallback_usage: bool = True,
ESPCNCallback_test_path: str = "test",
epoch_per_psnr: int = 20,
psnr_plot: bool = True,
model_type: str = "xs1",
loss_fns: list = None,
loss_fns_weight: list = None):optimizer : name of the optimizer (corently supported optimizers are : Adam, Adagrad, Adamax, Adadelta, SGD, RMSprop, Nadam)
epochs : number of epochs to train
ESPCNCallback_usage : wether to use ESPCNCallback or not
ESPCNCallback_test_path : a path to the test data directory for ESPCNCallback
epoch_per_psnr : if you use ESPCNCallback it will show a sample every few epochs you can choose that number here
psnr_plot : if you use ESPCNCall you can turn plutting a sample off
model_type : the type of the model (there are some different types in this package but for now i recommend using using the diffault xs1)
loss_fns : a list containing loss functions you want to use if you want only one loss function to use you can pass a list with only on loss functions
(corently supported loss functions are : mse, mae, mape, ssim, psnr, ipcusl (this is a custom loss function for more information read IPCUSL.md), charbonnier, tv (total variation), tvd (total variation difference))
loss_fns_weight : if you use multiple loss functions the model will calculate their weighted sum and this is a list that contains their sum in this order (mse,mae,mape,ssim,psnr,ipcusl,charbonnier,tv,tvd)
def fine_tuning(self, lr: float = 0.001,
epochs: int = 50,
model_name: str = "super_res",
ESPCNCallback_usage: bool = True,
ESPCNCallback_test_path: str = "test",
epoch_per_psnr: int = 20,
psnr_plot: bool = True,
recompile: bool = False,
optimizer: str = None,
loss_fns: list = None,
loss_fns_weight: list = None)its almost equivalent to the train method except you should load a model first
def load_model(self, model_name: str = "super_res")model_name : the name of the model
def save_model(self, model_name: str = "super_res")model_name : the name of the model
def benchmark(self, image_path: str = "testimage.jpg",
input_size: tuple = (300, 300))this method will benchmark the model based on a single image
input_size : the image will be resized to the given size image and then upscaled by model
def benchmark_from_directory(self, image_directory_path: str = "test",
input_size: tuple = None)this method will benchmark the model based on a directory of images
input_size : the image will be resized to the given size image and then upscaled by model
from CustomIntents import SuperRes
model = SuperRes(input_size=(300, 300), upscale_factor=3)
model.load_training_data(dataset_path="dataset", batch_size=8)
model.train(epochs=5, model_type="xs1", psnr_plot=True, loss_fns=["IPCUSL"], epoch_per_psnr=4)from CustomIntents import SuperRes
model = SuperRes(input_size=(100, 100), upscale_factor=3)
model.load_training_data(dataset_path="dataset", batch_size=32)
model.fine_tuning(epochs=2, lr=0.00008, model_name="CSR3X-1.1.3", psnr_plot=False, loss_fns=["mse", "mae"])from CustomIntents import SuperRes
model = SuperRes(input_size=(300, 300), upscale_factor=3)
model.load_model("CSR3X-1.1.2")
model.upscale_image_from_path(img_path="test_image_2_300x300.jpg", save_name="test_result_300x300_to_900x900_7.jpg")
you can easily use state of the art StableDiffiusion model with this class
def __init__(self, *,
model: str = "StableDiffusion",
img_height: int = 256,
img_width: int = 256,
jit_compile: bool = False,
cpu_only: bool = False):model : for now only StableDiffusion is available
img_height : it's the height of the genarated image it should be a multiple of 128
img_width : it's the width of the genarated image it should be a multiple of 128
jit_comple : it's a boolean indicating using just in time compliling
cpu_only : it's a boolean indicating whether to use CPU only or GPU
note every argument should be passed as an keyword argument
def generate(self, *,
prompt: str = "Iron man making breakfast",
batch_size: int = 1,
filename: str = "sample",
num_steps: int = 50):prompt : it's the prompt to create the image from
batch_size : how many of images to create
filename : the name of file to save
num_steps : the number of steps to run the image through the model bigger the number it will generate better images but also it will take longer to generate
this method will create a gradio preview
this method doesn't get any arguments
from CustomIntents import ImageGenerator
model = ImageGenerator(model="StableDiffusion",
img_width=512,
img_height=512,
cpu_only=True,
jit_compile=True)
model.gradio_preview()this code will generate two images and save them as "a sample image.jpg"
from ImageGenerator import ImageGenerator
model = ImageGenerator(model="StableDiffusion",
img_width=512,
img_height=512,
cpu_only=True,
jit_compile=True)
model.generate(prompt="a cat lying on a bed",
batch_size=2,
filename="a sample image",
num_steps=50)from CustomIntents import ChatBot
chatbot = ChatBot(model_name="test_model", intents="intents.json")
assistant.train_model()
assistant.save_model()
done = False
while not done:
message = input("Enter a message: ")
if message == "STOP":
done = True
else:
assistant.request(message)this is inspired by neuralintents
from CustomIntents import ChatBot
def function_for_greetings():
print("You triggered the greetings intent!")
# Some action you want to take
def function_for_stocks():
print("You triggered the stocks intent!")
# Some action you want to take
mappings = {'greeting' : function_for_greetings, 'stocks' : function_for_stocks}
assistant = ChatBot('intents.json', intent_methods=mappings ,model_name="test_model")
assistant.train_model()
assistant.save_model()
done = False
while not done:
message = input("Enter a message: ")
if message == "STOP":
done = True
else:
assistant.request(message){"intents": [
{"tag": "greeting",
"patterns": ["Hi", "Salam", "Nice to meet you", "Hello", "Good day", "Hey", "greetings"],
"responses": ["Hello!", "Good to see you again!", "Hi there, how can I help?"]
},
{"tag": "goodbye",
"patterns": ["bye", "good bye", "see you later"],
"responses": ["bye", "good bye"],
"context_set": ""
},
{"tag": "something",
"patterns": ["something", "something else", "etc"],
"responses": ["the response to something"],
}
]
}the first class in CustomIntent package is ChatBot its exacly what you thing a chatbot
def __init__(self, intents, intent_methods, model_name="assistant_model", threshold=0.25, w_and_b=False,
tensorboard=False):intents : its the path of your intents file
intents_method : its a dictionary of mapped functions
model_name : its just the name of your model
threshold : its the accuracy threshold of your model its set to 0.25 by default
w_and_b : it will connect to wandb if set to True (you will need to login first)
tensorboard : Not available at the time
you can start training your model with one function call train_model
training model arguments :
def train_model(self, epoch=None, batch_size=5, learning_rate=None,
ignore_letters=None, timeIt=True, model_type='s1',
validation_split=0, optimizer=None, accuracy_and_loss_plot=True):epoch: An epoch refers to one cycle of training the neural network with all the training data. This argument specifies the number of cycles that the network will undergo.
batch_size: An integer or None. This determines the number of samples per gradient update. You can ignore this argument if you like.
learning_rate: The learning rate is a hyper-parameter that controls the weights of the neural network with respect to the loss gradient. It defines how quickly the network updates the concepts it has learned. In simple terms, a larger learning rate makes the model learn faster but it can also deviate from the correct path more easily.
ignore_letters: A list of letters that you want to ignore. By default, it ignores the characters (? . , !). You can pass an empty list if you don't want to ignore any characters.
timeIt: This argument times the training process.
model_type: You can select one of the predefined models (which will be described later).
validation_split: You can split a portion of your data for validation only, meaning the model will not be trained on these samples. This argument should be a float between 0 and 1. I recommend not creating a validation split unless you have a very large dataset with many similar patterns.
optimizer: You can choose between SGD, Adam, Adamx, and Adagard.
it will save your model as two .pkl files and a .h5 file (don't add .h5 or .pkl)
def save_model(self, model_name=None):model_name : if its None (defualt), it will save the files like (model_name.h5)(model_name_words.pkl)(model_name_classes.pkl) where the model_name is the name you specified in the first place
it will load a model from those three files
def load_model(self, model_name=None):model_name : if its None (defualt), it will look for files like (model_name.h5)(model_name_words.pkl)(model_name_classes.pkl) where the model_name is the name you specified in the first place
you will pass it a massege and it will return the predicted tag for you
def request_tag(self, message, debug_mode=False, threshold=None):message : the actual message
debug_mode : it will print every step of the procces for debuging perpes
threshold : you can set a accuracy threshold if not specified it will use the threshold you set when initilizing the bot and if you didn't specified there either it is set to 0.25 by default
the same as request_tag but it will return a random response from intents
def request_response(self, message, debug_mode=False, threshold=None):it will open up a nice gui for testing your model in your browser
def gradio_preview(self, ask_for_threshold=False, share=False, inbrowser=True):ask_for_threshold : if set to True it will create a slider that you can set the threshold of the model with it
share : if set to True it will make the demo public
inbrowser : it will aoutomaticlly open a new browser page if set to True
def cli_preview(self):a simple cli interface for testing your model
a custom gui for triyng or even deploying your model
def gui_preview(self, user_name=""):user_name : it will only say hello to you if you pass your name for now
you can choose one of the defined models according to the size of diffrente patterns and tags you have (you can just try and see wich one is right for your use case)
xs1 : a very fast and small model
xs2 : still small but better for more tags
s1 : the default model (hidden layers : 128-64)
s2 : its better than s1 when you have small number of similar tags that s1 cant predict
s3 : most of the time you dont need this (hidden layers : 128-64-64)
s4 : its like a s2 on streoid its suited when you have a lot of patterns for tags that have similar patterns
s5 : most of the time you dont need this either (hidden layers : 128-64-64-32)
m1 : great balance of perfomance and accuracy for medium size intent files
m2 : great accuracy for medium size intent files
m3 : m3 to m1 is like s2 to s1 its more suited when you have smaller number of tags but hard to difrentiat
l1 - l2 - l3 - l4 - l5 - xl1 - xl2 - xl3 - xl5 : are bigger models for more information read MODELS.md
this class is used to add and edit Json files containing intents
def __init__(self, json_file_adrees):you just need to pass the path of the json file the function you want
its a function that ask you to input new patterns for tags (you can pass an especific tag to ask for that or it will cycle through them all and will go to the next tag by inputing D or d)
def add_pattern_app(self, tag=None):it will add new tags to your json file
def add_tag_app(self, tag=None, responses=None):tag : the name of the new tag you want to add
responses : a list of responses (you can add later on as well)
it will check for duplicate in patterns and deletes them for you
file = JsonIntents("internet intents.json")
file.delete_duplicate_app()
file.add_tag_app(tag="about")
file.add_pattern_app("about")the seccond class in CustomIntent package is ImageClassificator it let you create and train deep learning image classification models with just three line of code !!
def __init__(self, data_folder="data",
model_name="imageclassification_model",
number_of_classes=2,
classes_name=None,
gpu=None,
checkpoint_filepath='/tmp/checkpoint_epoch:{epoch}'):data_folder : the path to where you located your data
model_name : name your model
number_of_classes : number of difrent classes you have in your data (you should put the pictures of every class in a sub folder in your data folder)
classes_name : a list of names correspunding to your classes (they should be the same as the folder name of correspunding data folder for example if you have 3 sub folder in your data folder as banana apple pineapple you should pass ["banana", "apple", "pineapple"])
gpu : you can pass True or False if you dont pass anything it will try to use your gpu if you have a cuda enaibled graphic card and you have cudatoolkit and cuDNN installed and if you dont it will use your cpu
checkpoint_filepath : path to where you want your checkpoints
you can start training your model with one function call train_model
training model arguments :
def train_model(self, epochs=20,
model_type="s1",
logdir=None,
optimizer_type="adam",
learning_rate=0.00001,
class_weight=None,
prefetching=False,
plot_model=True,
validation_split=0.2,
test_split=0,
augment_data=False,
tensorboard_usage=False,
stop_early=False,
checkpoint=False):epoch : an epoch basicly means training the neural network with all the training data for one cycle and this arguament says how many of this circles it will go
model_type : you can select one of the defined models (we will look at the available models later on)
logdir : a directory to hold your tensorboard log files you can leave is empty if you dont care
optimizer_type : you can only choose adam right now
learning_rate : Learning rate is a hyper-parameter that controls the weights of our neural network with respect to the loss gradient. It defines how quickly the neural network updates the concepts it has learned. (in simple terms if its bigger our model learn faster but it can go of track faster)
class_weight : if you have an unbalanced dataset you can path a dictionary with the weight that you what to assosiate with every class ()
prefetching : prefetching data
plot_model : it will plot the model architecture for you
validation_split : you can split a portion of your data for validation only (model will not get trained on them) it should be float between 0 and 1 (i will recommend to not create a validation split unless you have a really huge data set with lots of similar patterns)
augment_data : if set to true the model will also be trained on augmented data
tensorboard_usage : it will use tensorboard
stop_early : if set to true it will stop training if validation loss is the same or increasing for more than 5 epochs
checkpoint : if set to true it will save checkpoints if the validation loss is the lovest ever seen
it will save your model a .h5 file
def save_model(self, model_file_name=None):model_name : if its None (defualt), it will save the files like (model_name.h5) where the model_name is the name you specified in the first place
it will load a model from those three files
def load_model(self, name="imageclassification_model"):model_name : if its None (defualt), it will look for files like (imageclassification_model.h5)
now you can predict
def predict(self, image, image_type=None, full_mode=False, accuracy=False):image : a path to an image file or a numpy array of the image or a cv2 image
image_type : if its None (defualt), it will try to detect if the image is a cv2 image or a numpy array of the image or a path to the image
full_mode : if you set it to true it will return every class and its probability
accuracy : if you set it to true it will return a tuple of the class name and the probability
(if both full_mode and accuracy set to false (defualt behavier) it will just return the most likly class name)
def predict_face(self, img, image_type=None, full_mode=False,
accuracy=False, return_picture=False,
save_returned_picture=False, saved_returned_picture_name="test.jpg",
show_returned_picture=False):img : a path to an image file or a numpy array of the image or a cv2 image
image_type : if its None (defualt), it will try to detect if the image is a cv2 image or a numpy array of the image or a path to the image
full_mode : if you set it to true it will return every class and its probability
accuracy : if you set it to true it will return a tuple of the class name and the probability
return_picture : if set to true it will return a picture with faces in rectangles and their predicted class writen on top of them
save_returned_picture : if set to True it will save the returned picture
saved_returned_picture_name : if you set save_returned_picture to true you can use this to especifie the name of the saved picture
show_returned_picture : if set to true it will open the returned picture in a cv2 preview
def realtime_prediction(self, src=0):src : if you have multiple webcams or virtual webcams it will let you choose from them if you only have one live it empty
its exacly like the realtime_prediction() method but it will detect facec with a haarcascadde and will feed the model with the facec to predict
def realtime_face_prediction(self, src=0, frame_rate=5):src : if you have multiple webcams or virtual webcams it will let you choose from them if you only have one live it empty
frame_rate : its the number of frames to skip before predicting again
it will open up a nice gui for testing your model in your browser
def gradio_preview(self, share=False, inbrowser=True, predict_face=False):share : if set to True it will make the demo public
inbrowser : it will aoutomaticlly open a new browser page if set to True
predict_face : if set to True it will look for faces and feed them to the model
in this example i have a folder in data/full that contains 4 sub folders (beni, khodm, matin, parsa) and in every one of them i have a lot of pictures of my friends (the folder name corredpunds to their names for example in beni folder there are beni's pictures, btw khodm means myself in my languge) and i want to train a model to detect which one of us we are in the picture
from CustomIntents import ImageClassificator
model = ImageClassificator(model_name="test_m1", data_folder="data/full", number_of_classes=4, classes_name=["beni", "khodm", "matin", "parsa"])
model.train_model(epochs=10, model_type="m1", logdir="logs", learning_rate=0.0001, prefetching=False)
model.save_model(model_file_name="test_m1")from CustomIntents import BinaryImageClassificator
model = ImageClassificator(model_name="test_m1", data_folder="data/full", number_of_classes=4, classes_name=["beni", "khodm", "matin", "parsa"])
model.load_model(name="test_m1")
result = model.realtime_face_prediction()
and as you see in the picture above you can see it under the that it is me in the picture with a really high accuracy
the fourth class in CustomIntent package is StyleTransformer it let you transform an image to the style of another image
def __init__(self, image_path=None,
style_reference_image_path=None,
result_prefix="test_generated"):image path : the path to the original image
style_reference_image_path : the path to the style reference image
result_prefix : the prefix for the result file
the main method of this class
def transfer(self, iterations=4000, iteration_per_save=100):iterations : the number of iterations
iteration_per_save : save every _ iteration (where _ is the number you pass)
a browser based app to use this class
def gradio_preview(self, share=False, inbrowser=True):share : if set to True it will make the demo public
inbrowser : it will aoutomaticlly open a new browser page if set to True
from CustomIntents import StyleTransformer
model = StyleTransformer(image_path="base_image.jpg", style_reference_image_path="style_reference_image.jpg")
model.transfer(iterations=500, iteration_per_save=50)this code will perform the teransformation for 500 times and save them every 50 steps
from CustomIntents import StyleTransformer
model = StyleTransformer()
model.gradio_preview()
don't use ridiculous numbers like 4000 like me, it took about 15 minutes on a 1660ti
the fourth class in CustomIntent package is BinaryImageClassificator it let you create and train deep learning image classification models with just three line of code !!
def __init__(self, data_folder="data", model_name="imageclassification_model",
first_class="1", second_class="2"):data_folder : it's the path of your data folder (you should put your training images in two subfolder representing their label (class))
model_name : your model's name
first_class : you can name your classes so when you whant to predict it returns their name insted of 1s and 2s
seccond_class : //
you can start training your model with one function call train_model
training model arguments :
def train_model(self, epochs=20, model_type="s1", logdir=None,
optimizer_type="adam", learning_rate=0.00001,
class_weight=None, prefetching=False, plot_model=True,
validation_split=0.2):epoch : an epoch basicly means training the neural network with all the training data for one cycle and this arguament says how many of this circles it will go
model_type : you can select one of the defined models (read MODELS.md for more information)
logdir : a directory to hold your tensorboard log files you can leave is empty if you don't care
optimizer_type : you can only choose adam right now
learning_rate : Learning rate is a hyper-parameter that controls the weights of our neural network with respect to the loss gradient. It defines how quickly the neural network updates the concepts it has learned. (in simple terms if its bigger our model learn faster but it can go of track faster)
class_weight : if you have an unbalanced dataset you can path a dictionary with the weight that you what to assosiate with every class ()
prefetching : prefetching data
plot_model : it will plot the model architecture for you
validation_split : you can split a portion of your data for validation only (model will not get trained on them) it should be float between 0 and 1 (i will recommend to not create a validation split unless you have a really huge data set with lots of similar patterns)
it will save your model a .h5 file (don't add .h5)
def save_model(self, model_file_name=None):model_name : if its None (defualt), it will save the files like (model_name.h5) where the model_name is the name you specified in the first place
it will load a model from those three files
def load_model(self, name="imageclassification_model"):model_name : if its None (defualt), it will look for files like (imageclassification_model.h5)
now you can predict
def predict(self, image, image_type=None, accuracy=False):image : a path to an image file or a numpy array of the image or a cv2 image
image_type : if its None (defualt), it will it will try to detect if the image is a cv2 image or a numpy array of the image or a path to the image
accuracy : if you set it to true it will return a tuple of the class name and the probability
it will predict what class the image blongs to from a path
def predict_from_files_path(self, image_file_path):image_file_path : the path of the image you want to predict
it will return the name of the class and the percentage that its correct
it will predict what class the image blongs to from a cv2 object
def predict_from_imshow(self, img):img : a cv2 image object
it will return the name of the class and the percentage that its correct
it will predict from a live video feed (it will open a live cv2 video feed)
def realtime_prediction(self, src=0):src : if you have multiple webcams or virtual webcams it will let you choose from them if you only have one live it empty
its exacly like the realtime_prediction() method but it will detect facec with a haarcascadde and will feed the model with the facec to predict
def realtime_face_prediction(self, src=0):src : if you have multiple webcams or virtual webcams it will let you choose from them if you only have one live it empty
it will open up a nice gui for testing your model in your browser
def gradio_preview(self, share=False, inbrowser=True):share : if set to True it will make the demo public
inbrowser : it will aoutomaticlly open a new browser page if set to True
from CustomIntents import BinaryImageClassificator
model = BinaryImageClassificator(model_name="test1", data_folder="data/parsa", first_class="sad", second_class="happy")
model.train_model(epochs=5, model_type="s1", logdir="logs", learning_rate=0.0001, prefetching=True) #, class_weight={0: 1.0, 1: 2.567750677506775})
model.save_model(model_file_name="test1")from CustomIntents import BinaryImageClassificator
model.load_model("models/test1")
model.realtime_face_prediction()it's a simple linear regression class with one input and one output
def __init__(self, data=None, x_axes1=None, y_axes1=None, model_name="test model"):data : if you have your data as a aray like [[x_axes], [y_axes]] you can pass it here
x_axes1 : if you have your x values (inputs) and y values (output) seperetly you can pass the array that contains x valeus here
y_axes1 : if you have your x values (inputs) and y values (output) seperetly you can pass the array that contains y valeus here
model_name : name your model
you will train your model on your data with this method
def train_model(self, algorythm="1", training_steps=10000,
start_step=None, verbose=1, plot_input_data=True,
learning_rate=0.01, plot_result=True):algorythm : you can choose bitween 1, 1.1 and 2 (1 is really simple and fast but 2 is the propper linear reggresion one)
training_steps : it's how many steps you want to train your model
start_step : it's the starting stepfor algorythm 1 and 1.1
verbose : if it's set to 1 it will show you the details of trainong in every step
plot_input_data : it will plot the training data
learning_rate : it's the learning rate used for algorythm 2
plot_result : it will plot the line of best fit that it found along the side of the training data
it will save your model as a csv file containing the information of the line of best fit
def save_model_to_csv(self, file_dir="test_model.csv"):file_dir : the name and dir you want to save your model to (include .csv)
it will load your model from a csv file containing the information of the line of best fit
def load_model_from_csv(self, file_dir="test_model.csv")file_dir : the name and dir you want to load your model from (include .csv)
it will make predictions for you
def make_prediction(self, x):x : your input data either in a numerical form or a numpy array containing multiple numerical values
it will return either a float (if you input is just a numerical value) or a numpy array containing multiple floats
this moudule is created for helping you scan faces this is helpful for person recognition emotion recognition etc.
def face_scanner(category: str, # category name
sub_category: str, # sub category name
base_dir: str, # base directory path
number_of_photos: int, # number of photos to take
number_of_frames_to_skip: int, # number of frames to skip before taking images
file_name: str, # file name
image_size: int = 256, # width and height of image (it will be square)
haar_file: str = None, # directory containing haarcascade
camera: int = 0, # camera
colored: bool = False): # True if you want to save the colored imagethis function will command app for facsScanner
from CustomIntents import scanner
scanner.facsScannerCliApp()it will command start a GUI app for faceScanner
from CustomIntents import scanner
scanner.faceScannerGuiApp()