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import spacy
import whisper
import numpy as np
from torch import nn
from torch import Tensor
from transformers import pipeline
from sklearn.tree import DecisionTreeRegressor
class PyAI:
def __init__(self, useGPU: bool):
if useGPU:
self.GPU = "cuda"
else:
self.GPU = "cpu"
def KNN(self, x, y, returnValues = 0):
distances = []
for axisX, axisY in zip(x, y):
distance = axisX - axisY
absDistance = np.absolute(distance)
distances.append(absDistance)
sortedDistances = []
checkDistance = min(distances, key = lambda x:np.absolute(x-i))
sortedDistances.append(checkDistance)
distances.remove(checkDistance)
if returnValues == 0:
return sortedDistances[0]
else:
return sortedDistances[0:returnValues-1]
def RNN(self, w: int, hx: int, useReLU: bool = False):
if useReLU:
RNN = nn.RNN(w, hx, 4, "relu").to(self.GPU)
else:
RNN = nn.RNN(w, hx, 4).to(self.GPU)
return self.Softmax(RNN)
def ReLU(self, x: list, *y: list, **u: list):
X, Y, U = [Tensor(x2) for x2 in x], [Tensor(y2) for y2 in y], [Tensor(u2) for u2 in u]
relu = nn.ReLU().to(self.GPU)
newX, newY, newU = [relu(x) for x in X], [relu(y) for y in Y], [relu(u) for u in U]
if newU is not None:
return newX, newY, newU
elif newY is not None:
return newX, newY
else:
return newX
def Softmax(self, x: list | Tensor):
if isinstance(x, list):
tensor = Tensor(x, 1).to(self.GPU)
soft = nn.Softmax(dim=1).to(self.GPU)(tensor)
return soft
else:
soft = nn.Softmax(dim=1).to(self.GPU)(x)
return soft
def Sigmoid(self, x: list | Tensor):
if isinstance(x, list):
tensor = Tensor(x, 1).to(self.GPU)
sigmod = nn.Sigmoid().to(self.GPU)(tensor)
return sigmod
else:
sigmod = nn.Sigmoid().to(self.GPU)(x)
return sigmod
def decisionTree(self, trainX: list, trainY: list, words: list):
w = np.array([len(a) for a in words]).reshape(-1, 1)
tree = DecisionTreeRegressor()
tree.fit(trainX, trainY)
return tree.predict(w).tolist()
class Audio:
def __init__(self, audio: str):
self.model = whisper.load_model("base")
self.audio = audio
def generateTextFromAudio(self) -> str:
aud = whisper.load_audio(self.audio)
aud = whisper.pad_or_trim(aud)
self.mel = whisper.log_mel_spectrogram(aud).to(self.model.device)
self.model.detect_language(self.mel)
options = whisper.DecodingOptions()
result = whisper.decode(self.model, self.mel, options)
return result.text
def translateText(self, text: str, dataSet: str) -> str:
with open(dataSet, "r") as d:
data = d.read()
translation = text.translate(data)
return translation
def getLang(self):
i, lang = self.model.detect_language(self.mel)
return max(lang, key=lang.get)
class NLP:
def __init__(self, text: str):
self.text = text
self.sentences = text.split(".")
self.words = text.split(" ")
self._past = ["was", "had", "did"]
self._present = ["is", "has"]
self._future = ["will", "shall"]
def setTokensTo(self, letters: bool, *words: bool, **sentences: bool):
self.tokens = []
if letters:
tokens = iter(self.text)
for t in tokens:
self.tokens.append(t)
elif words:
for t in self.words:
self.tokens.append(t)
elif sentences:
for t in self.sentences:
self.tokens.append(t)
else:
self.tokens.append("ERROR")
def getTense(self):
self.past = False
self.present = False
self.future = False
if self.sentences in self._past:
self.past = True
elif self.sentences in self._present:
self.present = True
elif self.sentences in self._future:
self.future = True
else:
return "ERROR - Tense :: Not Enough Data"
return self.past, self.present, self.future
def getWords(self):
return self.words
def getSentences(self):
return self.sentences
def getTokens(self):
return self.tokens
def getPartOfSpeech(self, text: str):
POS = spacy.load("en_core_web_sm")
return POS(text)[0].tag_
def BERT(self, text: str, model: str = "bert-base-uncased"):
BERT = pipeline("fill-mask", model=model)
return BERT(text)
class Transformers:
def __init__(self, text: str, typeOfOperation: str = "text-generation", model: str = "gpt2"):
transformer = pipeline(typeOfOperation, model=model)
return transformer(text) |