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README.md

@@ -1,6 +1,21 @@
----
-license: mit
----
-
-# PyAI
-Open-Source Python AI Model
+# PyAI
+This open-source library includes both an RNN and a KNN algorithm. In addition to these algorithms, PyAI offers a simple NLP and adds a way to convert audio into text through *Whisper*.
+
+Clone: `gh repo clone MitchellShibilski-Unkel/PyAI`
+
+# Algorithms
+- RNN
+- KNN
+- ReLU
+
+# NLP
+Able To Do:
+- Gets sentence tense
+- Gets each word and sentence
+- Able to change the type of tokenization `(letters, words, or sentences)`
+
+# Audio
+Able To Do:
+- Able to convert audio to text
+- Get the language used in the audio
+- Translate one language to another

pyai.py

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+import numpy as np
+import whisper
+from torch import nn
+from torch import Tensor
+
+
+class PyAI:
+    def __init__(self, useGPU: bool):
+        self.GPU = useGPU
+    
+    class Algorithms:
+        def KNN(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(w, u, b, x):
+            yt = 0
+            ht = 1 / 1 (w * x + u * yt ** -1 + b) ** -1
+            yt = 1 / 1 (w * ht + b) ** -1
+
+            return yt
+
+        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]
+            
+            if self.GPU:
+                relu = nn.ReLU().to("cuda")
+            else:
+                relu = nn.ReLU().to("cpu")
+                
+            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
+            
+    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