Added Decision Tree & Reorganized Imports

PyAI.egg-info/PKG-INFO

@@ -0,0 +1,44 @@
+Metadata-Version: 2.1
+Name: PyAI
+Version: 3.5
+Summary: PyAI
+Author: Mitchell Shibilski-Unkel
+Author-email: None
+Keywords: python,ai,machine_learning,nlp
+Classifier: Intended Audience :: Developers
+Classifier: Programming Language :: Python :: 3
+Classifier: Operating System :: Unix
+Classifier: Operating System :: MacOS :: MacOS X
+Classifier: Operating System :: Microsoft :: Windows
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: torch
+Requires-Dist: numpy
+Requires-Dist: whisper
+Requires-Dist: spacy
+
+
+# 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` <br />
+Installation: `pip install git+https://github.com/MitchellShibilski-Unkel/PyAI.git@main`
+
+# Algorithms
+- RNN
+- KNN
+- ReLU
+- Softmax
+
+# NLP
+Able To Do:
+- Gets sentence tense
+- Gets each word and sentence
+- Able to change the type of tokenization `(letters, words, or sentences)`
+- Able to get the part of speech of a word
+
+# Audio
+Able To Do:
+- Able to convert audio to text
+- Get the language used in the audio
+- Translate one language to another

PyAI.egg-info/SOURCES.txt

@@ -0,0 +1,8 @@
+LICENSE
+README.md
+setup.py
+PyAI.egg-info/PKG-INFO
+PyAI.egg-info/SOURCES.txt
+PyAI.egg-info/dependency_links.txt
+PyAI.egg-info/requires.txt
+PyAI.egg-info/top_level.txt

PyAI.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

PyAI.egg-info/requires.txt

@@ -0,0 +1,4 @@
+torch
+numpy
+whisper
+spacy

PyAI.egg-info/top_level.txt

@@ -0,0 +1 @@
+

pyai.py

@@ -1,8 +1,9 @@
-import numpy as np
-import whisper
 import spacy
+import whisper
+import numpy as np
 from torch import nn
 from torch import Tensor
+from sklearn.tree import DecisionTreeRegressor
 
 
 class PyAI:
@@ -60,6 +61,12 @@ def Softmax(self, x):
                 soft = nn.Softmax(dim=1).to("cpu")(x)
 
             return soft
+
+        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):
@@ -143,4 +150,4 @@ def getTokens(self):
 
         def getPartOfSpeech(self, text: str):
             POS = spacy.load("en_core_web_sm")
-            return POS(text)[0].tag_
+            return POS(text)[0].tag_

setup.py

@@ -7,7 +7,7 @@
 with codecs.open(os.path.join(here, "README.md"), encoding="utf-8") as fh:
     long_description = "\n" + fh.read()
 
-VERSION = 'v3.5'
+VERSION = 'v4.0'
 DESCRIPTION = 'PyAI'
 LONG_DESCRIPTION = '-'
 
@@ -21,7 +21,7 @@
     long_description_content_type="text/markdown",
     long_description=long_description,
     packages=find_packages(),
-    install_requires=['torch', 'numpy', 'whisper', 'spacy'],
+    install_requires=['torch', 'numpy', 'whisper', 'spacy', 'sklearn'],
     keywords=['python', 'ai', 'machine_learning', 'nlp'],
     classifiers=[
         "Intended Audience :: Developers",